BadgeWatch Search through Python CLI

[HiGregory]

As an end-user, I want to be able to search for cops by any parameters and see charts
based on the data to help give me a quick context of the data.

Challenge - Jose Hernandez has set this up in Python but is working on setting this up on the landing page.
Suggestion - Use Django web-based framework to host the front-end application

[josehernandez168]

Currently, the algorithm needs improvement in data storage, easy usage, adding an improved grading system, and allowing more parameters in the search options. Below is the program in its first stage as I have it on my computer:
`''' In this file, we will attempt to read
an Excel file and take some specific
information out of it by means of a
simple/ manual filtration algorithm.
This information is then stored in a
2D array. Then upon user input, it is
displayed a pie chart with police-
specific statistics.
'''

import xlrd
import math
import matplotlib.pyplot as plt

Opening and reading the Excel file

file_location = (r"C:\Users\jalbe\OneDrive\Documents\File_IN\2015-2020 CIP cases - Badge Watch.xlsx")
open_file = xlrd.open_workbook(file_location)
sheet = open_file.sheet_by_index(0)
row_num = sheet.nrows

Getting total number of officers

total_officers = []
for k in range (1, row_num):
if (sheet.cell_value(k,2) not in total_officers):
total_officers.append(sheet.cell_value(k,2))

Setting up variables and lists

grading_list = [[0 for i in range (13)] for j in range (len(total_officers)+1)] # Number of valid rows (rows with badge ID)
badge_ID = []
col = 0
prev_col = 0
BID = 0
name = ''
no_BID = 0

Filtration and data collection algorithm

for row in range (1, row_num):

if (sheet.cell_value(row,2) != "")and((type(sheet.cell_value(row,2))).__name__ != 'str'):
    n = 0
    total_complaints = 0
    if sheet.cell_value(row,2) not in badge_ID:
        BID  = int(sheet.cell_value(row,2))
        name = str(sheet.cell_value(row,3)+' '+sheet.cell_value(row,4))
        
        badge_ID.append(BID)
        grading_list[col][0] = BID
        grading_list[col][12] = name

        if sheet.cell_value(row,5) == 'Harassment':
            n = 1
        elif sheet.cell_value(row,5) == 'Police Involved Shooting':
            n = 2
        elif sheet.cell_value(row,5) == 'Improper Procedure':
            n = 3
        elif sheet.cell_value(row,5) == 'Negligence of Duty':
            n = 4
        elif sheet.cell_value(row,5) == 'Misconduct':
            n = 5
        elif sheet.cell_value(row,5) == 'Abusive Treatment':
            n = 6
        elif sheet.cell_value(row,5) == 'Discourtesy':
            n = 7
        elif sheet.cell_value(row,5) == 'Missing or Damaged Property':
            n = 8
        elif sheet.cell_value(row,5) == 'Bias Based Profiling':
            n = 9
        elif sheet.cell_value(row,5) == 'Excessive Force':
            n = 10
        elif sheet.cell_value(row,5) == '':
            n = 11
       
        grading_list[col][n] = (grading_list[col][n]) + 1
        for l in range (1, 11):
            total_complaints += grading_list[col][l]
            
        grading_list [col][11] = total_complaints
        col = col + 1
       
    else:
        prev_col = col
        
        for i in range (0, len(badge_ID)):
            if (badge_ID[i] == sheet.cell_value(row,2)):
                col = i
                break

        if sheet.cell_value(row,5) == 'Harassment':
            n = 1
        elif sheet.cell_value(row,5) == 'Police Involved Shooting':
            n = 2
        elif sheet.cell_value(row,5) == 'Improper Procedure':
            n = 3
        elif sheet.cell_value(row,5) == 'Negligence of Duty':
            n = 4
        elif sheet.cell_value(row,5) == 'Misconduct':
            n = 5
        elif sheet.cell_value(row,5) == 'Abusive Treatment':
            n = 6
        elif sheet.cell_value(row,5) == 'Discourtesy':
            n = 7
        elif sheet.cell_value(row,5) == 'Missing or Damaged Property':
            n = 8
        elif sheet.cell_value(row,5) == 'Bias Based Profiling':
            n = 9
        elif sheet.cell_value(row,5) == 'Excessive Force':
            n = 10
        elif sheet.cell_value(row,5) == '':
            n = 11

        grading_list[col][n] = (grading_list[col][n]) + 1
        for m in range (1, 11):
            total_complaints += grading_list[col][m]
            
        grading_list [col][11] = total_complaints
        col = prev_col
        
elif (sheet.cell_value(row,2) == ""):
    no_BID += 1

print(no_BID, " Complaints have no Badge ID")

Maintenance check

#print(len(total_officers))
#print(len(grading_list))
#print(*grading_list)
#print(*badge_ID)

Searching for specific officer (User inputed)

repeat = True
repeat_check = ''

while (repeat):

BID_input = int(input("Enter badge ID for the officer you look for: "))
officer_col_index = 0
officer_in_list = False

# Searching the database
for m in range (0, len(total_officers)+1):
    if (grading_list[m][0] == BID_input):
        officer_col_index = m
        officer_in_list = True
        break
    elif (m == len(total_officers)):
        print("Sorry, officer badge ID not found.")
if (officer_in_list):
    print("Information on:", str(grading_list[officer_col_index][12]))

    # Grading system
    most_complaints = 0
    for n in range (0, len(total_officers)+1):
        if (grading_list[n][11] > most_complaints):
            most_complaints = grading_list[n][11]

    print(str(grading_list[officer_col_index][12]),"'s Grade: ", math.ceil(100-((grading_list[officer_col_index][11]/most_complaints)*100)),"%")
    
# Displaying pie chart on the specific officer
if (officer_in_list):
    
    categories = ['Harassment','Police Involved Shooting','Improper Procedure','Negligence',\
                 'Misconduct','Abusive Treatment','Discourtesy','Missing/Damage Property',\
                 'Biased Profiling','Excessive Force']
    labels = []
    sizes = []
    explode = []
    total = grading_list[officer_col_index][11]
    
    for p in range (0, 10):
        if ((grading_list[officer_col_index][p+1]) > 0):
            labels.append(categories[p])
            sizes.append((grading_list[officer_col_index][p+1]/total)*100)
            explode.append(0.05)
            
    # Maintenance check
    #print(*labels)
    #print(*sizes)
    #print(*explode)

    # Creating plot image
    fig1, ax1 = plt.subplots()
    # Setting up pie chart
    ax1.pie(sizes, explode=explode, autopct='%1.1f%%',
            shadow= True, startangle=90, center = (0, 0))
    ax1.set_title(str(grading_list[officer_col_index][12])+"'s Statistics")
    ax1.legend(title = str(total)+ " total complaints", labels = labels, bbox_to_anchor=(.60, -.175, 0.5, 0.5))
    ax1.axis('equal')
#Showing image
plt.show()

repeat_check = input("Search again? Enter ('Y' or 'N')")
if (repeat_check == 'N') or (repeat_check == 'n'):
    repeat = False`

[josehernandez168]

Notice: As a team, we're currently undergoing issues getting Django ready on our computers. We plan to use Docker to get the environment ready and our code together.