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

Which regular `NumPy` data type will automatically be cast to a float data type when missing data is present?

Which regular <code>NumPy</code> data type will automatically be cast to a float data type when missing data is present?

**(True or False)** The `isnull()` and `notnull()` methods only work with `np.nan`, but not the newer `pd.NA` missing data representation.

(True or False) The <code>isnull()</code> and <code>notnull()</code> methods only work with <code>np.nan</code>, but not the newer <code>pd.NA</code> missing data representation.

The `hasnans` attribute allows us to check whether `pandas` objects like `Series` and entire DataFrames contain `NaN` values or not.

Cumulative functions like `cumsum()` and `cumprod()` ignore `NaN` values during the cumulative computations, but keep them in the resulting output.

When we sum an array containing a set of `np.nan` elements, we get an output of `NaN` due to the propagation of `NaN` values.

Although the default `skipna` parameter is `True`, there are times where it is beneficial to set it to `False`, such as for data integrity checks.

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

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

The `fillna()` method allows us to replace missing data with numerical or text (string) values.

The `np.nan` value is the overarching default representation of missing values in `pandas` objects.

When we use `fillna(method='bfill')`, we are propagating the last observed non-null value forward to replace the missing data.

If the data is missing, but not at random, dropping them would be an ideal initial strategy for managing the missing data.

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

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

Handling missing data

Handling missing data

Check your understanding of representing, detecting, analyzing, and managing missing data.

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