---
title: "Data Analysis with Python"
original_url: "https://tds.s-anand.net/#/data-analysis-with-python?id=data-analysis-with-python"
downloaded_at: "2025-06-08T23:24:24.926726"
---

[Data Analysis with Python](#/data-analysis-with-python?id=data-analysis-with-python)
-------------------------------------------------------------------------------------

[![Data Analysis with Python](https://i.ytimg.com/vi_webp/ZPfZH14FK90/sddefault.webp)](https://youtu.be/ZPfZH14FK90)

You’ll learn practical data analysis techniques in Python using Pandas, covering:

* **Reading Parquet Files**: Utilize Pandas to read Parquet file formats for efficient data handling.
* **Dataframe Inspection**: Methods to preview and understand the structure of a dataset.
* **Pivot Tables**: Creating and interpreting pivot tables to summarize data.
* **Percentage Calculations**: Normalize pivot table values to percentages for better insights.
* **Correlation Analysis**: Calculate and interpret correlation between variables, including significance testing.
* **Statistical Significance**: Use statistical tests to determine the significance of observed correlations.
* **Datetime Handling**: Extract and manipulate date and time information from datetime columns.
* **Data Visualization**: Generate and customize heat maps to visualize data patterns effectively.
* **Leveraging AI**: Use ChatGPT to generate and refine analytical code, enhancing productivity and accuracy.

Here are the links used in the video:

* [Data analysis with Python - Notebook](https://colab.research.google.com/drive/1wEUEeF_e2SSmS9uf2-3fZJQ2kEFRnxah)
* [Card transactions dataset (Parquet)](https://drive.google.com/file/u/3/d/1XGvuFjoTwlybkw0cc9u34horMF9vMhrB/view)
* [10 minutes to Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html)
* [Python Pandas tutorials](https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS)

[Previous

Outlier Detection with Excel](#/outlier-detection-with-excel)

[Next

Data Analysis with SQL](#/data-analysis-with-sql)