Gain insights into using Python Pandas for efficient data analysis and manipulation. Delve into detailed functions, parameters, and methods to elevate your data handling skills.

A Comprehensive Practical Guide to Python Pandas_ .png

There are several exercises that focus on how to use a particular function and method. The functions are covered in detail by explaining the important parameters and how to use them. By completing this course, you will be able to do data analysis and manipulation with Pandas easily and efficiently.

Mastering Data Analysis with Python Pandas

# Using the `unique` and `nunique` functions

The columns in a `DataFrame` might be categorical or continuous. The categorical columns may have many distinct but finite numbers of values, whereas continuous ones can take infinitely many values. In this sense, the columns can be considered as either discrete or categorical random variables.

Checking the number of distinct values in a categorical column is an essential part of exploratory data analysis. The `nunique` function returns the number of distinct values in a column and the `unique` function actually shows the unique values. We can apply them to the product group column in the `sales`.

Learn how to find the distinct values in a column of a Pandas DataFrame.

Course Introduction

Pandas Data Structures

Creating a Data Frame

Exploring a Data Frame

Filtering a Data Frame

String Manipulation with Pandas

Date Manipulation with Pandas

Handling Missing Values with Pandas

Data Analysis with Pandas

Data Visualization with Pandas

Combining DataFrames with Pandas

Final Challenge and Quiz

Different Values in a Column

Using the `unique` and `nunique` functions

Different Values in a Column

Using the unique and nunique functions

Using the `unique` and `nunique` functions