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The sliding average is often used to study time series data by calculating the average of data at a specific time interval. It is used to eliminate some short-term fluctuations and study data trends. When studying stock price trends, the simple sliding average is used a lot.

A weighted moving average places more emphasis on the most recent data than on older data.

The following chart will give a better understanding of the sliding average.

In this tutorial, we will discuss how to implement a sliding average for a numpy array in Python.

Use numpy.convolvethe method to calculate the sliding mean of a NumPy array.

convolve()The function is used for signal processing and can return the linear convolution of two arrays. What each step does is take the inner product between an array and the current window and take their sum.

The following code implements this functionality in a user-defined function.

import numpy as np


def moving_average(x, w):
    return np.convolve(x, np.ones(w), "valid") / w


data = np.array([10, 5, 8, 9, 15, 22, 26, 11, 15, 16, 18, 7])

print(moving_average(data, 4))

Output:

[ 8.    9.25 13.5  18.   18.5  18.5  17.   15.   14.  ]

Use scipy.convolvethe method to calculate the sliding mean of a NumPy array.

We can also use the numpy function in the same way scipy.convolve(). Let's say it is a bit faster. Another way to calculate the sliding average using the numpy module is to use cumsum()the numpy function. It calculates the cumulative sum of an array. This is a very simple non-weighted method to calculate a sliding average.

The following code uses this function to return a sliding average.

def moving_average(a, n):
    ret = np.cumsum(a, dtype=float)
    ret[n:] = ret[n:] - ret[:-n]
    return ret[n - 1 :] / n


data = np.array([10, 5, 8, 9, 15, 22, 26, 11, 15, 16, 18, 7])

print(moving_average(data, 4))

Output:

[ 8.    9.25 13.5  18.   18.5  18.5  17.   15.   14.  ]

Use bottleneckthe module to calculate the sliding average

bottleneckThe module is a compilation of fast numpy methods. The module has move_mean()a function that returns a sliding average of some data.

For example,

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))

Output:

[  nan   nan   nan  8.    9.25 13.5  18.   18.5  18.5  17.   15.   14.  ]

Since the time window interval is 4, there are three nan values ​​at the beginning because the sliding average cannot be calculated for them.

Use pandasthe module to calculate the sliding average

Time series data is primarily pandasassociated with DataFrames. Hence, this library is perfect for performing different computations on such data.

We can use the rolling()and mean()functions to calculate the moving average of time series data as shown below.

import pandas as pd
import numpy as np

data = np.array([10, 5, 8, 9, 15, 22, 26, 11, 15, 16, 18, 7])

d = pd.Series(data)

print(d.rolling(4).mean())

Output:

0       NaN
1       NaN
2       NaN
3      8.00
4      9.25
5     13.50
6     18.00
7     18.50
8     18.50
9     17.00
10    15.00
11    14.00
dtype: float64

We first convert the numpy array to a time series object, then use rolling()the function to perform calculations on the rolling window and mean()the function to calculate the sliding average.

This is also because the time window interval is 4, so there are three nan values ​​at the beginning because the sliding average cannot be calculated for them.