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

What is the two-dimensional data structure of Pandas?

What is the two-dimensional data structure of Pandas?

Which function is used to create a `DataFrame` from a CSV file?

Which function is used to create a <code>DataFrame</code> from a CSV file?

Suppose we have a `DataFrame` called `df`. How can we find the data types of the columns?

Suppose we have a <code>DataFrame</code> called <code>df</code>. How can we find the data types of the columns?

Which function is used to find the number of distinct values in a column?

Which function is used to find the number of distinct values in a column?

Suppose we have a `DataFrame` called `df`. How can we select the first five rows and the second column from this `DataFrame`?

Suppose we have a <code>DataFrame</code> called <code>df</code>. How can we select the first five rows and the second column from this <code>DataFrame</code>?

Suppose we have a `DataFrame` called `df` with an integer index. How can we select the first ten rows of the `product` column?

Suppose we have a <code>DataFrame</code> called <code>df</code> with an integer index. How can we select the first ten rows of the <code>product</code> column?

**(Select all that apply)** Suppose we have a `date` column in `df`. How can we add three weeks to the dates in this column?



(Select all that apply) Suppose we have a <code>date</code> column in <code>df</code>. How can we add three weeks to the dates in this column?

Suppose we have a `DataFrame` called `df`. How can we find the number of missing values in each column?

Suppose we have a <code>DataFrame</code> called <code>df</code>. How can we find the number of missing values in each column?

`df.groupby("month").mean(sales_quantity)`

`df.groupby("month")["sales_quantity"].mean()`

`df.groupby("month").agg(avg_sales=("sales_quantity", "mean")`


**(Select all that apply)** Suppose we have `month` and `sales_quantity` columns in `df`. How can we find the average monthly sales in this `df`?

(Select all that apply) Suppose we have <code>month</code> and <code>sales_quantity</code> columns in <code>df</code>. How can we find the average monthly sales in this <code>df</code>?

Which function is used to combine two `DataFrame`s based on the common values in shared columns?


Which function is used to combine two <code>DataFrame</code>s based on the common values in shared columns?

Final quiz

Final quiz

Let's take a final quiz on what we’ve learned in this course.

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

Final Quiz