Datasets:
add assertion testing
Browse files
code/exploratory_data_analysis.py
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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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# Set seaborn style
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sns.set(style="whitegrid")
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# Function to plot age distribution
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def plot_age_distribution(df):
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plt.figure(figsize=(8, 5))
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plt.title('Age Distribution')
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plt.xlabel('Age')
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plt.ylabel('Count')
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# Function to plot gender distribution (Donut Chart)
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def plot_gender_distribution(df):
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colors=sns.color_palette("pastel"))
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plt.title('Gender Distribution')
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plt.gca().set_aspect('equal')
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# Function to plot nationality distribution
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def plot_nationality_distribution(df):
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plt.figure(figsize=(8, 5))
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sns.countplot(y=df['Nationality'], palette='coolwarm')
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plt.title('Nationality Distribution')
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plt.gca().set_aspect('equal')
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# Function to plot native language distribution
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def plot_native_language_distribution(df):
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plt.figure(figsize=(8, 5))
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sns.countplot(y=df['Native Language'], palette='coolwarm')
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plt.title('Native Language Distribution')
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plt.gca().set_aspect('equal')
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# Function to plot familiarity with English distribution
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def plot_familiarity_with_english(df):
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plt.figure(figsize=(8, 5))
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sns.countplot(y=df['Familiarity with English'], palette='coolwarm')
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plt.title('Familiarity with English')
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plt.xlabel('Count')
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plt.ylabel('Familiarity Level')
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# Function to plot recording duration distribution
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def plot_duration_distribution(df):
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@@ -56,20 +81,31 @@ def plot_duration_distribution(df):
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plt.title('Recording Duration Distribution')
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plt.xlabel('Duration (seconds)')
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plt.ylabel('Count')
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def main():
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# Load the dataset
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df = pd.read_csv("metadata.csv")
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# Plot the distributions
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if __name__ == "__main__":
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import os
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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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# Set seaborn style
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sns.set(style="whitegrid")
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# Ensure the 'assets' directory exists
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if not os.path.exists('assets'):
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os.makedirs('assets')
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# Function to plot age distribution
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def plot_age_distribution(df):
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plt.figure(figsize=(8, 5))
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plt.title('Age Distribution')
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plt.xlabel('Age')
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plt.ylabel('Count')
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plot_filename = 'assets/age_distribution.png'
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plt.savefig(plot_filename)
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plt.show()
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plt.close()
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return plot_filename
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# Function to plot gender distribution (Donut Chart)
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def plot_gender_distribution(df):
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colors=sns.color_palette("pastel"))
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plt.title('Gender Distribution')
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plt.gca().set_aspect('equal')
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plot_filename = 'assets/gender_distribution.png'
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plt.savefig(plot_filename)
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plt.show()
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plt.close()
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return plot_filename
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# Function to plot nationality distribution
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def plot_nationality_distribution(df):
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plt.figure(figsize=(8, 5))
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sns.countplot(y=df['Nationality'], hue=df['Nationality'], palette='coolwarm', legend=False)
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plt.title('Nationality Distribution')
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plt.gca().set_aspect('equal')
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plot_filename = 'assets/nationality_distribution.png'
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plt.savefig(plot_filename)
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plt.show()
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plt.close()
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return plot_filename
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# Function to plot native language distribution
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def plot_native_language_distribution(df):
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plt.figure(figsize=(8, 5))
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sns.countplot(y=df['Native Language'], hue=df['Native Language'], palette='coolwarm', legend=False)
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plt.title('Native Language Distribution')
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plt.gca().set_aspect('equal')
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plot_filename = 'assets/native_language_distribution.png'
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plt.savefig(plot_filename)
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plt.show()
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plt.close()
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return plot_filename
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# Function to plot familiarity with English distribution
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def plot_familiarity_with_english(df):
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plt.figure(figsize=(8, 5))
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sns.countplot(y=df['Familiarity with English'], hue=df['Familiarity with English'], palette='coolwarm', legend=False)
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plt.title('Familiarity with English')
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plt.xlabel('Count')
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plt.ylabel('Familiarity Level')
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plot_filename = 'assets/familiarity_with_eng.png'
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plt.savefig(plot_filename)
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plt.show()
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plt.close()
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return plot_filename
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# Function to plot recording duration distribution
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def plot_duration_distribution(df):
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plt.title('Recording Duration Distribution')
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plt.xlabel('Duration (seconds)')
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plt.ylabel('Count')
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plot_filename = 'assets/recording_duration_distribution.png'
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plt.savefig(plot_filename)
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plt.show()
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plt.close()
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return plot_filename
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def main():
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# Load the dataset
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df = pd.read_csv("metadata.csv")
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# Plot the distributions and save files
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plot_files = [
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plot_age_distribution(df),
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plot_gender_distribution(df),
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plot_nationality_distribution(df),
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plot_native_language_distribution(df),
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plot_familiarity_with_english(df),
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plot_duration_distribution(df)
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]
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# Testing
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for plot_file in plot_files:
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assert os.path.exists(plot_file), f"Plot {plot_file} was not saved."
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print(f"Assertion passed for {plot_file}")
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if __name__ == "__main__":
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