This repository contains my hands-on practice and learning exercises using Python Pandas for data analysis and data manipulation.
The goal of this project is to build strong practical skills in:
- Data Cleaning
- Data Analysis (EDA)
- Data Transformation
- Statistical Analysis
- Data Visualization using Pandas
PANDAS_CODE_PRACTICE/ │ ├── INTRO_PD_1.ipynb # Pandas basics ├── INDEXING_3.ipynb # Indexing & selection ├── FILTERING_ORDERING_2.ipynb # Filtering & sorting ├── GROUPBY_AGGREGATION_4.ipynb # Groupby operations ├── MERGE_JOIN_CONCAT_5.ipynb # Combining datasets ├── DATA_CLEANING_7.ipynb # Cleaning real datasets ├── OUTLIER_REMOVAL.ipynb # Handling outliers ├── VISUALIZATION_6.ipynb # Data visualization ├── EDA_8.ipynb # Exploratory Data Analysis ├── COVARIANCE_CORELATION.ipynb # Statistical relationships ├── ANOVA.ipynb # Statistical testing ├── Z_TEST.ipynb # Hypothesis testing └── FILES/ # Sample datasets
- Pandas DataFrames & Series
- Data Cleaning Techniques
- Handling Missing Values
- GroupBy & Aggregations
- Statistical Analysis
- Data Filtering & Indexing
- Exploratory Data Analysis (EDA)
- Python
- Pandas
- NumPy
- Jupyter Notebook
This repository documents my learning journey toward becoming a Data Analyst / Data Scientist, focusing on practical data manipulation skills used in real-world projects.
Abishek T
This project is licensed under the MIT License.