Evaluate the sum of rows using the eval() function – Python Pandas

The eval() function in Pandas can be used to evaluate arithmetic expressions and create new columns. It's particularly useful for calculating row-wise sums across specified columns using a simple string expression.

Creating a Sample DataFrame

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

Using eval() to Sum Rows

The eval() function allows you to write arithmetic expressions as strings. The resultant column with the sum is also mentioned in the eval() expression ?

import pandas as pd

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

# Finding sum using eval()
# The expression displays the sum formula assigned to the resultant column
dataFrame = dataFrame.eval('Result_Sum = Opening_Stock + Closing_Stock')
print("Summing rows...\n", dataFrame)

Summing rows...
    Product  Opening_Stock  Closing_Stock  Result_Sum
0   SmartTV            300            200         500
1  ChromeCast            700            500        1200
2   Speaker           1200           1000        2200
3  Earphone           1500            900        2400

Key Benefits of eval()

The eval() method offers several advantages:

• Readable syntax: Write expressions as natural mathematical formulas
• Performance: Can be faster for complex expressions on large datasets
• Dynamic evaluation: Column names are referenced directly without bracket notation

Conclusion

The Pandas eval() function provides an intuitive way to perform row-wise calculations using string expressions. It's particularly useful for creating new columns based on arithmetic operations between existing columns.