Python - Sum only specific rows of a Pandas Dataframe

To sum only specific rows in a Pandas DataFrame, use the loc[] method to select specific row ranges and columns. The loc[] method allows you to specify both row indices and column names for precise data selection.

Let's start by creating a DataFrame with product inventory data ?

import pandas as pd

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

print("Original DataFrame:")
print(dataFrame)

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

Summing Specific Rows

To sum only the first two rows (indices 0 and 1) across specific columns, use loc[0:1, ["Opening_Stock", "Closing_Stock"]] and add the sum(axis=1) method ?

import pandas as pd

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

# Sum specific rows (0 and 1) for specific columns
dataFrame['Sum_Result'] = dataFrame.loc[0:1, ["Opening_Stock", "Closing_Stock"]].sum(axis=1)

print("DataFrame after summing specific rows:")
print(dataFrame)

DataFrame after summing specific rows:
    Product  Opening_Stock  Closing_Stock  Sum_Result
0   SmartTV            300            200       500.0
1  ChromeCast            700            500      1200.0
2   Speaker           1200           1000         NaN
3  Earphone           1500            900         NaN

Summing Different Row Ranges

You can also sum different row ranges by changing the indices in loc[] ?

import pandas as pd

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

# Sum rows 2 and 3 (Speaker and Earphone)
dataFrame['Sum_Last_Two'] = dataFrame.loc[2:3, ["Opening_Stock", "Closing_Stock"]].sum(axis=1)

print("Summing last two rows:")
print(dataFrame)

Summing last two rows:
    Product  Opening_Stock  Closing_Stock  Sum_Last_Two
0   SmartTV            300            200           NaN
1  ChromeCast            700            500           NaN
2   Speaker           1200           1000        2200.0
3  Earphone           1500            900        2400.0

Key Points

• Use loc[start_row:end_row, ["col1", "col2"]] to select specific rows and columns
• Add sum(axis=1) to sum across columns (horizontally)
• Rows not included in the range will show NaN values
• The range is inclusive on both ends (0:1 includes both row 0 and row 1)

Conclusion

The loc[] method with sum(axis=1) provides precise control for summing specific rows and columns in a DataFrame. This approach is useful for creating calculated fields based on subsets of your data.