mobility.py

import pandas as pd
import numpy as np


def fetch_total_population_df():
    train_df = pd.read_csv('training_data.csv', encoding="ISO-8859-1")
    matrix = train_df.groupby(['pickup_grid_number','dropoff_grid_number']).size().unstack().fillna(0)

    matrixByHours = train_df.groupby(['hour','pickup_grid_number','dropoff_grid_number']).size().unstack().fillna(0)

    pickup_matrix = train_df.groupby(['hour', 'pickup_grid_number']).size().unstack().fillna(0)
    dropoff_matrix = train_df.groupby(['hour', 'dropoff_grid_number']).size().unstack().fillna(0)
    pickup_matrix.fillna(0, inplace=True)
    dropoff_matrix.fillna(0, inplace=True)

    change_obj = {}
    total_population_obj = {}
    for i in range(1, 2500):
        try:
            if i in dropoff_matrix[:].columns and i in pickup_matrix[:].columns:
                change_obj[str(i)] = dropoff_matrix[:][i] - pickup_matrix[:][i]
                try:
                    total_population_obj[str(i)] = total_population_obj[str(i-1)] + change_obj[str(i)]
                except KeyError:
                    total_population_obj[str(i)] = change_obj[str(i)]
                    pass
            elif i in dropoff_matrix[:].columns:
                change_obj[str(i)] = dropoff_matrix[:][i]
                try:
                    total_population_obj[str(i)] = total_population_obj[str(i-1)] + change_obj[str(i)]
                except KeyError:
                    total_population_obj[str(i)] = change_obj[str(i)]
                    pass
            elif i in pickup_matrix[:].columns:
                change_obj[str(i)] = -pickup_matrix[:][i]
                try:
                    total_population_obj[str(i)] = total_population_obj[str(i-1)] + change_obj[str(i)]
                except KeyError:                    
                    total_population_obj[str(i)] = change_obj[str(i)]
                    pass


        except KeyError:
            pass


    #net_change_df is a 24x1010 (hour by zone) matrix containing the net change in a given zone
    #a positive value implies more people entering the zone then leaving
    #a negative value implies more people leaving the zone then entering
    net_change_df = pd.DataFrame(change_obj)
    total_population_df = pd.DataFrame(total_population_obj)

    return total_population_df