Pandas DataFrame any() Method – Be on the Right Side of Change

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

The Pandas library enables access to/from a DataFrame.
The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required libraries.

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd
import numpy as np

DataFrame any()

The any() method evaluates each element to determine if the value is True/False on a specified axis. This method returns True if a DataFrame axis is Non-Zero or Non-Empty, else False returns.

The syntax for this method is as follows:

DataFrame.any(axis=0, bool_only=None, skipna=True, level=None, **kwargs)

Parameters	Description
`axis`	If zero (0) or index is selected, apply to each column. Default 0. If one (1) apply to each row.
`bool_only`	Includes only Boolean DataFrame columns. If `None`, this parameter will attempt to use everything. Not supported for Series.
`skipna`	This parameter excludes NaN/NULL values. If the row/column is NaN and `skipna=True`, the result is `True`. For an empty row/column and `skipna=False`, then NaN is treated as `True` because they are not equal to 0.
`level`	If the `axis` is `MultiLevel`, count along with a specific level and collapse into a Series.
`**kwargs`	Additional keywords have no effect.

For this example, Rivers Clothing assumes every item in their inventory contains a valid value. To confirm this, run the following code.

df_inv = pd.DataFrame({'Tops':     [36, 23, 0],
                       'Tanks':    [10, 20, 0],
                       'Pants':    [61, 33, 0],
                       'Sweats':   [88, 38, 0]})

index_ = ['Small', 'Medium', 'Large']
df_inv.index = index_

result = df_inv.any(axis='columns')
print(result)

Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_inv.
Line [2-3] creates and sets the index for the DataFrame (Small/Medium/Large).
Line [4] checks all elements of the DataFrame based on the specified axis and saves to the result variable.
Line [5] outputs the result to the terminal.

Output

There is an issue with the Large size of all items in inventory. They all contain zero values.

Small	True
Medium	True
Large	False
dtype: bool

More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

👈 df.all()

df.append() 👉

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.