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.
numpy.mean()
functionThe numpy.mean()
function returns the arithmetic average of a given NumPy array along the specified axis.
numpy.mean(
arr,
axis=None,
out=None,
overwrite_input=False,
dtype= data-type
)
arr
: This represents the input array.
axis
: This represents the axis on which we want to calculate the mean. If the axis is 0
, the direction is down the row. If it is 1
, 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.
The following code shows how to use the numpy.mean()
function in Python:
# import numpy import numpy as np # create a list my_list = [24,8,3,4,86,42,56,34,8] # convert the list to numpy array np_list = np.array(my_list) # compute the mean and store it np_list_mean = np.mean(np_list) print(f"The mean is {np_list_mean}")
numpy
library.my_list
.np_list
.np.mean()
function to compute the mean for the np_list
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