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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

**(True or False)** Encoding a categorical column from `dtype='object'` to `dtype='category'` increases memory usage significantly.

(True or False) Encoding a categorical column from <code>dtype='object'</code> to <code>dtype='category'</code> increases memory usage significantly.

The elements in the list of categories we pass into the `CategoricalDtype` instance must be unique.

We can automatically remove unused categories (i.e., categories not present in the actual data) with `remove_categories()`.

The `get_dummies()` method only works on columns with `dtype='category'`.

The `add_categories()` method appends new categories to the end of the existing list of categories.

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

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

Minimum (`min()`) and Maximum (`max()`) operations

**(Select all that apply.)** Which operations can we apply to a DataFrame column with unordered categories? 

(Select all that apply.) Which operations can we apply to a DataFrame column with unordered categories?

The `colnames` parameter of `crosstab()` specifies the values to group by in the columns.

Under the hood of `crosstab()`, it calls the `unpivot()` method as part of the function code.

When we set `normalize='index'` in the `crosstab()` method, each cell value is divided by the sum of its row.

The `factorize()` method generates two outputs that’s a numerical representation of the categories and a sequence of the unique categories.

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

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

The `union_categoricals()` method works for objects containing categories of different data types.

Missing values in a categorical variable with `dtype='category'` are defined as `-1` in its numerical representation.

When we combine `Series` or DataFrames with the same categories, we can expect the category `dtype` to be preserved.

A DataFrame with categorical variables encoded with `dtype='category'` will have the `dtypes` preserved when written to a CSV file.

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

Handling categorical data

Handling categorical data

Check your understanding of the different ways to work effectively with categorical 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