📄️ DataFrames in Pandas
In this tutorial, we'll delve into DataFrames in Pandas, a powerful two-dimensional labeled data structure widely used for data manipulation and analysis. We'll cover the fundamentals of DataFrames, how to create them, access their attributes, and perform basic operations.
📄️ Series and DataFrames
In this tutorial, we'll explore common operations on Pandas Series and DataFrames, highlighting their similarities and differences. We'll cover creating Series and DataFrames, performing basic operations like summation, and understanding the concept of axes.
📄️ Retrieving Useful Information from Data Frames
In this tutorial, we will explore how to retrieve valuable information from data frames using the Pandas library in Python. We will use a dataset containing information about the top 1000 movies and TV shows. This dataset includes missing values and requires some data cleaning. Let's dive in step by step.
📄️ Sorting Data
In this tutorial, we'll explore how to efficiently sort data in a Pandas DataFrame using the nlargest and nsmallest functions. These functions are particularly useful when you need to identify the largest or smallest values in a DataFrame based on one or multiple columns.
📄️ Modifying DataFrame Index
In this tutorial, we will explore how to modify the index of a DataFrame using various methods provided by the Pandas library in Python. We will cover the setindex() and resetindex() methods along with their parameters, such as drop and inplace, to understand how they affect the DataFrame's structure.
📄️ Removing Columns and Rows
In this tutorial, we'll explore how to efficiently remove both columns and rows from Pandas DataFrames using the drop method. This method provides a convenient way to manipulate the structure of your DataFrame by eliminating unwanted columns or rows. We'll cover various scenarios, including removing columns by name, removing rows by index, and specifying the axis explicitly.