Python Pandas - Create a subset by choosing specific values from columns based on indexes

To create a subset by choosing specific values from columns based on indexes, use the iloc() method. The iloc() method allows you to select data by integer position, making it perfect for choosing specific rows and columns from a DataFrame.

Creating a DataFrame

Let us first create a sample DataFrame with product records ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"],
    "Opening_Stock": [300, 700, 1200, 1500],
    "Closing_Stock": [200, 500, 1000, 900]
})

print("DataFrame...\n", dataFrame)

DataFrame...
  Closing_Stock  Opening_Stock    Product
0           200            300    SmartTV
1           500            700  ChromeCast
2          1000           1200    Speaker
3           900           1500   Earphone

Creating Subsets Using iloc()

Single Column Subset

You can select a specific column by its index position ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"],
    "Opening_Stock": [300, 700, 1200, 1500],
    "Closing_Stock": [200, 500, 1000, 900]
})

# Select first column (Product) for all rows
subset_column = dataFrame.iloc[:, 0]
print("Single column subset:\n", subset_column)

Single column subset:
0      SmartTV
1    ChromeCast
2       Speaker
3     Earphone
Name: Product, dtype: object

Multiple Columns and Rows Subset

Create a subset with specific rows and columns using row and column index ranges ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"],
    "Opening_Stock": [300, 700, 1200, 1500],
    "Closing_Stock": [200, 500, 1000, 900]
})

# Create subset with first 2 rows and first 2 columns
subset_multiple = dataFrame.iloc[0:2, 0:2]
print("Subset using iloc() with 2 rows and 2 columns:\n", subset_multiple)

Subset using iloc() with 2 rows and 2 columns:
    Product  Opening_Stock
0   SmartTV            300
1  ChromeCast            700

Specific Rows and Columns

Select non-consecutive rows and columns using lists of indexes ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"],
    "Opening_Stock": [300, 700, 1200, 1500],
    "Closing_Stock": [200, 500, 1000, 900]
})

# Select specific rows (0, 2) and columns (0, 2)
subset_specific = dataFrame.iloc[[0, 2], [0, 2]]
print("Specific rows and columns:\n", subset_specific)

Specific rows and columns:
   Product  Closing_Stock
0  SmartTV            200
2  Speaker           1000

Key Points

• iloc[row_indexer, column_indexer] uses integer positions
• Use slices like 0:2 for ranges (excludes the end index)
• Use lists like [0, 2] for specific non-consecutive positions
• Use : to select all rows or columns

Conclusion

The iloc() method provides flexible integer-based indexing for creating DataFrame subsets. Use slices for consecutive elements or lists for specific positions to extract exactly the data you need.