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Pandas is an easy-to-use and performant Python library for data manipulation and analysis. It is an important toolkit in a world where the volume of data created and captured is growing exponentially.

In this course, you’ll discover an in-depth exploration of the pandas library. The course covers advanced  techniques to reshape data, manipulate data, and handle various data types. You will also learn how to use less conventional pandas features to streamline workflows, while also leveraging extended pandas libraries for enhanced data analysis.

Whether you are an aspiring data scientist, a researcher, or a seasoned professional looking to sharpen your skills, this course is your pathway to becoming proficient in using pandas to analyze and manipulate data effectively and efficiently.

Roshan

This course was transformative for my data science skills. It offered a deep dive into complex data manipulation techniques, making advanced concepts easily understandable. The practical exercises were directly applicable to real-world data challenges

Data Science Analyst

Advanced pandas—Going Beyond the Basics

Creating profile reports with summary statistics and data visualizations.


Parallel computation across multiple machines.

Automatic model training and hyperparameter tuning.

Data transformation and feature engineering.

Which functionality is directly provided by `ydata-profiling` when analyzing a `pandas` DataFrame?

Which functionality is directly provided by <code>ydata-profiling</code> when analyzing a <code>pandas</code> DataFrame?

**(True or False)** The `pandarallel` library shouldn’t be used if our data can’t fit into memory within `pandas` itself.

(True or False) The <code>pandarallel</code> library shouldn’t be used if our data can’t fit into memory within <code>pandas</code> itself.

It’s compulsory to pass a list of data types into dtypes` when we utilize the `read_csv()` method in `polars`.

The `scan_csv()` delays the parsing of a CSV dataset, and instead lazily reads it and returns a holder known as a LazyFrame (rather than a DataFrame).

The `polars` library only provides four expression methods---`select()`, 
`with_columns()`, 
`filter()`, and 
`groupby()`.

The `with_columns()` method can be used to create new columns.

**(Select all that apply.)** Which  statements are *true*? 

(Select all that apply.) Which statements are true?

A `pandasAI` `SmartDataFrame` can only accept natural language queries.

We can’t pass in multiple DataFrames to `pandasAI`.

An OpenAI API key is required when selecting to use the OpenAI's language models in `pandasAI`.

If we want to enforce data privacy, we can instantiate `pandasAI` with `enforce_privacy=True`, which will then avoid sending the head of the DataFrame to the LLM.

Utilizing extended libraries

Utilizing extended libraries

Check your understanding of utilizing the extended libraries of pandas.

Before We Begin

Reading Data into pandas

Combining Data

Reshaping and Manipulating Data

Encoding Data Types

Handling Numerical Data

Handling Categorical Data

Handling Text Data

Handling Time Series Data

Handling Sparse Data Structures

Handling Missing Data

Leveraging Further Features of pandas

Utilizing Extended Libraries

Wrap Up

Appendix

Quiz