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

# The `query` function

The `query` function is another way to filter values in a `DataFrame`.

It differs from previous functions because it can write the conditions as text. It’s quite useful and is more practical in various cases.

Here’s how we can select products with a price higher than 100 by using the `query` function:

```
sales_filtered = sales.query("price > 100")
```

We can apply multiple conditions with the `query` function as well. 

In the previous lesson, we created a condition based on the `price` and `stock quantity` columns. Let's do the same using the `query` function.


Learn how to use the query function for filtering a 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

Data Visualization with Seaborn for Walmart Sales Projection

Final Challenge and Quiz

Filtering Rows with the Query Function

The `query` function

Data Visualization with Seaborn for Walmart Sales Projection

Filtering Rows with the Query Function

The query function

The `query` function