Merge pull request #84 from Dnyanesh182/patch-2

Create CleaningScript.py

data-processing/CleaningScript.py

@@ -0,0 +1,70 @@
+# data_visualization.py
+# Project: CSV Data Visualizer
+# Description: This script provides basic data visualization functions to analyze the cleaned dataset.
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def plot_missing_values(df):
+    """
+    Plot missing values per column in a bar chart.
+
+    Contributors: Customize the chart or add options for displaying in percentage or absolute values.
+    """
+    missing = df.isnull().sum()
+    missing = missing[missing > 0]
+    if missing.empty:
+        print("No missing values in the dataset.")
+    else:
+        plt.figure(figsize=(10, 5))
+        missing.plot(kind='bar', color='skyblue')
+        plt.title("Missing Values per Column")
+        plt.xlabel("Columns")
+        plt.ylabel("Number of Missing Values")
+        plt.show()
+
+def plot_numerical_distributions(df):
+    """
+    Plot distributions of numerical columns.
+
+    Contributors: Add parameters for bin sizes or range customization.
+    """
+    numeric_cols = df.select_dtypes(include=['number']).columns
+    if not numeric_cols.empty:
+        df[numeric_cols].hist(bins=15, figsize=(15, 10), color='lightblue')
+        plt.suptitle("Distributions of Numerical Columns")
+        plt.show()
+    else:
+        print("No numerical columns to display.")
+
+def plot_correlation_heatmap(df):
+    """
+    Plot a heatmap of correlations among numerical columns.
+
+    Contributors: Enhance this function to support different correlation methods or interactive visualizations.
+    """
+    numeric_cols = df.select_dtypes(include=['number']).columns
+    if not numeric_cols.empty:
+        plt.figure(figsize=(10, 8))
+        correlation = df[numeric_cols].corr()
+        sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt=".2f")
+        plt.title("Correlation Heatmap")
+        plt.show()
+    else:
+        print("No numerical columns for correlation heatmap.")
+
+def main():
+    # Path to the cleaned dataset
+    file_path = 'path/to/your/cleaned_dataset.csv'
+
+    # Load the cleaned dataset
+    df = pd.read_csv(file_path)
+
+    # Generate visualizations
+    plot_missing_values(df)
+    plot_numerical_distributions(df)
+    plot_correlation_heatmap(df)
+
+if __name__ == '__main__':
+    main()