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Python Data Analysis Toolkit

$39

Pandas/NumPy analysis templates with data cleaning, EDA notebooks, statistical testing, and visualization recipes using Matplotlib/Seaborn.

📁 40 files
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📁 File Structure 40 files

python-data-analysis-toolkit/ ├── LICENSE ├── README.md ├── data/ │ └── sample_saas_metrics.csv ├── free-sample.zip ├── guide/ │ ├── choosing_the_right_chart.md │ └── statistical_tests_cheatsheet.md ├── guides/ │ ├── choosing_the_right_chart.md │ └── statistical_tests_cheatsheet.md ├── index.html ├── notebooks/ │ ├── 01_loading_and_cleaning.py │ ├── 02_eda_workflow.py │ ├── 03_statistical_testing.py │ └── __pycache__/ │ ├── 01_loading_and_cleaning.cpython-312.pyc │ ├── 02_eda_workflow.cpython-312.pyc │ └── 03_statistical_testing.cpython-312.pyc ├── requirements.txt ├── src/ │ └── datkit/ │ ├── __init__.py │ ├── __pycache__/ │ │ ├── __init__.cpython-312.pyc │ │ ├── cleaning.cpython-312.pyc │ │ ├── correlations.cpython-312.pyc │ │ ├── distributions.cpython-312.pyc │ │ ├── eda.cpython-312.pyc │ │ ├── loading.cpython-312.pyc │ │ ├── main.cpython-312.pyc │ │ ├── statistics.cpython-312.pyc │ │ └── visualization.cpython-312.pyc │ ├── cleaning.py │ ├── correlations.py │ ├── distributions.py │ ├── eda.py │ ├── loading.py │ ├── main.py │ ├── statistics.py │ └── visualization.py └── tests/ ├── __init__.py ├── __pycache__/ │ ├── __init__.cpython-312.pyc │ ├── test_cleaning.cpython-312.pyc │ └── test_statistics.cpython-312.pyc ├── test_cleaning.py └── test_statistics.py

📖 Documentation Preview README excerpt

Python Data Analysis Toolkit

A comprehensive collection of pandas, NumPy, and visualization modules for data analysts. Provides battle-tested patterns for data loading, cleaning, EDA, statistical testing, and publication-ready visualization.

Features

  • Data Loading & Cleaning — Multi-format loaders with automatic type inference and validation
  • Exploratory Data Analysis — Automated profiling, distribution analysis, correlation matrices
  • Statistical Testing — t-tests, chi-square, ANOVA, normality checks with plain-English interpretation
  • Visualization Recipes — 15+ chart templates for Matplotlib/Seaborn, ready for reports and presentations
  • Sample Data — Included CSV dataset for testing all modules immediately

Quick Start


pip install -r requirements.txt
python -m src.datkit.main

Structure


src/datkit/
├── __init__.py         # Package exports
├── main.py             # Demo runner showcasing all modules
├── loading.py          # Data loading utilities (CSV, Excel, JSON, Parquet)
├── cleaning.py         # Missing values, type coercion, deduplication
├── eda.py              # Automated EDA profiling and summaries
├── distributions.py    # Distribution fitting and normality testing
├── correlations.py     # Correlation analysis with significance testing
├── statistics.py       # Hypothesis testing helpers
├── visualization.py    # Chart recipes and styling
notebooks/
├── 01_loading_and_cleaning.py   # Guided walkthrough of data loading
├── 02_eda_workflow.py           # Complete EDA workflow example
├── 03_statistical_testing.py    # Hypothesis testing examples
tests/
├── test_cleaning.py    # Unit tests for cleaning module
├── test_statistics.py  # Unit tests for statistical functions
data/
├── sample_saas_metrics.csv  # 1000-row sample dataset
guides/
├── choosing_the_right_chart.md  # Decision framework for visualization
├── statistical_tests_cheatsheet.md  # When to use which test

Modules

`loading.py`

Load data from any format with automatic type detection:


from src.datkit.loading import smart_load
df = smart_load("data/sample_saas_metrics.csv")

`cleaning.py`

Handle missing values, duplicates, and type issues:


from src.datkit.cleaning import DataCleaner
cleaner = DataCleaner(df)

*... continues with setup instructions, usage examples, and more.*

📄 Code Sample .py preview

src/datkit/cleaning.py """ Data cleaning pipeline with chainable operations. Provides a fluent interface for common cleaning tasks: missing value handling, deduplication, type coercion, and outlier removal. """ from __future__ import annotations import logging from dataclasses import dataclass, field from typing import Literal import numpy as np import pandas as pd logger = logging.getLogger(__name__) @dataclass class CleaningReport: """Summary of all cleaning operations performed.""" rows_before: int rows_after: int columns_modified: list[str] = field(default_factory=list) nulls_filled: int = 0 duplicates_removed: int = 0 outliers_removed: int = 0 types_coerced: dict[str, str] = field(default_factory=dict) def __str__(self) -> str: lines = [ f"Cleaning Report:", f" Rows: {self.rows_before} → {self.rows_after} ({self.rows_before - self.rows_after} removed)", f" Nulls filled: {self.nulls_filled}", f" Duplicates removed: {self.duplicates_removed}", f" Outliers removed: {self.outliers_removed}", f" Types coerced: {len(self.types_coerced)} columns", ] return "\n".join(lines) class DataCleaner: """Chainable data cleaning pipeline. Usage: cleaner = DataCleaner(df) result = ( cleaner .handle_missing(strategy="smart") # ... 204 more lines ...
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