Datasets:
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import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Set seaborn style
sns.set(style="whitegrid")
# Ensure the 'assets' directory exists
if not os.path.exists('assets'):
os.makedirs('assets')
# Function to plot age distribution
def plot_age_distribution(df):
plt.figure(figsize=(8, 5))
sns.histplot(df['Age'], kde=False, color='skyblue', bins=5)
plt.title('Age Distribution')
plt.xlabel('Age')
plt.ylabel('Count')
plot_filename = 'assets/age_distribution.png'
plt.savefig(plot_filename)
plt.show()
plt.close()
return plot_filename
# Function to plot gender distribution (Donut Chart)
def plot_gender_distribution(df):
gender_counts = df['Gender'].value_counts()
plt.figure(figsize=(7, 7))
plt.pie(gender_counts, labels=gender_counts.index, autopct='%1.1f%%', startangle=90,
colors=sns.color_palette("pastel"))
plt.title('Gender Distribution')
plt.gca().set_aspect('equal')
plot_filename = 'assets/gender_distribution.png'
plt.savefig(plot_filename)
plt.show()
plt.close()
return plot_filename
# Function to plot nationality distribution
def plot_nationality_distribution(df):
plt.figure(figsize=(8, 5))
sns.countplot(y=df['Nationality'], hue=df['Nationality'], palette='coolwarm', legend=False)
plt.title('Nationality Distribution')
plt.gca().set_aspect('equal')
plot_filename = 'assets/nationality_distribution.png'
plt.savefig(plot_filename)
plt.show()
plt.close()
return plot_filename
# Function to plot native language distribution
def plot_native_language_distribution(df):
plt.figure(figsize=(8, 5))
sns.countplot(y=df['Native Language'], hue=df['Native Language'], palette='coolwarm', legend=False)
plt.title('Native Language Distribution')
plt.gca().set_aspect('equal')
plot_filename = 'assets/native_language_distribution.png'
plt.savefig(plot_filename)
plt.show()
plt.close()
return plot_filename
# Function to plot familiarity with English distribution
def plot_familiarity_with_english(df):
plt.figure(figsize=(8, 5))
sns.countplot(y=df['Familiarity with English'], hue=df['Familiarity with English'], palette='coolwarm', legend=False)
plt.title('Familiarity with English')
plt.xlabel('Count')
plt.ylabel('Familiarity Level')
plot_filename = 'assets/familiarity_with_eng.png'
plt.savefig(plot_filename)
plt.show()
plt.close()
return plot_filename
# Function to plot recording duration distribution
def plot_duration_distribution(df):
plt.figure(figsize=(8, 5))
sns.histplot(df['Duration (secs)'], kde=False, color='coral', bins=10)
plt.title('Recording Duration Distribution')
plt.xlabel('Duration (seconds)')
plt.ylabel('Count')
plot_filename = 'assets/recording_duration_distribution.png'
plt.savefig(plot_filename)
plt.show()
plt.close()
return plot_filename
def main():
# Load the dataset
df = pd.read_csv("metadata.csv")
# Plot the distributions and save files
plot_files = [
plot_age_distribution(df),
plot_gender_distribution(df),
plot_nationality_distribution(df),
plot_native_language_distribution(df),
plot_familiarity_with_english(df),
plot_duration_distribution(df)
]
# Testing
for plot_file in plot_files:
assert os.path.exists(plot_file), f"Plot {plot_file} was not saved."
print(f"Assertion passed for {plot_file}")
if __name__ == "__main__":
main()
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