Select or Access Multiple Rows and Columns from a Pandas DataFrame

Опубликовано: 02 Январь 2025
на канале: Ishant Insight

Hello Everyone and Welcome to our YouTube tutorial on accessing or selecting multiple rows and columns in a DataFrame ! In this video, we'll explore various techniques and functions that will help you efficiently navigate and extract data from your pandas DataFrame.

It doesn't matter whether you're a beginner or an intermediate data analyst, this tutorial is designed to provide you with step-by-step instructions on accessing and selecting specific rows and columns based on your desired criteria. We'll cover important concepts to give you a comprehensive understanding of DataFrame manipulation.

By the end of this tutorial, you'll be equipped with the skills to extract the data you need, enabling you to perform insightful analysis and make informed decisions. Additionally, we've included useful code snippets and resources in the description box below to help you practice and enhance your skills.

Please Join us now and level up your DataFrame manipulation game! Don't forget to like, comment, and share this video with other data enthusiasts. And if you haven't already, be sure to subscribe to our channel for more exciting tutorials on data analysis and programming.
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Time Stamps in (mm:ss)
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00:00 Introduction
00:25 Creating a DataFrame
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ASSIGNING USER DEFINED ROW(S) & COLUMN(S)
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03:00 Assigning Column Labels to index in a DataFrame
05:16 Assigning Row Labels to index in a DataFrame

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ACCESSING LABELLED COLUMN(S) IN DATAFRAME
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07:31 Accessing a Column in a DataFrame using df_name.col_name
11:11 Accessing a Column in a DataFrame using single square bracket
12:23 Accessing Multiple Column(s) in a DataFrame using double square brackets

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ACCESSING LABELLED ROW(S) IN DATAFRAME
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13:46 Accessing a Row using .loc[ ]
15:41 Accessing Multiple Columns using .loc[ ]

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ACCESSING CONSECUTIVE COLUMNS
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16:06 Accessing last consecutive columns in DataFrame

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ACCESSING OR SELECTING MULTIPLE ROW(S) & COLUMN(S) IN DATAFRAME
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17:44 Accessing Multiple rows and multiple columns