**π‘ Problem Formulation:** Data analysis often requires summing up the values in your dataset. Consider a Pandas DataFrame as an input representing a dataset with multiple rows and columns. The challenge is to calculate the sum of all the elements in each row, generating a Series or a new DataFrame as the desired output. This article demonstrates different methods to accomplish this task efficiently in Python using Pandas.

## Method 1: Using DataFrame.sum() method with axis=1

The `DataFrame.sum()`

method is used to return the sum of the values for the requested axis. By setting `axis=1`

, the method will sum across the rows, which is the solution to our problem.

Here’s an example:

import pandas as pd # Create a sample DataFrame df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}) # Sum all the rows row_sums = df.sum(axis=1) print(row_sums)

Output:

0 12 1 15 2 18 dtype: int64

This code snippet creates a DataFrame with three columns and three rows. By using `df.sum(axis=1)`

, we calculate the sum of each row, which gives us a Pandas Series with the total for each row. This is one of the simplest and most direct methods to achieve the row-wise sum.

## Method 2: Using apply() method with a lambda function

The `apply()`

method applies a function along an axis of the DataFrame. When combined with a lambda function that sums up the row, this method can also be used to calculate the sum of each row.

Here’s an example:

import pandas as pd # Creating a sample DataFrame df = pd.DataFrame({'A': [10, 20, 30], 'B': [40, 50, 60], 'C': [70, 80, 90]}) # Use apply() to sum all the rows row_sums = df.apply(lambda x: x.sum(), axis=1) print(row_sums)

Output:

0 120 1 150 2 180 dtype: int64

This code snippet again creates a DataFrame, this time with different values. Then, `df.apply()`

is used in combination with a lambda function that takes an x (row) and returns its sum. This method offers flexibility, as various functions can be applied to the DataFrame, but it may be less efficient for simple operations like summing rows.

## Method 3: Summing with a list comprehension

A more manual approach to summing the rows of a DataFrame can be achieved with a list comprehension, iterating over each row and calculating the sum manually.

Here’s an example:

import pandas as pd # Create a DataFrame df = pd.DataFrame({'A': [100, 200, 300], 'B': [400, 500, 600], 'C': [700, 800, 900]}) # List comprehension to sum all rows row_sums = [sum(row) for row in df.values] print(row_sums)

Output:

[1200, 1500, 1800]

In this example, we iterate over `df.values`

, which returns a numpy array of the DataFrame’s values. Each row is summed up inside the list comprehension, resulting in a list of the summed rows. This method provides a clear and explicit way to perform operations; however, it may not be the most idiomatic Pandas solution and might be slower for large datasets.

## Method 4: Using np.sum() function directly

Numpy has a highly optimized sum function, `np.sum()`

, which can be faster than using Pandas methods for large datasets. It is applied directly on the DataFrame’s underlying numeral array for a quick and efficient sum calculation.

Here’s an example:

import pandas as pd import numpy as np # Create a DataFrame df = pd.DataFrame({'A': [2, 4, 6], 'B': [8, 10, 12], 'C': [14, 16, 18]}) # Numpy's sum function row_sums = np.sum(df, axis=1) print(row_sums)

Output:

0 24 1 30 2 36 dtype: int64

This code uses the `np.sum()`

function on the DataFrame. By setting `axis=1`

, Numpy sums over the rows, outputting a Pandas Series similar to the DataFrame’s sum method. This can be a better choice for performance on large-scale data.

## Bonus One-Liner Method 5: Summing using np.sum() within a DataFrame constructor

Combining Numpy’s sum function right within the DataFrame constructor is a slick one-liner that can yield a new DataFrame containing the row sums. It’s a quick and effective method when we need to preserve the result as DataFrame.

Here’s an example:

import pandas as pd import numpy as np # Create a DataFrame df = pd.DataFrame({'A': [1, 4, 7], 'B': [2, 5, 8], 'C': [3, 6, 9]}) # One-liner to sum rows and preserve as DataFrame row_sum_df = pd.DataFrame(np.sum(df, axis=1), columns=['Sum']) print(row_sum_df)

Output:

Sum 0 6 1 15 2 24

This concise line creates a new DataFrame by summing the original `df`

with `np.sum()`

and specifying `axis=1`

. The result is then passed as data to the DataFrame constructor, which creates a single-column DataFrame with the sums. This method excels in readability and compactness.

## Summary/Discussion

**Method 1:** DataFrame.sum() method. Straightforward and idiomatic Pandas usage. May not be the fastest for very large datasets.

**Method 2:** apply() method with a lambda function. Very flexible and allows for complex row-wise operations. Less performance efficient for simple sum operations.

**Method 3:** List comprehension for manual sum. Explicit iteration can be clearer for some users but deviates from typical Pandas operations, possibly leading to slower performance.

**Method 4:** np.sum() function. Can be faster than Pandas methods, especially on large data. It works well with Pandas but introduces an additional dependency.

**Method 5:** One-Liner np.sum() within DataFrame constructor. Neat and compact for including sum directly as a DataFrame. Excellent for quick manipulations.