Tutorial 4 Concatenating and Transforming Data.ipynb

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   "source": [
    "# The Engineering WOrld - A Dot To Explore ...\n",
    "___\n",
    "##  - Concatenating and transforming data"
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from pandas import Series, DataFrame"
   ]
  },
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   "execution_count": 7,
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       "      <td>24</td>\n",
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       "      <td>26</td>\n",
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       "    0   1   2   3   4   5\n",
       "0   0   1   2   3   4   5\n",
       "1   6   7   8   9  10  11\n",
       "2  12  13  14  15  16  17\n",
       "3  18  19  20  21  22  23\n",
       "4  24  25  26  27  28  29\n",
       "5  30  31  32  33  34  35"
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   "source": [
    "DF_obj = pd.DataFrame(np.arange(36).reshape(6,6))\n",
    "DF_obj"
   ]
  },
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       "    0   1   2\n",
       "0   0   1   2\n",
       "1   3   4   5\n",
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   "source": [
    "DF_obj_2 = pd.DataFrame(np.arange(15).reshape(5,3))\n",
    "DF_obj_2"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenating data"
   ]
  },
  {
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       "    0   1   2   3   4   5     0     1     2\n",
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    "# pd.concat([left_object, right_object], axis=1)\n",
    "# ♔┈♔┈♔┈( WHAT THIS DOES )┈♔┈♔┈♔\n",
    "# The concat() method joins data from seperate sources into one combined data table. If you want to \n",
    "# join objects based on their row index values, just call the pd.concat() method on the objects you \n",
    "# want joined, and then pass in the axis=1 argument. The axis=1 argument tells Python to concatenate \n",
    "# the DataFrames by adding columns (in other words, joining on the row index values).\n",
    "pd.concat([DF_obj, DF_obj_2], axis =1)"
   ]
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       "    0   1   2     3     4     5\n",
       "0   0   1   2   3.0   4.0   5.0\n",
       "1   6   7   8   9.0  10.0  11.0\n",
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     "execution_count": 11,
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   "source": [
    "pd.concat([DF_obj, DF_obj_2])"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming data\n",
    "#### Dropping data"
   ]
  },
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   "execution_count": 12,
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    "# object_name.drop([row indexes])\n",
    "# ♔┈♔┈♔┈( WHAT THIS DOES )┈♔┈♔┈♔\n",
    "# You can easily drop rows from a DataFrame by calling the .drop() method and passing in the index \n",
    "# values for the rows you want dropped.\n",
    "DF_obj.drop([0,2])"
   ]
  },
  {
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   "execution_count": 13,
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       "    1   3   4   5\n",
       "0   1   3   4   5\n",
       "1   7   9  10  11\n",
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       "3  19  21  22  23\n",
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   "source": [
    "DF_obj.drop([0,2], axis=1)"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "### Adding data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    4\n",
       "5    5\n",
       "Name: added_variable, dtype: int32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_obj = Series(np.arange(6))\n",
    "series_obj.name = \"added_variable\"\n",
    "series_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
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    {
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       "      <th>5</th>\n",
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       "      <td>34</td>\n",
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       "    0   1   2   3   4   5  added_variable\n",
       "0   0   1   2   3   4   5               0\n",
       "1   6   7   8   9  10  11               1\n",
       "2  12  13  14  15  16  17               2\n",
       "3  18  19  20  21  22  23               3\n",
       "4  24  25  26  27  28  29               4\n",
       "5  30  31  32  33  34  35               5"
      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "# DataFrame.join(left_object, right_object)\n",
    "# ♔┈♔┈♔┈( WHAT THIS DOES )┈♔┈♔┈♔\n",
    "# You can use .join() method two join two data sources into one. The .join() method works by joining \n",
    "# the two sources on their row index values.\n",
    "variable_added = DataFrame.join(DF_obj, series_obj)\n",
    "variable_added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "    0   1   2   3   4   5  added_variable\n",
       "0   0   1   2   3   4   5               0\n",
       "1   6   7   8   9  10  11               1\n",
       "2  12  13  14  15  16  17               2\n",
       "3  18  19  20  21  22  23               3\n",
       "4  24  25  26  27  28  29               4\n",
       "5  30  31  32  33  34  35               5\n",
       "0   0   1   2   3   4   5               0\n",
       "1   6   7   8   9  10  11               1\n",
       "2  12  13  14  15  16  17               2\n",
       "3  18  19  20  21  22  23               3\n",
       "4  24  25  26  27  28  29               4\n",
       "5  30  31  32  33  34  35               5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "added_datatable = variable_added.append(variable_added, ignore_index=False)\n",
    "added_datatable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "     0   1   2   3   4   5  added_variable\n",
       "0    0   1   2   3   4   5               0\n",
       "1    6   7   8   9  10  11               1\n",
       "2   12  13  14  15  16  17               2\n",
       "3   18  19  20  21  22  23               3\n",
       "4   24  25  26  27  28  29               4\n",
       "5   30  31  32  33  34  35               5\n",
       "6    0   1   2   3   4   5               0\n",
       "7    6   7   8   9  10  11               1\n",
       "8   12  13  14  15  16  17               2\n",
       "9   18  19  20  21  22  23               3\n",
       "10  24  25  26  27  28  29               4\n",
       "11  30  31  32  33  34  35               5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "added_datatable = variable_added.append(variable_added, ignore_index=True)\n",
    "added_datatable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    0   1   2   3   4   5\n",
       "5  30  31  32  33  34  35\n",
       "4  24  25  26  27  28  29\n",
       "3  18  19  20  21  22  23\n",
       "2  12  13  14  15  16  17\n",
       "1   6   7   8   9  10  11\n",
       "0   0   1   2   3   4   5"
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# object_name.sort_values(by=[index value], ascending=[False])\n",
    "# ♔┈♔┈♔┈( WHAT THIS DOES )┈♔┈♔┈♔\n",
    "# To sort rows in a DataFrame, either in ascending or descending order, call the .sort_values() \n",
    "# method off of the DataFrame, and pass in the by argument to specify the column index upon which \n",
    "# the DataFrame should be sorted.\n",
    "DF_sorted = DF_obj.sort_values(by=[5], ascending=[False])\n",
    "DF_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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