Create 1b.py

1b.py

@@ -0,0 +1,53 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+
+
+df = pd.read_csv('Trips_by_Distance.csv')
+
+df['Date'] = pd.to_datetime(df['Date'])
+
+# Filter datasets for specific conditions
+df_10_25 = df[df['Number of Trips 10-25'] > 10000000]
+df_50_100 = df[df['Number of Trips 50-100'] > 10000000]
+
+# Plot for 'Number of Trips 10-25'
+plt.figure(figsize=(20, 10))
+plt.scatter(df_10_25['Date'], df_10_25['Number of Trips 10-25'], color='blue')
+plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
+plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=1))
+plt.gcf().autofmt_xdate()  # Automatically rotates dates for better readability
+plt.title('Trips of 10-25 Miles Over Time')
+plt.xlabel('Date')
+plt.ylabel('Number of Trips')
+plt.tight_layout()
+plt.savefig('trips_10_25_miles_over_time.png')  # Saving the plot as PNG
+plt.close()  # Close the plot to avoid display overlap
+
+# Plot for 'Number of Trips 50-100'
+plt.figure(figsize=(20, 10))
+plt.scatter(df_50_100['Date'], df_50_100['Number of Trips 50-100'], color='red')
+plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
+plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=1))
+plt.gcf().autofmt_xdate()
+plt.title('Trips of 50-100 Miles Over Time')
+plt.xlabel('Date')
+plt.ylabel('Number of Trips')
+plt.tight_layout()
+plt.savefig('trips_50_100_miles_over_time.png')  # Save this plot as well
+plt.close()
+
+
+
+
+trips_full_data_cleaned = trips_full_data_cleaned.drop_duplicates()
+trips_by_distance_cleaned = trips_by_distance.drop_duplicates()
+
+
+trips_full_data_cleaned['Date'] = pd.to_datetime(trips_full_data_cleaned['Date'], errors='coerce')
+
+trips_by_distance_cleaned.loc[trips_by_distance_cleaned['Distance'] > 1000, 'Distance'] = np.nan
+
+
+print(trips_full_data_cleaned.isnull().sum())
+print(trips_by_distance_cleaned.isnull().sum())