add data_exploration.py - create charts

preprocessing/data_exploration.py

@@ -0,0 +1,75 @@
+import seaborn as sns
+from preprocessing.preprocessing import DataPreprocessing
+import matplotlib.pyplot as plt
+
+preprocessing = DataPreprocessing("data/sbahn_hamburg.csv")
+df = preprocessing.create_df()
+df = preprocessing.rename_columns(df)
+df = preprocessing.change_datatypes(df)
+df = df.loc[df['sBahnID'] == 'S1']
+
+df['Hour'] = df['Arrival'].dt.hour
+
+# Creating the scatter plot
+
+plt.figure(figsize=(10, 6))
+sns.scatterplot(x='Boardings', y='Alightings', data=df)
+plt.title('Scatter Plot of Boardings vs. Alightings')
+plt.xlabel('Boarding Counts')
+plt.ylabel('Alighting Counts')
+plt.show()
+
+
+# Preparing the data for heat map
+# Aggregating data by station and hour
+df_heat_map_agg = df.groupby(['Station', 'Hour']).agg({'Boardings': 'sum', 'Alightings': 'sum'}).reset_index()
+
+# Pivoting the DataFrame for seaborn heatmap
+heatmap_data = df_heat_map_agg.pivot("Station", "Hour", "Boardings")  # Example for Boardings
+
+# Creating the heat map
+plt.figure(figsize=(15, 8))
+sns.heatmap(heatmap_data, #annot=True, #fmt="d", 
+            cmap="YlGnBu")
+plt.title('Heat Map of Boarding Counts by Station and Hour')
+plt.xlabel('Hour of the Day')
+plt.ylabel('Station')
+plt.show()
+
+
+# Aggregating data by station
+df_station_aggregated = df.groupby('Station').sum().reset_index()
+
+# Melting the DataFrame for seaborn barplot
+df_station_melted = df_station_aggregated.melt('Station', var_name='Type', value_name='Counts')
+
+# Creating the bar chart
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Station', y='Counts', hue='Type', data=df_station_melted)
+plt.title('Station-wise Boarding and Alighting Counts')
+plt.xlabel('Station')
+plt.xticks(rotation=45)
+plt.ylabel('Total Counts')
+plt.show()
+
+
+# Plotting histograms
+
+df = df.loc[df['sBahnID'] == 'S1']
+
+plt.figure(figsize=(12, 6))
+
+plt.subplot(1, 2, 1)
+sns.histplot(df['Boardings'], kde=False, color='blue')
+plt.title('Histogram of Boardings')
+plt.xlabel('Number of Boardings')
+plt.ylabel('Frequency')
+
+plt.subplot(1, 2, 2)
+sns.histplot(df['Alightings'], kde=False, color='green')
+plt.title('Histogram of Alightings')
+plt.xlabel('Number of Alightings')
+plt.ylabel('Frequency')
+
+plt.tight_layout()
+plt.show()