Python - Summing all the rows of a Pandas Dataframe

To sum all the rows of a DataFrame, use the sum() function with axis=1. Setting the axis parameter to 1 adds values across columns for each row, while axis=0 (default) would sum down columns.

Creating a Sample DataFrame

Let's start by creating a DataFrame with stock data ?

import pandas as pd

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

print("DataFrame...")
print(dataFrame)

DataFrame...
   Opening_Stock  Closing_Stock
0            300            200
1            700            500
2           1200           1000
3           1500            900

Summing Rows with axis=1

Use sum(axis=1) to add values across columns for each row ?

import pandas as pd

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

# Finding sum of row values
row_sums = dataFrame.sum(axis=1)
print("Summing rows...")
print(row_sums)

Summing rows...
0     500
1    1200
2    2200
3    2400
dtype: int64

Adding Row Sum as a New Column

You can also add the row sums as a new column to the original DataFrame ?

import pandas as pd

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

# Add row sums as a new column
dataFrame['Total_Stock'] = dataFrame.sum(axis=1)
print(dataFrame)

   Opening_Stock  Closing_Stock  Total_Stock
0            300            200          500
1            700            500         1200
2           1200           1000         2200
3           1500            900         2400

Key Points

• axis=1 sums across columns (row-wise)
• axis=0 sums down rows (column-wise)
• The result is a pandas Series with row sums
• You can assign the result to a new column

Conclusion

Use dataFrame.sum(axis=1) to sum all values across columns for each row. This returns a Series containing the sum of each row, which can be used for analysis or added back to the DataFrame.