Explore advanced pandas techniques for reshaping, manipulating, and handling data. Gain insights into using unconventional features and extended libraries to streamline workflows and enhance data analysis.

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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

It only allows the application of built-in `pandas` functions on a DataFrame or `Series`.

It allows the execution of a sequence of functions on a DataFrame or `Series`, including custom user-defined functions.

It performs a pipe operation to join two DataFrames on their indexes.

It sends the DataFrame or `Series` to a pipeline that automatically saves the output.

What statement correctly describes the usage of the `pipe()` method in `pandas`?


What statement correctly describes the usage of the <code>pipe()</code> method in <code>pandas</code>?

**(True or False)** By default, the `assert_frame_equal()` is used to check whether two DataFrames are identical, even if their data types differ for the corresponding columns.

(True or False) By default, the <code>assert_frame_equal()</code> is used to check whether two DataFrames are identical, even if their data types differ for the corresponding columns.

`Stylers` can be used to apply CSS styling to specific cells in a DataFrame.

`Stylers` are used to modify the underlying data of a DataFrame.

The `Styler.set_table_styles()` method can only apply one specific style to the entire DataFrame.

The `Styler.applymap()` method applies a function element-wise to the entire DataFrame.

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

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

The `pd.option_context()` function can be used to reset an option to its default value.

We can use the `pd.set_option()` function to change `pandas`' default behavior for displaying numerical precision.

The `pandas` options can only be changed at the beginning of a script and remain fixed throughout its execution.

The `pd.options.display.max_columns` attribute allows us to control the number of columns displayed when printing a DataFrame.

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

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

**(True or False)** In `pandas`, using vectorized operations is generally slower than iterating through rows with a for-loop and applying a function, while the `apply()` method can be used as a more efficient alternative to both.


(True or False) In <code>pandas</code>, using vectorized operations is generally slower than iterating through rows with a for-loop and applying a function, while the <code>apply()</code> method can be used as a more efficient alternative to both.

Leveraging the advanced features of `pandas`

Leveraging the advanced features of <code>pandas</code>

Check your understanding of leveraging further advanced features 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