Slicing Pandas Dataframe

Slicing a Pandas DataFrame is an important skill for extracting specific data subsets. Whether selecting rows, columns or individual cells, Pandas provides efficient methods such as iloc[]and loc[]. It focuses on using integer-based and label-based indexing to slice DataFrames effectively.

Create a Custom Dataframe

Let's import pandas library and create pandas dataframe from custom nested list.

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Slicing Using iloc[] (Integer-Based Indexing)

The iloc[] method in Pandas allows us to extract specific rows and columns based on their integer positions starting from 0. Each number represents a position in the DataFrame not the actual label of the row or column.

Slicing Rows in dataframe

Row slicing means selecting a specific set of rows from the DataFrame while keeping all columns.

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Slicing Columns in dataframe

Column slicing means selecting a specific set of columns from the DataFrame while keeping all rows.

Output:

Selecting a Specific Cell in a Pandas DataFrame

If you need a single value from a DataFrame you can specify the exact row and column position

Output:

Specific Cell Value: 8428000

Using Boolean Conditions in a Pandas DataFrame

Instead of selecting rows by index we can use Boolean conditions (e.g., Age > 35) to filter rows dynamically.

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Slicing Using loc[]

Slicing can also be performed using the loc[] function in Pandas. Since loc[] is label-based, it selects data using row and column labels. As a result, when working with custom indices, it is important to reference the correct labels during selection.

Slicing Rows in Dataframe

With loc[] we can extract a range of rows by their labels instead of integer positions.

Output:

Selecting Specified cell in Dataframe

loc[] allows us to fetch a specific value based on row and column labels

Output:

Value of the Specific Cell (V.Kohli, Salary): 8428000