Upload exploratory_data_analysis.py

code/exploratory_data_analysis.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Set seaborn style
+sns.set(style="whitegrid")
+
+# 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')
+    plt.show()
+
+# 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')
+    plt.show()
+
+# Function to plot nationality distribution
+def plot_nationality_distribution(df):
+    plt.figure(figsize=(8, 5))
+    sns.countplot(y=df['Nationality'], palette='coolwarm')
+    plt.title('Nationality Distribution')
+    plt.gca().set_aspect('equal')
+    plt.show()
+
+# Function to plot native language distribution
+def plot_native_language_distribution(df):
+    plt.figure(figsize=(8, 5))
+    sns.countplot(y=df['Native Language'], palette='coolwarm')
+    plt.title('Native Language Distribution')
+    plt.gca().set_aspect('equal')
+    plt.show()
+
+# 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'], palette='coolwarm')
+    plt.title('Familiarity with English')
+    plt.xlabel('Count')
+    plt.ylabel('Familiarity Level')
+    plt.show()
+
+# 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')
+    plt.show()
+
+
+def main():
+    # Load the dataset
+    df = pd.read_csv("metadata.csv")
+    
+    # Plot the distributions
+    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)
+
+
+if __name__ == "__main__":
+    main()