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

If we want to perform a `groupby()` operation along the column axis, what value should we pass into the `axis` parameter?

If we want to perform a <code>groupby()</code> operation along the column axis, what value should we pass into the <code>axis</code> parameter?

When performed on grouped data, both the aggregation and transformation operations generate an output that has the same shape as the original DataFrame.

The `agg()` method allows us to run multiple aggregations on multiple columns, which `transform()` can’t do.

The `filter()` method goes one step beyond the `transform()` method by only including elements from groups that satisfy the filter criteria we specify.

The `agg()` method doesn’t accept  the Lambda functions or user-defined Python functions.

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

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

If we are working with the numeric columns of a dataset and we plan to reshape and perform aggregations on them, `pivot_table()` is the go-to choice.

We can display the subtotals and grand total of a pivot table by setting `margins=False`.

The `aggfunc` parameter can only accept a string value to indicate the type of aggregation to be performed.

The `pivot_table()` method only permits columns with numerical data types to be passed into the values parameter.

The `value_vars` parameter in `melt()` allows us to indicate the columns by which to unpivot a DataFrame.

The `unstack()` method works by reshaping a DataFrame and pivoting a level of the column labels (i.e., column names outside the index).

The `assign()` method allows us to create multiple new columns in a DataFrame with a single method call.

The default level in `stack()` is at `level=1`, which refers to the outermost level.

Using the `loc` accessor with slicing is a valid way to select data in a MultiIndex DataFrame.

Using the `xs()` cross section method is a valid way to select data in a MultiIndex DataFrame.

Using the `unstack` and `unpivot` methods are valid ways to select data in a MultiIndex DataFrame.

Using `IndexSlice` together with the `squeeze` method is a valid way to select data in a MultiIndex DataFrame.

**(Select all that apply.)** Which options are valid ways to select data in a MultiIndex DataFrame? 

(Select all that apply.) Which options are valid ways to select data in a MultiIndex DataFrame?

Reshaping and manipulating data

Reshaping and manipulating data

Check your understanding of the different techniques used for reshaping and manipulating 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