Plotting for dask.py

#!/usr/bin/env python
# coding: utf-8

# In[ ]:


import pandas as pd
import dask.dataframe as dd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


column_types = {
    'Level': 'str',
    'Date': 'str',
    'State FIPS': 'str',
    'State Postal Code': 'str',
    'County FIPS': 'str',
    'County Name': 'str',
    'Population Staying at Home': 'float64',
    'Population Not Staying at Home': 'float64',
    'Number of Trips': 'float64',
    'Number of Trips <1': 'float64',
    'Number of Trips 1-3': 'float64',
    'Number of Trips 3-5': 'float64',
    'Number of Trips 5-10': 'float64',
    'Number of Trips 10-25': 'float64',
    'Number of Trips 25-50': 'float64',
    'Number of Trips 50-100': 'float64',
    'Number of Trips 100-250': 'float64',
    'Number of Trips 250-500': 'float64',
    'Number of Trips >=500': 'float64',
    'Row ID': 'str',
    'Week': 'int64',
    'Month': 'int64'
}

block_size = "20MB"


data_df = dd.read_csv("Cleaned_Trips_by_Distance.csv", blocksize=block_size, dtype=column_types)


full_data = pd.read_csv("Trips_Full Data.csv")

def clean_dataframe_col(df, cols):
    for col in cols:
        df = df.dropna(subset=[col])
    return df

def convert_to_Date(df, type_df):
    if type_df == "pd":
        df['Date'] = pd.to_datetime(df['Date'])
    elif type_df == "dd":
        df['Date'] = df['Date'].astype('M8[us]')
    return df

def number_of_trips(df):
    total_trips = df['Number of Trips'].sum().compute()
    print(f"Total number of trips: {total_trips}")


def clean_populations(data_df, full_data):
    data = clean_dataframe_col(data_df, ['Population Staying at Home'])
    
   
    weekly_means = data.groupby('Week')['Population Staying at Home'].mean().compute()
    print("Weekly Means:\n", weekly_means)
    print("Total Mean:\n", int(weekly_means.mean()))
    
    
    plt.figure(figsize=(10, 6))
    weekly_means.plot(kind='bar')
    plt.title('BarChart of People Staying at Home vs Week')
    plt.xlabel('Week')
    plt.ylabel('Average Population Staying at Home')
    plt.show()
    
    
    trips_columns = ['Trips <1 Mile', 'Trips 1-3 Miles', 'Trips 3-5 Miles', 
                'Trips 5-10 Miles', 'Trips 10-25 Miles', 'Trips 25-50 Miles', 
                'Trips 50-100 Miles', 'Trips 100-250 Miles', 'Trips 250-500 Miles', 
                'Trips 500+ Miles']
    
    
    trip_values = full_data[trips_columns].mean()
    
    
    plt.figure(figsize=(12, 8))
    sns.barplot(x=trip_values.index, y=trip_values.values, palette='viridis')
    plt.xlabel('Distance Range', fontsize=12)
    plt.ylabel('Average Number of Trips', fontsize=12)
    plt.title('Average Number of Trips for Each Distance Range', fontsize=14)
    plt.xticks(rotation=45, ha='right')  
    plt.tight_layout()  
    plt.show()


clean_populations(data_df, full_data)