How to use the Numpy.mean() function in Python
Overview
NumPy is a library in Python that allows us to work with numeric data. Numeric data can be created and stored in a data structure called a NumPy array.
NumPy has various functions to perform calculations on the arrays of numeric data. One of these is the mean() function.
The numpy.mean() function
The numpy.mean() function returns the arithmetic average of a given NumPy array along the specified axis.
Syntax
numpy.mean(
arr,
axis=None,
out=None,
overwrite_input=False,
dtype= data-type
)
Parameters
-
arr: This represents the input array. -
axis: This represents the axis on which we want to calculate the mean. If the axis is0, the direction is down the row. If it is1, the direction is down the column. -
out: This is an optional parameter that saves the NumPy result. -
dtype: This is an optional parameter that specifies the type to use when computing the mean.
Code
The following code shows how to use the numpy.mean() function in Python:
# import numpyimport numpy as np# create a listmy_list = [24,8,3,4,86,42,56,34,8]# convert the list to numpy arraynp_list = np.array(my_list)# compute the mean and store itnp_list_mean = np.mean(np_list)print(f"The mean is {np_list_mean}")
Explanation
- Line 2: We import the
numpylibrary. - Line 4: We create a list called
my_list. - Line 6: We convert the list to a NumPy array and store it in a variable,
np_list. - Line 8: We use the
np.mean()function to compute the mean for thenp_list. - Line 10: We display the result.