.mean()

The .mean() method calculates and returns the arithmetic mean of elements in a NumPy array. It computes the average by summing all elements along the specified axis and dividing by the number of elements. This method is one of the fundamental statistical functions in NumPy that data scientists and analysts use to understand the central tendency of numerical data.

NumPy’s .mean() is highly versatile, allowing calculation of means across entire arrays or along specific dimensions. It’s commonly used in data analysis, scientific computing, and machine learning for tasks such as feature normalization, statistical analysis, and data preprocessing.

Syntax

numpy.mean(a, axis=None, dtype=None, out=None, keepdims=<no value>, where=<no value>)

Parameters:

• a: The array containing numbers whose mean is to be calculated.
• axis (Optional): Axis or axes along which the means are computed. If None, the array is flattened before computation.
• dtype (Optional): The data type used for calculating the mean. By default, float64 is used for integers, and the input data type is preserved for floating-point numbers.
• out (Optional): Alternative output array to store the result. Must have the same shape as expected output.
• keepdims (Optional): If True, retains the reduced dimensions as size one, ensuring consistency for broadcasting.
• where (Optional): Elements to include in the mean calculation. Must be a boolean array with the same shape as a.

Return value:

The .mean() method returns an ndarray containing the mean values. If axis is None, the result is a scalar value.

Example 1: Basic Mean Calculation

This example demonstrates how to calculate the mean of a one-dimensional NumPy array:

import numpy as np

# Create a 1D array
array1 = np.array([0, 1, 2, 3, 4, 5, 6, 7])

# Calculate the mean of the array
avg = np.mean(array1)

print("Array:", array1)
print("Mean value:", avg)

to clipboard
Copy to clipboard

This example results in the following output:

Array: [0 1 2 3 4 5 6 7]
Mean value: 3.5

to clipboard
Copy to clipboard

In this example, a 1D array with values from 0 to 7 is created, and the arithmetic mean is calculated, which is 3.5 (the sum of all elements divided by the number of elements).

Example 2: Calculating Mean Across Different Axes

This example shows how to compute the mean along different axes of a multi-dimensional array, which is useful in many data analysis scenarios:

import numpy as np

# Create a 3D array
array1 = np.array([[[1, 2], [3, 4]],
                   [[5, 6], [7, 8]]])

# Print the array shape and the array itself
print("Array shape:", array1.shape)
print("Array:\n", array1)

# Find the mean of entire array
mean1 = np.mean(array1)

# Find the mean across axis 0
mean2 = np.mean(array1, axis=0)

# Find the mean across axis 0 and 1
mean3 = np.mean(array1, (0, 1))

print("\nMean of the entire array:", mean1)
print("Mean across axis 0:\n", mean2)
print("Mean across axis 0 and 1:", mean3)

to clipboard
Copy to clipboard

This example results in the following output:

Array shape: (2, 2, 2)
Array:
 [[[1 2]
  [3 4]]

 [[5 6]
  [7 8]]]

Mean of the entire array: 4.5
Mean across axis 0:
 [[3. 4.]
 [5. 6.]]
Mean across axis 0 and 1: [4. 5.]

to clipboard
Copy to clipboard

When calculating the mean without specifying an axis, all elements are averaged. When specifying axis=0, the mean is calculated along the first dimension, resulting in a 2D array. When specifying both axes 0 and 1 with (0, 1), the result is a 1D array with the mean of all elements in each 2D slice.

Example 3: Data Analysis with Real-world Data

This example demonstrates how to use .mean() to analyze temperature data, a common application in environmental science and meteorology:

import numpy as np

# Monthly average temperatures (°C) for a city over 2 years
# Rows: Years (2023, 2024)
# Columns: Months (Jan to Dec)
temperatures = np.array([
    [5.2, 6.8, 9.3, 13.5, 18.2, 22.6, 25.1, 24.3, 19.7, 14.2, 9.1, 6.3],  # 2023
    [4.8, 6.5, 8.9, 14.1, 17.9, 23.2, 26.0, 25.2, 19.5, 13.8, 8.5, 5.9]   # 2024
])

print("Temperature data shape:", temperatures.shape)

# Calculate the average temperature for each year
yearly_avg = np.mean(temperatures, axis=1)
print("\nYearly average temperatures:")
for year, avg in zip([2023, 2024], yearly_avg):
  print(f"{year}: {avg:.2f}°C")

# Calculate the average temperature for each month across years
monthly_avg = np.mean(temperatures, axis=0)
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
          "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]

print("\nMonthly average temperatures across years:")
for month, avg in zip(months, monthly_avg):
  print(f"{month}: {avg:.2f}°C")

# Calculate the overall average temperature
overall_avg = np.mean(temperatures)
print("\nOverall average temperature: {:.2f}°C".format(overall_avg))

to clipboard
Copy to clipboard

This example results in the following output:

Temperature data shape: (2, 12)

Yearly average temperatures:
2023: 14.52°C
2024: 14.53°C

Monthly average temperatures across years:
Jan: 5.00°C
Feb: 6.65°C
Mar: 9.10°C
Apr: 13.80°C
May: 18.05°C
Jun: 22.90°C
Jul: 25.55°C
Aug: 24.75°C
Sep: 19.60°C
Oct: 14.00°C
Nov: 8.80°C
Dec: 6.10°C

Overall average temperature: 14.53°C

to clipboard
Copy to clipboard

This example shows how .mean() can be used to analyze temperature data by calculating yearly averages, monthly averages across years, and the overall average temperature.

Codebyte Example: Student Exam Score Analysis

This example demonstrates how to use .mean() to analyze student exam scores, a common task in educational assessment:

us
Visit us
code
Hide code
Code
Output
Hide output
Hide output
Loading...
Copy Codebyte
Copy to your clipboard
Run

FAQs