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

They produce more accurate arithmetic output

They can select the axis for arithmetic operation i.e., either at row or column level

**(Select all that apply.)** What are the benefits of using arithmetic methods like `add()` over standard operators like `+`?



(Select all that apply.) What are the benefits of using arithmetic methods like <code>add()</code> over standard operators like <code>+</code>?

The `gt()` method allows us to compare whether a value is greater than or equal to another value.

The process of aligning by index and then comparing each value iteratively is known as broadcasting.

When comparing a DataFrame to a sequence like a list or `Series`, the number of columns (for column-wise comparison) or the number of rows (for index-wise comparison) must match the number of elements in the sequence.

There are reverse methods (i.e., methods with a prefix `r`) for all the arithmetic methods, including modulo, power, and dot product. 

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

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

The `where()` method applies an if-else logic so that the elements that meet the criteria (i.e., returns True) are kept in the DataFrame.

If we pass the argument 5 into `nlargest()`, the number of rows returned in the output will never exceed 5.

When we set `method='first'` in the `rank()` method to handle tied records, it will return us the highest rank in the group.

There are three different ways that `NaN` values are ranked in `rank()`, namely keeping them as `NaN`, assigning the lowest rank, or assigning them the highest rank.

Range (difference between highest and lowest values) with `range()`.

Skewness (asymmetry of distribution) with `skew()`.

Kurtosis (peakedness or flatness of distribution relative to normal distribution) with `kurtosis()`.

**(Select all that apply.)** What are the correct ways to obtain different measures of variability in a dataset?

(Select all that apply.) What are the correct ways to obtain different measures of variability in a dataset?

The `applymap()` method is used to apply a function element-wise to a DataFrame.

The `map()` method is used to substitute values of a `Series` object with another based on a dictionary or function.

When we set `result_type='expand'` in the `apply()` method, the result is broadcasted and replaces the original values in the columns of the DataFrame.

Cumulative methods like `cumsum()` and `cumprod()` don’t work with `groupby()` operations.

Handling numerical data

Handling numerical data

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