np.any() and np.all() in NumPy

Introduction

These functions help check if some or all values in data meet a condition. They make it easy to quickly understand your data.

Check if any sensor reading in a list is above a danger level.

Verify if all students passed an exam from their scores.

Find out if any missing values exist in a dataset.

Confirm if all entries in a column meet a quality standard.

Syntax

NumPy

np.any(array, axis=None, keepdims=False)
np.all(array, axis=None, keepdims=False)

array is your data, usually a NumPy array.

axis lets you check across rows, columns, or the whole array.

Examples

Returns True because at least one value is True.

NumPy

np.any([False, True, False])

Returns False because not all values are True.

NumPy

np.all([True, True, False])

Checks each column; returns [False, True] because the second column has a True (1).

NumPy

np.any([[0, 0], [0, 1]], axis=0)

Checks each row; returns [True, True] because all values in each row are True (1).

NumPy

np.all([[1, 1], [1, 1]], axis=1)

Sample Program

This code checks if any temperature is above 30 degrees and if all temperatures in each row are above 20 degrees. It helps quickly understand temperature data.

NumPy

import numpy as np

# Sample data: temperatures in Celsius
temps = np.array([[22, 25, 19], [30, 35, 28], [15, 18, 20]])

# Check if any temperature is above 30
any_above_30 = np.any(temps > 30)

# Check if all temperatures in each row are above 20
all_above_20_per_row = np.all(temps > 20, axis=1)

print(f"Any temperature above 30? {any_above_30}")
print(f"All temperatures above 20 per row? {all_above_20_per_row}")

Important Notes

Use axis=None (default) to check the whole array.

These functions return a boolean or an array of booleans depending on the axis.

They are very useful for quick data quality checks.

Summary

np.any() checks if any value is True.

np.all() checks if all values are True.

Use the axis parameter to check along rows, columns, or the whole array.