Python_Data_Analysis

Python Data Analysis project involving data cleaning and visualization of statistical insights using bar graphs.

📊 Python Data Cleaning and Analysis Project

Welcome to this Python-based data analysis project where we perform data cleaning and statistical analysis using common Python libraries. This project is aimed at demonstrating the practical workflow of preparing messy data for meaningful insights through cleaning, transformation, and visualization.

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🔧 Technologies & Libraries Used

• Python 3.13.5
• NumPy – for numerical operations
• Pandas – for data manipulation and analysis
• Matplotlib – for static visualizations
• Seaborn – for statistical data visualization

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📁 Project Structure

data_analysis_project/
│
├── dataset.csv               # Raw dataset used for analysis
├── analysis.ipynb            # Jupyter Notebook with code and visualizations
└── README.md                 # Project documentation (this file)

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📥 Step 1: Importing Required Libraries

We start by importing the essential Python libraries for handling data and visualizing results.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

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📂 Step 2: Reading and Uploading CSV File

We read the dataset using Pandas:

df = pd.read_csv('dataset.csv')

This loads the dataset into a DataFrame df for further operations.

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🧹 Step 3: Data Cleaning

✅ 3.1 Handling Missing Values

We remove rows or columns with null values to prevent errors during analysis.

df.dropna(inplace=True)

🔤 3.2 Changing Data Types

We ensure all columns are of the correct data types:

df['column_name'] = df['column_name'].astype('desired_dtype')

🏷️ 3.3 Renaming Columns

For better readability and consistency:

df.rename(columns={'OldName': 'NewName'}, inplace=True)

📋 3.4 Describing the Data

We generate basic descriptive statistics:

df.describe()

This includes count, mean, standard deviation, min, max, etc.

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📊 Step 4: Data Analysis

📌 4.1 Grouping, Summing, and Sorting Data

To understand grouped behavior of data:

grouped_data = df.groupby('CategoryColumn')['ValueColumn'].sum().sort_values(ascending=False)

📈 4.2 Plotting Bar Graphs

We visualize grouped data using bar graphs:

grouped_data.plot(kind='bar', figsize=(10,6), color='skyblue')
plt.title('Total Value by Category')
plt.xlabel('Category')
plt.ylabel('Total Value')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

This helps us visually interpret trends and outliers in the data.

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📌 Statistical Outputs

We calculate key statistics such as:

• Mean
• Median
• Mode
• Standard Deviation
• Correlation

Example:

print("Mean:", df['column'].mean())
print("Standard Deviation:", df['column'].std())
print("Correlation Matrix:\n", df.corr())

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📎 Conclusion

This project demonstrates:

• Efficient handling of real-world, imperfect data.
• Extraction of meaningful insights through statistical measures.
• Effective use of bar graphs for data storytelling.

It serves as a solid foundation for beginners in data science and anyone looking to understand the basic EDA (Exploratory Data Analysis) workflow.

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📌 To Run the Project

1. Clone this repository:

   git clone https://github.com/your-username/data-analysis-project.git
   cd data-analysis-project

2. Install required libraries:

   pip install numpy pandas matplotlib seaborn

3. Open the notebook:

   jupyter notebook analysis.ipynb

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📬 Contact

For feedback or collaboration:

• ✉️ Email: deepanshraj03@gmail.com
• 🐙 GitHub: Deepansh3942