#8 Data frame Row Operations in Pandas- Full Tutorial
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Pandas Dataframe operations on columns
In this video, I demonstrate all column operations in a Pandas Dataframe in Python, working with the nls97.csv dataset.
What’s Covered in This Video:
1) Import libraries
2) Read the csv data
3) Set a column as index
4) Select rows by slicing
5) Slicing with a step
6) Slicing the last 3 rows using tail
7) Slicing the last 3 rows with negative indexing
8) Using "loc" to select rows
9) Using "iloc" to select rows
10) Select rows using a condition
11) Select rows with multiple conditions
12) Select both rows and columns in a dataframe using "loc"
Time Stamps:
00:00 Intro
00:24 Import Libraries and read the data
00:41 Set a column as index
00:56 Select rows by slicing
01:12 Transpose the data
01:21 Slicing with a step
01:45 Slicing the last 3 rows using tail()
01:55 Slicing the last 3 rows using negative indexing
02:20 Select rows using "loc" and index labels
03:02 Select rows using "iloc"
03:32 Select rows using a condition
03:47 Using describe() for statistical info
04:32 Using quantiles
05:17 Select rows with a boolean condition
05:52 Select rows with multiple conditions
07:47 Select rows with 3 conditions
08:22 Select both rows and columns with "loc" in a dataframe
Why Watch This Video?
• Learn Pandas fundamentals for real-world data analysis.
• Master row selection, indexing, and filtering techniques.
• Gain practical data manipulation skills for structured datasets.
• Ideal for beginners and data professionals working with large datasets.
Who Is This Video For?
• Data analysts and Python beginners exploring Pandas for EDA.
• Data science students working with real-world datasets.
• Anyone looking to filter, clean, and transform CSV data efficiently.