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

# Problem Statement

This challenge consists of a few steps. 

1.  We have a `DataFrame` called `grocery`, which contains some missing values in the `price` column. We need to fill them in with the average price of the product. For example, a missing price value for a tomato needs to be filled in with the average price of tomatoes.

2. Then, we need to create a column called `day_of_week` by extracting the day-of-week information from the `sales_date` column. Make sure to convert the data type of the `sales_date` column to `datetime64[ns]`.

3. The final step is to calculate the average sales quantity on each day of the week. We need to sort the values in descending order and select the index of the first value to find the day of the week with the most sales.

# Solution

Write your solution in the specified place and click on "test". 

- Make sure to uncomment the lines before execution.

Learn how to use the Pandas functions, fillna, groupby, dayofweek, and sort_values.

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

Data Visualization with Seaborn for Walmart Sales Projection

Final Challenge and Quiz

Final Challenge: Find the Day of Week with the Most Sales

Problem Statement