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 string methods in `pandas` is used to extract a part of a string based on its position?

Which string methods in <code>pandas</code> is used to extract a part of a string based on its position?

**(True or False)** The `partition()` method is similar to `split()` except that it splits the string at the last occurrence of the separator. 

(True or False) The <code>partition()</code> method is similar to <code>split()</code> except that it splits the string at the last occurrence of the separator.

The `join` parameter of the `cat()` string method allows us to align the indexes before concatenation.

The pattern matching in the `match()` method is case-sensitive because the default value for the `case` parameter is `True`. 

The `pat` separator parameter of the `split()` method only takes in string values and does not accept regular expressions.

The `match()` method makes it such that a match can occur at any position within the string.

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

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

The `extract()` and `extractall()` methods work on both string and regex patterns.

Through this line of code: `df['email'].str.replace('.com', '.org', regex=False, case=True)`, the email address ending with the string `.com` will have these suffixes replaced with `.org` in a case-sensitive manner.

When we set the `expand` parameter in the `extract()` string method as `True`, the extracted substring output will be in the form of a DataFrame column.

The `find()` method is used to get the first matching substring from a string column based on a regex pattern.

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

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

**(True or False)** The `ljust()` method pads the left side of string values with an arbitrary character defined in the `fillchar` parameter. It is equivalent to setting `side='left'` in the `pad()` method.

(True or False) The <code>ljust()</code> method pads the left side of string values with an arbitrary character defined in the <code>fillchar</code> parameter. It is equivalent to setting <code>side='left'</code> in the <code>pad()</code> method.

Handling text data

Handling text data

Check your understanding of the string methods for handling and manipulating string data in 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