pandas.DataFrame.sum¶

Return the sum of the values for the requested axis.

This is equivalent to the method numpy.sum.

param axis

{index (0), columns (1)}: Axis for the function to be applied on.

param skipna

bool, default True: Exclude NA/null values when computing the result.

param level

int or level name, default None: If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

param numeric_only

bool, default None: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

param min_count

int, default 0

The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

New in version 0.22.0.

Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1. **kwargs Additional keyword arguments to be passed to the function.

return

Series or DataFrame (if level specified)

Limitations¶

Parameters axis, level, numeric_only and min_count are unsupported.

Examples¶

Return the sum of the values for the columns.¶

import pandas as pd
import numpy as np
from numba import njit


@njit
def dataframe_sum():
    df = pd.DataFrame({"A": [.2, .0, .6, .2],
                       "B": [2, 0, 6, 2],
                       "C": [-1, np.nan, 1, np.inf]})

    return df.sum()


print(dataframe_sum())

$ python ./dataframe/dataframe_sum.py
A     1.0
B    10.0
C     inf
dtype: float64

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pandas.DataFrame.sum¶

Limitations¶

Examples¶