
- NumPy - Home
- NumPy - Introduction
- NumPy - Environment
- NumPy Arrays
- NumPy - Ndarray Object
- NumPy - Data Types
- NumPy Creating and Manipulating Arrays
- NumPy - Array Creation Routines
- NumPy - Array Manipulation
- NumPy - Array from Existing Data
- NumPy - Array From Numerical Ranges
- NumPy - Iterating Over Array
- NumPy - Reshaping Arrays
- NumPy - Concatenating Arrays
- NumPy - Stacking Arrays
- NumPy - Splitting Arrays
- NumPy - Flattening Arrays
- NumPy - Transposing Arrays
- NumPy Indexing & Slicing
- NumPy - Indexing & Slicing
- NumPy - Indexing
- NumPy - Slicing
- NumPy - Advanced Indexing
- NumPy - Fancy Indexing
- NumPy - Field Access
- NumPy - Slicing with Boolean Arrays
- NumPy Array Attributes & Operations
- NumPy - Array Attributes
- NumPy - Array Shape
- NumPy - Array Size
- NumPy - Array Strides
- NumPy - Array Itemsize
- NumPy - Broadcasting
- NumPy - Arithmetic Operations
- NumPy - Array Addition
- NumPy - Array Subtraction
- NumPy - Array Multiplication
- NumPy - Array Division
- NumPy Advanced Array Operations
- NumPy - Swapping Axes of Arrays
- NumPy - Byte Swapping
- NumPy - Copies & Views
- NumPy - Element-wise Array Comparisons
- NumPy - Filtering Arrays
- NumPy - Joining Arrays
- NumPy - Sort, Search & Counting Functions
- NumPy - Searching Arrays
- NumPy - Union of Arrays
- NumPy - Finding Unique Rows
- NumPy - Creating Datetime Arrays
- NumPy - Binary Operators
- NumPy - String Functions
- NumPy - Matrix Library
- NumPy - Linear Algebra
- NumPy - Matplotlib
- NumPy - Histogram Using Matplotlib
- NumPy Sorting and Advanced Manipulation
- NumPy - Sorting Arrays
- NumPy - Sorting along an axis
- NumPy - Sorting with Fancy Indexing
- NumPy - Structured Arrays
- NumPy - Creating Structured Arrays
- NumPy - Manipulating Structured Arrays
- NumPy - Record Arrays
- Numpy - Loading Arrays
- Numpy - Saving Arrays
- NumPy - Append Values to an Array
- NumPy - Swap Columns of Array
- NumPy - Insert Axes to an Array
- NumPy Handling Missing Data
- NumPy - Handling Missing Data
- NumPy - Identifying Missing Values
- NumPy - Removing Missing Data
- NumPy - Imputing Missing Data
- NumPy Performance Optimization
- NumPy - Performance Optimization with Arrays
- NumPy - Vectorization with Arrays
- NumPy - Memory Layout of Arrays
- Numpy Linear Algebra
- NumPy - Linear Algebra
- NumPy - Matrix Library
- NumPy - Matrix Addition
- NumPy - Matrix Subtraction
- NumPy - Matrix Multiplication
- NumPy - Element-wise Matrix Operations
- NumPy - Dot Product
- NumPy - Matrix Inversion
- NumPy - Determinant Calculation
- NumPy - Eigenvalues
- NumPy - Eigenvectors
- NumPy - Singular Value Decomposition
- NumPy - Solving Linear Equations
- NumPy - Matrix Norms
- NumPy Element-wise Matrix Operations
- NumPy - Sum
- NumPy - Mean
- NumPy - Median
- NumPy - Min
- NumPy - Max
- NumPy Set Operations
- NumPy - Unique Elements
- NumPy - Intersection
- NumPy - Union
- NumPy - Difference
- NumPy Random Number Generation
- NumPy - Random Generator
- NumPy - Permutations & Shuffling
- NumPy - Uniform distribution
- NumPy - Normal distribution
- NumPy - Binomial distribution
- NumPy - Poisson distribution
- NumPy - Exponential distribution
- NumPy - Rayleigh Distribution
- NumPy - Logistic Distribution
- NumPy - Pareto Distribution
- NumPy - Visualize Distributions With Sea born
- NumPy - Matplotlib
- NumPy - Multinomial Distribution
- NumPy - Chi Square Distribution
- NumPy - Zipf Distribution
- NumPy File Input & Output
- NumPy - I/O with NumPy
- NumPy - Reading Data from Files
- NumPy - Writing Data to Files
- NumPy - File Formats Supported
- NumPy Mathematical Functions
- NumPy - Mathematical Functions
- NumPy - Trigonometric functions
- NumPy - Exponential Functions
- NumPy - Logarithmic Functions
- NumPy - Hyperbolic functions
- NumPy - Rounding functions
- NumPy Fourier Transforms
- NumPy - Discrete Fourier Transform (DFT)
- NumPy - Fast Fourier Transform (FFT)
- NumPy - Inverse Fourier Transform
- NumPy - Fourier Series and Transforms
- NumPy - Signal Processing Applications
- NumPy - Convolution
- NumPy Polynomials
- NumPy - Polynomial Representation
- NumPy - Polynomial Operations
- NumPy - Finding Roots of Polynomials
- NumPy - Evaluating Polynomials
- NumPy Statistics
- NumPy - Statistical Functions
- NumPy - Descriptive Statistics
- NumPy Datetime
- NumPy - Basics of Date and Time
- NumPy - Representing Date & Time
- NumPy - Date & Time Arithmetic
- NumPy - Indexing with Datetime
- NumPy - Time Zone Handling
- NumPy - Time Series Analysis
- NumPy - Working with Time Deltas
- NumPy - Handling Leap Seconds
- NumPy - Vectorized Operations with Datetimes
- NumPy ufunc
- NumPy - ufunc Introduction
- NumPy - Creating Universal Functions (ufunc)
- NumPy - Arithmetic Universal Function (ufunc)
- NumPy - Rounding Decimal ufunc
- NumPy - Logarithmic Universal Function (ufunc)
- NumPy - Summation Universal Function (ufunc)
- NumPy - Product Universal Function (ufunc)
- NumPy - Difference Universal Function (ufunc)
- NumPy - Finding LCM with ufunc
- NumPy - ufunc Finding GCD
- NumPy - ufunc Trigonometric
- NumPy - Hyperbolic ufunc
- NumPy - Set Operations ufunc
- NumPy Useful Resources
- NumPy - Quick Guide
- NumPy - Cheatsheet
- NumPy - Useful Resources
- NumPy - Discussion
- NumPy Compiler
NumPy mean() Function
The NumPy mean() function computes the arithmetic mean (average) of the elements in an array along a specified axis. The mean is the sum of the data values divided by the number of data points. The average is taken over the flattened array by default, otherwise over the specified axis. The float64 data type is intermediated and return values are used for integer inputs.
In statistics, the mean (also known as the average) is the sum of the data values divided by the number of data points. The formula is mean = (sum of all elements) / (number of elements).
For a one-dimensional array, the mean is computed over all elements. For multi-dimensional arrays, the mean is computed along the specified axis.
Syntax
Following is the syntax of the NumPy mean() function −
numpy.mean(a, axis=None, dtype=None, out=None, keepdims=<no value>, where=<no value>)
Parameters
Following are the parameters of the NumPy mean() function −
- a: Input array, which can be a NumPy array, list, or a scalar value.
- axis (optional): Axis along which to compute the mean. Default is None, which means the mean is computed over the entire array.
- dtype (optional): The data type used to compute the mean. Default is None, meaning the data type of the input array is used.
- out (optional): A location into which the result is stored. If provided, it must have the same shape as the expected output.
- keepdims (optional): If True, the reduced dimensions are retained as dimensions of size one in the output. Default is False.
- where: This provides elements to be included in the mean
Return Values
This function returns the mean of the input array. If the axis parameter is specified, the mean is computed along that axis. The result is a scalar for one-dimensional input and an array for multi-dimensional input.
Example
Following is a basic example to compute the mean of an array using the NumPy mean() function −
import numpy as np # input array x = np.array([1, 2, 3, 4, 5]) # applying mean result = np.mean(x) print("Mean Result:", result)
Output
Following is the output of the above code −
Mean Result: 3.0
Example: Specifying an Axis
The mean() function can compute the mean along a specific axis of a multi-dimensional array. In the following example, we have computed the mean along axis 0 (columns) and axis 1 (rows) of a 2D array −
import numpy as np # 2D array x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # applying mean along axis 0 (columns) result_axis0 = np.mean(x, axis=0) # applying mean along axis 1 (rows) result_axis1 = np.mean(x, axis=1) print("Mean along axis 0:", result_axis0) print("Mean along axis 1:", result_axis1)
Output
Following is the output of the above code −
Mean along axis 0: [4. 5. 6.] Mean along axis 1: [2. 5. 8.]
Example: Usage of 'keepdims' Parameter
The keepdims parameter allows the result to retain the reduced dimensions as size one. This is useful for broadcasting the result back to the original shape. In the following example, we have computed the mean along axis 0 and retained the reduced dimensions −
import numpy as np # 2D array x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) result = np.mean(x, axis=0, keepdims=True) print("Mean with keepdims=True:", result)
Output
Following is the output of the above code −
Mean with keepdims=True: [[4. 5. 6.]]
Example: Plotting 'mean()' Function
In the following example, we have plotted the behavior of the mean() function. We have calculated and plotted the mean for different sizes of input arrays by importing Numpy and matplotlib.pyplot module −
import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 10, 100) y = np.full_like(x, np.mean(x)) plt.plot(x, y, label="Mean") plt.title("Mean Function") plt.xlabel("Input") plt.ylabel("Mean Value") plt.legend() plt.grid() plt.show()
Output
The plot demonstrates the constant nature of the mean value across the input range −
