From 1b15beae8b6f95e8ef89854a8c45f2690e01c259 Mon Sep 17 00:00:00 2001
From: Shreyas GN <136763743+Shreyas-GN@users.noreply.github.com>
Date: Mon, 14 Oct 2024 16:19:08 +0530
Subject: [PATCH] Add files via upload

---
 Data Analysis Tool/tool.py | 84 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 Data Analysis Tool/tool.py

diff --git a/Data Analysis Tool/tool.py b/Data Analysis Tool/tool.py
new file mode 100644
index 00000000..5ec44f80
--- /dev/null
+++ b/Data Analysis Tool/tool.py	
@@ -0,0 +1,84 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Function to load data from a CSV or Excel file
+def load_data(file_path):
+    """Load data from a CSV or Excel file."""
+    if file_path.endswith('.csv'):
+        data = pd.read_csv(file_path)
+    elif file_path.endswith('.xlsx'):
+        data = pd.read_excel(file_path)
+    else:
+        raise ValueError("Unsupported file format. Please use .csv or .xlsx")
+    return data
+
+# Function to clean data
+def clean_data(df):
+    """Clean the DataFrame by handling missing values and duplicates."""
+    # Remove duplicate rows
+    df = df.drop_duplicates()
+
+    # Fill missing values with the mean for numerical columns
+    for column in df.select_dtypes(include=['float64', 'int64']).columns:
+        df[column].fillna(df[column].mean(), inplace=True)
+    
+    # Drop rows with missing values in non-numerical columns
+    df = df.dropna()
+    
+    return df
+
+# Function to visualize data
+def visualize_data(df, column_name):
+    """Generate bar and pie charts for a specified column in the DataFrame."""
+    # Check if the specified column exists
+    if column_name not in df.columns:
+        raise ValueError(f"Column '{column_name}' does not exist in the DataFrame.")
+
+    # Generate a bar chart
+    df[column_name].value_counts().plot(kind='bar', color='skyblue')
+    plt.title(f'Bar Chart of {column_name}')
+    plt.xlabel(column_name)
+    plt.ylabel('Count')
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    plt.show()
+
+    # Generate a pie chart
+    df[column_name].value_counts().plot(kind='pie', autopct='%1.1f%%', startangle=90, colors=plt.cm.Paired.colors)
+    plt.title(f'Pie Chart of {column_name}')
+    plt.ylabel('')  # Hide the y-label
+    plt.tight_layout()
+    plt.show()
+
+# Main function
+def main():
+    # Specify the file path (change this to your file location)
+    file_path = 'data.csv'  # Replace with your CSV or Excel file path
+    
+    try:
+        # Load the data
+        data = load_data(file_path)
+        print("Data Loaded Successfully!")
+        
+        # Display the first few rows of the data
+        print("\nFirst few rows of the data:")
+        print(data.head())
+        
+        # Clean the data
+        cleaned_data = clean_data(data)
+        print("\nData Cleaned Successfully!")
+        
+        # Display the cleaned data
+        print("\nFirst few rows of the cleaned data:")
+        print(cleaned_data.head())
+        
+        # Visualize a specified column
+        column_name = input("\nEnter the column name you want to visualize: ")
+        visualize_data(cleaned_data, column_name)
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+# Run the program
+if __name__ == "__main__":
+    main()
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