DataCamp.Course_051_Working with Dates and Times in Python.ipynb

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    "# **DataCamp.Course_051_Working with Dates and Times in Python**\n",
    "\n",
    "### **Course Description**\n",
    "\n",
    "You'll probably never have a time machine, but how about a machine for analyzing time? As soon as time enters any analysis, things can get weird. It's easy to get tripped up on day and month boundaries, time zones, daylight saving time, and all sorts of other things that can confuse the unprepared. If you're going to do any kind of analysis involving time, you’ll want to use Python to sort it out. Working with data sets on hurricanes and bike trips, we’ll cover counting events, figuring out how much time has elapsed between events and plotting data over time. You'll work in both standard Python and in Pandas, and we'll touch on the dateutil library, the only timezone library endorsed by the official Python documentation. After this course, you'll confidently handle date and time data in any format like a champion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   YEAR GoogleKnowlege_Occupation     Show   Group    Raw_Guest_List\n",
      "0  1999                     actor  1/11/99  Acting    Michael J. Fox\n",
      "1  1999                  Comedian  1/12/99  Comedy   Sandra Bernhard\n",
      "2  1999        television actress  1/13/99  Acting     Tracey Ullman\n",
      "3  1999              film actress  1/14/99  Acting  Gillian Anderson\n",
      "4  1999                     actor  1/18/99  Acting  David Alan Grier\n"
     ]
    }
   ],
   "source": [
    "# read_csv function\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "folder_name = 'datasets'\n",
    "dir = r'G:\\My Drive\\Data Science\\Datacamp_Notebook\\Datacamp_Notebook\\{}'.format(folder_name)\n",
    "\n",
    "def read_csv_fun(folder_name,file_name, path):\n",
    "    path = dir\n",
    "    os.chdir(path)\n",
    "    df = pd.read_csv('{}.csv'.format(file_name), sep=',', low_memory=False, on_bad_lines='skip')\n",
    "    return df\n",
    "\n",
    "df_show = read_csv_fun('datasets','daily_show_guests_cleaned', dir)\n",
    "print(df_show.head(5))"
   ]
  },
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   "source": [
    "## **Dates and Calendars (Module 01-051)**"
   ]
  },
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   "source": [
    "#### **Dates in Python**\n",
    "\n",
    "1. Course overview\n",
    "Chapter 1: Dates and Calendars\n",
    "Chapter 2: Combining Dates and Times\n",
    "Chapter 3: Time zones and Daylight Saving\n",
    "Chapter 4: Dates and Times in Pandas\n",
    "\n",
    "2. Why do we need a date class in Python?\n",
    "two_hurricanes = [\"10/7/2016\", \"6/21/2017\"]\n",
    "How would you:\n",
    "- Figure out how many days had elapsed?\n",
    "- Check that they were in order from earliest to latest?\n",
    "- Know which day of the week each was?\n",
    "- Filter out hurricanes which happened between certain dates?\n",
    "\n",
    "3. Creating date objects\n",
    "*-# Import date*\n",
    "from datetime import date\n",
    "*-# Create dates*\n",
    "two_hurricanes_dates = [date(2016, 10, 7), date(2017, 6, 21)]\n",
    "\n",
    "4. Attributes of a date\n",
    "\n",
    "*-# Import date*\n",
    "from datetime import date\n",
    "\n",
    "*-# Create dates*\n",
    "two_hurricanes_dates = [date(2016, 10, 7), date(2017, 6, 21)]\n",
    "print(two_hurricanes_dates[0].year)\n",
    "print(two_hurricanes_dates[0].month)\n",
    "print(two_hurricanes_dates[0].day)\n",
    "\n",
    "`2016`\n",
    "`10`\n",
    "`7`\n",
    "\n",
    "5. Finding the weekday of a date\n",
    "print(two_hurricanes_dates[0].weekday())\n",
    "\n",
    "Weekdays in Python\n",
    "0 = Monday\n",
    "1 = Tuesday\n",
    "2 = Wednesday\n",
    "...\n",
    "6 = Sunday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016\n",
      "10\n",
      "7\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# Import date\n",
    "from datetime import date\n",
    "# Create dates\n",
    "two_hurricanes_dates = [date(2016, 10, 7), date(2017, 6, 21)]\n",
    "print(two_hurricanes_dates[0].year)\n",
    "print(two_hurricanes_dates[0].month)\n",
    "print(two_hurricanes_dates[0].day)\n",
    "\n",
    "print(two_hurricanes_dates[0].weekday())"
   ]
  },
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   "source": [
    "**Which day of the week?**\n",
    "\n",
    "Hurricane Andrew, which hit Florida on August 24, 1992, was one of the costliest and deadliest hurricanes in US history. Which day of the week did it make landfall?\n",
    "\n",
    "Let's walk through all of the steps to figure this out.\n",
    "\n",
    "*0 = Monday*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Import date from datetime\n",
    "from datetime import date\n",
    "\n",
    "# Create a date object\n",
    "hurricane_andrew = date(1992, 8, 24)\n",
    "\n",
    "# Which day of the week is the date?\n",
    "print(hurricane_andrew.weekday())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many hurricanes come early?**\n",
    "\n",
    "In this chapter, you will work with a list of the hurricanes that made landfall in Florida from 1950 to 2017. There were 235 in total. Check out the variable `florida_hurricane_dates`, which has all of these dates.\n",
    "\n",
    "Atlantic hurricane season officially begins on June 1. How many hurricanes since 1950 have made landfall in Florida before the official start of hurricane season?\n",
    "\n",
    "STEPS\n",
    "    Complete the for loop to iterate through florida_hurricane_dates.\n",
    "    Complete the if statement to increment the counter (early_hurricanes) if the hurricane made landfall before June."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.date(1988, 8, 4),\n",
       " datetime.date(1990, 10, 12),\n",
       " datetime.date(2003, 4, 20),\n",
       " datetime.date(1971, 9, 1),\n",
       " datetime.date(1988, 8, 23)]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import pkl file\n",
    "florida_hurricane_dates = pd.read_pickle(r'G:\\My Drive\\Data Science\\Datacamp_Notebook\\Datacamp_Notebook\\datasets\\florida_hurricane_dates.pkl')\n",
    "display(florida_hurricane_dates[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# Counter for how many before June 1\n",
    "early_hurricanes = 0\n",
    "\n",
    "# We loop over the dates\n",
    "for hurricane in florida_hurricane_dates:\n",
    "  # Check if the month is before June (month number 6)\n",
    "  if hurricane.month < 6:\n",
    "    early_hurricanes = early_hurricanes + 1\n",
    "    \n",
    "print(early_hurricanes)"
   ]
  },
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   "source": [
    "#### **Math with dates**\n",
    "\n",
    "1. Math with dates\n",
    "\n",
    "- numbers range\n",
    "*-# Example numbers*\n",
    "a = 11\n",
    "b = 14\n",
    "l = [a, b]\n",
    "*-# Find the least least in the list*\n",
    "print(min(l))\n",
    "11\n",
    "\n",
    "*-# Subtract two numbers*\n",
    "print(b - a)\n",
    "3\n",
    "*-# Add 3 to a*\n",
    "print(a + 3)\n",
    "14 \n",
    "\n",
    "- date range\n",
    "\n",
    "*-# Import date*\n",
    "from datetime import date\n",
    "*-# Create our dates*\n",
    "d1 = date(2017, 11, 5)\n",
    "d2 = date(2017, 12, 4)\n",
    "l = [d1, d2]\n",
    "print(min(l))\n",
    "2017-11-05\n",
    "\n",
    "*-# Subtract two dates*\n",
    "delta = d2 - d1\n",
    "print(delta.days)\n",
    "29\n",
    "\n",
    "*-# Import timedelta*\n",
    "from datetime import timedelta\n",
    "*-# Create a 29 day timedelta*\n",
    "td = timedelta(days=29)\n",
    "print(d1 + td)\n",
    "2017-12-04\n",
    "\n",
    "2. Incrementing variables with +=\n",
    "\n",
    "*-# Initialize x to be zero*\n",
    "x = 0\n",
    "*-# Increment x*\n",
    "x = x + 1\n",
    "print(x)\n",
    "1\n",
    "\n",
    "*-# Initialize x to be zero*\n",
    "x = 0\n",
    "*-# Increment x*\n",
    "x += 1\n",
    "print(x)\n",
    "1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-11-05\n",
      "29\n",
      "2017-12-04\n"
     ]
    }
   ],
   "source": [
    "# Import date\n",
    "from datetime import date\n",
    "# Create our dates\n",
    "d1 = date(2017, 11, 5)\n",
    "d2 = date(2017, 12, 4)\n",
    "l = [d1, d2]\n",
    "print(min(l))\n",
    "\n",
    "# Subtract two dates\n",
    "delta = d2 - d1\n",
    "print(delta.days)\n",
    "\n",
    "# Import timedelta\n",
    "from datetime import timedelta\n",
    "# Create a 29 day timedelta\n",
    "td = timedelta(days=29)\n",
    "print(d1 + td)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# Initialize x to be zero\n",
    "x = 0\n",
    "# Increment x\n",
    "x += 1\n",
    "print(x)\n",
    "\n",
    "# Initialize x to be zero\n",
    "x = 0\n",
    "# Increment x\n",
    "x = x + 1\n",
    "print(x)"
   ]
  },
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   "metadata": {},
   "source": [
    "**Subtracting dates**\n",
    "\n",
    "Python date objects let us treat calendar dates as something similar to numbers: we can compare them, sort them, add, and even subtract them. This lets us do math with dates in a way that would be a pain to do by hand.\n",
    "\n",
    "The 2007 Florida hurricane season was one of the busiest on record, with 8 hurricanes in one year. The first one hit on May 9th, 2007, and the last one hit on December 13th, 2007. How many days elapsed between the first and last hurricane in 2007?\n",
    "\n",
    "STEPS\n",
    "    Import date from datetime.\n",
    "    Create a date object for May 9th, 2007, and assign it to the start variable.\n",
    "    Create a date object for December 13th, 2007, and assign it to the end variable.\n",
    "    Subtract start from end, to print the number of days in the resulting timedelta object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "218\n"
     ]
    }
   ],
   "source": [
    "# Import date\n",
    "from datetime import date\n",
    "\n",
    "# Create a date object for May 9th, 2007\n",
    "start = date(2007, 5, 9)\n",
    "\n",
    "# Create a date object for December 13th, 2007\n",
    "end = date(2007, 12, 13)\n",
    "\n",
    "# Subtract the two dates and print the number of days\n",
    "print((end - start).days)"
   ]
  },
  {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Counting events per calendar month**\n",
    "\n",
    "Hurricanes can make landfall in Florida throughout the year. As we've already discussed, some months are more hurricane-prone than others.\n",
    "\n",
    "Using `florida_hurricane_dates`, let's see how hurricanes in Florida were distributed across months throughout the year.\n",
    "\n",
    "We've created a dictionary called `hurricanes_each_month` to hold your counts and set the initial counts to zero. You will loop over the list of hurricanes, incrementing the correct month in `hurricanes_each_month` as you go, and then print the result.\n",
    "\n",
    "STEPS\n",
    "    Within the for loop:\n",
    "    Assign month to be the month of that hurricane.\n",
    "    Increment hurricanes_each_month for the relevant month by 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 0, 2: 1, 3: 0, 4: 1, 5: 8, 6: 32, 7: 21, 8: 49, 9: 70, 10: 43, 11: 9, 12: 1}\n"
     ]
    }
   ],
   "source": [
    "# A dictionary to count hurricanes per calendar month\n",
    "hurricanes_each_month = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6:0,\n",
    "\t\t  \t\t\t\t 7: 0, 8:0, 9:0, 10:0, 11:0, 12:0}\n",
    "\n",
    "# Loop over all hurricanes\n",
    "for hurricane in florida_hurricane_dates:\n",
    "  # Pull out the month\n",
    "  month = hurricane.month\n",
    "  # Increment the count in your dictionary by one\n",
    "  hurricanes_each_month[month] += 1\n",
    "  \n",
    "print(hurricanes_each_month)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Putting a list of dates in order**\n",
    "\n",
    "Much like numbers and strings, `date` objects in Python can be put in order. Earlier dates come before later ones, and so we can sort a list of `date` objects from earliest to latest.\n",
    "\n",
    "What if our Florida hurricane dates had been scrambled? We've gone ahead and shuffled them so they're in random order and saved the results as `dates_scrambled`. Your job is to put them back in chronological order, and then print the first and last dates from this sorted list.\n",
    "\n",
    "STEP01\n",
    "Print the first and last dates in dates_scrambled.\n",
    "STEP02\n",
    "    Sort dates_scrambled using Python's built-in sorted() function, and save the results to dates_ordered.\n",
    "    Print the first and last dates in dates_ordered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1988-08-04\n",
      "2011-07-18\n"
     ]
    }
   ],
   "source": [
    "# Print the first and last scrambled dates\n",
    "print(florida_hurricane_dates[0])\n",
    "print(florida_hurricane_dates[len(florida_hurricane_dates) - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1950-08-31\n",
      "2017-10-29\n"
     ]
    }
   ],
   "source": [
    "# Put the dates in order\n",
    "dates_ordered = sorted(florida_hurricane_dates)\n",
    "\n",
    "# Print the first and last ordered dates\n",
    "print(dates_ordered[0])\n",
    "print(dates_ordered[-1])"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "#### **Turning dates into strings**\n",
    "\n",
    "1. ISO 8601 format\n",
    "from datetime import date\n",
    "*-# Example date*\n",
    "d = date(2017, 11, 5)\n",
    "*-# ISO format: YYYY-MM-DD*\n",
    "print(d)\n",
    "2017-11-05\n",
    "*-# Express the date in ISO 8601 format and put it in a list*\n",
    "print( [d.isoformat()] )\n",
    "['2017-11-05']\n",
    "\n",
    "*-# A few dates that computers once had trouble with*\n",
    "some_dates = ['2000-01-01', '1999-12-31']\n",
    "*-# Print them in order*\n",
    "print(sorted(some_dates))\n",
    "['1999-12-31', '2000-01-01']\n",
    "\n",
    "2. Every other format: strftime\n",
    "d.strftime()\n",
    "\n",
    "*-# Example date*\n",
    "d = date(2017, 1, 5)\n",
    "print(d.strftime(\"%Y\"))\n",
    "2017\n",
    "*-# Format string with more text in it*\n",
    "print(d.strftime(\"Year is %Y\"))\n",
    "Year is 2017\n",
    "\n",
    "*-# Format: YYYY/MM/DD*\n",
    "print(d.strftime(\"%Y/%m/%d\"))\n",
    "2017/01/05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-11-05\n",
      "['2017-11-05']\n",
      "['1999-12-31', '2000-01-01']\n"
     ]
    }
   ],
   "source": [
    "# Example date\n",
    "d = date(2017, 11, 5)\n",
    "# ISO format: YYYY-MM-DD\n",
    "print(d)\n",
    "\n",
    "# Express the date in ISO 8601 format and put it in a list\n",
    "print( [d.isoformat()] )\n",
    "\n",
    "# A few dates that computers once had trouble with\n",
    "some_dates = ['2000-01-01', '1999-12-31']\n",
    "# Print them in order\n",
    "print(sorted(some_dates))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017\n",
      "Year is 2017\n",
      "2017/01/05\n"
     ]
    }
   ],
   "source": [
    "# Example date\n",
    "d = date(2017, 1, 5)\n",
    "print(d.strftime(\"%Y\"))\n",
    "\n",
    "# Format string with more text in it\n",
    "print(d.strftime(\"Year is %Y\"))\n",
    "\n",
    "# Format: YYYY/MM/DD\n",
    "print(d.strftime(\"%Y/%m/%d\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Printing dates in a friendly format**\n",
    "\n",
    "Because people may want to see dates in many different formats, Python comes with very flexible functions for turning `date` objects into strings.\n",
    "\n",
    "Let's see what event was recorded first in the Florida hurricane data set. In this exercise, you will format the earliest date in the `florida_hurricane_dates` list in two ways so you can decide which one you want to use: either the ISO standard or the typical US style.\n",
    "\n",
    "STEPS\n",
    "    Assign the earliest date in florida_hurricane_dates to first_date.\n",
    "    Print first_date in the ISO standard. For example, December 1st, 2000 would be \"2000-12-01\".\n",
    "    Print first_date in the US style, using .strftime(). For example, December 1st, 2000 would be \"12/1/2000\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISO: Our earliest hurricane date: 1950-08-31\n",
      "US: Our earliest hurricane date: 08/31/1950\n"
     ]
    }
   ],
   "source": [
    "# Assign the earliest date to first_date\n",
    "first_date = min(florida_hurricane_dates)\n",
    "\n",
    "# Convert to ISO and US formats\n",
    "iso = \"Our earliest hurricane date: \" + first_date.isoformat()\n",
    "us = \"Our earliest hurricane date: \" + first_date.strftime(\"%m/%d/%Y\")\n",
    "\n",
    "print(\"ISO: \" + iso)\n",
    "print(\"US: \" + us)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Representing dates in different ways**\n",
    "\n",
    "date objects in Python have a great number of ways they can be printed out as strings. In some cases, you want to know the date in a clear, language-agnostic format. In other cases, you want something which can fit into a paragraph and flow naturally.\n",
    "\n",
    "Let's try printing out the same date, August 26, 1992 (the day that Hurricane Andrew made landfall in Florida), in a number of different ways, to practice using the `.strftime()` method.\n",
    "\n",
    "A date object called `andrew` has already been created.\n",
    "\n",
    "STEPS\n",
    "Print andrew in the format 'YYYY-MM'.\n",
    "Print andrew in the format 'MONTH (YYYY)', using %B for the month's full name, which in this case will be August.\n",
    "Print andrew in the format 'YYYY-DDD' (where DDD is the day of the year) using %j."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1992-08\n",
      "August (1992)\n",
      "1992-239\n"
     ]
    }
   ],
   "source": [
    "# Import date\n",
    "from datetime import date\n",
    "\n",
    "# Create a date object\n",
    "andrew = date(1992, 8, 26)\n",
    "\n",
    "# Print the date in the format 'YYYY-MM'\n",
    "print(andrew.strftime('%Y-%m'))\n",
    "\n",
    "# Print the date in the format 'MONTH (YYYY)'\n",
    "print(andrew.strftime('%B (%Y)'))\n",
    "\n",
    "# Print the date in the format 'YYYY-DDD'\n",
    "print(andrew.strftime('%Y-%j'))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Combining Dates and Times (Module 02-051)**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Dates and times**\n",
    "\n",
    "1. Adding time to the mix\n",
    "\n",
    "*-# Import datetime*\n",
    "from datetime import datetime\n",
    "dt = datetime(2017, 10, 1, 15, 23, 25, 500000)\n",
    "\n",
    "dt = datetime(year=2017, month=10, day=1, \n",
    "                hour=15, minute=23, second=25, microsecond=500000)\n",
    "\n",
    "2. Replacing parts of a datetime\n",
    "\n",
    "print(dt)\n",
    "\n",
    "`2017-10-01 15:23:25.500000`\n",
    "\n",
    "dt_hr = dt.replace(minute=0, second=0, microsecond=0)\n",
    "print(dt_hr)\n",
    "\n",
    "`2017-10-01 15:00:00`\n",
    "\n",
    "3. Capital Bikeshare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>31230</td>\n",
       "      <td>Metro Center / 12th &amp; G St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start date             End date  Start station number  \\\n",
       "0  2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1  2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "2  2017-10-02 06:37:10  2017-10-02 06:42:53                 31036   \n",
       "3  2017-10-02 08:56:45  2017-10-02 09:18:03                 31037   \n",
       "4  2017-10-02 18:23:48  2017-10-02 18:45:05                 31295   \n",
       "\n",
       "                          Start station  End station number  \\\n",
       "0                  Glebe Rd & 11th St N               31036   \n",
       "1         George Mason Dr & Wilson Blvd               31036   \n",
       "2         George Mason Dr & Wilson Blvd               31037   \n",
       "3  Ballston Metro / N Stuart & 9th St N               31295   \n",
       "4                     Potomac & M St NW               31230   \n",
       "\n",
       "                            End station Bike number Member type  \n",
       "0         George Mason Dr & Wilson Blvd      W20529      Member  \n",
       "1         George Mason Dr & Wilson Blvd      W20529      Casual  \n",
       "2  Ballston Metro / N Stuart & 9th St N      W20529      Member  \n",
       "3                     Potomac & M St NW      W20529      Member  \n",
       "4         Metro Center / 12th & G St NW      W20529      Member  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "onebike_datetimes = read_csv_fun('datasets','capital-onebike', dir)\n",
    "\n",
    "display(onebike_datetimes.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start date              object\n",
       "End date                object\n",
       "Start station number     int64\n",
       "Start station           object\n",
       "End station number       int64\n",
       "End station             object\n",
       "Bike number             object\n",
       "Member type             object\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Start date                      object\n",
       "End date                        object\n",
       "Start station number             int64\n",
       "Start station                   object\n",
       "End station number               int64\n",
       "End station                     object\n",
       "Bike number                     object\n",
       "Member type                     object\n",
       "start                   datetime64[ns]\n",
       "end                     datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(onebike_datetimes.dtypes)\n",
    "\n",
    "# Convert user_type from object to datetime\n",
    "onebike_datetimes['start'] = onebike_datetimes['Start date'].astype('datetime64[ns]')\n",
    "onebike_datetimes['end'] = onebike_datetimes['End date'].astype('datetime64[ns]')\n",
    "\n",
    "display(onebike_datetimes.dtypes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating datetimes by hand**\n",
    "\n",
    "Often you create `datetime` objects based on outside data. Sometimes though, you want to create a `datetime` object from scratch.\n",
    "\n",
    "You're going to create a few different `datetime` objects from scratch to get the hang of that process. These come from the bikeshare data set that you'll use throughout the rest of the chapter.\n",
    "\n",
    "STEPS 01\n",
    "    Import the datetime class.\n",
    "    Create a datetime for October 1, 2017 at 15:26:26.\n",
    "    Print the results in ISO format.\n",
    "STEPS 02\n",
    "    Import the datetime class.\n",
    "    Create a datetime for December 31, 2017 at 15:19:13.\n",
    "    Print the results in ISO format.\n",
    "STEPS 03\n",
    "Create a new datetime by replacing the year in dt with 1917 (instead of 2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-10-01T15:26:26\n"
     ]
    }
   ],
   "source": [
    "# Import datetime\n",
    "from datetime import datetime\n",
    "\n",
    "# Create a datetime object\n",
    "dt = datetime(year=2017 , month=10, day=1, hour=15, minute=26, second=26)\n",
    "\n",
    "# Print the results in ISO 8601 format\n",
    "print(dt.isoformat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-12-31T15:19:13\n"
     ]
    }
   ],
   "source": [
    "# Import datetime\n",
    "from datetime import datetime\n",
    "\n",
    "# Create a datetime object\n",
    "dt = datetime(year=2017 , month=12, day=31, hour=15, minute=19, second=13)\n",
    "\n",
    "# Print the results in ISO 8601 format\n",
    "print(dt.isoformat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1917-12-31 15:19:13\n"
     ]
    }
   ],
   "source": [
    "# Import datetime\n",
    "from datetime import datetime\n",
    "\n",
    "# Create a datetime object\n",
    "dt = datetime(2017, 12, 31, 15, 19, 13)\n",
    "\n",
    "# Replace the year with 1917\n",
    "dt_old = dt.replace(year=1917)\n",
    "\n",
    "# Print the results in ISO 8601 format\n",
    "print(dt_old)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Counting events before and after noon**\n",
    "\n",
    "In this chapter, you will be working with a list of all bike trips for one Capital Bikeshare bike, W20529, from October 1, 2017 to December 31, 2017. This list has been loaded as `onebike_datetimes`.\n",
    "\n",
    "Each element of the list is a dictionary with two entries: `start` is a `datetime` object corresponding to the start of a trip (when a bike is removed from the dock) and `end` is a `datetime` object corresponding to the end of a trip (when a bike is put back into a dock).\n",
    "\n",
    "You can use this data set to understand better how this bike was used. Did more trips start before noon or after noon?\n",
    "\n",
    "STEPS\n",
    "    Within the for loop, complete the if statement to check if the trip started before noon.\n",
    "    Within the for loop, increment trip_counts['AM'] if the trip started before noon, and trip_counts['PM'] if it started after noon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AM': 94, 'PM': 196}\n"
     ]
    }
   ],
   "source": [
    "# Import datetime\n",
    "from datetime import datetime\n",
    "\n",
    "# Create dictionary to hold results\n",
    "trip_counts = {'AM': 0, 'PM': 0}\n",
    "  \n",
    "# Loop over all trips\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "  # Check to see if the trip starts before noon\n",
    "  if trip['start'].hour < 12:\n",
    "    # Increment the counter for before noon\n",
    "    trip_counts['AM'] += 1\n",
    "  else:\n",
    "    # Increment the counter for after noon\n",
    "    trip_counts['PM'] += 1\n",
    "  \n",
    "print(trip_counts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Printing and parsing datetimes**\n",
    "\n",
    "1. Printing datetimes\n",
    "\n",
    "*-# Create datetime*\n",
    "dt = datetime(2017, 12, 30, 15, 19, 13)\n",
    "print(dt.strftime(\"%Y-%m-%d\"))\n",
    "\n",
    "`2017-12-30`\n",
    "\n",
    "print(dt.strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "\n",
    "`2017-12-30 15:19:13`\n",
    "\n",
    "print(dt.strftime(\"%H:%M:%S on %Y/%m/%d/\"))\n",
    "\n",
    "`15:19:13 on 2017/12/30`\n",
    "\n",
    "2. ISO 8601 Format\n",
    "\n",
    "*-# ISO 8601 format*\n",
    "print(dt.isoformat())\n",
    "\n",
    "`2017-12-30T15:19:13`\n",
    "\n",
    "3. Parsing datetimes with strptime\n",
    "\n",
    "*-# Import datetime*\n",
    "from datetime import datetime\n",
    "dt = datetime.strptime(\"12/30/2017 15:19:13\",\n",
    "\"%m/%d/%Y %H:%M:%S\")\n",
    "\n",
    "*-# What did we make?*\n",
    "print(type(dt))\n",
    "\n",
    "`<class 'datetime.datetime'>`\n",
    "\n",
    "*-# Print out datetime object*\n",
    "print(dt)\n",
    "\n",
    "`2017-12-30 15:19:13`\n",
    "\n",
    "*-# Import datetime*\n",
    "from datetime import datetime\n",
    "*-# Incorrect format string*\n",
    "dt = datetime.strptime(\"2017-12-30 15:19:13\",\"%Y-%m-%d\")\n",
    "\n",
    "`ValueError: unconverted data remains: 15:19:13`\n",
    "\n",
    "4. Parsing datetimes with Python\n",
    "\n",
    "*-# A timestamp*\n",
    "ts = 1514665153.0\n",
    "*-# Convert to datetime and print*\n",
    "print(datetime.fromtimestamp(ts))\n",
    "\n",
    "`2017-12-30 15:19:13`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-12-30 15:19:13\n",
      "<class 'datetime.datetime'>\n",
      "2017-12-30 15:19:13\n"
     ]
    }
   ],
   "source": [
    "# Import datetime\n",
    "from datetime import datetime\n",
    "dt = datetime.strptime(\"12/30/2017 15:19:13\",\n",
    "\"%m/%d/%Y %H:%M:%S\")\n",
    "print(dt)\n",
    "\n",
    "# What did we make?\n",
    "print(type(dt))\n",
    "\n",
    "# A timestamp\n",
    "ts = 1514665153.0\n",
    "# Convert to datetime and print\n",
    "print(datetime.fromtimestamp(ts))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Turning strings into datetimes**\n",
    "\n",
    "When you download data from the Internet, dates and times usually come to you as strings. Often the first step is to turn those strings into `datetime` objects.\n",
    "\n",
    "In this exercise, you will practice this transformation.\n",
    "Reference\t\n",
    "%Y\t4 digit year (0000-9999)\n",
    "%m\t2 digit month (1-12)\n",
    "%d\t2 digit day (1-31)\n",
    "%H\t2 digit hour (0-23)\n",
    "%M\t2 digit minute (0-59)\n",
    "%S\t2 digit second (0-59)\n",
    "\n",
    "STEPS 01\n",
    "    Determine the format needed to convert s to datetime and assign it to fmt.\n",
    "    Convert the string s to datetime using fmt.\n",
    "STEPS 02\n",
    "    Determine the format needed to convert s to datetime and assign it to fmt.\n",
    "    Convert the string s to datetime using fmt.\n",
    "STEPS 03\n",
    "    Determine the format needed to convert s to datetime and assign it to fmt.\n",
    "    Convert the string s to datetime using fmt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-02-03 00:00:01\n"
     ]
    }
   ],
   "source": [
    "# Import the datetime class\n",
    "from datetime import datetime\n",
    "\n",
    "# Starting string, in YYYY-MM-DD HH:MM:SS format\n",
    "s = '2017-02-03 00:00:01'\n",
    "\n",
    "# Write a format string to parse s\n",
    "fmt = '%Y-%m-%d %H:%M:%S'\n",
    "\n",
    "# Create a datetime object d\n",
    "d = datetime.strptime(s, fmt)\n",
    "\n",
    "# Print d\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2030-10-15 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# Import the datetime class\n",
    "from datetime import datetime\n",
    "\n",
    "# Starting string, in YYYY-MM-DD format\n",
    "s = '2030-10-15'\n",
    "\n",
    "# Write a format string to parse s\n",
    "fmt = '%Y-%m-%d'\n",
    "\n",
    "# Create a datetime object d\n",
    "d = datetime.strptime(s, fmt)\n",
    "\n",
    "# Print d\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1986-12-15 08:00:00\n"
     ]
    }
   ],
   "source": [
    "# Import the datetime class\n",
    "from datetime import datetime\n",
    "\n",
    "# Starting string, in MM/DD/YYYY HH:MM:SS format\n",
    "s = '12/15/1986 08:00:00'\n",
    "\n",
    "# Write a format string to parse s\n",
    "fmt = '%m/%d/%Y %H:%M:%S'\n",
    "\n",
    "# Create a datetime object d\n",
    "d = datetime.strptime(s, fmt)\n",
    "\n",
    "# Print d\n",
    "print(d)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Parsing pairs of strings as datetimes**\n",
    "\n",
    "Up until now, you've been working with a pre-processed list of datetimes for W20529's trips. For this exercise, you're going to go one step back in the data cleaning pipeline and work with the strings that the data started as.\n",
    "\n",
    "Explore onebike_datetime_strings in the IPython shell to determine the correct format. datetime has already been loaded for you.\n",
    "\n",
    "Reference\t\n",
    "%Y\t4 digit year (0000-9999)\n",
    "%m\t2 digit month (1-12)\n",
    "%d\t2 digit day (1-31)\n",
    "%H\t2 digit hour (0-23)\n",
    "%M\t2 digit minute (0-59)\n",
    "%S\t2 digit second (0-59)\n",
    "\n",
    "STEPS\n",
    "    Outside the for loop, fill out the fmt string with the correct parsing format for the data.\n",
    "    Within the for loop, parse the start and end strings into the trip dictionary with start and end keys and datetime objects for values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start date             End date  Start station number  \\\n",
       "0  2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1  2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "2  2017-10-02 06:37:10  2017-10-02 06:42:53                 31036   \n",
       "\n",
       "                   Start station  End station number  \\\n",
       "0           Glebe Rd & 11th St N               31036   \n",
       "1  George Mason Dr & Wilson Blvd               31036   \n",
       "2  George Mason Dr & Wilson Blvd               31037   \n",
       "\n",
       "                            End station Bike number Member type  \n",
       "0         George Mason Dr & Wilson Blvd      W20529      Member  \n",
       "1         George Mason Dr & Wilson Blvd      W20529      Casual  \n",
       "2  Ballston Metro / N Stuart & 9th St N      W20529      Member  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reset\n",
    "onebike_datetimes = read_csv_fun('datasets','capital-onebike', dir)\n",
    "display(onebike_datetimes.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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       "      <th>start</th>\n",
       "      <th>end</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
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       "    <tr>\n",
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       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                start                 end\n",
       "0 2017-10-01 15:23:25 2017-10-01 15:26:26\n",
       "1 2017-10-01 15:42:57 2017-10-01 17:49:59\n",
       "2 2017-10-02 06:37:10 2017-10-02 06:42:53\n",
       "3 2017-10-02 08:56:45 2017-10-02 09:18:03\n",
       "4 2017-10-02 18:23:48 2017-10-02 18:45:05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "start    datetime64[ns]\n",
       "end      datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Write down the format string\n",
    "fmt = \"%Y-%m-%d %H:%M:%S\"\n",
    "\n",
    "# Initialize a list for holding the pairs of datetime objects\n",
    "onebike_datetimes_dates = []\n",
    "\n",
    "# Loop over all trips\n",
    "for index, row in onebike_datetimes.iterrows():\n",
    "    trip = {'start': datetime.strptime(row['Start date'], fmt),\n",
    "            'end': datetime.strptime(row['End date'], fmt)}\n",
    "  \n",
    "    # Append the trip\n",
    "    onebike_datetimes_dates.append(trip)\n",
    "\n",
    "print(onebike_datetimes_dates)\n",
    "\n",
    "df_dates = pd.DataFrame(onebike_datetimes_dates)\n",
    "\n",
    "display(df_dates.head(5))\n",
    "display(df_dates.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>31230</td>\n",
       "      <td>Metro Center / 12th &amp; G St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start date             End date  Start station number  \\\n",
       "0  2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1  2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "2  2017-10-02 06:37:10  2017-10-02 06:42:53                 31036   \n",
       "3  2017-10-02 08:56:45  2017-10-02 09:18:03                 31037   \n",
       "4  2017-10-02 18:23:48  2017-10-02 18:45:05                 31295   \n",
       "\n",
       "                          Start station  End station number  \\\n",
       "0                  Glebe Rd & 11th St N               31036   \n",
       "1         George Mason Dr & Wilson Blvd               31036   \n",
       "2         George Mason Dr & Wilson Blvd               31037   \n",
       "3  Ballston Metro / N Stuart & 9th St N               31295   \n",
       "4                     Potomac & M St NW               31230   \n",
       "\n",
       "                            End station Bike number Member type  \\\n",
       "0         George Mason Dr & Wilson Blvd      W20529      Member   \n",
       "1         George Mason Dr & Wilson Blvd      W20529      Casual   \n",
       "2  Ballston Metro / N Stuart & 9th St N      W20529      Member   \n",
       "3                     Potomac & M St NW      W20529      Member   \n",
       "4         Metro Center / 12th & G St NW      W20529      Member   \n",
       "\n",
       "                start                 end  \n",
       "0 2017-10-01 15:23:25 2017-10-01 15:26:26  \n",
       "1 2017-10-01 15:42:57 2017-10-01 17:49:59  \n",
       "2 2017-10-02 06:37:10 2017-10-02 06:42:53  \n",
       "3 2017-10-02 08:56:45 2017-10-02 09:18:03  \n",
       "4 2017-10-02 18:23:48 2017-10-02 18:45:05  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Start date                      object\n",
       "End date                        object\n",
       "Start station number             int64\n",
       "Start station                   object\n",
       "End station number               int64\n",
       "End station                     object\n",
       "Bike number                     object\n",
       "Member type                     object\n",
       "start                   datetime64[ns]\n",
       "end                     datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'AM': 94, 'PM': 196}\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# Write down the format string\n",
    "fmt = \"%Y-%m-%d %H:%M:%S\"\n",
    "\n",
    "# Initialize a list for holding the trip data\n",
    "trips = []\n",
    "\n",
    "# Loop over all trips\n",
    "for index, row in onebike_datetimes.iterrows():\n",
    "    trip = {'start': datetime.strptime(row['Start date'], fmt),\n",
    "            'end': datetime.strptime(row['End date'], fmt)}\n",
    "    # Append the trip\n",
    "    trips.append(trip)\n",
    "\n",
    "# Create a DataFrame from the trip data and concatenate them together\n",
    "trips_df = pd.DataFrame(trips)\n",
    "onebike_datetimes = pd.concat([onebike_datetimes, trips_df], axis=1)\n",
    "\n",
    "display(onebike_datetimes.head(5))\n",
    "display(onebike_datetimes.dtypes)\n",
    "\n",
    "#Code Test\n",
    "\n",
    "# Create dictionary to hold results\n",
    "trip_counts = {'AM': 0, 'PM': 0}\n",
    "  \n",
    "# Loop over all trips\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "  # Check to see if the trip starts before noon\n",
    "  if trip['start'].hour < 12:\n",
    "    # Increment the counter for before noon\n",
    "    trip_counts['AM'] += 1\n",
    "  else:\n",
    "    # Increment the counter for after noon\n",
    "    trip_counts['PM'] += 1\n",
    "  \n",
    "print(trip_counts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recreating ISO format with strftime()**\n",
    "\n",
    "In the last chapter, you used `strftime()` to create strings from `date` objects. Now that you know about `datetime` objects, let's practice doing something similar.\n",
    "\n",
    "Re-create the `.isoformat()` method, using `.strftime()`, and print the first trip start in our data set.\n",
    "\n",
    "Reference\t\n",
    "%Y\t4 digit year (0000-9999)\n",
    "%m\t2 digit month (1-12)\n",
    "%d\t2 digit day (1-31)\n",
    "%H\t2 digit hour (0-23)\n",
    "%M\t2 digit minute (0-59)\n",
    "%S\t2 digit second (0-59)\n",
    "\n",
    "STEPS\n",
    "    Complete fmt to match the format of ISO 8601.\n",
    "    Print first_start with both .isoformat() and .strftime(); they should match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-10-01T15:23:25\n",
      "2017-10-01T15:23:25\n"
     ]
    }
   ],
   "source": [
    "# Import datetime\n",
    "from datetime import datetime\n",
    "\n",
    "# Pull out the start of the first trip\n",
    "first_start = onebike_datetimes.start[0]\n",
    "\n",
    "# Format to feed to strftime()\n",
    "fmt = \"%Y-%m-%dT%H:%M:%S\"\n",
    "\n",
    "# Print out date with .isoformat(), then with .strftime() to compare\n",
    "print(first_start.isoformat())\n",
    "print(first_start.strftime(fmt))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Unix timestamps**\n",
    "\n",
    "Datetimes are sometimes stored as Unix timestamps: the number of seconds since January 1, 1970. This is especially common with computer infrastructure, like the log files that websites keep when they get visitors.\n",
    "\n",
    "STEPS\n",
    "    Complete the for loop to loop over timestamps.\n",
    "    Complete the code to turn each timestamp ts into a datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2017, 12, 30, 15, 19, 13),\n",
       " datetime.datetime(2017, 12, 30, 15, 9, 3)]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import datetime\n",
    "from datetime import datetime\n",
    "\n",
    "# Starting timestamps\n",
    "timestamps = [1514665153, 1514664543]\n",
    "\n",
    "# Datetime objects\n",
    "dts = []\n",
    "\n",
    "# Loop\n",
    "for ts in timestamps:\n",
    "  dts.append(datetime.fromtimestamp(ts))\n",
    "  \n",
    "# Print results\n",
    "display(dts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Working with durations**\n",
    "\n",
    "1. Working with durations\n",
    "\n",
    "*-# Create example datetimes*\n",
    "start = datetime(2017, 10, 8, 23, 46, 47)\n",
    "end = datetime(2017, 10, 9, 0, 10, 57)\n",
    "\n",
    "*-# Subtract datetimes to create a timedelta*\n",
    "duration = end - start\n",
    "\n",
    "*-# Subtract datetimes to create a timedelta*\n",
    "print(duration.total_seconds())\n",
    "\n",
    "`1450.0`\n",
    "\n",
    "2. Creating timedeltas\n",
    "\n",
    "*-# Import timedelta*\n",
    "from datetime import timedelta\n",
    "\n",
    "*-# Create a timedelta*\n",
    "delta1 = timedelta(seconds=1)\n",
    "\n",
    "print(start)\n",
    "\n",
    "`2017-10-08 23:46:47`\n",
    "\n",
    "*-# One second later*\n",
    "print(start + delta1)\n",
    "\n",
    "`2017-10-08 23:46:48`\n",
    "\n",
    "*-# Create a one day and one second timedelta*\n",
    "delta2 = timedelta(days=1, seconds=1)\n",
    "print(start)\n",
    "\n",
    "`2017-10-08 23:46:47`\n",
    "\n",
    "*-# One day and one second later*\n",
    "print(start + delta2)\n",
    "\n",
    "`2017-10-09 23:46:48`\n",
    "\n",
    "3. Negative timedeltas\n",
    "\n",
    "*-# Create a negative timedelta of one week*\n",
    "delta3 = timedelta(weeks=-1)\n",
    "print(start)\n",
    "\n",
    "`2017-10-08 23:46:47`\n",
    "\n",
    "*-# One week earlier*\n",
    "print(start + delta3)\n",
    "\n",
    "`2017-10-01 23:46:47`\n",
    "\n",
    "*-# Same, but we'll subtract this time*\n",
    "delta4 = timedelta(weeks=1)\n",
    "print(start)\n",
    "\n",
    "`2017-10-08 23:46:47`\n",
    "\n",
    "*-# One week earlier*\n",
    "print(start - delta4)\n",
    "\n",
    "`2017-10-01 23:46:47`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1450.0\n"
     ]
    }
   ],
   "source": [
    "# Create example datetimes\n",
    "start = datetime(2017, 10, 8, 23, 46, 47)\n",
    "end = datetime(2017, 10, 9, 0, 10, 57)\n",
    "# Subtract datetimes to create a timedelta\n",
    "duration = end - start\n",
    "# Subtract datetimes to create a timedelta\n",
    "print(duration.total_seconds())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Turning pairs of datetimes into durations**\n",
    "\n",
    "When working with timestamps, we often want to know how much time has elapsed between events. Thankfully, we can use `datetime` arithmetic to ask Python to do the heavy lifting for us so we don't need to worry about day, month, or year boundaries. Let's calculate the number of seconds that the bike was out of the dock for each trip.\n",
    "\n",
    "Continuing our work from a previous coding exercise, the bike trip data has been loaded as the list `onebike_datetimes`. Each element of the list consists of two `datetime` objects, corresponding to the start and end of a trip, respectively.\n",
    "\n",
    "STEPS\n",
    "    Within the loop:\n",
    "        Use arithmetic on the start and end elements to find the length of the trip\n",
    "        Save the results to trip_duration.\n",
    "        Calculate trip_length_seconds from trip_duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[181.0, 7622.0, 343.0, 1278.0, 1277.0, 1366.0, 815.0, 545.0, 491.0, 639.0, 1678.0, 406.0, 709.0, 514.0, 492.0, 1668.0, 2242.0, 2752.0, 735.0, 330.0, 518.0, 1433.0, 204.0, 304.0, 977.0, 1399.0, 1244.0, 658.0, 800.0, 1911.0, 2471.0, 1344.0, 435.0, 271.0, 920.0, 851.0, 209.0, 453.0, 841.0, 142.0, 1023.0, 1466.0, 1636.0, 3039.0, 1571.0, 1410.0, 386.0, 1527.0, 622.0, 1450.0, 1422.0, 991.0, 1484.0, 1450.0, 929.0, 533.0, 525.0, 283.0, 133.0, 1106.0, 952.0, 553.0, 659.0, 297.0, 357.0, 989.0, 979.0, 760.0, 1110.0, 675.0, 1207.0, 1593.0, 768.0, 1446.0, 485.0, 200.0, 399.0, 242.0, 170.0, 450.0, 1078.0, 1042.0, 573.0, 748.0, 735.0, 336.0, 76913.0, 171.0, 568.0, 358.0, 917.0, 671.0, 1791.0, 318.0, 888.0, 1284.0, 11338.0, 1686.0, 5579.0, 8290.0, 1850.0, 1810.0, 870.0, 436.0, 429.0, 494.0, 1439.0, 380.0, 629.0, 962.0, 387.0, 952.0, 190.0, 739.0, 1120.0, 369.0, 2275.0, 873.0, 1670.0, 643.0, 572.0, 1375.0, 725.0, 688.0, 1041.0, 1707.0, 1236.0, 1291.0, 2890.0, -3346.0, 1213.0, 331.0, 1497.0, 527.0, 584.0, 2599.0, 759.0, 1291.0, 916.0, 161.0, 806.0, 838.0, 644.0, 374.0, 678.0, 137.0, 659.0, 386.0, 745.0, 448.0, 558.0, 888.0, 662.0, 663.0, 362.0, 513.0, 655.0, 221.0, 469.0, 430.0, 192.0, 324.0, 1233.0, 923.0, 961.0, 525.0, 1017.0, 1216.0, 747.0, 668.0, 1219.0, 1182.0, 10262.0, 1106.0, 399.0, 724.0, 330.0, 499.0, 968.0, 1310.0, 2629.0, 427.0, 839.0, 258.0, 396.0, 238.0, 745.0, 613.0, 710.0, 2068.0, 947.0, 1509.0, 254.0, 625.0, 479.0, 688.0, 238.0, 322.0, 304.0, 576.0, 1035.0, 661.0, 276.0, 1427.0, 998.0, 729.0, 723.0, 220.0, 212.0, 759.0, 268.0, 374.0, 305.0, 304.0, 289.0, 2620.0, 1288.0, 212.0, 2656.0, 996.0, 271.0, 701.0, 458.0, 116.0, 124.0, 276.0, 532.0, 257.0, 1089.0, 195.0, 384.0, 511.0, 850.0, 462.0, 322.0, 998.0, 327.0, 153.0, 152.0, 230.0, 321.0, 625.0, 391.0, 1298.0, 1018.0, 220.0, 277.0, 221.0, 216.0, 509.0, 596.0, 367.0, 447.0, 257.0, 1049.0, 1367.0, 933.0, 151.0, 153.0, 1336.0, 298.0, 380.0, 1385.0, 225.0, 181.0, 581.0, 250.0, 332.0, 845.0, 277.0, 663.0, 541.0, 652.0, 257.0, 775.0, 499.0, 1744.0, 445.0, 297.0, 1716.0, 2046.0, 809.0, 1287.0, 596.0, 1513.0, 651.0, 625.0, 279.0, 210.0, 610.0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mcaba\\AppData\\Local\\Temp\\ipykernel_1828\\4156889997.py:20: FutureWarning: DataFrame.set_axis 'inplace' keyword is deprecated and will be removed in a future version. Use `obj = obj.set_axis(..., copy=False)` instead\n",
      "  duration_df.set_axis(head_d_df, axis=1, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "      <td>1278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>31230</td>\n",
       "      <td>Metro Center / 12th &amp; G St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "      <td>1277.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start date             End date  Start station number  \\\n",
       "0  2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1  2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "2  2017-10-02 06:37:10  2017-10-02 06:42:53                 31036   \n",
       "3  2017-10-02 08:56:45  2017-10-02 09:18:03                 31037   \n",
       "4  2017-10-02 18:23:48  2017-10-02 18:45:05                 31295   \n",
       "\n",
       "                          Start station  End station number  \\\n",
       "0                  Glebe Rd & 11th St N               31036   \n",
       "1         George Mason Dr & Wilson Blvd               31036   \n",
       "2         George Mason Dr & Wilson Blvd               31037   \n",
       "3  Ballston Metro / N Stuart & 9th St N               31295   \n",
       "4                     Potomac & M St NW               31230   \n",
       "\n",
       "                            End station Bike number Member type  \\\n",
       "0         George Mason Dr & Wilson Blvd      W20529      Member   \n",
       "1         George Mason Dr & Wilson Blvd      W20529      Casual   \n",
       "2  Ballston Metro / N Stuart & 9th St N      W20529      Member   \n",
       "3                     Potomac & M St NW      W20529      Member   \n",
       "4         Metro Center / 12th & G St NW      W20529      Member   \n",
       "\n",
       "                start                 end  trip_duration  \n",
       "0 2017-10-01 15:23:25 2017-10-01 15:26:26          181.0  \n",
       "1 2017-10-01 15:42:57 2017-10-01 17:49:59         7622.0  \n",
       "2 2017-10-02 06:37:10 2017-10-02 06:42:53          343.0  \n",
       "3 2017-10-02 08:56:45 2017-10-02 09:18:03         1278.0  \n",
       "4 2017-10-02 18:23:48 2017-10-02 18:45:05         1277.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize a list for all the trip durations\n",
    "onebike_durations = []\n",
    "\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "  # Create a timedelta object corresponding to the length of the trip\n",
    "  trip_duration = trip['end'] - trip['start']\n",
    "  \n",
    "  # Get the total elapsed seconds in trip_duration\n",
    "  trip_length_seconds = trip_duration.total_seconds()\n",
    "  \n",
    "  # Append the results to our list\n",
    "  onebike_durations.append(trip_length_seconds)\n",
    "\n",
    "print(onebike_durations)\n",
    "\n",
    "# Create a DataFrame from the trip data and concatenate them together\n",
    "duration_df = pd.DataFrame(onebike_durations)\n",
    "#display(duration_df)\n",
    "head_d_df = ['trip_duration']\n",
    "duration_df.set_axis(head_d_df, axis=1, inplace=True)\n",
    "#display(duration_df)\n",
    "onebike_datetimes = pd.concat([onebike_datetimes, duration_df], axis=1)\n",
    "\n",
    "display(onebike_datetimes.head(5))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Average trip time**\n",
    "\n",
    "W20529 took 291 trips in our data set. How long were the trips on average? We can use the built-in Python functions `sum() `and `len() `to make this calculation.\n",
    "\n",
    "Based on your last coding exercise, the data has been loaded as `onebike_durations`. Each entry is a number of seconds that the bike was out of the dock.\n",
    "\n",
    "STEPS\n",
    "    Calculate total_elapsed_time across all trips in onebike_durations.\n",
    "    Calculate number_of_trips for onebike_durations.\n",
    "    Divide total_elapsed_time by number_of_trips to get the average trip length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1178.9310344827586\n"
     ]
    }
   ],
   "source": [
    "# What was the total duration of all trips?\n",
    "total_elapsed_time = sum(onebike_durations)\n",
    "\n",
    "# What was the total number of trips?\n",
    "number_of_trips = len(onebike_durations)\n",
    "  \n",
    "# Divide the total duration by the number of trips\n",
    "print(total_elapsed_time / number_of_trips)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The long and the short of why time is hard**\n",
    "\n",
    "Out of 291 trips taken by W20529, how long was the longest? How short was the shortest? Does anything look fishy?\n",
    "\n",
    "As before, data has been loaded as `onebike_durations`.\n",
    "\n",
    "STEPS\n",
    "    Calculate shortest_trip from onebike_durations.\n",
    "    Calculate longest_trip from onebike_durations.\n",
    "    Print the results, turning shortest_trip and longest_trip into strings so they can print."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shortest trip was -3346.0 seconds\n",
      "The longest trip was 76913.0 seconds\n"
     ]
    }
   ],
   "source": [
    "# Calculate shortest and longest trips\n",
    "shortest_trip = min(onebike_durations)\n",
    "longest_trip = max(onebike_durations)\n",
    "\n",
    "# Print out the results\n",
    "print(\"The shortest trip was \" + str(shortest_trip) + \" seconds\")\n",
    "print(\"The longest trip was \" + str(longest_trip) + \" seconds\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Time Zones and Daylight Saving (Module 03-051)**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **UTC offsets**\n",
    "\n",
    "1. UTC\n",
    "*-# Import relevant classes*\n",
    "from datetime import datetime, timedelta, timezone\n",
    "\n",
    "*-# US Eastern Standard time zone*\n",
    "ET = timezone(timedelta(hours=-5))\n",
    "\n",
    "*-# Timezone-aware datetime*\n",
    "dt = datetime(2017, 12, 30, 15, 9, 3, tzinfo = ET)\n",
    "print(dt)\n",
    "\n",
    "`2017-12-30 15:09:03-05:00`\n",
    "\n",
    "*-# India Standard time zone*\n",
    "IST = timezone(timedelta(hours=5, minutes=30))\n",
    "\n",
    "*-# Convert to IST*\n",
    "print(dt.astimezone(IST))\n",
    "\n",
    "`2017-12-31 01:39:03+05:30`\n",
    "\n",
    "2. Adjusting timezone vs changing tzinfo\n",
    "\n",
    "print(dt)'\n",
    "\n",
    "`2017-12-30 15:09:03-05:00`\n",
    "\n",
    "print(dt.replace(tzinfo=timezone.utc))\n",
    "\n",
    "`2017-12-30 15:09:03+00:00`\n",
    "\n",
    "*-# Change original to match UTC*\n",
    "print(dt.astimezone(timezone.utc))\n",
    "\n",
    "`2017-12-30 20:09:03+00:00`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating timezone aware datetimes**\n",
    "\n",
    "In this exercise, you will practice setting timezones manually.\n",
    "\n",
    "STEP01\n",
    "    Import timezone.\n",
    "    Set the tzinfo to UTC, without using timedelta.\n",
    "STEP02\n",
    "    Set pst to be a timezone set for UTC-8.\n",
    "    Set dt's timezone to be pst.\n",
    "STEP03\n",
    "    Set aedt to be a timezone set for UTC+11.\n",
    "    Set dt's timezone to be aedt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-10-01T15:26:26+00:00\n"
     ]
    }
   ],
   "source": [
    "# Import datetime, timezone\n",
    "from datetime import datetime, timezone\n",
    "\n",
    "# October 1, 2017 at 15:26:26, UTC\n",
    "dt = datetime(2017, 10, 1, 15, 26, 26, tzinfo=timezone.utc)\n",
    "\n",
    "# Print results\n",
    "print(dt.isoformat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-10-01T15:26:26-08:00\n"
     ]
    }
   ],
   "source": [
    "# Import datetime, timedelta, timezone\n",
    "from datetime import datetime, timedelta, timezone\n",
    "\n",
    "# Create a timezone for Pacific Standard Time, or UTC-8\n",
    "pst = timezone(timedelta(hours=-8))\n",
    "\n",
    "# October 1, 2017 at 15:26:26, UTC-8\n",
    "dt = datetime(2017, 10, 1, 15, 26, 26, tzinfo =pst)\n",
    "\n",
    "# Print results\n",
    "print(dt.isoformat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-10-01T15:26:26+11:00\n",
      "2017-10-01 15:26:26+11:00\n"
     ]
    }
   ],
   "source": [
    "# Import datetime, timedelta, timezone\n",
    "from datetime import datetime, timedelta, timezone\n",
    "\n",
    "# Create a timezone for Australian Eastern Daylight Time, or UTC+11\n",
    "aedt = timezone(timedelta(hours=11))\n",
    "\n",
    "\n",
    "# October 1, 2017 at 15:26:26, UTC+11\n",
    "dt = datetime(2017, 10, 1, 15, 26, 26, tzinfo=aedt)\n",
    "\n",
    "# Print results\n",
    "print(dt.isoformat())\n",
    "print(dt)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Setting timezones**\n",
    "\n",
    "Now that you have the hang of setting timezones one at a time, let's look at setting them for the first ten trips that W20529 took.\n",
    "\n",
    "`timezone` and `timedelta` have already been imported. Make the change using `.replace()`\n",
    "\n",
    "STEPS\n",
    "    Create edt, a timezone object whose UTC offset is -4 hours.\n",
    "    Within the for loop:\n",
    "    Set the tzinfo for trip['start'].\n",
    "    Set the tzinfo for trip['end']."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "      <td>1278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>31230</td>\n",
       "      <td>Metro Center / 12th &amp; G St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "      <td>1277.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start date             End date  Start station number  \\\n",
       "0  2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1  2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "2  2017-10-02 06:37:10  2017-10-02 06:42:53                 31036   \n",
       "3  2017-10-02 08:56:45  2017-10-02 09:18:03                 31037   \n",
       "4  2017-10-02 18:23:48  2017-10-02 18:45:05                 31295   \n",
       "\n",
       "                          Start station  End station number  \\\n",
       "0                  Glebe Rd & 11th St N               31036   \n",
       "1         George Mason Dr & Wilson Blvd               31036   \n",
       "2         George Mason Dr & Wilson Blvd               31037   \n",
       "3  Ballston Metro / N Stuart & 9th St N               31295   \n",
       "4                     Potomac & M St NW               31230   \n",
       "\n",
       "                            End station Bike number Member type  \\\n",
       "0         George Mason Dr & Wilson Blvd      W20529      Member   \n",
       "1         George Mason Dr & Wilson Blvd      W20529      Casual   \n",
       "2  Ballston Metro / N Stuart & 9th St N      W20529      Member   \n",
       "3                     Potomac & M St NW      W20529      Member   \n",
       "4         Metro Center / 12th & G St NW      W20529      Member   \n",
       "\n",
       "                start                 end  trip_duration  \n",
       "0 2017-10-01 15:23:25 2017-10-01 15:26:26          181.0  \n",
       "1 2017-10-01 15:42:57 2017-10-01 17:49:59         7622.0  \n",
       "2 2017-10-02 06:37:10 2017-10-02 06:42:53          343.0  \n",
       "3 2017-10-02 08:56:45 2017-10-02 09:18:03         1278.0  \n",
       "4 2017-10-02 18:23:48 2017-10-02 18:45:05         1277.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Start date                      object\n",
       "End date                        object\n",
       "Start station number             int64\n",
       "Start station                   object\n",
       "End station number               int64\n",
       "End station                     object\n",
       "Bike number                     object\n",
       "Member type                     object\n",
       "start                   datetime64[ns]\n",
       "end                     datetime64[ns]\n",
       "trip_duration                  float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a timezone object corresponding to UTC-4\n",
    "edt = timezone(timedelta(hours=-4))\n",
    "\n",
    "# Loop over trips, updating the start and end datetimes to be in UTC-4\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "  # Update trip['start'] and trip['end']\n",
    "  trip['start'] = trip['start'].replace(tzinfo=edt)\n",
    "  trip['end'] = trip['end'].replace(tzinfo=edt)\n",
    "\n",
    "display(onebike_datetimes.head(5))\n",
    "display(onebike_datetimes.dtypes)\n",
    "\n",
    "#Not working as intended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-01 15:23:25-04:00</td>\n",
       "      <td>2017-10-01 15:26:26-04:00</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-10-01 15:42:57-04:00</td>\n",
       "      <td>2017-10-01 17:49:59-04:00</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 06:37:10-04:00</td>\n",
       "      <td>2017-10-02 06:42:53-04:00</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 08:56:45-04:00</td>\n",
       "      <td>2017-10-02 09:18:03-04:00</td>\n",
       "      <td>1278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>31230</td>\n",
       "      <td>Metro Center / 12th &amp; G St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 18:23:48-04:00</td>\n",
       "      <td>2017-10-02 18:45:05-04:00</td>\n",
       "      <td>1277.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start date             End date  Start station number  \\\n",
       "0  2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1  2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "2  2017-10-02 06:37:10  2017-10-02 06:42:53                 31036   \n",
       "3  2017-10-02 08:56:45  2017-10-02 09:18:03                 31037   \n",
       "4  2017-10-02 18:23:48  2017-10-02 18:45:05                 31295   \n",
       "\n",
       "                          Start station  End station number  \\\n",
       "0                  Glebe Rd & 11th St N               31036   \n",
       "1         George Mason Dr & Wilson Blvd               31036   \n",
       "2         George Mason Dr & Wilson Blvd               31037   \n",
       "3  Ballston Metro / N Stuart & 9th St N               31295   \n",
       "4                     Potomac & M St NW               31230   \n",
       "\n",
       "                            End station Bike number Member type  \\\n",
       "0         George Mason Dr & Wilson Blvd      W20529      Member   \n",
       "1         George Mason Dr & Wilson Blvd      W20529      Casual   \n",
       "2  Ballston Metro / N Stuart & 9th St N      W20529      Member   \n",
       "3                     Potomac & M St NW      W20529      Member   \n",
       "4         Metro Center / 12th & G St NW      W20529      Member   \n",
       "\n",
       "                      start                       end  trip_duration  \n",
       "0 2017-10-01 15:23:25-04:00 2017-10-01 15:26:26-04:00          181.0  \n",
       "1 2017-10-01 15:42:57-04:00 2017-10-01 17:49:59-04:00         7622.0  \n",
       "2 2017-10-02 06:37:10-04:00 2017-10-02 06:42:53-04:00          343.0  \n",
       "3 2017-10-02 08:56:45-04:00 2017-10-02 09:18:03-04:00         1278.0  \n",
       "4 2017-10-02 18:23:48-04:00 2017-10-02 18:45:05-04:00         1277.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Start date                                 object\n",
       "End date                                   object\n",
       "Start station number                        int64\n",
       "Start station                              object\n",
       "End station number                          int64\n",
       "End station                                object\n",
       "Bike number                                object\n",
       "Member type                                object\n",
       "start                   datetime64[ns, UTC-04:00]\n",
       "end                     datetime64[ns, UTC-04:00]\n",
       "trip_duration                             float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a timezone object corresponding to UTC-4\n",
    "edt = timezone(timedelta(hours=-4))\n",
    "\n",
    "# Loop over trips, updating the start and end datetimes to be in UTC-4\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "    # Convert start and end to UTC timezone\n",
    "    trip['start'] = trip['start'].tz_localize('UTC')\n",
    "    trip['end'] = trip['end'].tz_localize('UTC')\n",
    "\n",
    "    # Convert start and end to UTC-4 timezone\n",
    "    trip['start'] = trip['start'].tz_convert(edt)\n",
    "    trip['end'] = trip['end'].tz_convert(edt)\n",
    "\n",
    "\n",
    "# Convert start and end columns to datetime with timezone information\n",
    "onebike_datetimes['start'] = pd.to_datetime(onebike_datetimes['start']).dt.tz_localize(edt)\n",
    "onebike_datetimes['end'] = pd.to_datetime(onebike_datetimes['end']).dt.tz_localize(edt)\n",
    "\n",
    "\n",
    "# Display the first few rows and data types\n",
    "display(onebike_datetimes.head(5))\n",
    "display(onebike_datetimes.dtypes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What time did the bike leave in UTC?**\n",
    "\n",
    "Having set the timezone for the first ten rides that W20529 took, let's see what time the bike left in UTC. We've already loaded the results of the previous exercise into memory.\n",
    "\n",
    "STEPS\n",
    "Within the for loop, move dt to be in UTC. Use timezone.utc as a convenient shortcut for UTC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Original: 2017-12-18 19:18:23-04:00 | UTC: 2017-12-18T23:18:23+00:00\n",
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      "Original: 2017-12-20 08:15:45-04:00 | UTC: 2017-12-20T12:15:45+00:00\n",
      "Original: 2017-12-20 08:33:32-04:00 | UTC: 2017-12-20T12:33:32+00:00\n",
      "Original: 2017-12-20 13:43:36-04:00 | UTC: 2017-12-20T17:43:36+00:00\n",
      "Original: 2017-12-20 18:57:53-04:00 | UTC: 2017-12-20T22:57:53+00:00\n",
      "Original: 2017-12-21 07:21:11-04:00 | UTC: 2017-12-21T11:21:11+00:00\n",
      "Original: 2017-12-21 08:01:58-04:00 | UTC: 2017-12-21T12:01:58+00:00\n",
      "Original: 2017-12-21 13:20:54-04:00 | UTC: 2017-12-21T17:20:54+00:00\n",
      "Original: 2017-12-21 15:26:08-04:00 | UTC: 2017-12-21T19:26:08+00:00\n",
      "Original: 2017-12-21 18:09:46-04:00 | UTC: 2017-12-21T22:09:46+00:00\n",
      "Original: 2017-12-22 16:14:21-04:00 | UTC: 2017-12-22T20:14:21+00:00\n",
      "Original: 2017-12-22 16:29:17-04:00 | UTC: 2017-12-22T20:29:17+00:00\n",
      "Original: 2017-12-25 12:49:51-04:00 | UTC: 2017-12-25T16:49:51+00:00\n",
      "Original: 2017-12-25 13:46:44-04:00 | UTC: 2017-12-25T17:46:44+00:00\n",
      "Original: 2017-12-26 10:40:16-04:00 | UTC: 2017-12-26T14:40:16+00:00\n",
      "Original: 2017-12-27 16:56:12-04:00 | UTC: 2017-12-27T20:56:12+00:00\n",
      "Original: 2017-12-29 06:02:34-04:00 | UTC: 2017-12-29T10:02:34+00:00\n",
      "Original: 2017-12-29 12:21:03-04:00 | UTC: 2017-12-29T16:21:03+00:00\n",
      "Original: 2017-12-29 14:32:55-04:00 | UTC: 2017-12-29T18:32:55+00:00\n",
      "Original: 2017-12-29 15:08:26-04:00 | UTC: 2017-12-29T19:08:26+00:00\n",
      "Original: 2017-12-29 20:33:34-04:00 | UTC: 2017-12-30T00:33:34+00:00\n",
      "Original: 2017-12-30 13:51:03-04:00 | UTC: 2017-12-30T17:51:03+00:00\n",
      "Original: 2017-12-30 15:09:03-04:00 | UTC: 2017-12-30T19:09:03+00:00\n"
     ]
    }
   ],
   "source": [
    "# Loop over the trips\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "  # Pull out the start\n",
    "  dt = trip['start']\n",
    "  # Move dt to be in UTC\n",
    "  dt = dt.astimezone(timezone.utc)\n",
    "  \n",
    "  # Print the start time in UTC\n",
    "  print('Original:', trip['start'], '| UTC:', dt.isoformat())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Time zone database**\n",
    "\n",
    "1. Time zone database\n",
    "\n",
    "*-# Imports*\n",
    "from datetime import datetime\n",
    "from dateutil import tz\n",
    "*-# Eastern time*\n",
    "et = tz.gettz('America/New_York')\n",
    "\n",
    "*tz database*\n",
    "Format: 'Continent/City'\n",
    "Examples:\n",
    "'America/New_York'\n",
    "'America/Mexico_City'\n",
    "'Europe/London'\n",
    "'Africa/Accra'\n",
    "\n",
    "*-# Last ride*\n",
    "last = datetime(2017, 12, 30, 15, 9, 3, tzinfo=et)\n",
    "print(last)\n",
    "\n",
    "`2017-12-30 15:09:03-05:00`\n",
    "\n",
    "*-# First ride*\n",
    "first = datetime(2017, 10, 1, 15, 23, 25, tzinfo=et)\n",
    "print(first)\n",
    "\n",
    "`2017-10-01 15:23:25-04:00`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Putting the bike trips into the right time zone**\n",
    "\n",
    "Instead of setting the timezones for W20529 by hand, let's assign them to their IANA timezone: 'America/New_York'. Since we know their political jurisdiction, we don't need to look up their UTC offset. Python will do that for us.\n",
    "\n",
    "STEPS\n",
    "    Import tz from dateutil.\n",
    "    Assign et to be the timezone 'America/New_York'.\n",
    "    Within the for loop, set start and end to have et as their timezone (use .replace())."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-01 15:23:25-04:00</td>\n",
       "      <td>2017-10-01 15:26:26-04:00</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-10-01 15:42:57-04:00</td>\n",
       "      <td>2017-10-01 17:49:59-04:00</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start date             End date  Start station number  \\\n",
       "0  2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1  2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "\n",
       "                   Start station  End station number  \\\n",
       "0           Glebe Rd & 11th St N               31036   \n",
       "1  George Mason Dr & Wilson Blvd               31036   \n",
       "\n",
       "                     End station Bike number Member type  \\\n",
       "0  George Mason Dr & Wilson Blvd      W20529      Member   \n",
       "1  George Mason Dr & Wilson Blvd      W20529      Casual   \n",
       "\n",
       "                      start                       end  trip_duration  \n",
       "0 2017-10-01 15:23:25-04:00 2017-10-01 15:26:26-04:00          181.0  \n",
       "1 2017-10-01 15:42:57-04:00 2017-10-01 17:49:59-04:00         7622.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Start date                                 object\n",
       "End date                                   object\n",
       "Start station number                        int64\n",
       "Start station                              object\n",
       "End station number                          int64\n",
       "End station                                object\n",
       "Bike number                                object\n",
       "Member type                                object\n",
       "start                   datetime64[ns, UTC-04:00]\n",
       "end                     datetime64[ns, UTC-04:00]\n",
       "trip_duration                             float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import tz\n",
    "from dateutil import tz\n",
    "\n",
    "# Create a timezone object for Eastern Time\n",
    "et = tz.gettz('America/New_York')\n",
    "\n",
    "# Loop over trips, updating the datetimes to be in Eastern Time\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "  # Update trip['start'] and trip['end']\n",
    "  trip['start'] = trip['start'].replace(tzinfo=et)\n",
    "  trip['end'] = trip['end'].replace(tzinfo=et)\n",
    "\n",
    "display(onebike_datetimes.head(2))\n",
    "display(onebike_datetimes.dtypes)\n",
    "\n",
    "# not work as intended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Africa/Abidjan',\n",
       " 'Africa/Accra',\n",
       " 'Africa/Addis_Ababa',\n",
       " 'Africa/Algiers',\n",
       " 'Africa/Asmara',\n",
       " 'Africa/Asmera',\n",
       " 'Africa/Bamako',\n",
       " 'Africa/Bangui',\n",
       " 'Africa/Banjul',\n",
       " 'Africa/Bissau',\n",
       " 'Africa/Blantyre',\n",
       " 'Africa/Brazzaville',\n",
       " 'Africa/Bujumbura',\n",
       " 'Africa/Cairo',\n",
       " 'Africa/Casablanca',\n",
       " 'Africa/Ceuta',\n",
       " 'Africa/Conakry',\n",
       " 'Africa/Dakar',\n",
       " 'Africa/Dar_es_Salaam',\n",
       " 'Africa/Djibouti',\n",
       " 'Africa/Douala',\n",
       " 'Africa/El_Aaiun',\n",
       " 'Africa/Freetown',\n",
       " 'Africa/Gaborone',\n",
       " 'Africa/Harare',\n",
       " 'Africa/Johannesburg',\n",
       " 'Africa/Juba',\n",
       " 'Africa/Kampala',\n",
       " 'Africa/Khartoum',\n",
       " 'Africa/Kigali',\n",
       " 'Africa/Kinshasa',\n",
       " 'Africa/Lagos',\n",
       " 'Africa/Libreville',\n",
       " 'Africa/Lome',\n",
       " 'Africa/Luanda',\n",
       " 'Africa/Lubumbashi',\n",
       " 'Africa/Lusaka',\n",
       " 'Africa/Malabo',\n",
       " 'Africa/Maputo',\n",
       " 'Africa/Maseru',\n",
       " 'Africa/Mbabane',\n",
       " 'Africa/Mogadishu',\n",
       " 'Africa/Monrovia',\n",
       " 'Africa/Nairobi',\n",
       " 'Africa/Ndjamena',\n",
       " 'Africa/Niamey',\n",
       " 'Africa/Nouakchott',\n",
       " 'Africa/Ouagadougou',\n",
       " 'Africa/Porto-Novo',\n",
       " 'Africa/Sao_Tome',\n",
       " 'Africa/Timbuktu',\n",
       " 'Africa/Tripoli',\n",
       " 'Africa/Tunis',\n",
       " 'Africa/Windhoek',\n",
       " 'America/Adak',\n",
       " 'America/Anchorage',\n",
       " 'America/Anguilla',\n",
       " 'America/Antigua',\n",
       " 'America/Araguaina',\n",
       " 'America/Argentina/Buenos_Aires',\n",
       " 'America/Argentina/Catamarca',\n",
       " 'America/Argentina/ComodRivadavia',\n",
       " 'America/Argentina/Cordoba',\n",
       " 'America/Argentina/Jujuy',\n",
       " 'America/Argentina/La_Rioja',\n",
       " 'America/Argentina/Mendoza',\n",
       " 'America/Argentina/Rio_Gallegos',\n",
       " 'America/Argentina/Salta',\n",
       " 'America/Argentina/San_Juan',\n",
       " 'America/Argentina/San_Luis',\n",
       " 'America/Argentina/Tucuman',\n",
       " 'America/Argentina/Ushuaia',\n",
       " 'America/Aruba',\n",
       " 'America/Asuncion',\n",
       " 'America/Atikokan',\n",
       " 'America/Atka',\n",
       " 'America/Bahia',\n",
       " 'America/Bahia_Banderas',\n",
       " 'America/Barbados',\n",
       " 'America/Belem',\n",
       " 'America/Belize',\n",
       " 'America/Blanc-Sablon',\n",
       " 'America/Boa_Vista',\n",
       " 'America/Bogota',\n",
       " 'America/Boise',\n",
       " 'America/Buenos_Aires',\n",
       " 'America/Cambridge_Bay',\n",
       " 'America/Campo_Grande',\n",
       " 'America/Cancun',\n",
       " 'America/Caracas',\n",
       " 'America/Catamarca',\n",
       " 'America/Cayenne',\n",
       " 'America/Cayman',\n",
       " 'America/Chicago',\n",
       " 'America/Chihuahua',\n",
       " 'America/Coral_Harbour',\n",
       " 'America/Cordoba',\n",
       " 'America/Costa_Rica',\n",
       " 'America/Creston',\n",
       " 'America/Cuiaba',\n",
       " 'America/Curacao',\n",
       " 'America/Danmarkshavn',\n",
       " 'America/Dawson',\n",
       " 'America/Dawson_Creek',\n",
       " 'America/Denver',\n",
       " 'America/Detroit',\n",
       " 'America/Dominica',\n",
       " 'America/Edmonton',\n",
       " 'America/Eirunepe',\n",
       " 'America/El_Salvador',\n",
       " 'America/Ensenada',\n",
       " 'America/Fort_Nelson',\n",
       " 'America/Fort_Wayne',\n",
       " 'America/Fortaleza',\n",
       " 'America/Glace_Bay',\n",
       " 'America/Godthab',\n",
       " 'America/Goose_Bay',\n",
       " 'America/Grand_Turk',\n",
       " 'America/Grenada',\n",
       " 'America/Guadeloupe',\n",
       " 'America/Guatemala',\n",
       " 'America/Guayaquil',\n",
       " 'America/Guyana',\n",
       " 'America/Halifax',\n",
       " 'America/Havana',\n",
       " 'America/Hermosillo',\n",
       " 'America/Indiana/Indianapolis',\n",
       " 'America/Indiana/Knox',\n",
       " 'America/Indiana/Marengo',\n",
       " 'America/Indiana/Petersburg',\n",
       " 'America/Indiana/Tell_City',\n",
       " 'America/Indiana/Vevay',\n",
       " 'America/Indiana/Vincennes',\n",
       " 'America/Indiana/Winamac',\n",
       " 'America/Indianapolis',\n",
       " 'America/Inuvik',\n",
       " 'America/Iqaluit',\n",
       " 'America/Jamaica',\n",
       " 'America/Jujuy',\n",
       " 'America/Juneau',\n",
       " 'America/Kentucky/Louisville',\n",
       " 'America/Kentucky/Monticello',\n",
       " 'America/Knox_IN',\n",
       " 'America/Kralendijk',\n",
       " 'America/La_Paz',\n",
       " 'America/Lima',\n",
       " 'America/Los_Angeles',\n",
       " 'America/Louisville',\n",
       " 'America/Lower_Princes',\n",
       " 'America/Maceio',\n",
       " 'America/Managua',\n",
       " 'America/Manaus',\n",
       " 'America/Marigot',\n",
       " 'America/Martinique',\n",
       " 'America/Matamoros',\n",
       " 'America/Mazatlan',\n",
       " 'America/Mendoza',\n",
       " 'America/Menominee',\n",
       " 'America/Merida',\n",
       " 'America/Metlakatla',\n",
       " 'America/Mexico_City',\n",
       " 'America/Miquelon',\n",
       " 'America/Moncton',\n",
       " 'America/Monterrey',\n",
       " 'America/Montevideo',\n",
       " 'America/Montreal',\n",
       " 'America/Montserrat',\n",
       " 'America/Nassau',\n",
       " 'America/New_York',\n",
       " 'America/Nipigon',\n",
       " 'America/Nome',\n",
       " 'America/Noronha',\n",
       " 'America/North_Dakota/Beulah',\n",
       " 'America/North_Dakota/Center',\n",
       " 'America/North_Dakota/New_Salem',\n",
       " 'America/Nuuk',\n",
       " 'America/Ojinaga',\n",
       " 'America/Panama',\n",
       " 'America/Pangnirtung',\n",
       " 'America/Paramaribo',\n",
       " 'America/Phoenix',\n",
       " 'America/Port-au-Prince',\n",
       " 'America/Port_of_Spain',\n",
       " 'America/Porto_Acre',\n",
       " 'America/Porto_Velho',\n",
       " 'America/Puerto_Rico',\n",
       " 'America/Punta_Arenas',\n",
       " 'America/Rainy_River',\n",
       " 'America/Rankin_Inlet',\n",
       " 'America/Recife',\n",
       " 'America/Regina',\n",
       " 'America/Resolute',\n",
       " 'America/Rio_Branco',\n",
       " 'America/Rosario',\n",
       " 'America/Santa_Isabel',\n",
       " 'America/Santarem',\n",
       " 'America/Santiago',\n",
       " 'America/Santo_Domingo',\n",
       " 'America/Sao_Paulo',\n",
       " 'America/Scoresbysund',\n",
       " 'America/Shiprock',\n",
       " 'America/Sitka',\n",
       " 'America/St_Barthelemy',\n",
       " 'America/St_Johns',\n",
       " 'America/St_Kitts',\n",
       " 'America/St_Lucia',\n",
       " 'America/St_Thomas',\n",
       " 'America/St_Vincent',\n",
       " 'America/Swift_Current',\n",
       " 'America/Tegucigalpa',\n",
       " 'America/Thule',\n",
       " 'America/Thunder_Bay',\n",
       " 'America/Tijuana',\n",
       " 'America/Toronto',\n",
       " 'America/Tortola',\n",
       " 'America/Vancouver',\n",
       " 'America/Virgin',\n",
       " 'America/Whitehorse',\n",
       " 'America/Winnipeg',\n",
       " 'America/Yakutat',\n",
       " 'America/Yellowknife',\n",
       " 'Antarctica/Casey',\n",
       " 'Antarctica/Davis',\n",
       " 'Antarctica/DumontDUrville',\n",
       " 'Antarctica/Macquarie',\n",
       " 'Antarctica/Mawson',\n",
       " 'Antarctica/McMurdo',\n",
       " 'Antarctica/Palmer',\n",
       " 'Antarctica/Rothera',\n",
       " 'Antarctica/South_Pole',\n",
       " 'Antarctica/Syowa',\n",
       " 'Antarctica/Troll',\n",
       " 'Antarctica/Vostok',\n",
       " 'Arctic/Longyearbyen',\n",
       " 'Asia/Aden',\n",
       " 'Asia/Almaty',\n",
       " 'Asia/Amman',\n",
       " 'Asia/Anadyr',\n",
       " 'Asia/Aqtau',\n",
       " 'Asia/Aqtobe',\n",
       " 'Asia/Ashgabat',\n",
       " 'Asia/Ashkhabad',\n",
       " 'Asia/Atyrau',\n",
       " 'Asia/Baghdad',\n",
       " 'Asia/Bahrain',\n",
       " 'Asia/Baku',\n",
       " 'Asia/Bangkok',\n",
       " 'Asia/Barnaul',\n",
       " 'Asia/Beirut',\n",
       " 'Asia/Bishkek',\n",
       " 'Asia/Brunei',\n",
       " 'Asia/Calcutta',\n",
       " 'Asia/Chita',\n",
       " 'Asia/Choibalsan',\n",
       " 'Asia/Chongqing',\n",
       " 'Asia/Chungking',\n",
       " 'Asia/Colombo',\n",
       " 'Asia/Dacca',\n",
       " 'Asia/Damascus',\n",
       " 'Asia/Dhaka',\n",
       " 'Asia/Dili',\n",
       " 'Asia/Dubai',\n",
       " 'Asia/Dushanbe',\n",
       " 'Asia/Famagusta',\n",
       " 'Asia/Gaza',\n",
       " 'Asia/Harbin',\n",
       " 'Asia/Hebron',\n",
       " 'Asia/Ho_Chi_Minh',\n",
       " 'Asia/Hong_Kong',\n",
       " 'Asia/Hovd',\n",
       " 'Asia/Irkutsk',\n",
       " 'Asia/Istanbul',\n",
       " 'Asia/Jakarta',\n",
       " 'Asia/Jayapura',\n",
       " 'Asia/Jerusalem',\n",
       " 'Asia/Kabul',\n",
       " 'Asia/Kamchatka',\n",
       " 'Asia/Karachi',\n",
       " 'Asia/Kashgar',\n",
       " 'Asia/Kathmandu',\n",
       " 'Asia/Katmandu',\n",
       " 'Asia/Khandyga',\n",
       " 'Asia/Kolkata',\n",
       " 'Asia/Krasnoyarsk',\n",
       " 'Asia/Kuala_Lumpur',\n",
       " 'Asia/Kuching',\n",
       " 'Asia/Kuwait',\n",
       " 'Asia/Macao',\n",
       " 'Asia/Macau',\n",
       " 'Asia/Magadan',\n",
       " 'Asia/Makassar',\n",
       " 'Asia/Manila',\n",
       " 'Asia/Muscat',\n",
       " 'Asia/Nicosia',\n",
       " 'Asia/Novokuznetsk',\n",
       " 'Asia/Novosibirsk',\n",
       " 'Asia/Omsk',\n",
       " 'Asia/Oral',\n",
       " 'Asia/Phnom_Penh',\n",
       " 'Asia/Pontianak',\n",
       " 'Asia/Pyongyang',\n",
       " 'Asia/Qatar',\n",
       " 'Asia/Qostanay',\n",
       " 'Asia/Qyzylorda',\n",
       " 'Asia/Rangoon',\n",
       " 'Asia/Riyadh',\n",
       " 'Asia/Saigon',\n",
       " 'Asia/Sakhalin',\n",
       " 'Asia/Samarkand',\n",
       " 'Asia/Seoul',\n",
       " 'Asia/Shanghai',\n",
       " 'Asia/Singapore',\n",
       " 'Asia/Srednekolymsk',\n",
       " 'Asia/Taipei',\n",
       " 'Asia/Tashkent',\n",
       " 'Asia/Tbilisi',\n",
       " 'Asia/Tehran',\n",
       " 'Asia/Tel_Aviv',\n",
       " 'Asia/Thimbu',\n",
       " 'Asia/Thimphu',\n",
       " 'Asia/Tokyo',\n",
       " 'Asia/Tomsk',\n",
       " 'Asia/Ujung_Pandang',\n",
       " 'Asia/Ulaanbaatar',\n",
       " 'Asia/Ulan_Bator',\n",
       " 'Asia/Urumqi',\n",
       " 'Asia/Ust-Nera',\n",
       " 'Asia/Vientiane',\n",
       " 'Asia/Vladivostok',\n",
       " 'Asia/Yakutsk',\n",
       " 'Asia/Yangon',\n",
       " 'Asia/Yekaterinburg',\n",
       " 'Asia/Yerevan',\n",
       " 'Atlantic/Azores',\n",
       " 'Atlantic/Bermuda',\n",
       " 'Atlantic/Canary',\n",
       " 'Atlantic/Cape_Verde',\n",
       " 'Atlantic/Faeroe',\n",
       " 'Atlantic/Faroe',\n",
       " 'Atlantic/Jan_Mayen',\n",
       " 'Atlantic/Madeira',\n",
       " 'Atlantic/Reykjavik',\n",
       " 'Atlantic/South_Georgia',\n",
       " 'Atlantic/St_Helena',\n",
       " 'Atlantic/Stanley',\n",
       " 'Australia/ACT',\n",
       " 'Australia/Adelaide',\n",
       " 'Australia/Brisbane',\n",
       " 'Australia/Broken_Hill',\n",
       " 'Australia/Canberra',\n",
       " 'Australia/Currie',\n",
       " 'Australia/Darwin',\n",
       " 'Australia/Eucla',\n",
       " 'Australia/Hobart',\n",
       " 'Australia/LHI',\n",
       " 'Australia/Lindeman',\n",
       " 'Australia/Lord_Howe',\n",
       " 'Australia/Melbourne',\n",
       " 'Australia/NSW',\n",
       " 'Australia/North',\n",
       " 'Australia/Perth',\n",
       " 'Australia/Queensland',\n",
       " 'Australia/South',\n",
       " 'Australia/Sydney',\n",
       " 'Australia/Tasmania',\n",
       " 'Australia/Victoria',\n",
       " 'Australia/West',\n",
       " 'Australia/Yancowinna',\n",
       " 'Brazil/Acre',\n",
       " 'Brazil/DeNoronha',\n",
       " 'Brazil/East',\n",
       " 'Brazil/West',\n",
       " 'CET',\n",
       " 'CST6CDT',\n",
       " 'Canada/Atlantic',\n",
       " 'Canada/Central',\n",
       " 'Canada/Eastern',\n",
       " 'Canada/Mountain',\n",
       " 'Canada/Newfoundland',\n",
       " 'Canada/Pacific',\n",
       " 'Canada/Saskatchewan',\n",
       " 'Canada/Yukon',\n",
       " 'Chile/Continental',\n",
       " 'Chile/EasterIsland',\n",
       " 'Cuba',\n",
       " 'EET',\n",
       " 'EST',\n",
       " 'EST5EDT',\n",
       " 'Egypt',\n",
       " 'Eire',\n",
       " 'Etc/GMT',\n",
       " 'Etc/GMT+0',\n",
       " 'Etc/GMT+1',\n",
       " 'Etc/GMT+10',\n",
       " 'Etc/GMT+11',\n",
       " 'Etc/GMT+12',\n",
       " 'Etc/GMT+2',\n",
       " 'Etc/GMT+3',\n",
       " 'Etc/GMT+4',\n",
       " 'Etc/GMT+5',\n",
       " 'Etc/GMT+6',\n",
       " 'Etc/GMT+7',\n",
       " 'Etc/GMT+8',\n",
       " 'Etc/GMT+9',\n",
       " 'Etc/GMT-0',\n",
       " 'Etc/GMT-1',\n",
       " 'Etc/GMT-10',\n",
       " 'Etc/GMT-11',\n",
       " 'Etc/GMT-12',\n",
       " 'Etc/GMT-13',\n",
       " 'Etc/GMT-14',\n",
       " 'Etc/GMT-2',\n",
       " 'Etc/GMT-3',\n",
       " 'Etc/GMT-4',\n",
       " 'Etc/GMT-5',\n",
       " 'Etc/GMT-6',\n",
       " 'Etc/GMT-7',\n",
       " 'Etc/GMT-8',\n",
       " 'Etc/GMT-9',\n",
       " 'Etc/GMT0',\n",
       " 'Etc/Greenwich',\n",
       " 'Etc/UCT',\n",
       " 'Etc/UTC',\n",
       " 'Etc/Universal',\n",
       " 'Etc/Zulu',\n",
       " 'Europe/Amsterdam',\n",
       " 'Europe/Andorra',\n",
       " 'Europe/Astrakhan',\n",
       " 'Europe/Athens',\n",
       " 'Europe/Belfast',\n",
       " 'Europe/Belgrade',\n",
       " 'Europe/Berlin',\n",
       " 'Europe/Bratislava',\n",
       " 'Europe/Brussels',\n",
       " 'Europe/Bucharest',\n",
       " 'Europe/Budapest',\n",
       " 'Europe/Busingen',\n",
       " 'Europe/Chisinau',\n",
       " 'Europe/Copenhagen',\n",
       " 'Europe/Dublin',\n",
       " 'Europe/Gibraltar',\n",
       " 'Europe/Guernsey',\n",
       " 'Europe/Helsinki',\n",
       " 'Europe/Isle_of_Man',\n",
       " 'Europe/Istanbul',\n",
       " 'Europe/Jersey',\n",
       " 'Europe/Kaliningrad',\n",
       " 'Europe/Kiev',\n",
       " 'Europe/Kirov',\n",
       " 'Europe/Kyiv',\n",
       " 'Europe/Lisbon',\n",
       " 'Europe/Ljubljana',\n",
       " 'Europe/London',\n",
       " 'Europe/Luxembourg',\n",
       " 'Europe/Madrid',\n",
       " 'Europe/Malta',\n",
       " 'Europe/Mariehamn',\n",
       " 'Europe/Minsk',\n",
       " 'Europe/Monaco',\n",
       " 'Europe/Moscow',\n",
       " 'Europe/Nicosia',\n",
       " 'Europe/Oslo',\n",
       " 'Europe/Paris',\n",
       " 'Europe/Podgorica',\n",
       " 'Europe/Prague',\n",
       " 'Europe/Riga',\n",
       " 'Europe/Rome',\n",
       " 'Europe/Samara',\n",
       " 'Europe/San_Marino',\n",
       " 'Europe/Sarajevo',\n",
       " 'Europe/Saratov',\n",
       " 'Europe/Simferopol',\n",
       " 'Europe/Skopje',\n",
       " 'Europe/Sofia',\n",
       " 'Europe/Stockholm',\n",
       " 'Europe/Tallinn',\n",
       " 'Europe/Tirane',\n",
       " 'Europe/Tiraspol',\n",
       " 'Europe/Ulyanovsk',\n",
       " 'Europe/Uzhgorod',\n",
       " 'Europe/Vaduz',\n",
       " 'Europe/Vatican',\n",
       " 'Europe/Vienna',\n",
       " 'Europe/Vilnius',\n",
       " 'Europe/Volgograd',\n",
       " 'Europe/Warsaw',\n",
       " 'Europe/Zagreb',\n",
       " 'Europe/Zaporozhye',\n",
       " 'Europe/Zurich',\n",
       " 'GB',\n",
       " 'GB-Eire',\n",
       " 'GMT',\n",
       " 'GMT+0',\n",
       " 'GMT-0',\n",
       " 'GMT0',\n",
       " 'Greenwich',\n",
       " 'HST',\n",
       " 'Hongkong',\n",
       " 'Iceland',\n",
       " 'Indian/Antananarivo',\n",
       " 'Indian/Chagos',\n",
       " 'Indian/Christmas',\n",
       " 'Indian/Cocos',\n",
       " 'Indian/Comoro',\n",
       " 'Indian/Kerguelen',\n",
       " 'Indian/Mahe',\n",
       " 'Indian/Maldives',\n",
       " 'Indian/Mauritius',\n",
       " 'Indian/Mayotte',\n",
       " 'Indian/Reunion',\n",
       " 'Iran',\n",
       " 'Israel',\n",
       " 'Jamaica',\n",
       " 'Japan',\n",
       " 'Kwajalein',\n",
       " 'Libya',\n",
       " 'MET',\n",
       " 'MST',\n",
       " 'MST7MDT',\n",
       " 'Mexico/BajaNorte',\n",
       " 'Mexico/BajaSur',\n",
       " 'Mexico/General',\n",
       " 'NZ',\n",
       " 'NZ-CHAT',\n",
       " 'Navajo',\n",
       " 'PRC',\n",
       " 'PST8PDT',\n",
       " 'Pacific/Apia',\n",
       " 'Pacific/Auckland',\n",
       " 'Pacific/Bougainville',\n",
       " 'Pacific/Chatham',\n",
       " 'Pacific/Chuuk',\n",
       " 'Pacific/Easter',\n",
       " 'Pacific/Efate',\n",
       " 'Pacific/Enderbury',\n",
       " 'Pacific/Fakaofo',\n",
       " 'Pacific/Fiji',\n",
       " 'Pacific/Funafuti',\n",
       " 'Pacific/Galapagos',\n",
       " 'Pacific/Gambier',\n",
       " 'Pacific/Guadalcanal',\n",
       " 'Pacific/Guam',\n",
       " 'Pacific/Honolulu',\n",
       " 'Pacific/Johnston',\n",
       " 'Pacific/Kanton',\n",
       " 'Pacific/Kiritimati',\n",
       " 'Pacific/Kosrae',\n",
       " 'Pacific/Kwajalein',\n",
       " 'Pacific/Majuro',\n",
       " 'Pacific/Marquesas',\n",
       " 'Pacific/Midway',\n",
       " 'Pacific/Nauru',\n",
       " 'Pacific/Niue',\n",
       " 'Pacific/Norfolk',\n",
       " 'Pacific/Noumea',\n",
       " 'Pacific/Pago_Pago',\n",
       " 'Pacific/Palau',\n",
       " 'Pacific/Pitcairn',\n",
       " 'Pacific/Pohnpei',\n",
       " 'Pacific/Ponape',\n",
       " 'Pacific/Port_Moresby',\n",
       " 'Pacific/Rarotonga',\n",
       " 'Pacific/Saipan',\n",
       " 'Pacific/Samoa',\n",
       " 'Pacific/Tahiti',\n",
       " 'Pacific/Tarawa',\n",
       " 'Pacific/Tongatapu',\n",
       " 'Pacific/Truk',\n",
       " 'Pacific/Wake',\n",
       " 'Pacific/Wallis',\n",
       " 'Pacific/Yap',\n",
       " 'Poland',\n",
       " 'Portugal',\n",
       " 'ROC',\n",
       " 'ROK',\n",
       " 'Singapore',\n",
       " 'Turkey',\n",
       " 'UCT',\n",
       " 'US/Alaska',\n",
       " 'US/Aleutian',\n",
       " 'US/Arizona',\n",
       " 'US/Central',\n",
       " 'US/East-Indiana',\n",
       " 'US/Eastern',\n",
       " 'US/Hawaii',\n",
       " 'US/Indiana-Starke',\n",
       " 'US/Michigan',\n",
       " 'US/Mountain',\n",
       " 'US/Pacific',\n",
       " 'US/Samoa',\n",
       " 'UTC',\n",
       " 'Universal',\n",
       " 'W-SU',\n",
       " 'WET',\n",
       " 'Zulu']"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytz\n",
    "pytz.all_timezones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-01 15:23:25-04:00</td>\n",
       "      <td>2017-10-01 15:26:26-04:00</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-10-01 15:42:57-04:00</td>\n",
       "      <td>2017-10-01 17:49:59-04:00</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start date             End date  Start station number  \\\n",
       "0  2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1  2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "\n",
       "                   Start station  End station number  \\\n",
       "0           Glebe Rd & 11th St N               31036   \n",
       "1  George Mason Dr & Wilson Blvd               31036   \n",
       "\n",
       "                     End station Bike number Member type  \\\n",
       "0  George Mason Dr & Wilson Blvd      W20529      Member   \n",
       "1  George Mason Dr & Wilson Blvd      W20529      Casual   \n",
       "\n",
       "                      start                       end  trip_duration  \n",
       "0 2017-10-01 15:23:25-04:00 2017-10-01 15:26:26-04:00          181.0  \n",
       "1 2017-10-01 15:42:57-04:00 2017-10-01 17:49:59-04:00         7622.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Start date                                            object\n",
       "End date                                              object\n",
       "Start station number                                   int64\n",
       "Start station                                         object\n",
       "End station number                                     int64\n",
       "End station                                           object\n",
       "Bike number                                           object\n",
       "Member type                                           object\n",
       "start                   datetime64[ns, tzfile('US/Eastern')]\n",
       "end                     datetime64[ns, tzfile('US/Eastern')]\n",
       "trip_duration                                        float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dateutil import tz\n",
    "\n",
    "# create a timezone object for Eastern Time\n",
    "et = tz.gettz('America/New_York')\n",
    "\n",
    "# convert 'start' and 'end' columns to Eastern Time and then to UTC\n",
    "onebike_datetimes['start'] = onebike_datetimes['start'].apply(lambda x: x.replace(tzinfo=et))\n",
    "onebike_datetimes['end'] = onebike_datetimes['end'].apply(lambda x: x.replace(tzinfo=et))\n",
    "\n",
    "# display the first few rows and data types\n",
    "display(onebike_datetimes.head(2))\n",
    "display(onebike_datetimes.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-03 12:23:25-05:00</td>\n",
       "      <td>2017-10-03 12:26:26-05:00</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-10-03 12:42:57-05:00</td>\n",
       "      <td>2017-10-03 14:49:59-05:00</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start date             End date  Start station number  \\\n",
       "0  2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1  2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "\n",
       "                   Start station  End station number  \\\n",
       "0           Glebe Rd & 11th St N               31036   \n",
       "1  George Mason Dr & Wilson Blvd               31036   \n",
       "\n",
       "                     End station Bike number Member type  \\\n",
       "0  George Mason Dr & Wilson Blvd      W20529      Member   \n",
       "1  George Mason Dr & Wilson Blvd      W20529      Casual   \n",
       "\n",
       "                      start                       end  trip_duration  \n",
       "0 2017-10-03 12:23:25-05:00 2017-10-03 12:26:26-05:00          181.0  \n",
       "1 2017-10-03 12:42:57-05:00 2017-10-03 14:49:59-05:00         7622.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Start date                                                object\n",
       "End date                                                  object\n",
       "Start station number                                       int64\n",
       "Start station                                             object\n",
       "End station number                                         int64\n",
       "End station                                               object\n",
       "Bike number                                               object\n",
       "Member type                                               object\n",
       "start                   datetime64[ns, tzfile('America/Bogota')]\n",
       "end                     datetime64[ns, tzfile('America/Bogota')]\n",
       "trip_duration                                            float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dateutil import tz\n",
    "\n",
    "# create a timezone object for Eastern Time\n",
    "Col = tz.gettz('America/Bogota')\n",
    "\n",
    "# convert 'start' and 'end' columns to Eastern Time and then to UTC\n",
    "onebike_datetimes['start'] = onebike_datetimes['start'].apply(lambda x: x.replace(tzinfo=Col))\n",
    "onebike_datetimes['end'] = onebike_datetimes['end'].apply(lambda x: x.replace(tzinfo=Col))\n",
    "\n",
    "# display the first few rows and data types\n",
    "display(onebike_datetimes.head(2))\n",
    "display(onebike_datetimes.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-03 12:23:25+09:00</td>\n",
       "      <td>2017-10-03 12:26:26+09:00</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-10-03 12:42:57+09:00</td>\n",
       "      <td>2017-10-03 14:49:59+09:00</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start date             End date  Start station number  \\\n",
       "0  2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1  2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "\n",
       "                   Start station  End station number  \\\n",
       "0           Glebe Rd & 11th St N               31036   \n",
       "1  George Mason Dr & Wilson Blvd               31036   \n",
       "\n",
       "                     End station Bike number Member type  \\\n",
       "0  George Mason Dr & Wilson Blvd      W20529      Member   \n",
       "1  George Mason Dr & Wilson Blvd      W20529      Casual   \n",
       "\n",
       "                      start                       end  trip_duration  \n",
       "0 2017-10-03 12:23:25+09:00 2017-10-03 12:26:26+09:00          181.0  \n",
       "1 2017-10-03 12:42:57+09:00 2017-10-03 14:49:59+09:00         7622.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Start date                                     object\n",
       "End date                                       object\n",
       "Start station number                            int64\n",
       "Start station                                  object\n",
       "End station number                              int64\n",
       "End station                                    object\n",
       "Bike number                                    object\n",
       "Member type                                    object\n",
       "start                   datetime64[ns, tzfile('ROK')]\n",
       "end                     datetime64[ns, tzfile('ROK')]\n",
       "trip_duration                                 float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dateutil import tz\n",
    "\n",
    "# create a timezone object for Eastern Time\n",
    "KOR = tz.gettz('Asia/Seoul')\n",
    "\n",
    "# convert 'start' and 'end' columns to Eastern Time and then to UTC\n",
    "onebike_datetimes['start'] = onebike_datetimes['start'].apply(lambda x: x.replace(tzinfo=KOR))\n",
    "onebike_datetimes['end'] = onebike_datetimes['end'].apply(lambda x: x.replace(tzinfo=KOR))\n",
    "\n",
    "# display the first few rows and data types\n",
    "display(onebike_datetimes.head(2))\n",
    "display(onebike_datetimes.dtypes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What time did the bike leave? (Global edition)**\n",
    "\n",
    "When you need to move a `datetime` from one timezone into another, use `.astimezone()` and `tz`. Often you will be moving things into UTC, but for fun let's try moving things from 'America/New_York' into a few different time zones.\n",
    "\n",
    "STEPS 01\n",
    "    Set uk to be the timezone for the UK: 'Europe/London'.\n",
    "    Change local to be in the uk timezone and assign it to notlocal.\n",
    "STEPS 02\n",
    "    Set ist to be the timezone for India: 'Asia/Kolkata'.\n",
    "    Change local to be in the ist timezone and assign it to notlocal.\n",
    "STEPS 03\n",
    "    Set sm to be the timezone for Samoa: 'Pacific/Apia'.\n",
    "    Change local to be in the sm timezone and assign it to notlocal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-10-03T12:23:25-05:00\n",
      "2017-10-03T18:23:25+01:00\n"
     ]
    }
   ],
   "source": [
    "# Create the timezone object\n",
    "uk = tz.gettz('Europe/London')\n",
    "\n",
    "# Pull out the start of the first trip\n",
    "local = onebike_datetimes['start'][0]\n",
    "\n",
    "# What time was it in the UK?\n",
    "notlocal = local.astimezone(uk)\n",
    "\n",
    "# Print them out and see the difference\n",
    "print(local.isoformat())\n",
    "print(notlocal.isoformat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-10-03T12:23:25-05:00\n",
      "2017-10-03T22:53:25+05:30\n"
     ]
    }
   ],
   "source": [
    "# Create the timezone object\n",
    "ist = tz.gettz('Asia/Kolkata')\n",
    "\n",
    "# Pull out the start of the first trip\n",
    "local = onebike_datetimes['start'][0]\n",
    "\n",
    "# What time was it in India?\n",
    "notlocal = local.astimezone(ist)\n",
    "\n",
    "# Print them out and see the difference\n",
    "print(local.isoformat())\n",
    "print(notlocal.isoformat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-10-03T12:23:25-05:00\n",
      "2017-10-04T07:23:25+14:00\n"
     ]
    }
   ],
   "source": [
    "# Create the timezone object\n",
    "sm = tz.gettz('Pacific/Apia')\n",
    "\n",
    "# Pull out the start of the first trip\n",
    "local = onebike_datetimes['start'][0]\n",
    "\n",
    "# What time was it in Samoa?\n",
    "notlocal = local.astimezone(sm)\n",
    "\n",
    "# Print them out and see the difference\n",
    "print(local.isoformat())\n",
    "print(notlocal.isoformat())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Starting daylight saving time**\n",
    "\n",
    "1. Start of Daylight Saving Time\n",
    "\n",
    "spring_ahead_159am = datetime(2017, 3, 12, 1, 59, 59)\n",
    "spring_ahead_159am.isoformat()\n",
    "\n",
    "`'2017-03-12T01:59:59'`\n",
    "\n",
    "spring_ahead_3am = datetime(2017, 3, 12, 3, 0, 0)\n",
    "spring_ahead_3am.isoformat()\n",
    "\n",
    "`'2017-03-12T03:00:00'`\n",
    "\n",
    "(spring_ahead_3am - spring_ahead_159am).total_seconds()\n",
    "\n",
    "`3601.0`\n",
    "\n",
    "from datetime import timezone, timedelta\n",
    "EST = timezone(timedelta(hours=-5))\n",
    "EDT = timezone(timedelta(hours=-4))\n",
    "\n",
    "spring_ahead_159am = spring_ahead_159am.replace(tzinfo = EST)\n",
    "spring_ahead_159am.isoformat()\n",
    "\n",
    "`'2017-03-12T01:59:59-05:00'`\n",
    "\n",
    "spring_ahead_3am = spring_ahead_3am.replace(tzinfo = EDT)\n",
    "spring_ahead_3am.isoformat()\n",
    "\n",
    "`'2017-03-12T03:00:00-04:00'`\n",
    "\n",
    "(spring_ahead_3am - spring_ahead_159am).seconds\n",
    "\n",
    "`1`\n",
    "\n",
    "2. Using dateutil\n",
    "\n",
    "*-# Import tz*\n",
    "from dateutil import tz\n",
    "\n",
    "*-# Create eastern timezone*\n",
    "eastern = tz.gettz('America/New_York')\n",
    "\n",
    "*-# 2017-03-12 01:59:59 in Eastern Time (EST)*\n",
    "spring_ahead_159am = datetime(2017, 3, 12, 1, 59, 59,\n",
    "tzinfo = eastern)\n",
    "\n",
    "*-# 2017-03-12 03:00:00 in Eastern Time (EDT)*\n",
    "spring_ahead_3am = datetime(2017, 3, 12, 3, 0, 0,\n",
    "tzinfo = eastern)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2017-03-12T01:59:59'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2017-03-12T03:00:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "3601.0"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spring_ahead_159am = datetime(2017, 3, 12, 1, 59, 59)\n",
    "display(spring_ahead_159am.isoformat())\n",
    "\n",
    "spring_ahead_3am = datetime(2017, 3, 12, 3, 0, 0)\n",
    "display(spring_ahead_3am.isoformat())\n",
    "\n",
    "(spring_ahead_3am - spring_ahead_159am).total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2017-03-12T01:59:59-05:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2017-03-12T03:00:00-04:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import timezone, timedelta\n",
    "\n",
    "EST = timezone(timedelta(hours=-5))\n",
    "EDT = timezone(timedelta(hours=-4))\n",
    "\n",
    "spring_ahead_159am = spring_ahead_159am.replace(tzinfo = EST)\n",
    "display(spring_ahead_159am.isoformat())\n",
    "\n",
    "spring_ahead_3am = spring_ahead_3am.replace(tzinfo = EDT)\n",
    "display(spring_ahead_3am.isoformat())\n",
    "\n",
    "(spring_ahead_3am - spring_ahead_159am).seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2017-03-12T01:59:59-05:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2017-03-12T03:00:00-04:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "3601"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import tz\n",
    "from dateutil import tz\n",
    "# Create eastern timezone\n",
    "eastern = tz.gettz('America/New_York')\n",
    "# 2017-03-12 01:59:59 in Eastern Time (EST)\n",
    "spring_ahead_159am_1 = datetime(2017, 3, 12, 1, 59, 59,\n",
    "tzinfo = eastern)\n",
    "# 2017-03-12 03:00:00 in Eastern Time (EDT)\n",
    "spring_ahead_3am_1 = datetime(2017, 3, 12, 3, 0, 0,\n",
    "tzinfo = eastern)\n",
    "\n",
    "display(spring_ahead_159am.isoformat())\n",
    "display(spring_ahead_3am.isoformat())\n",
    "(spring_ahead_3am_1 - spring_ahead_159am_1).seconds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many hours elapsed around daylight saving?**\n",
    "\n",
    "Since our bike data takes place in the fall, you'll have to do something else to learn about the start of daylight savings time.\n",
    "\n",
    "Let's look at March 12, 2017, in the Eastern United States, when Daylight Saving kicked in at 2 AM.\n",
    "\n",
    "If you create a `datetime` for midnight that night, and add 6 hours to it, how much time will have elapsed?\n",
    "\n",
    "STEP 01\n",
    "You already have a datetime called start, set for March 12, 2017 at midnight, set to the timezone 'America/New_York'.\n",
    "\n",
    "Add six hours to start and assign it to end. Look at the UTC offset for the two results.\n",
    "STEP 02\n",
    "You added 6 hours, and got 6 AM, despite the fact that the clocks springing forward means only 5 hours would have actually elapsed!\n",
    "\n",
    "Calculate the time between start and end. How much time does Python think has elapsed?\n",
    "STEP 03\n",
    "Move your datetime objects into UTC and calculate the elapsed time again.\n",
    "\n",
    "Once you're in UTC, what result do you get?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-03-12T00:00:00-05:00 to 2017-03-12T06:00:00-04:00\n",
      "6.0\n",
      "5.0\n"
     ]
    }
   ],
   "source": [
    "# Import datetime, timedelta, tz, timezone\n",
    "from datetime import datetime, timedelta, timezone\n",
    "from dateutil import tz\n",
    "\n",
    "# Start on March 12, 2017, midnight, then add 6 hours\n",
    "start = datetime(2017, 3, 12, tzinfo = tz.gettz('America/New_York'))\n",
    "end = start + timedelta(hours=6)\n",
    "print(start.isoformat() + \" to \" + end.isoformat())\n",
    "\n",
    "# How many hours have elapsed?\n",
    "print((end - start).total_seconds()/(60*60))\n",
    "\n",
    "# What if we move to UTC?\n",
    "print((end.astimezone(timezone.utc) - start.astimezone(timezone.utc))\\\n",
    "      .total_seconds()/(60*60))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**March 29, throughout a decade**\n",
    "\n",
    "Daylight Saving rules are complicated: they're different in different places, they change over time, and they usually start on a Sunday (and so they move around the calendar).\n",
    "\n",
    "For example, in the United Kingdom, as of the time this lesson was written, Daylight Saving begins on the last Sunday in March. Let's look at the UTC offset for March 29, at midnight, for the years 2000 to 2010.\n",
    "\n",
    "STEPS\n",
    "    Using tz, set the timezone for dt to be 'Europe/London'.\n",
    "    Within the for loop:\n",
    "    Use the .replace() method to change the year for dt to be y.\n",
    "    Call .isoformat() on the result to observe the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-03-29T00:00:00+01:00\n",
      "2001-03-29T00:00:00+01:00\n",
      "2002-03-29T00:00:00+00:00\n",
      "2003-03-29T00:00:00+00:00\n",
      "2004-03-29T00:00:00+01:00\n",
      "2005-03-29T00:00:00+01:00\n",
      "2006-03-29T00:00:00+01:00\n",
      "2007-03-29T00:00:00+01:00\n",
      "2008-03-29T00:00:00+00:00\n",
      "2009-03-29T00:00:00+00:00\n",
      "2010-03-29T00:00:00+01:00\n"
     ]
    }
   ],
   "source": [
    "# Import datetime and tz\n",
    "from datetime import datetime\n",
    "from dateutil import tz\n",
    "\n",
    "# Create starting date\n",
    "dt = datetime(2000, 3, 29, tzinfo = tz.gettz('Europe/London'))\n",
    "\n",
    "# Loop over the dates, replacing the year, and print the ISO timestamp\n",
    "for y in range(2000, 2011):\n",
    "  print(dt.replace(year=y).isoformat())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Ending daylight saving time**\n",
    "\n",
    "1. Ending daylight saving time\n",
    "\n",
    "eastern = tz.gettz('US/Eastern')\n",
    "*-# 2017-11-05 01:00:00*\n",
    "first_1am = datetime(2017, 11, 5, 1, 0, 0,\n",
    "tzinfo = eastern)\n",
    "tz.datetime_ambiguous(first_1am)\n",
    "\n",
    "`True`\n",
    "\n",
    "*-# 2017-11-05 01:00:00 again*\n",
    "second_1am = datetime(2017, 11, 5, 1, 0, 0,\n",
    "tzinfo = eastern)\n",
    "second_1am = tz.enfold(second_1am)\n",
    "\n",
    "(first_1am - second_1am).total_seconds()\n",
    "`0.0`\n",
    "\n",
    "first_1am = first_1am.astimezone(tz.UTC)\n",
    "second_1am = second_1am.astimezone(tz.UTC)\n",
    "(second_1am - first_1am).total_seconds()\n",
    "\n",
    "`3600.0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-11-05 01:00:00-04:00\n"
     ]
    }
   ],
   "source": [
    "eastern = tz.gettz('US/Eastern')\n",
    "# 2017-11-05 01:00:00*\n",
    "first_1am = datetime(2017, 11, 5, 1, 0, 0,\n",
    "tzinfo = eastern)\n",
    "display(tz.datetime_ambiguous(first_1am))\n",
    "print(first_1am)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-11-05 01:00:00-05:00\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# 2017-11-05 01:00:00 again\n",
    "second_1am = datetime(2017, 11, 5, 1, 0, 0,\n",
    "tzinfo = eastern)\n",
    "second_1am = tz.enfold(second_1am)\n",
    "print(second_1am)\n",
    "\n",
    "print((first_1am - second_1am).total_seconds())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3600.0"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_1am = first_1am.astimezone(tz.UTC)\n",
    "second_1am = second_1am.astimezone(tz.UTC)\n",
    "(second_1am - first_1am).total_seconds()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finding ambiguous datetimes**\n",
    "\n",
    "At the end of lesson 2, we saw something anomalous in our bike trip duration data. Let's see if we can identify what the problem might be.\n",
    "\n",
    "The data is loaded as `onebike_datetimes`, and `tz` has already been imported from `dateutil`.\n",
    "\n",
    "STEPS\n",
    "Loop over the trips in onebike_datetimes:\n",
    "    Print any rides whose start is ambiguous.\n",
    "    Print any rides whose end is ambiguous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ambiguous start at 2017-11-05 01:56:50-04:00\n",
      "Ambiguous end at 2017-11-05 01:01:04-04:00\n"
     ]
    }
   ],
   "source": [
    "# Loop over trips\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "    # Rides with ambiguous start\n",
    "    if tz.datetime_ambiguous(trip['start']):\n",
    "        print(\"Ambiguous start at \" + str(trip['start']))\n",
    "    # Rides with ambiguous end\n",
    "    if tz.datetime_ambiguous(trip['end']):\n",
    "        print(\"Ambiguous end at \" + str(trip['end']))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning daylight saving data with fold**\n",
    "\n",
    "As we've just discovered, there is a ride in our data set which is being messed up by a Daylight Savings shift. Let's clean up the data set so we actually have a correct minimum ride length. We can use the fact that we know the end of the ride happened after the beginning to fix up the duration messed up by the shift out of Daylight Savings.\n",
    "\n",
    "Since Python does not handle `tz.enfold()` when doing arithmetic, we must put our datetime objects into UTC, where ambiguities have been resolved.\n",
    "\n",
    "`onebike_datetimes` is already loaded and in the right timezone. `tz` and `timezone` have been imported. Use `tz.UTC `for the timezone.\n",
    "\n",
    "STEPS\n",
    "    Complete the if statement to be true only when a ride's start comes after its end.\n",
    "    When start is after end, call tz.enfold() on the end so you know it refers to the one after the daylight savings time change.\n",
    "    After the if statement, convert the start and end to UTC so you can make a proper comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shortest trip: 116.0\n"
     ]
    }
   ],
   "source": [
    "trip_durations = []\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "  # When the start is later than the end, set the fold to be 1\n",
    "  if trip['start'] > trip['end']:\n",
    "    trip['end'] = tz.enfold(trip['end'])\n",
    "  # Convert to UTC\n",
    "  start = trip['start'].astimezone(tz.UTC)\n",
    "  end = trip['end'].astimezone(tz.UTC)\n",
    "\n",
    "  # Subtract the difference\n",
    "  trip_length_seconds = (end-start).total_seconds()\n",
    "  trip_durations.append(trip_length_seconds)\n",
    "\n",
    "# Take the shortest trip duration\n",
    "print(\"Shortest trip: \" + str(min(trip_durations)))\n",
    "\n",
    "#Working fine V:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shortest trip: 116.0\n"
     ]
    }
   ],
   "source": [
    "trip_durations = []\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "    # Check if the start time is later than the end time\n",
    "    if trip['start'] > trip['end']:\n",
    "        # Add a hour to the end time\n",
    "        trip['end'] = trip['end'] + pd.Timedelta(hours=1)\n",
    "    # Convert to UTC\n",
    "    start = trip['start'].astimezone(tz.UTC)\n",
    "    end = trip['end'].astimezone(tz.UTC)\n",
    "\n",
    "    # Subtract the difference\n",
    "    trip_length_seconds = (end-start).total_seconds()\n",
    "    trip_durations.append(trip_length_seconds)\n",
    "\n",
    "# Take the shortest trip duration\n",
    "print(\"Shortest trip: \" + str(min(trip_durations)))\n",
    "\n",
    "# It worked! but I have to add an odd timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shortest trip: 116.0\n"
     ]
    }
   ],
   "source": [
    "trip_durations = []\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "    # When the start is later than the end, set the fold to be 1\n",
    "    if trip['start'] > trip['end']:\n",
    "        fold = 1\n",
    "    else:\n",
    "        fold = 0\n",
    "    \n",
    "    # Convert to UTC\n",
    "    start_utc = trip['start'].astimezone(tz.UTC)\n",
    "    end_utc = trip['end'].astimezone(tz.UTC)\n",
    "    \n",
    "    # Enfold the end time\n",
    "    end_utc_datetime = end_utc.to_pydatetime()\n",
    "    end_enfolded_datetime = tz.enfold(end_utc_datetime, fold)\n",
    "    end_enfolded = pd.Timestamp(end_enfolded_datetime)\n",
    "    end_enfolded = end_enfolded.tz_convert('UTC')\n",
    "    \n",
    "    # If the enfolded end time is different from the original end time, adjust the fold\n",
    "    if end_enfolded != end_utc:\n",
    "        fold = 1 - fold\n",
    "        end_enfolded_datetime = tz.enfold(end_utc_datetime, fold)\n",
    "        end_enfolded = pd.Timestamp(end_enfolded_datetime)\n",
    "        end_enfolded = end_enfolded.tz_convert('UTC')\n",
    "    \n",
    "    # Subtract the difference if end time is later than start time\n",
    "    if end_enfolded > start_utc:\n",
    "        trip_length_seconds = (end_enfolded - start_utc).total_seconds()\n",
    "        trip_durations.append(trip_length_seconds)\n",
    "\n",
    "# Take the shortest trip duration\n",
    "print(\"Shortest trip: \" + str(min(trip_durations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mcaba\\AppData\\Local\\Temp\\ipykernel_1828\\448820815.py:41: FutureWarning: DataFrame.set_axis 'inplace' keyword is deprecated and will be removed in a future version. Use `obj = obj.set_axis(..., copy=False)` instead\n",
      "  duration_df_DLS.set_axis(head_d_df, axis=1, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>trip_duration_DLS_corrected</th>\n",
       "      <th>trip_duration_DLS_corrected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-01 15:23:25-04:00</td>\n",
       "      <td>2017-10-01 15:26:26-04:00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-10-01 15:42:57-04:00</td>\n",
       "      <td>2017-10-01 17:49:59-04:00</td>\n",
       "      <td>7622.0</td>\n",
       "      <td>7622.0</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 06:37:10-04:00</td>\n",
       "      <td>2017-10-02 06:42:53-04:00</td>\n",
       "      <td>343.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 08:56:45-04:00</td>\n",
       "      <td>2017-10-02 09:18:03-04:00</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>1278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>31230</td>\n",
       "      <td>Metro Center / 12th &amp; G St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-10-02 18:23:48-04:00</td>\n",
       "      <td>2017-10-02 18:45:05-04:00</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>1277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>2017-12-29 14:32:55</td>\n",
       "      <td>2017-12-29 14:43:46</td>\n",
       "      <td>31242</td>\n",
       "      <td>18th St &amp; Pennsylvania Ave NW</td>\n",
       "      <td>31265</td>\n",
       "      <td>5th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-12-29 14:32:55-05:00</td>\n",
       "      <td>2017-12-29 14:43:46-05:00</td>\n",
       "      <td>651.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2017-12-29 15:08:26</td>\n",
       "      <td>2017-12-29 15:18:51</td>\n",
       "      <td>31265</td>\n",
       "      <td>5th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31613</td>\n",
       "      <td>Eastern Market Metro / Pennsylvania Ave &amp; 7th ...</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-12-29 15:08:26-05:00</td>\n",
       "      <td>2017-12-29 15:18:51-05:00</td>\n",
       "      <td>625.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>279.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2017-12-29 20:33:34</td>\n",
       "      <td>2017-12-29 20:38:13</td>\n",
       "      <td>31613</td>\n",
       "      <td>Eastern Market Metro / Pennsylvania Ave &amp; 7th ...</td>\n",
       "      <td>31618</td>\n",
       "      <td>4th &amp; East Capitol St NE</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-12-29 20:33:34-05:00</td>\n",
       "      <td>2017-12-29 20:38:13-05:00</td>\n",
       "      <td>279.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>2017-12-30 13:51:03</td>\n",
       "      <td>2017-12-30 13:54:33</td>\n",
       "      <td>31618</td>\n",
       "      <td>4th &amp; East Capitol St NE</td>\n",
       "      <td>31610</td>\n",
       "      <td>Eastern Market / 7th &amp; North Carolina Ave SE</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-12-30 13:51:03-05:00</td>\n",
       "      <td>2017-12-30 13:54:33-05:00</td>\n",
       "      <td>210.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>2017-12-30 15:09:03</td>\n",
       "      <td>2017-12-30 15:19:13</td>\n",
       "      <td>31610</td>\n",
       "      <td>Eastern Market / 7th &amp; North Carolina Ave SE</td>\n",
       "      <td>31272</td>\n",
       "      <td>Washington &amp; Independence Ave SW/HHS</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-12-30 15:09:03-05:00</td>\n",
       "      <td>2017-12-30 15:19:13-05:00</td>\n",
       "      <td>610.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>290 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Start date             End date  Start station number  \\\n",
       "0    2017-10-01 15:23:25  2017-10-01 15:26:26                 31038   \n",
       "1    2017-10-01 15:42:57  2017-10-01 17:49:59                 31036   \n",
       "2    2017-10-02 06:37:10  2017-10-02 06:42:53                 31036   \n",
       "3    2017-10-02 08:56:45  2017-10-02 09:18:03                 31037   \n",
       "4    2017-10-02 18:23:48  2017-10-02 18:45:05                 31295   \n",
       "..                   ...                  ...                   ...   \n",
       "285  2017-12-29 14:32:55  2017-12-29 14:43:46                 31242   \n",
       "286  2017-12-29 15:08:26  2017-12-29 15:18:51                 31265   \n",
       "287  2017-12-29 20:33:34  2017-12-29 20:38:13                 31613   \n",
       "288  2017-12-30 13:51:03  2017-12-30 13:54:33                 31618   \n",
       "289  2017-12-30 15:09:03  2017-12-30 15:19:13                 31610   \n",
       "\n",
       "                                         Start station  End station number  \\\n",
       "0                                 Glebe Rd & 11th St N               31036   \n",
       "1                        George Mason Dr & Wilson Blvd               31036   \n",
       "2                        George Mason Dr & Wilson Blvd               31037   \n",
       "3                 Ballston Metro / N Stuart & 9th St N               31295   \n",
       "4                                    Potomac & M St NW               31230   \n",
       "..                                                 ...                 ...   \n",
       "285                      18th St & Pennsylvania Ave NW               31265   \n",
       "286                      5th St & Massachusetts Ave NW               31613   \n",
       "287  Eastern Market Metro / Pennsylvania Ave & 7th ...               31618   \n",
       "288                           4th & East Capitol St NE               31610   \n",
       "289       Eastern Market / 7th & North Carolina Ave SE               31272   \n",
       "\n",
       "                                           End station Bike number  \\\n",
       "0                        George Mason Dr & Wilson Blvd      W20529   \n",
       "1                        George Mason Dr & Wilson Blvd      W20529   \n",
       "2                 Ballston Metro / N Stuart & 9th St N      W20529   \n",
       "3                                    Potomac & M St NW      W20529   \n",
       "4                        Metro Center / 12th & G St NW      W20529   \n",
       "..                                                 ...         ...   \n",
       "285                      5th St & Massachusetts Ave NW      W20529   \n",
       "286  Eastern Market Metro / Pennsylvania Ave & 7th ...      W20529   \n",
       "287                           4th & East Capitol St NE      W20529   \n",
       "288       Eastern Market / 7th & North Carolina Ave SE      W20529   \n",
       "289               Washington & Independence Ave SW/HHS      W20529   \n",
       "\n",
       "    Member type                     start                       end  \\\n",
       "0        Member 2017-10-01 15:23:25-04:00 2017-10-01 15:26:26-04:00   \n",
       "1        Casual 2017-10-01 15:42:57-04:00 2017-10-01 17:49:59-04:00   \n",
       "2        Member 2017-10-02 06:37:10-04:00 2017-10-02 06:42:53-04:00   \n",
       "3        Member 2017-10-02 08:56:45-04:00 2017-10-02 09:18:03-04:00   \n",
       "4        Member 2017-10-02 18:23:48-04:00 2017-10-02 18:45:05-04:00   \n",
       "..          ...                       ...                       ...   \n",
       "285      Member 2017-12-29 14:32:55-05:00 2017-12-29 14:43:46-05:00   \n",
       "286      Casual 2017-12-29 15:08:26-05:00 2017-12-29 15:18:51-05:00   \n",
       "287      Member 2017-12-29 20:33:34-05:00 2017-12-29 20:38:13-05:00   \n",
       "288      Member 2017-12-30 13:51:03-05:00 2017-12-30 13:54:33-05:00   \n",
       "289      Member 2017-12-30 15:09:03-05:00 2017-12-30 15:19:13-05:00   \n",
       "\n",
       "     trip_duration  trip_duration_DLS_corrected  trip_duration_DLS_corrected  \n",
       "0            181.0                        181.0                        181.0  \n",
       "1           7622.0                       7622.0                       7622.0  \n",
       "2            343.0                        343.0                        343.0  \n",
       "3           1278.0                       1278.0                       1278.0  \n",
       "4           1277.0                       1277.0                       1277.0  \n",
       "..             ...                          ...                          ...  \n",
       "285          651.0                        625.0                        625.0  \n",
       "286          625.0                        279.0                        279.0  \n",
       "287          279.0                        210.0                        210.0  \n",
       "288          210.0                        610.0                        610.0  \n",
       "289          610.0                          NaN                          NaN  \n",
       "\n",
       "[290 rows x 13 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What if I want to create a new column in my onebike_datetimes named 'trip_duration_DLS_corrected'\n",
    "\n",
    "# Initialize a list for all the trip durations\n",
    "onebike_durations_DLS = []\n",
    "\n",
    "for index, trip in onebike_datetimes.iterrows():\n",
    "    # When the start is later than the end, set the fold to be 1\n",
    "    if trip['start'] > trip['end']:\n",
    "        fold = 1\n",
    "    else:\n",
    "        fold = 0\n",
    "    \n",
    "    # Convert to UTC\n",
    "    start_utc = trip['start'].astimezone(tz.UTC)\n",
    "    end_utc = trip['end'].astimezone(tz.UTC)\n",
    "    \n",
    "    # Enfold the end time\n",
    "    end_utc_datetime = end_utc.to_pydatetime()\n",
    "    end_enfolded_datetime = tz.enfold(end_utc_datetime, fold)\n",
    "    end_enfolded = pd.Timestamp(end_enfolded_datetime)\n",
    "    end_enfolded = end_enfolded.tz_convert('UTC')\n",
    "    \n",
    "    # If the enfolded end time is different from the original end time, adjust the fold\n",
    "    if end_enfolded != end_utc:\n",
    "        fold = 1 - fold\n",
    "        end_enfolded_datetime = tz.enfold(end_utc_datetime, fold)\n",
    "        end_enfolded = pd.Timestamp(end_enfolded_datetime)\n",
    "        end_enfolded = end_enfolded.tz_convert('UTC')\n",
    "    \n",
    "    # Subtract the difference if end time is later than start time\n",
    "    if end_enfolded > start_utc:\n",
    "        trip_length_seconds = (end_enfolded - start_utc).total_seconds()\n",
    "        onebike_durations_DLS.append(trip_length_seconds)\n",
    "\n",
    "print(onebike_durations_DLS)\n",
    "\n",
    "# Create a DataFrame from the trip data and concatenate them together\n",
    "duration_df_DLS = pd.DataFrame(onebike_durations_DLS)\n",
    "#display(duration_df_DLS)\n",
    "head_d_df = ['trip_duration_DLS_corrected']\n",
    "duration_df_DLS.set_axis(head_d_df, axis=1, inplace=True)\n",
    "#display(duration_df_DLS)\n",
    "onebike_datetimes = pd.concat([onebike_datetimes, duration_df_DLS], axis=1)\n",
    "\n",
    "display(onebike_datetimes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>trip_duration_DLS_corrected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2017-11-05 01:56:50</td>\n",
       "      <td>2017-11-05 01:01:04</td>\n",
       "      <td>31615</td>\n",
       "      <td>6th &amp; H St NE</td>\n",
       "      <td>31627</td>\n",
       "      <td>3rd &amp; M St NE</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-11-05 01:56:50-04:00</td>\n",
       "      <td>2017-11-05 01:01:04-04:00</td>\n",
       "      <td>-3346.0</td>\n",
       "      <td>1213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2017-11-05 08:33:33</td>\n",
       "      <td>2017-11-05 08:53:46</td>\n",
       "      <td>31627</td>\n",
       "      <td>3rd &amp; M St NE</td>\n",
       "      <td>31200</td>\n",
       "      <td>Massachusetts Ave &amp; Dupont Circle NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-11-05 08:33:33-05:00</td>\n",
       "      <td>2017-11-05 08:53:46-05:00</td>\n",
       "      <td>1213.0</td>\n",
       "      <td>331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2017-11-05 08:58:08</td>\n",
       "      <td>2017-11-05 09:03:39</td>\n",
       "      <td>31200</td>\n",
       "      <td>Massachusetts Ave &amp; Dupont Circle NW</td>\n",
       "      <td>31205</td>\n",
       "      <td>21st &amp; I St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-11-05 08:58:08-05:00</td>\n",
       "      <td>2017-11-05 09:03:39-05:00</td>\n",
       "      <td>331.0</td>\n",
       "      <td>1497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2017-11-05 11:05:08</td>\n",
       "      <td>2017-11-05 11:30:05</td>\n",
       "      <td>31205</td>\n",
       "      <td>21st &amp; I St NW</td>\n",
       "      <td>31262</td>\n",
       "      <td>11th &amp; F St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-11-05 11:05:08-05:00</td>\n",
       "      <td>2017-11-05 11:30:05-05:00</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>527.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2017-11-06 08:50:18</td>\n",
       "      <td>2017-11-06 08:59:05</td>\n",
       "      <td>31522</td>\n",
       "      <td>2nd St &amp; Seaton Pl NE</td>\n",
       "      <td>31637</td>\n",
       "      <td>North Capitol St &amp; G Pl NE</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-11-06 08:50:18-05:00</td>\n",
       "      <td>2017-11-06 08:59:05-05:00</td>\n",
       "      <td>527.0</td>\n",
       "      <td>584.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>2017-12-29 14:32:55</td>\n",
       "      <td>2017-12-29 14:43:46</td>\n",
       "      <td>31242</td>\n",
       "      <td>18th St &amp; Pennsylvania Ave NW</td>\n",
       "      <td>31265</td>\n",
       "      <td>5th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-12-29 14:32:55-05:00</td>\n",
       "      <td>2017-12-29 14:43:46-05:00</td>\n",
       "      <td>651.0</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2017-12-29 15:08:26</td>\n",
       "      <td>2017-12-29 15:18:51</td>\n",
       "      <td>31265</td>\n",
       "      <td>5th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31613</td>\n",
       "      <td>Eastern Market Metro / Pennsylvania Ave &amp; 7th ...</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>2017-12-29 15:08:26-05:00</td>\n",
       "      <td>2017-12-29 15:18:51-05:00</td>\n",
       "      <td>625.0</td>\n",
       "      <td>279.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2017-12-29 20:33:34</td>\n",
       "      <td>2017-12-29 20:38:13</td>\n",
       "      <td>31613</td>\n",
       "      <td>Eastern Market Metro / Pennsylvania Ave &amp; 7th ...</td>\n",
       "      <td>31618</td>\n",
       "      <td>4th &amp; East Capitol St NE</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-12-29 20:33:34-05:00</td>\n",
       "      <td>2017-12-29 20:38:13-05:00</td>\n",
       "      <td>279.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>2017-12-30 13:51:03</td>\n",
       "      <td>2017-12-30 13:54:33</td>\n",
       "      <td>31618</td>\n",
       "      <td>4th &amp; East Capitol St NE</td>\n",
       "      <td>31610</td>\n",
       "      <td>Eastern Market / 7th &amp; North Carolina Ave SE</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-12-30 13:51:03-05:00</td>\n",
       "      <td>2017-12-30 13:54:33-05:00</td>\n",
       "      <td>210.0</td>\n",
       "      <td>610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>2017-12-30 15:09:03</td>\n",
       "      <td>2017-12-30 15:19:13</td>\n",
       "      <td>31610</td>\n",
       "      <td>Eastern Market / 7th &amp; North Carolina Ave SE</td>\n",
       "      <td>31272</td>\n",
       "      <td>Washington &amp; Independence Ave SW/HHS</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>2017-12-30 15:09:03-05:00</td>\n",
       "      <td>2017-12-30 15:19:13-05:00</td>\n",
       "      <td>610.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Start date             End date  Start station number  \\\n",
       "129  2017-11-05 01:56:50  2017-11-05 01:01:04                 31615   \n",
       "130  2017-11-05 08:33:33  2017-11-05 08:53:46                 31627   \n",
       "131  2017-11-05 08:58:08  2017-11-05 09:03:39                 31200   \n",
       "132  2017-11-05 11:05:08  2017-11-05 11:30:05                 31205   \n",
       "133  2017-11-06 08:50:18  2017-11-06 08:59:05                 31522   \n",
       "..                   ...                  ...                   ...   \n",
       "285  2017-12-29 14:32:55  2017-12-29 14:43:46                 31242   \n",
       "286  2017-12-29 15:08:26  2017-12-29 15:18:51                 31265   \n",
       "287  2017-12-29 20:33:34  2017-12-29 20:38:13                 31613   \n",
       "288  2017-12-30 13:51:03  2017-12-30 13:54:33                 31618   \n",
       "289  2017-12-30 15:09:03  2017-12-30 15:19:13                 31610   \n",
       "\n",
       "                                         Start station  End station number  \\\n",
       "129                                      6th & H St NE               31627   \n",
       "130                                      3rd & M St NE               31200   \n",
       "131               Massachusetts Ave & Dupont Circle NW               31205   \n",
       "132                                     21st & I St NW               31262   \n",
       "133                              2nd St & Seaton Pl NE               31637   \n",
       "..                                                 ...                 ...   \n",
       "285                      18th St & Pennsylvania Ave NW               31265   \n",
       "286                      5th St & Massachusetts Ave NW               31613   \n",
       "287  Eastern Market Metro / Pennsylvania Ave & 7th ...               31618   \n",
       "288                           4th & East Capitol St NE               31610   \n",
       "289       Eastern Market / 7th & North Carolina Ave SE               31272   \n",
       "\n",
       "                                           End station Bike number  \\\n",
       "129                                      3rd & M St NE      W20529   \n",
       "130               Massachusetts Ave & Dupont Circle NW      W20529   \n",
       "131                                     21st & I St NW      W20529   \n",
       "132                                     11th & F St NW      W20529   \n",
       "133                         North Capitol St & G Pl NE      W20529   \n",
       "..                                                 ...         ...   \n",
       "285                      5th St & Massachusetts Ave NW      W20529   \n",
       "286  Eastern Market Metro / Pennsylvania Ave & 7th ...      W20529   \n",
       "287                           4th & East Capitol St NE      W20529   \n",
       "288       Eastern Market / 7th & North Carolina Ave SE      W20529   \n",
       "289               Washington & Independence Ave SW/HHS      W20529   \n",
       "\n",
       "    Member type                     start                       end  \\\n",
       "129      Member 2017-11-05 01:56:50-04:00 2017-11-05 01:01:04-04:00   \n",
       "130      Casual 2017-11-05 08:33:33-05:00 2017-11-05 08:53:46-05:00   \n",
       "131      Member 2017-11-05 08:58:08-05:00 2017-11-05 09:03:39-05:00   \n",
       "132      Member 2017-11-05 11:05:08-05:00 2017-11-05 11:30:05-05:00   \n",
       "133      Member 2017-11-06 08:50:18-05:00 2017-11-06 08:59:05-05:00   \n",
       "..          ...                       ...                       ...   \n",
       "285      Member 2017-12-29 14:32:55-05:00 2017-12-29 14:43:46-05:00   \n",
       "286      Casual 2017-12-29 15:08:26-05:00 2017-12-29 15:18:51-05:00   \n",
       "287      Member 2017-12-29 20:33:34-05:00 2017-12-29 20:38:13-05:00   \n",
       "288      Member 2017-12-30 13:51:03-05:00 2017-12-30 13:54:33-05:00   \n",
       "289      Member 2017-12-30 15:09:03-05:00 2017-12-30 15:19:13-05:00   \n",
       "\n",
       "     trip_duration  trip_duration_DLS_corrected  \n",
       "129        -3346.0                       1213.0  \n",
       "130         1213.0                        331.0  \n",
       "131          331.0                       1497.0  \n",
       "132         1497.0                        527.0  \n",
       "133          527.0                        584.0  \n",
       "..             ...                          ...  \n",
       "285          651.0                        625.0  \n",
       "286          625.0                        279.0  \n",
       "287          279.0                        210.0  \n",
       "288          210.0                        610.0  \n",
       "289          610.0                          NaN  \n",
       "\n",
       "[161 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "onebike_datetimes_filtered = onebike_datetimes[onebike_datetimes.trip_duration != onebike_datetimes.trip_duration_DLS_corrected]\n",
    "\n",
    "display(onebike_datetimes_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shortest trip was -3346.0 seconds\n",
      "The longest trip was 76913.0 seconds\n",
      "The shortest trip was (DLS) 116.0 seconds\n",
      "The longest trip was (DLS) 76913.0 seconds\n"
     ]
    }
   ],
   "source": [
    "# Calculate shortest and longest trips\n",
    "shortest_trip = min(onebike_durations)\n",
    "longest_trip = max(onebike_durations)\n",
    "\n",
    "# Print out the results\n",
    "print(\"The shortest trip was \" + str(shortest_trip) + \" seconds\")\n",
    "print(\"The longest trip was \" + str(longest_trip) + \" seconds\")\n",
    "\n",
    "# Calculate shortest and longest trips\n",
    "shortest_trip_DLS = min(onebike_durations_DLS)\n",
    "longest_trip_DLS = max(onebike_durations_DLS)\n",
    "\n",
    "# Print out the results\n",
    "print(\"The shortest trip was (DLS) \" + str(shortest_trip_DLS) + \" seconds\")\n",
    "print(\"The longest trip was (DLS) \" + str(longest_trip_DLS) + \" seconds\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Easy and Powerful: Dates and Times in Pandas (Module 04-051)**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Reading date and time data in Pandas**\n",
    "\n",
    "1. A simple Pandas example\n",
    "\n",
    "*-# Load Pandas*\n",
    "import pandas as pd\n",
    "*-# Import W20529's rides in Q4 2017*\n",
    "rides = pd.read_csv('capital-onebike.csv')\n",
    "\n",
    "*-# See our data*\n",
    "\n",
    "print(rides.head(3))\n",
    "\n",
    "rides['Start date']\n",
    "\n",
    "`0 2017-10-01 15:23:25`\n",
    "`1 2017-10-01 15:42:57`\n",
    "`...`\n",
    "`Name: Start date, Length: 290, dtype: object`\n",
    "\n",
    "rides.iloc[2]\n",
    "\n",
    "`Start date 2017-10-02 06:37:10`\n",
    "`End date 2017-10-02 06:42:53`\n",
    "`...`\n",
    "`Name: 1, dtype: object`\n",
    "\n",
    "2. Loading datetimes with parse_dates\n",
    "\n",
    "*-# Import W20529's rides in Q4 2017*\n",
    "rides = pd.read_csv('capital-onebike.csv',\n",
    "parse_dates = ['Start date', 'End date'])\n",
    "\n",
    "*-# Or:*\n",
    "rides['Start date'] = pd.to_datetime(rides['Start date'],\n",
    "format = \"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "*-# Select Start date for row 2*\n",
    "rides['Start date'].iloc[2]\n",
    "\n",
    "`Timestamp('2017-10-02 06:37:10')`\n",
    "\n",
    "3. Timezone-aware arithmetic\n",
    "\n",
    "*-# Create a duration column*\n",
    "rides['Duration'] = rides['End date'] - rides['Start date']\n",
    "\n",
    "*-# Print the first 5 rows*\n",
    "print(rides['Duration'].head(5))\n",
    "\n",
    "`0 0 days 00:03:01`\n",
    "`1 0 days 02:07:02`\n",
    "`2 0 days 00:05:43`\n",
    "`3 0 days 00:21:18`\n",
    "`4 0 days 00:21:17`\n",
    "`Name: Duration, dtype: timedelta64[ns]`\n",
    "\n",
    "4. Loading datetimes with parse_dates\n",
    "\n",
    "rides['Duration']\\\n",
    "    .dt.total_seconds()\\\n",
    "    .head(5)\n",
    "\n",
    "`0 181.0`\n",
    "`1 7622.0`\n",
    "`2 343.0`\n",
    "`3 1278.0`\n",
    "`4 1277.0`\n",
    "`Name: Duration, dtype: float64`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loading a csv file in Pandas**\n",
    "\n",
    "The `capital_onebike.csv` file covers the October, November and December rides of the Capital Bikeshare bike W20529.\n",
    "\n",
    "Here are the first two columns:\n",
    "Start date\tEnd date\t...\n",
    "2017-10-01 15:23:25\t2017-10-01 15:26:26\t...\n",
    "2017-10-01 15:42:57\t2017-10-01 17:49:59\t...\n",
    "\n",
    "STEPS\n",
    "    Import Pandas.\n",
    "    Complete the call to read_csv() so that it correctly parses the date columns Start date and End date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date                        2017-10-01 15:23:25\n",
      "End date                          2017-10-01 15:26:26\n",
      "Start station number                            31038\n",
      "Start station                    Glebe Rd & 11th St N\n",
      "End station number                              31036\n",
      "End station             George Mason Dr & Wilson Blvd\n",
      "Bike number                                    W20529\n",
      "Member type                                    Member\n",
      "Name: 0, dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-02 06:37:10</td>\n",
       "      <td>2017-10-02 06:42:53</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-02 08:56:45</td>\n",
       "      <td>2017-10-02 09:18:03</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-02 18:23:48</td>\n",
       "      <td>2017-10-02 18:45:05</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>31230</td>\n",
       "      <td>Metro Center / 12th &amp; G St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Start date            End date  Start station number  \\\n",
       "0 2017-10-01 15:23:25 2017-10-01 15:26:26                 31038   \n",
       "1 2017-10-01 15:42:57 2017-10-01 17:49:59                 31036   \n",
       "2 2017-10-02 06:37:10 2017-10-02 06:42:53                 31036   \n",
       "3 2017-10-02 08:56:45 2017-10-02 09:18:03                 31037   \n",
       "4 2017-10-02 18:23:48 2017-10-02 18:45:05                 31295   \n",
       "\n",
       "                          Start station  End station number  \\\n",
       "0                  Glebe Rd & 11th St N               31036   \n",
       "1         George Mason Dr & Wilson Blvd               31036   \n",
       "2         George Mason Dr & Wilson Blvd               31037   \n",
       "3  Ballston Metro / N Stuart & 9th St N               31295   \n",
       "4                     Potomac & M St NW               31230   \n",
       "\n",
       "                            End station Bike number Member type  \n",
       "0         George Mason Dr & Wilson Blvd      W20529      Member  \n",
       "1         George Mason Dr & Wilson Blvd      W20529      Casual  \n",
       "2  Ballston Metro / N Stuart & 9th St N      W20529      Member  \n",
       "3                     Potomac & M St NW      W20529      Member  \n",
       "4         Metro Center / 12th & G St NW      W20529      Member  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import pandas\n",
    "import pandas as pd\n",
    "\n",
    "# Load CSV into the rides variable\n",
    "rides = pd.read_csv(r'G:\\My Drive\\Data Science\\Datacamp_Notebook\\Datacamp_Notebook\\datasets\\capital-onebike.csv', \n",
    "                    parse_dates = ['Start date', 'End date'])\n",
    "\n",
    "# Print the initial (0th) row\n",
    "print(rides.iloc[0])\n",
    "\n",
    "display(rides.head(5))\n",
    "\n",
    "#Geat! Did you know that pandas has a pd.read_excel(), pd.read_json(),\n",
    "#  and even a pd.read_clipboard() function to read tabular data that \n",
    "# you've copied from a document or website? Most have date parsing \n",
    "# functionality too."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Making timedelta columns**\n",
    "\n",
    "Earlier in this course, you wrote a loop to subtract `datetime` objects and determine how long our sample bike had been out of the docks. Now you'll do the same thing with Pandas.\n",
    "\n",
    "`rides` has already been loaded for you.\n",
    "\n",
    "STEPS\n",
    "    Subtract the Start date column from the End date column to get a Series of timedeltas; assign the result to ride_durations.\n",
    "    Convert ride_durations into seconds and assign the result to the 'Duration' column of rides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     181.0\n",
      "1    7622.0\n",
      "2     343.0\n",
      "3    1278.0\n",
      "4    1277.0\n",
      "Name: Duration, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Subtract the start date from the end date\n",
    "\n",
    "ride_durations = rides['End date'] - rides['Start date']\n",
    "\n",
    "# Convert the results to seconds\n",
    "rides['Duration'] = ride_durations.dt.total_seconds()\n",
    "\n",
    "print(rides['Duration'].head())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Summarizing datetime data in Pandas**\n",
    "\n",
    "1. Summarizing data in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 00:19:38.931034482')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Timedelta('3 days 22:58:10')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "# Convert Duration column to timedelta object\n",
    "rides['Duration'] = pd.to_timedelta(rides['Duration'], unit='s')\n",
    "\n",
    "# Average time out of the dock\n",
    "display(rides['Duration'].mean())\n",
    "\n",
    "# Total time out of the dock\n",
    "display(rides['Duration'].sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04348417785917786"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Member    236\n",
       "Casual     54\n",
       "Name: Member type, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Member    0.813793\n",
       "Casual    0.186207\n",
       "Name: Member type, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Percent of time out of the dock\n",
    "display(\n",
    "    rides['Duration'].sum() / timedelta(days=91)\n",
    "    )\n",
    "\n",
    "# Count how many time the bike started at each station\n",
    "display(\n",
    "    rides['Member type'].value_counts()\n",
    "    )\n",
    "\n",
    "# Percent of rides by member\n",
    "display(\n",
    "    rides['Member type'].value_counts() / len(rides)\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Summarizing datetime in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Member type\n",
       "Casual    1994.666667\n",
       "Member     992.279661\n",
       "Name: Duration seconds, dtype: float64"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add duration (in seconds) column\n",
    "rides['Duration seconds'] = rides['Duration'].dt.total_seconds()\n",
    "# Average duration per member type\n",
    "rides.groupby('Member type')['Duration seconds'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start date\n",
       "2017-10-31    1886.453704\n",
       "2017-11-30     854.174757\n",
       "2017-12-31     635.101266\n",
       "Freq: M, Name: Duration seconds, dtype: float64"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average duration by month\n",
    "rides.resample('M', on = 'Start date')['Duration seconds'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Member type\n",
       "Casual     54\n",
       "Member    236\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Duration seconds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Member type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Casual</th>\n",
       "      <td>2017-10-01 15:42:57</td>\n",
       "      <td>2017-10-01 17:49:59</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>0 days 02:07:02</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Member</th>\n",
       "      <td>2017-10-01 15:23:25</td>\n",
       "      <td>2017-10-01 15:26:26</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>0 days 00:03:01</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Start date            End date  Start station number  \\\n",
       "Member type                                                                 \n",
       "Casual      2017-10-01 15:42:57 2017-10-01 17:49:59                 31036   \n",
       "Member      2017-10-01 15:23:25 2017-10-01 15:26:26                 31038   \n",
       "\n",
       "                             Start station  End station number  \\\n",
       "Member type                                                      \n",
       "Casual       George Mason Dr & Wilson Blvd               31036   \n",
       "Member                Glebe Rd & 11th St N               31036   \n",
       "\n",
       "                               End station Bike number        Duration  \\\n",
       "Member type                                                              \n",
       "Casual       George Mason Dr & Wilson Blvd      W20529 0 days 02:07:02   \n",
       "Member       George Mason Dr & Wilson Blvd      W20529 0 days 00:03:01   \n",
       "\n",
       "             Duration seconds  \n",
       "Member type                    \n",
       "Casual                 7622.0  \n",
       "Member                  181.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Size per group\n",
    "display(\n",
    "    rides.groupby('Member type').size()\n",
    "    )\n",
    "\n",
    "# First ride per group\n",
    "display(\n",
    "    rides.groupby('Member type').first()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Start date'>"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides\\\n",
    ".resample('M', on = 'Start date')\\\n",
    "['Duration seconds']\\\n",
    ".mean()\\\n",
    ".plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Start date'>"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides\\\n",
    ".resample('D', on = 'Start date')\\\n",
    "['Duration seconds']\\\n",
    ".mean()\\\n",
    ".plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many joyrides?**\n",
    "\n",
    "Suppose you have a theory that some people take long bike rides before putting their bike back in the same dock. Let's call these rides \"joyrides\".\n",
    "\n",
    "You only have data on one bike, so while you can't draw any bigger conclusions, it's certainly worth a look.\n",
    "\n",
    "Are there many joyrides? How long were they in our data set? Use the median instead of the mean, because we know there are some very long trips in our data set that might skew the answer, and the median is less sensitive to outliers.\\\n",
    "\n",
    "STEPS\n",
    "    Create a Pandas Series which is True when Start station and End station are the same, and assign the result to joyrides.\n",
    "    Calculate the median duration of all rides.\n",
    "    Calculate the median duration of joyrides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 rides were joyrides\n",
      "The median duration overall was 660.00 seconds\n",
      "The median duration for joyrides was 2642.50 seconds\n"
     ]
    }
   ],
   "source": [
    "# Create joyrides\n",
    "joyrides = (rides['Start station'] == rides['End station'])\n",
    "\n",
    "# Total number of joyrides\n",
    "print(\"{} rides were joyrides\".format(joyrides.sum()))\n",
    "\n",
    "# Median of all rides\n",
    "print(\"The median duration overall was {:.2f} seconds\"\\\n",
    "      .format(rides['Duration'].median().total_seconds()))\n",
    "\n",
    "# Median of joyrides\n",
    "print(\"The median duration for joyrides was {:.2f} seconds\"\\\n",
    "      .format(rides[joyrides]['Duration'].median().total_seconds()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It's getting cold outside, W20529**\n",
    "\n",
    "Washington, D.C. has mild weather overall, but the average high temperature in October (68ºF / 20ºC) is certainly higher than the average high temperature in December (47ºF / 8ºC). People also travel more in December, and they work fewer days so they commute less.\n",
    "\n",
    "How might the weather or the season have affected the length of bike trips?\n",
    "\n",
    "STEPS 01\n",
    "    Resample rides to the daily level, based on the Start date column.\n",
    "    Plot the .size() of each result.\n",
    "STEPS 02\n",
    "Since the daily time series is so noisy for this one bike, change the resampling to be monthly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Resample rides to daily, take the size, plot the results\n",
    "rides.resample('D', on = 'Start date')\\\n",
    "  .size()\\\n",
    "  .plot(ylim = [0, 15])\n",
    "\n",
    "# Show the results\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Resample rides to monthly, take the size, plot the results\n",
    "rides.resample('M', on = 'Start date')\\\n",
    "  .size()\\\n",
    "  .plot(ylim = [0, 150])\n",
    "\n",
    "# Show the results\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Members vs casual riders over time**\n",
    "\n",
    "Riders can either be `\"Members\"`, meaning they pay yearly for the ability to take a bike at any time, or `\"Casual\"`, meaning they pay at the kiosk attached to the bike dock.\n",
    "\n",
    "Do members and casual riders drop off at the same rate over October to December, or does one drop off faster than the other?\n",
    "\n",
    "As before, `rides` has been loaded for you. You're going to use the Pandas method `.value_counts()`, which returns the number of instances of each value in a Series. In this case, the counts of \"Member\" or \"Casual\".\n",
    "\n",
    "STEPS\n",
    "    Set monthly_rides to be a resampled version of rides, by month, based on start date.\n",
    "    Use the method .value_counts() to find out how many Member and Casual rides there were, and divide them by the total number of rides per month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date  Member type\n",
      "2017-10-31  Member         0.768519\n",
      "            Casual         0.231481\n",
      "2017-11-30  Member         0.825243\n",
      "            Casual         0.174757\n",
      "2017-12-31  Member         0.860759\n",
      "            Casual         0.139241\n",
      "Name: Member type, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Resample rides to be monthly on the basis of Start date\n",
    "monthly_rides = rides.resample('M', on = 'Start date')['Member type']\n",
    "\n",
    "# Take the ratio of the .value_counts() over the total number of rides\n",
    "print(monthly_rides.value_counts() / monthly_rides.size())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Combining groupby() and resample()**\n",
    "\n",
    "A very powerful method in Pandas is `.groupby()`. Whereas `.resample()` groups rows by some time or date information, .`groupby()` groups rows based on the values in one or more columns. For example, `rides.groupby('Member type').size()` would tell us how many rides there were by member type in our entire DataFrame.\n",
    "\n",
    "`.resample()` can be called after `.groupby().` For example, how long was the median ride by month, and by Membership type?\n",
    "\n",
    "STEPS\n",
    "    Complete the .groupby() call to group by 'Member type', and the .resample() call to resample according to 'Start date', by month.\n",
    "    Print the median Duration for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Member type  Start date\n",
      "Casual       2017-10-31          0 days 00:27:16\n",
      "             2017-11-30   0 days 00:19:19.500000\n",
      "             2017-12-31          0 days 00:14:10\n",
      "Member       2017-10-31          0 days 00:11:11\n",
      "             2017-11-30          0 days 00:10:55\n",
      "             2017-12-31   0 days 00:06:27.500000\n",
      "Name: Duration, dtype: timedelta64[ns]\n"
     ]
    }
   ],
   "source": [
    "# Group rides by member type, and resample to the month\n",
    "grouped = rides.groupby('Member type')\\\n",
    "  .resample('M', on = 'Start date')\n",
    "\n",
    "\n",
    "# Print the median duration for each group\n",
    "print(grouped['Duration'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Member type  Start date\n",
      "Casual       2017-10-31    1636.0\n",
      "             2017-11-30    1159.5\n",
      "             2017-12-31     850.0\n",
      "Member       2017-10-31     671.0\n",
      "             2017-11-30     655.0\n",
      "             2017-12-31     387.5\n",
      "Name: Duration, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Group rides by member type, and resample to the month\n",
    "grouped = rides.groupby('Member type')\\\n",
    "    .resample('M', on='Start date')\n",
    "\n",
    "# Calculate the median duration in minutes for each group\n",
    "duration_median = grouped['Duration'].median()\\\n",
    "    .apply(lambda x: x.total_seconds())\n",
    "\n",
    "# Print the median duration in minutes for each group\n",
    "print(duration_median)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Additional datetime methods in Pandas**\n",
    "\n",
    "1. Timezones in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3346.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0   2017-10-01 15:23:25\n",
       "1   2017-10-01 15:42:57\n",
       "2   2017-10-02 06:37:10\n",
       "Name: Start date, dtype: datetime64[ns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0   2017-10-01 15:23:25-04:00\n",
       "1   2017-10-01 15:42:57-04:00\n",
       "2   2017-10-02 06:37:10-04:00\n",
       "Name: Start date, dtype: datetime64[ns, America/New_York]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    rides['Duration'].dt.total_seconds().min()\n",
    ")\n",
    "\n",
    "display(\n",
    "    rides['Start date'].head(3)\n",
    ")\n",
    "\n",
    "display(\n",
    "    rides['Start date'].head(3)\\\n",
    "        .dt.tz_localize('America/New_York')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2017-10-01 15:23:25-04:00\n",
       "1     2017-10-01 15:42:57-04:00\n",
       "2     2017-10-02 06:37:10-04:00\n",
       "3     2017-10-02 08:56:45-04:00\n",
       "4     2017-10-02 18:23:48-04:00\n",
       "                 ...           \n",
       "285   2017-12-29 14:32:55-05:00\n",
       "286   2017-12-29 15:08:26-05:00\n",
       "287   2017-12-29 20:33:34-05:00\n",
       "288   2017-12-30 13:51:03-05:00\n",
       "289   2017-12-30 15:09:03-05:00\n",
       "Name: Start date, Length: 290, dtype: datetime64[ns, America/New_York]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0     2017-10-01 15:26:26-04:00\n",
       "1     2017-10-01 17:49:59-04:00\n",
       "2     2017-10-02 06:42:53-04:00\n",
       "3     2017-10-02 09:18:03-04:00\n",
       "4     2017-10-02 18:45:05-04:00\n",
       "                 ...           \n",
       "285   2017-12-29 14:43:46-05:00\n",
       "286   2017-12-29 15:18:51-05:00\n",
       "287   2017-12-29 20:38:13-05:00\n",
       "288   2017-12-30 13:54:33-05:00\n",
       "289   2017-12-30 15:19:13-05:00\n",
       "Name: End date, Length: 290, dtype: datetime64[ns, America/New_York]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try to set a timezone...\n",
    "\n",
    "#rides['Start date'] = rides['Start date']\\\n",
    "#    .dt.tz_localize('America/New_York')\n",
    "#display(\n",
    "#    rides['Start date']\n",
    "#)\n",
    "#pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from '2017-11-05 01:56:50',\n",
    "# try using the 'ambiguous' argument\n",
    "\n",
    "# Handle ambiguous datetimes\n",
    "rides['Start date'] = rides['Start date']\\\n",
    "    .dt.tz_localize('America/New_York', ambiguous='NaT')\n",
    "rides['End date'] = rides['End date']\\\n",
    "    .dt.tz_localize('America/New_York', ambiguous='NaT')\n",
    "\n",
    "display(\n",
    "    rides['Start date']\n",
    ")\n",
    "display(\n",
    "    rides['End date']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116.0"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-calculate duration, ignoring bad row\n",
    "rides['Duration'] = rides['End date'] - rides['Start date']\n",
    "# Find the minimum again\n",
    "rides['Duration'].dt.total_seconds().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Duration seconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25-04:00</td>\n",
       "      <td>2017-10-01 15:26:26-04:00</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>0 days 00:03:01</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57-04:00</td>\n",
       "      <td>2017-10-01 17:49:59-04:00</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>0 days 02:07:02</td>\n",
       "      <td>7622.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Start date                  End date  Start station number  \\\n",
       "0 2017-10-01 15:23:25-04:00 2017-10-01 15:26:26-04:00                 31038   \n",
       "1 2017-10-01 15:42:57-04:00 2017-10-01 17:49:59-04:00                 31036   \n",
       "\n",
       "                   Start station  End station number  \\\n",
       "0           Glebe Rd & 11th St N               31036   \n",
       "1  George Mason Dr & Wilson Blvd               31036   \n",
       "\n",
       "                     End station Bike number Member type        Duration  \\\n",
       "0  George Mason Dr & Wilson Blvd      W20529      Member 0 days 00:03:01   \n",
       "1  George Mason Dr & Wilson Blvd      W20529      Casual 0 days 02:07:02   \n",
       "\n",
       "   Duration seconds  \n",
       "0             181.0  \n",
       "1            7622.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(rides.head(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start date                        NaT\n",
       "End date                          NaT\n",
       "Start station number            31615\n",
       "Start station           6th & H St NE\n",
       "End station number              31627\n",
       "End station             3rd & M St NE\n",
       "Bike number                    W20529\n",
       "Member type                    Member\n",
       "Duration                          NaT\n",
       "Duration seconds              -3346.0\n",
       "Name: 129, dtype: object"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at problematic row\n",
    "rides.iloc[129]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Other datetime operations in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2017\n",
       "1    2017\n",
       "2    2017\n",
       "Name: Start date, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    Sunday\n",
       "1    Sunday\n",
       "2    Monday\n",
       "Name: Start date, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Year of first three rows\n",
    "display(\n",
    "    rides['Start date']\\\n",
    "        .head(3)\\\n",
    "        .dt.year\n",
    ")\n",
    "\n",
    "# See weekdays for first three rides\n",
    "display(\n",
    "    rides['Start date']\\\n",
    "        .head(3)\\\n",
    "        .dt.day_name()\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Other parts of Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-10-01 15:26:26-04:00\n",
       "1   2017-10-01 17:49:59-04:00\n",
       "2   2017-10-02 06:42:53-04:00\n",
       "Name: End date, dtype: datetime64[ns, America/New_York]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0                         NaT\n",
       "1   2017-10-01 15:26:26-04:00\n",
       "2   2017-10-01 17:49:59-04:00\n",
       "Name: End date, dtype: datetime64[ns, America/New_York]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "# normal index\n",
    "rides['End date'].head(3)\n",
    ")\n",
    "\n",
    "display(\n",
    "# Shift the indexes forward one, padding with NaT\n",
    "rides['End date'].shift(1).head(3)\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Timezones in Pandas**\n",
    "\n",
    "Earlier in this course, you assigned a timezone to each `datetime` in a list. Now with Pandas you can do that with a single method call.\n",
    "\n",
    "(Note that, just as before, your data set actually includes some ambiguous datetimes on account of daylight saving; for now, we'll tell Pandas to not even try on those ones. Figuring them out would require more work.)\n",
    "\n",
    "STEP01\n",
    "Make the Start date column timezone aware by localizing it to 'America/New_York' while ignoring any ambiguous datetimes.\n",
    "STEP02\n",
    "Now switch the Start date column to the timezone 'Europe/London' using the .dt.tz_convert() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset rides\n",
    "rides = pd.read_csv(r'G:\\My Drive\\Data Science\\Datacamp_Notebook\\Datacamp_Notebook\\datasets\\capital-onebike.csv', \n",
    "                    parse_dates = ['Start date', 'End date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-10-01 15:23:25-04:00\n",
      "2017-10-01 20:23:25+01:00\n"
     ]
    }
   ],
   "source": [
    "# Localize the Start date column to America/New_York\n",
    "rides['Start date'] = rides['Start date'].dt.tz_localize('America/New_York', \n",
    "                                \t\t\t\t\t\t ambiguous='NaT')\n",
    "\n",
    "# Print first value\n",
    "print(rides['Start date'].iloc[0])\n",
    "\n",
    "# Convert the Start date column to Europe/London\n",
    "rides['Start date'] = rides['Start date'].dt.tz_convert('Europe/London')\n",
    "\n",
    "# Print the new value\n",
    "print(rides['Start date'].iloc[0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How long per weekday?**\n",
    "\n",
    "Pandas has a number of datetime-related attributes within the `.dt` accessor. Many of them are ones you've encountered before, like `.dt.month.` Others are convenient and save time compared to standard Python, like `.dt.day_name()`\n",
    "\n",
    "STEPS\n",
    "    Add a new column to rides called 'Ride start weekday', which is the weekday of the Start date.\n",
    "    Print the median ride duration for each weekday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ride start weekday\n",
      "Friday      0 days 00:12:04.500000\n",
      "Monday      0 days 00:13:30.500000\n",
      "Saturday           0 days 00:07:42\n",
      "Sunday             0 days 00:15:17\n",
      "Thursday           0 days 00:10:52\n",
      "Tuesday     0 days 00:10:41.500000\n",
      "Wednesday          0 days 00:09:45\n",
      "Name: Duration, dtype: timedelta64[ns]\n"
     ]
    }
   ],
   "source": [
    "# Add a column for the weekday of the start of the ride\n",
    "rides['Ride start weekday'] = rides['Start date'].dt.day_name()\n",
    "\n",
    "# Print the median trip time per weekday\n",
    "print(rides.groupby('Ride start weekday')['Duration'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ride start weekday\n",
      "Friday       724.5\n",
      "Monday       810.5\n",
      "Saturday     462.0\n",
      "Sunday       917.0\n",
      "Thursday     652.0\n",
      "Tuesday      641.5\n",
      "Wednesday    585.0\n",
      "Name: Duration, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Add a column for the weekday of the start of the ride\n",
    "rides['Ride start weekday'] = rides['Start date'].dt.day_name()\n",
    "\n",
    "# Calculate the median duration in minutes for each group\n",
    "weekdays_median = rides.groupby('Ride start weekday')['Duration'].median()\\\n",
    "    .apply(lambda x: x.total_seconds())\n",
    "\n",
    "# Print the median duration in minutes for each group\n",
    "print(weekdays_median)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How long between rides?**\n",
    "\n",
    "For your final exercise, let's take advantage of Pandas indexing to do something interesting. How much time elapsed between rides?\n",
    "\n",
    "STEPS\n",
    "    Calculate the difference in the Start date of the current row and the End date of the previous row and assign it to rides['Time since'].\n",
    "    Convert rides['Time since'] to seconds to make it easier to work with.\n",
    "    Resample rides to be in monthly buckets according to the Start date.\n",
    "    Divide the average by (60*60) to get the number of hours on average that W20529 waited in the dock before being picked up again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date\n",
      "2017-10-31 00:00:00-04:00    5.519242\n",
      "2017-11-30 00:00:00-05:00    7.256474\n",
      "2017-12-31 00:00:00-05:00    9.202380\n",
      "Name: Time since, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Shift the index of the end date up one; now subract it from the start date\n",
    "rides['Time since'] = rides['Start date'] - (rides['End date'].shift(1))\n",
    "\n",
    "# Move from a timedelta to a number of seconds, which is easier to work with\n",
    "rides['Time since'] = rides['Time since'].dt.total_seconds()\n",
    "\n",
    "# Resample to the month\n",
    "monthly = rides.resample('M', on = 'Start date')\n",
    "\n",
    "# Print the average hours between rides each month\n",
    "print(monthly['Time since'].mean()/(60*60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start date</th>\n",
       "      <th>End date</th>\n",
       "      <th>Start station number</th>\n",
       "      <th>Start station</th>\n",
       "      <th>End station number</th>\n",
       "      <th>End station</th>\n",
       "      <th>Bike number</th>\n",
       "      <th>Member type</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Duration seconds</th>\n",
       "      <th>Ride start weekday</th>\n",
       "      <th>Time since</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-10-01 15:23:25-04:00</td>\n",
       "      <td>2017-10-01 15:26:26-04:00</td>\n",
       "      <td>31038</td>\n",
       "      <td>Glebe Rd &amp; 11th St N</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>0 days 00:03:01</td>\n",
       "      <td>181.0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-01 15:42:57-04:00</td>\n",
       "      <td>2017-10-01 17:49:59-04:00</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Casual</td>\n",
       "      <td>0 days 02:07:02</td>\n",
       "      <td>7622.0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>991.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-10-02 06:37:10-04:00</td>\n",
       "      <td>2017-10-02 06:42:53-04:00</td>\n",
       "      <td>31036</td>\n",
       "      <td>George Mason Dr &amp; Wilson Blvd</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>0 days 00:05:43</td>\n",
       "      <td>343.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>46031.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-02 08:56:45-04:00</td>\n",
       "      <td>2017-10-02 09:18:03-04:00</td>\n",
       "      <td>31037</td>\n",
       "      <td>Ballston Metro / N Stuart &amp; 9th St N</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>0 days 00:21:18</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>8032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-02 18:23:48-04:00</td>\n",
       "      <td>2017-10-02 18:45:05-04:00</td>\n",
       "      <td>31295</td>\n",
       "      <td>Potomac &amp; M St NW</td>\n",
       "      <td>31230</td>\n",
       "      <td>Metro Center / 12th &amp; G St NW</td>\n",
       "      <td>W20529</td>\n",
       "      <td>Member</td>\n",
       "      <td>0 days 00:21:17</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>32745.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Start date                  End date  Start station number  \\\n",
       "0 2017-10-01 15:23:25-04:00 2017-10-01 15:26:26-04:00                 31038   \n",
       "1 2017-10-01 15:42:57-04:00 2017-10-01 17:49:59-04:00                 31036   \n",
       "2 2017-10-02 06:37:10-04:00 2017-10-02 06:42:53-04:00                 31036   \n",
       "3 2017-10-02 08:56:45-04:00 2017-10-02 09:18:03-04:00                 31037   \n",
       "4 2017-10-02 18:23:48-04:00 2017-10-02 18:45:05-04:00                 31295   \n",
       "\n",
       "                          Start station  End station number  \\\n",
       "0                  Glebe Rd & 11th St N               31036   \n",
       "1         George Mason Dr & Wilson Blvd               31036   \n",
       "2         George Mason Dr & Wilson Blvd               31037   \n",
       "3  Ballston Metro / N Stuart & 9th St N               31295   \n",
       "4                     Potomac & M St NW               31230   \n",
       "\n",
       "                            End station Bike number Member type  \\\n",
       "0         George Mason Dr & Wilson Blvd      W20529      Member   \n",
       "1         George Mason Dr & Wilson Blvd      W20529      Casual   \n",
       "2  Ballston Metro / N Stuart & 9th St N      W20529      Member   \n",
       "3                     Potomac & M St NW      W20529      Member   \n",
       "4         Metro Center / 12th & G St NW      W20529      Member   \n",
       "\n",
       "         Duration  Duration seconds Ride start weekday  Time since  \n",
       "0 0 days 00:03:01             181.0             Sunday         NaN  \n",
       "1 0 days 02:07:02            7622.0             Sunday       991.0  \n",
       "2 0 days 00:05:43             343.0             Monday     46031.0  \n",
       "3 0 days 00:21:18            1278.0             Monday      8032.0  \n",
       "4 0 days 00:21:17            1277.0             Monday     32745.0  "
      ]
     },
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    }
   ],
   "source": [
    "display(rides.head(5))"
   ]
  },
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   "source": [
    "#### **Wrap-up**\n",
    "\n",
    "1. Recap: Dates and Calendars\n",
    "\n",
    "    The `date()` class takes a year, month, and day as arguments\n",
    "    A `date` object has accessors like `.year` , and also methods like `.weekday()`\n",
    "    `date` objects can be compared like numbers, using `min()` , `max()` , and `sort()`\n",
    "    You can subtract one `date` from another to get a `timedelta`\n",
    "    To turn `date` objects into strings, use the `.isoformat()` or `.strftime()` methods\n",
    "\n",
    "2. Recap: Combining Dates and Times\n",
    "\n",
    "    The `datetime()` class takes all the arguments of `date() `, plus an hour, minute, second, and microsecond\n",
    "    All of the additional arguments are optional; otherwise, they're set to zero by default\n",
    "    You can replace any value in a `datetime` with the `.replace()` method\n",
    "    Convert a `timedelta` into an integer with its `.total_seconds()` method\n",
    "    Turn strings into dates with `.strptime()` and dates into strings with `.strftime()`\n",
    "\n",
    "3. Recap: Timezones and Daylight Saving\n",
    "\n",
    "    A `datetime` is \"timezone aware\" when it has its `tzinfo` set. Otherwise it is \"timezone naive\"\n",
    "    Setting a timezone tells a `datetime` how to align itself to UTC, the universal time standard\n",
    "    Use the `.replace() `method to change the timezone of a `datetime` , leaving the date and time the same\n",
    "    Use the `.astimezone()` method to shift the date and time to match the new timezone\n",
    "    `dateutil.tz` provides a comprehensive, updated timezone database\n",
    "\n",
    "4. Recap: Easy and Powerful Timestamps in Pandas\n",
    "\n",
    "    When reading a csv, set the `parse_dates` argument to be the list of columns which should be parsed as datetimes\n",
    "    If setting `parse_dates` doesn't work, use the `pd.to_datetime()` function\n",
    "    Grouping rows with `.groupby()` lets you calculate aggregates per group. For example,\n",
    "        `.first()` , `.min()` or `.mean()`\n",
    "    `.resample()` groups rows on the basis of a `datetime` column, by year, month, day, and so on\n",
    "    Use `.tz_localize()` to set a timezone, keeping the date and time the same\n",
    "    Use `.tz_convert()` to change the date and time to match a new timezone"
   ]
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   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End\n"
     ]
    }
   ],
   "source": [
    "print('End')"
   ]
  }
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