Part 1

Datasets/during_the_pandemic (1).csv

@@ -1,10 +1,10 @@
-Industry/Month,20-Jan,20-Feb,20-Mar,20-Apr,20-May,20-Jun,20-Jul,20-Aug,20-Sep,20-Oct,20-Nov,20-Dec
-Agriculture,38.9,39,41.2,42.3,48.8,45.3,42.7,46,49.5,44.6,42,38.9
-"Forestry, fishing, mining, quarrying, oil and gas",46.3,46.2,44.5,42.3,43.8,45.1,44.2,45.7,45.2,42,42.9,44.1
-Construction,37.8,38.6,37.4,35.9,38.3,39.5,39.3,39.8,40.6,37.1,38.5,39.5
-Wholesale and retail trade,32.6,32.7,33.1,33.6,34.3,33.7,33.3,33.3,32.9,31.2,31.9,33.1
-Transportation and warehousing,39.5,39.5,39.4,38.8,39.1,39.4,40.1,39.4,38.9,37.2,37.9,39.4
-Educational services,32.5,31.5,30.9,32,34,34.2,30.5,30.8,33.8,30.8,30.8,33.6
-Health care and social assistance,33.6,33.7,32.9,33.3,33.8,33.7,33.9,33.9,33.8,32.2,32.9,34.2
-Accommodation and food services,27.2,28,25.2,29.8,28.7,28.2,28,29.2,28.1,27.5,26.9,26.6
-Public administration,36.1,36,35.6,34.8,37.2,36.9,36.4,36.3,36.5,34.3,33.2,37
+Industry/Month,20-Jan,20-Feb,20-Mar,20-Apr,20-May,20-Jun,20-Jul,20-Aug,20-Sep,20-Oct,20-Nov,20-Dec
+Agriculture,38.9,39,41.2,42.3,48.8,45.3,42.7,46,49.5,44.6,42,38.9
+"Forestry, fishing, mining, quarrying, oil and gas",46.3,46.2,44.5,42.3,43.8,45.1,44.2,45.7,45.2,42,42.9,44.1
+Construction,37.8,38.6,37.4,35.9,38.3,39.5,39.3,39.8,40.6,37.1,38.5,39.5
+Wholesale and retail trade,32.6,32.7,33.1,33.6,34.3,33.7,33.3,33.3,32.9,31.2,31.9,33.1
+Transportation and warehousing,39.5,39.5,39.4,38.8,39.1,39.4,40.1,39.4,38.9,37.2,37.9,39.4
+Educational services,32.5,31.5,30.9,32,34,34.2,30.5,30.8,33.8,30.8,30.8,33.6
+Health care and social assistance,33.6,33.7,32.9,33.3,33.8,33.7,33.9,33.9,33.8,32.2,32.9,34.2
+Accommodation and food services,27.2,28,25.2,29.8,28.7,28.2,28,29.2,28.1,27.5,26.9,26.6
+Public administration,36.1,36,35.6,34.8,37.2,36.9,36.4,36.3,36.5,34.3,33.2,37

Datasets/pre_pandemic.csv

@@ -1,10 +1,10 @@
-Industry/Month,19-Jan,19-Feb,19-Mar,19-Apr,19-May,19-Jun,19-Jul,19-Aug,19-Sep,19-Oct,19-Nov,19-Dec
-Agriculture,40.2,38.9,40.7,41.7,47.5,44.2,44,44.4,47.6,45.1,43.8,41.5
-"Forestry, fishing, mining, quarrying, oil and gas",45,46.2,45.4,42.1,45.9,46.8,45.5,46.1,46.8,43,44.7,46.2
-Construction,39.3,38.2,39.4,34.9,39.7,40.8,40.7,40.9,41,36.7,37.5,39.5
-Wholesale and retail trade,32.5,32.1,32.7,30.6,33.3,33.8,34,34.2,33.5,30.7,32.3,33.5
-Transportation and warehousing,39.8,39.1,40.4,36.9,39.8,40.1,40.8,40.7,40.2,37.6,39,40.7
-Educational services,32.5,31.4,31.6,28.9,33.4,34.3,30.9,31,33.7,29.3,30.8,32.3
-Health care and social assistance,33.9,33.5,34.1,31.3,33.8,34.4,34,34.2,33.8,30.8,32.4,33.7
-Accommodation and food services,28.8,28.5,28.5,27.7,29.5,30.5,31.3,31.4,29.2,28.1,27.7,28.2
-Public administration,36.8,35.7,36.3,32.3,36.3,36.8,36.3,36.7,36.7,32.5,33.4,36.1
+Industry/Month,19-Jan,19-Feb,19-Mar,19-Apr,19-May,19-Jun,19-Jul,19-Aug,19-Sep,19-Oct,19-Nov,19-Dec
+Agriculture,40.2,38.9,40.7,41.7,47.5,44.2,44,44.4,47.6,45.1,43.8,41.5
+"Forestry, fishing, mining, quarrying, oil and gas",45,46.2,45.4,42.1,45.9,46.8,45.5,46.1,46.8,43,44.7,46.2
+Construction,39.3,38.2,39.4,34.9,39.7,40.8,40.7,40.9,41,36.7,37.5,39.5
+Wholesale and retail trade,32.5,32.1,32.7,30.6,33.3,33.8,34,34.2,33.5,30.7,32.3,33.5
+Transportation and warehousing,39.8,39.1,40.4,36.9,39.8,40.1,40.8,40.7,40.2,37.6,39,40.7
+Educational services,32.5,31.4,31.6,28.9,33.4,34.3,30.9,31,33.7,29.3,30.8,32.3
+Health care and social assistance,33.9,33.5,34.1,31.3,33.8,34.4,34,34.2,33.8,30.8,32.4,33.7
+Accommodation and food services,28.8,28.5,28.5,27.7,29.5,30.5,31.3,31.4,29.2,28.1,27.7,28.2
+Public administration,36.8,35.7,36.3,32.3,36.3,36.8,36.3,36.7,36.7,32.5,33.4,36.1

project_part_1.py

@@ -0,0 +1,132 @@
+""" project_part_1.py - Plotting the Graph
+
+The purpose here is to plot the graph according to the selected industry regarding the data
+from before the pandemic (2019) and during the pandemic (2020) the two year will correspond
+to 2 different graphs and the x-axis will represent the average working hour while the y-axis
+will be representing in the form of year and month.
+
+This module contains all the necessary code to implement the first part of the project, and must be imported in the main.py file.
+
+"""
+import matplotlib.pyplot as plot
+import pandas as p
+from matplotlib.figure import Figure
+
+during_pandemic_data = p.read_csv('Datasets/during_the_pandemic (1).csv')
+pre_pandemic_data = p.read_csv('Datasets/pre_pandemic.csv')
+# Gets the DataFrame from the csv dataset.
+
+industry_and_its_index = {'Agriculture': 0,
+                          'Forestry, fishing, mining, quarrying, oil and gas': 1,
+                          'Construction': 2,
+                          'Wholesale and retail trade': 3,
+                          'Transportation and warehousing': 4,
+                          'Educational services': 5,
+                          'Health care and social assistance': 6,
+                          'Accommodation and food services': 7,
+                          'Public administration': 8}
+# A dictionary so that each industry is connected to the following data set from the DataFrame.
+
+
+def plotting_the_graph(industry: str) -> Figure:
+    """plots the graph based on the industry chosen
+    for aesthetic purposes, in the function, the name will be changed so that all word will
+    capitalize their first letter.
+
+    E.g: 'Educational services' -> 'Educational Services'
+
+    """
+
+    pre_pandemic_points = points_of_pre_pandemic(industry)
+    during_pandemic_points = points_of_during_pandemic(industry)
+    # Getting the list of coordinates of the chosen industry as (Month, Average Working Hours)
+    # using helper functions.
+
+    industry_name = industry.split(' ')
+    industry_name_fixed = [industry_name[0]]
+    # Accumulator for the new industry name so that the first alphabet of each word is capitalized,
+    # index 0 doesn't count because its first letter is capitalized by default.
+
+    for i in range(1, len(industry_name)):
+        if industry_name[i][0].islower() and industry_name[i] != 'and':
+            industry_name_fixed.append(industry_name[i].replace(industry_name[i][0],
+                                       industry_name[i][0].capitalize(), 1))
+        else:
+            industry_name_fixed.append(industry_name[i])
+
+    # If the first alphabet of the word is lower cased then change it to upper case,
+    # the first word of the industry should always have an uppercase letter
+
+    new_industry_name = ' '.join(industry_name_fixed)
+
+    pre_pandemic_time_axis = []
+    pre_pandemic_stat_axis = []
+    during_pandemic_time_axis = []
+    during_pandemic_stat_axis = []
+    # Accumulators for the x and y axis for the 2 graphs.
+
+    for points in pre_pandemic_points:
+        time, stat = points
+        pre_pandemic_time_axis.append(time)
+        pre_pandemic_stat_axis.append(stat)
+    for points in during_pandemic_points:
+        time, stat = points
+        during_pandemic_time_axis.append(time)
+        during_pandemic_stat_axis.append(stat)
+    # Creating the list of the following x and y axis that matplotlib will use.
+
+    max_y_axis = int(round(max(during_pandemic_stat_axis + pre_pandemic_stat_axis) / 5) * 5)
+    min_y_axis = int(round(min(during_pandemic_stat_axis + pre_pandemic_stat_axis) / 5) * 5)
+    # Simple algorithm that creates the min/max of y-axis based on the multiple of 5.
+
+    figure, graphs = plot.subplots(2)
+
+    # Declaring that there will be 2 different graphs.
+
+    graphs[0].set_title(new_industry_name + "'s Average Working Hour Before the Pandemic (2019)")
+    graphs[1].set_title(new_industry_name + "'s Average Working Hour During the Pandemic (2020)")
+    # Creating the titles for the 2 graphs.
+
+    graphs[0].plot(pre_pandemic_time_axis, pre_pandemic_stat_axis, color='red', marker='o')
+    graphs[1].plot(during_pandemic_time_axis, during_pandemic_stat_axis, color='blue', marker='o')
+    # Plotting the line graph with distinct color while also plotting each point with a marker.
+
+    plot.setp(graphs, ylabel='Average Working Hours',
+              xlabel='Month', ylim=(int(min_y_axis - 5), int(max_y_axis + 5)))
+    # Labeling each axis and using the min/max values to indicate the range of the graph's y-axis.
+
+    figure.set_size_inches(16, 10)
+    # Setting the window size so it isn't too small.
+    plot.close('all')
+    return figure
+    # Return the figure to the main file to update the canvas
+
+
+def points_of_pre_pandemic(industry: str) -> list[tuple[int, int]]:
+    """Returns the list of point from the dataset based on the chosen industry
+        based on the average working hour and month before the pandemic
+    """
+    index = industry_and_its_index[industry]
+    before_pandemic = pre_pandemic_data.loc[index].to_dict()
+    keys = list(before_pandemic.keys())
+    pre_pandemic_points = []
+
+    for month in keys[1:13]:
+        pre_pandemic_points.append((month, before_pandemic[month]))
+
+    return pre_pandemic_points
+
+
+def points_of_during_pandemic(industry: str) -> list[tuple[int, int]]:
+    """Returns the list of point from the dataset based on the chosen industry
+        based on the average working hour and month during the pandemic
+    """
+    index = industry_and_its_index[industry]
+    during_pandemic = during_pandemic_data.loc[index].to_dict()
+    keys = list(during_pandemic.keys())
+    during_pandemic_points = []
+
+    for month in keys[1:13]:
+        during_pandemic_points.append((month, during_pandemic[month]))
+
+    return during_pandemic_points

to_do.txt

@@ -7,6 +7,11 @@ Make the necessary dataframes to be rendered with the interactive panel. (This i
 ###
 Distribution of tasks: (To be filled and updated regularly.)
 
+Eren: Configure the tkinter window to display the buttons.  (Until tonight, or tmrw after lecture at the latest.) Also, after the graphs are set, work on rendering them.
+(Deadline to be determined) Test if the graphs work. Document the module(s)
+Thomas : Make the necessary line graphs to be displayed when the buttons are pressed.  (Until friday night.)
+Khizer: Help Thomas. (for now.) I can assign you additional tasks tomorrow if you want.
+Sheldon: Show a sign of life. 
 
 ###