In this Python tutorial, we will discuss **Python NumPy Average **and also cover the below examples:

- Python numpy average vs mean
- Python numpy average of columns
- Python numpy average 2d array
- Python numpy average function
- Python numpy average ignore nan
- Python numpy average of matrix
- Python numpy moving average filter
- Python numpy average value

**Table of Contents**show

## Python numpy average

- In this section, we will learn and discuss the Python numpy average.
- To find the average of a numpy array, you can use numpy.average() function.
- The numpy library of Python provides a function called np. average(), used for calculating the weight mean along the specified axis.

**Syntax:**

Here is the syntax of the NumPy average

```
numpy.average
(
arr,
axis=None,
Weights=None,
returned=False
)
```

- It consists of few parameters
**arr**: An array stored data to be averaged.**Axis:**Axis along which to average a. The default, axis=None, will average over all the values of the input array. If the axis is negative it will counts from the end to the first axis.**Weights:**It is an optional parameter. Each value in a contributes to the mean according to its given weight.**Returns:**it will Return the average along the given axis. When the average value is True with the mean as the first element and the sum of the weights as the second element.

**Example:**

Letâ€™s take an example to check **how to calculate numpy average in python**.

```
import numpy as np
arr = np.arange(1, 5)
avg = np.average(arr)
print(avg)
```

In the above code, we will import a NumPy library and create an array by using the function numpy.arange.

Here is the Screenshot of the following given code

Read Python NumPy absolute value with examples

## Python numpy average vs mean

- In this method, we will learn and discuss the numpy average vs mean.
- Both these functions can be used to calculate the arithmetic and statistic value to find mean or average.
- np.mean() function can have many other parameters like dtype, out, where and more which are not available in the np.average() function.
- This function can compute a weighted mean if the weights parameter is supplied.
- np.average does not take into boolean masks, so it will generate the average over the whole set of data. While the case of mean takes into boolean masks, so compute the mean only over unmasked values.

**Example:**

```
import numpy as np
arr = np.arange(1, 5)
avg = np.average(arr)
mea = np.mean(arr)
print(avg)
print(mea)
```

Here is the Screenshot of the following given code

Read Python NumPy square with examples

## Python numpy average of columns

- In this method, we will learn and discuss the Python numpy average of columns.
- np.average() function is to calculate mean values across dimensions in an array.
- It will return the average of a numpy array of all values along the given axis.
- x as equal to 0 and then 1 to calculate the mean value of each column and then row in numpy module

**Syntax:**

```
numpy.average
(
arr,
axis=None,
Weights=None,
returned=False
)
```

**Example:**

```
import numpy as np
arr = np.array([[2,3,4],
[3,6,7],
[5,7,8]])
a= np.average(arr,axis=0)
print(a)
```

Here is the Screenshot of the following given code

Read Python NumPy to list with examples

## Python numpy average 2d array

- In this method, we will learn and discuss the Python numpy average 2d array.
- To calculate the average of all values in a 2 dimensional NumPy array called matrix, use the numpy.average(matrix) function.
- The output will display a numpy array that has three average values, one per column of the input given array.

**Syntax:**

Here is the Syntax of the numpy average

```
numpy.average
(
arr,
axis=None,
Weights=None,
returned=False
)
```

**Example:**

```
import numpy as np
arr = np.array([[4,5,6],
[4,6,7]])# 2D array
a= np.average(arr)
print(a)
```

Here is the Screenshot of the following given code

## Python numpy average function

- In this method, we will learn and discuss the numpy average function
- The numpy library of Python provides a function called numpy.average(). Basically, it is used for calculating the weighted average along the given axis.
- To find the mean of a numpy array, you can use np.average() statistical function.
- These weights will be multiplied with the values and then the mean of the resulting is calculated.

**Syntax:**

Here is the Syntax of the **NumPy average function**

```
numpy.average
(
arr,
axis=None,
Weights=None,
returned=False
)
```

**Example:**

```
import numpy as np
c = np.array([2, 3, 4, 7]).reshape(2,2)
d = np.average(c, axis=0, weights=[0.3,0.7])# average along axis=0
print(d)
```

Here is the Screenshot of the following given code

Read Python NumPy log

## Python numpy average ignore nan

**In this method, we will learn and discuss the numpy average ignore nan.**- If the numpy array has a NaN value and we can easily find out the average without the effect of the NaN value. axis: we can use axis=1 means row-wise or column-wise.
- In this method, we will calculate our weighted average and create a numpy array.
- numpy.average does take into account masks, so it will generate the average over the whole set of data. While in case of average takes into boolean masks, so compute the mean only over unmasked values.

**Example:**

```
import numpy as np
avg = np.array([[4,5,6], [7,8,np.NaN], [np.NaN,6,np.NaN], [0,0,0]])
data = np.ma.masked_array(avg, np.isnan(avg))
weights = [1, 1, 1]
average = np.ma.average(data, axis=1, weights=weights)
result = average.filled(np.nan)
print(result)
```

Here is the Screenshot of the following given code

Read Python NumPy where with examples

## Python numpy average of matrix

- In this method, we will learn and discuss the numpy average matrix.
- To calculate the average individually for each column of the 2Dimension matrix, use the function call numpy. average(array, axis=0) setting the axis parameter to 0.
- It will always return the mean value of the matrix.
- In this method, we can easily use the function np.average().

**Example:**

```
import numpy as np
x = np.matrix(np.arange(12).reshape((3, 4)))
y = np.average(x)
print(y)
```

Here is the Screenshot of the following given code

## Python numpy average filter

- In this method, we will learn and discuss the numpy moving average filter.
- Weighted moving average puts more effort on the recent data than the older data.
- Moving np. average is currently used in study time-series data by calculating the average of the data at given intervals.
- In this method we can easily use the function numpy.convolve to calculate the moving average for numpy arrays.
- The convolve() function is used in signal processing and can return the linear numpy convolution of two arrays.

**Example:**

```
import numpy as np
def moving_average(x, w):
return np.convolve(x, np.ones(w), 'valid') / w
data = np.array([2,3,8,4,6,7,8,11,14,17,9,7])
print(moving_average(data,4))
```

Here is the Screenshot of the following given code

Another way to calculate the average for NumPy arrays using scipy.convolve() function.

The scipy.convolve() function in the same way. It is consumed to be a little faster. Another way to check the moving mean using the Python module is with the cumsum() function.

**Example:**

```
import numpy as np
def average(a, n) :
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n - 1:] / n
data = np.array([5,4,3,2,10,11,13,14,15,16,19,7])
print(average(data,4))
```

Here is the Screenshot of the following given code

Another method to calculate the moving **average for NumPy arrays** using a bottleneck.

The bottleneck module is a compilation of quick NumPy modules. This module has the move_average() function, which can return the moving average of some data.

**Example:**

```
import numpy as np
import bottleneck as bn
import numpy as np
def rollavg_bottlneck(a,n):
return bn.move_mean(a, window=n,min_count = None)
data = np.array([10,5,8,9,15,22,26,11,15,16,18,7])
print(rollavg_bottlneck(data, 4))
```

Here is the Screenshot of the following given code

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## Python numpy average value

- In this method, we will learn and discuss the numpy average value.
- In this method, we will check how to use the function np.average() to evaluate the average value of a given array.
- This function returns the arithmetic statistics average value of elements in the array.
- Else on the given axis, float 64 datatypes is compute as well as return values are used for integer inputs.

**Syntax:**

Here is the Syntax of the **NumPy average**

```
numpy.average
(
arr,
axis=None,
Weights=None,
returned=False
)
```

**Example:**

```
import numpy as np
arr = np.array([[1,2,4],
[3,4,7],
[5,7,8]])
a= np.average(arr,axis=0)
print(a)
```

Here is the Screenshot of the following given code

You may like the following Python NumPy articles:

In this Python tutorial, we will discuss **Python NumPy Average **and also cover the below examples:

- Python numpy average vs mean
- Python numpy average of columns
- Python numpy average 2d array
- Python numpy average function
- Python numpy average ignore nan
- Python numpy average of matrix
- Python numpy moving average filter
- Python numpy average value

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