DataNinja 0.2

A comprehensive data analysis and visualization toolkit.

markdown Copier le code

DataNinja Package

Overview

DataNinja is an all-in-one data analysis toolkit designed to simplify data manipulation, statistical analysis, and visualization. It integrates popular libraries like pandas, numpy, matplotlib, and others into a single package, making it easier for data analysts and scientists to perform common data tasks.

Features

• DataFrame Creation: Create DataFrames with ease.
• Statistics Calculation: Compute basic statistics such as mean, median, variance, and standard deviation.
• Data Profiling: Generate comprehensive data profile reports.
• Missing Data Visualization: Visualize missing data patterns in your DataFrame.
• Missing Value Handling: Handle missing values using different methods.
• Visualization: Create various plots to analyze and visualize your data.

Installation

To install the DataNinja package, use pip:

pip install dataninja
Usage
1. DataFrame Creation
Create a DataFrame using the create_dataframe function:

python
Copier le code
import dataninja

data = {
    'A': [1, 2, None, 4],
    'B': [None, 2, 3, 4],
    'C': [1, None, None, 4]
}

df = dataninja.create_dataframe(data)
print(df)
2. Statistics Calculation
Calculate basic statistics using the calculate_statistics function:

python
Copier le code
# Assuming df is already created
stats = dataninja.calculate_statistics(df)
print(stats)
3. Data Profiling
Generate a data profile report using the generate_data_profile function. This can be rendered in Jupyter Notebooks:

python
Copier le code
# Assuming df is already created
report = dataninja.generate_data_profile(df)
report.to_notebook_iframe()  # For Jupyter Notebooks
4. Missing Data Visualization
Visualize missing data patterns with the plot_missing_data function:

python
Copier le code
# Assuming df is already created
dataninja.plot_missing_data(df)
5. Missing Value Handling
Fill missing values using different methods:

python
Copier le code
# Assuming df is already created
df_filled = dataninja.fill_missing_values(df, method='mean')
print(df_filled)
Example
Here’s a complete example of using the DataNinja package:

python
Copier le code
import dataninja

# Create a DataFrame
data = {
    'A': [1, 2, None, 4],
    'B': [None, 2, 3, 4],
    'C': [1, None, None, 4]
}
df = dataninja.create_dataframe(data)

# Calculate statistics
stats = dataninja.calculate_statistics(df)
print("Statistics:\n", stats)

# Generate data profile report
report = dataninja.generate_data_profile(df)
report.to_notebook_iframe()  # For Jupyter Notebooks

# Visualize missing data
dataninja.plot_missing_data(df)

# Fill missing values
df_filled = dataninja.fill_missing_values(df, method='mean')
print("Filled DataFrame:\n", df_filled)
Development
To contribute to the DataNinja package:

Clone the Repository:

bash
Copier le code
git clone https://github.com/ShelbyTO/DataNinja.git
Navigate to the Project Directory:

bash
Copier le code
cd DataNinja
Install Dependencies:

bash
Copier le code
pip install -r requirements.txt
Run Tests:

bash
Copier le code
pytest
Make Your Changes and submit a pull request.

License
This package is licensed under the MIT License. See the LICENSE file for details.

Contact
For any issues or questions, please contact:

Author: Nicolas Prieur
Email: [e-mail]