Introduction to Pandas DataFrame.sum()

The DataFrame.sum() method in the Pandas library is used to calculate the sum of values across different axes in a DataFrame. This function is often used to calculate the total sum of numerical columns or rows.

Syntax

DataFrame.sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0)

• axis: It specifies the axis along which the sum operation is performed. By default, it sums the values vertically (axis=0). To sum horizontally, use axis=1.
• skipna: It is a boolean value that indicates whether to exclude missing or NaN values when performing the sum operation. By default, it is set to None, which means missing values are included.
• level: It is used when the DataFrame has hierarchical columns. It specifies the level for which the sum is calculated.
• numeric_only: It is a boolean value that specifies whether only numeric columns should be included in the summation. By default, it is set to None.
• min_count: It specifies the minimum number of valid values required for the summation. By default, it is set to 0, which means any number of valid values are considered.

Return Value

The DataFrame.sum() method returns a new Series object that contains the sums of values across the specified axis.

Examples

Consider the following example DataFrame:

import pandas as pd

data = {'A': [1, 2, 3],
        'B': [4, 5, 6],
        'C': [7, 8, 9]}

df = pd.DataFrame(data)

By default, df.sum() will return the sum of each column:

# Column-wise sum
df.sum()

| | A | B | C | | ---- | -- | -- | -- | | Sum | 6 | 15 | 24 |

To calculate the sum row-wise, use axis=1:

# Row-wise sum
df.sum(axis=1)

| | Sum | | -- | --- | | 0 | 12 | | 1 | 15 | | 2 | 18 |

You can exclude missing values from the sum calculation by setting skipna=True:

# Exclude missing values
df.sum(skipna=True)

| | A | B | C | | ---- | -- | -- | -- | | Sum | 6 | 15 | 24 |

It is also possible to calculate the sum for a specific column or a subset of columns:

# Sum for specific columns
df[['A', 'B']].sum()

| | Sum | | ---- | --- | | A | 6 | | B | 15 |

These are just a few examples of how to use the DataFrame.sum() method in Pandas. It is a powerful function that is often used in data analysis and manipulation tasks.

Note: This is just a brief overview of the DataFrame.sum() function. Refer to the Pandas documentation for more detailed information.