56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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from .basePlotAnalysis import BasePlotAnalysis
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import matplotlib
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matplotlib.use('Agg')
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class PlotActivityHeatmap(BasePlotAnalysis):
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"""
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Class for analyzing user activity trends over multiple days and generating a heatmap.
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Attributes:
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name (str): The name of the analysis.
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description (str): A brief description of the analysis.
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plot_filename (str): The filename for the output plot.
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note (str): Additional notes for the analysis.
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"""
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name = "Activity Heatmap"
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description = "Displays user activity trends over multiple days using a heatmap. Generates a downloadable PNG image."
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plot_filename = "activity_heatmap.png"
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note = ""
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def transform_data(self, df: pd.DataFrame) -> pd.DataFrame:
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"""
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Transform data for the heatmap.
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Parameters:
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df (pd.DataFrame): The input DataFrame containing user activity data.
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Returns:
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pd.DataFrame: The transformed DataFrame with activity counts by hour.
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"""
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active_counts = df[df['was_active']].pivot_table(
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index='name',
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columns='hour',
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values='was_active',
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aggfunc='sum',
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fill_value=0
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)
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active_counts['total_active_minutes'] = active_counts.sum(axis=1)
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return active_counts.sort_values(by='total_active_minutes', ascending=False)
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def plot_data(self, df: pd.DataFrame):
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"""
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Generate heatmap plot.
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Parameters:
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df (pd.DataFrame): The transformed DataFrame containing activity counts by hour.
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"""
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plt.figure(figsize=(12, 8))
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sns.heatmap(df.loc[:, df.columns != 'total_active_minutes'], cmap='viridis', cbar_kws={'label': 'Count of was_active == True'})
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plt.xlabel('Hour of Day')
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plt.ylabel('User ID')
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plt.title('User Activity Heatmap')
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