#7 Data frame Column 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. Loading and Exploring CSV Data:
• Import Pandas and NumPy.
• Load nls97.csv into a Pandas DataFrame.
• Inspect dataset structure and data types using .info().
2. Column Operations & Data Selection:
• Display column names (df.columns).
• Loop through object-type columns for transformations.
• Convert selected columns to categorical data types.
3. Indexing and Filtering Data:
• Set personid as the index using .set_index().
• Select columns using .loc[], .iloc[], and direct indexing.
• Extract multiple columns (gender, maritalstatus, highestgradecompleted).
• Filter columns containing specific keywords (weeksworked, income).
4. Data Type Analysis & Transformation:
• Count occurrences of different data types (df.dtypes.value_counts()).
• Select categorical and numeric columns separately.
• Convert columns to appropriate data types.
Time Stamps:
00:00 Intro
00:18 Launch Jupyter lab notebook
00:31 Read the csv data
00:57 Info about the datframe
01:15 Set the "personid" column as index
01:35 Convert the datatype of columns using for loop
02:35 Select a column from the dataframe using bracket notation
03:27 Selecting the column and making it a dataframe
04:02 Select column using "loc" selector pandas dataframe
04:47 Select column using "iloc" selector pandas dataframe
05:27 Select multiple columns using "loc" selector
05:57 Select multiple columns based on a list of column names
06:36 Select columns using "filter"
07:07 Select columns based on "datatype"
07:55 Select columns using a regex pattern
Why Watch This Video?
• Learn Pandas fundamentals for real-world data analysis.
• Master column 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.
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