A moving average is a statistical calculation that helps in determining the average value of a set of data points over a specified window of time. It is a simple calculation that gives a smoothed representation of the timeseries data. This concept is widely used in finance, stock market analysis, and various other applications.
In this article, we'll look at how to calculate the moving average of a list of numbers in Python. We'll first see how to calculate the moving average using the traditional method, and then move on to the implementation using the NumPy library.
Moving Average Using Traditional Method
The traditional method of calculating the moving average involves the following steps:
 Initialize the window size and create an empty list to store the moving average values
 Slide the window over the list of numbers and calculate the average for each window
 Append the average to the list of moving average values
Here's the implementation of the traditional method to calculate the moving average:
def moving_average(data, window_size):
moving_average_values = []
for i in range(len(data)  window_size + 1):
window = data[i:i + window_size]
window_average = sum(window) / window_size
moving_average_values.append(window_average)
return moving_average_values
Let's test the function with an example.
data = [3, 5, 7, 2, 8, 10, 11, 65, 72, 81, 99, 100, 150]
window_size = 3
print(moving_average(data, window_size))
The output will be:
[5.0, 4.666666666666667, 5.666666666666667, 6.666666666666667, 9.666666666666666, 28.666666666666668, 49.333333333333336, 72.66666666666667, 84.0, 93.33333333333333, 116.33333333333333]
As we can see, the moving average of the given list of numbers has been calculated for each window of size 3
.
Moving Average Using NumPy
NumPy is a popular library for numerical computing in Python. It provides a simple and efficient way of calculating the moving average. The numpy.convolve
function can be used to calculate the moving average of a list of numbers.
Here's the implementation of the moving average using NumPy:
import numpy as np
def moving_average_numpy(data, window_size):
window = np.ones(int(window_size))/float(window_size)
return np.convolve(data, window, 'valid')
Let's test the function with the same example as before.
data = [3, 5, 7, 2, 8, 10, 11, 65, 72, 81, 99, 100, 150]
window_size = 3
print(moving_average_numpy(data, window_size))
The output will be:
[5. 4.66666667 5.66666667 6.66666667 9.66666667 28.66666667
49.33333333 72.66666667 84. 93.33333333 116.33333333]
As we can see, the output of the
Sure! Let's continue to explore related topics that are relevant to the moving average calculation.
Applications of Moving Average
The moving average is a simple and powerful tool that can be used in a variety of applications, including:

Financial Analysis: In finance, the moving average is used to smooth out the fluctuations in stock prices and other financial data to help identify trends and make investment decisions.

Timeseries Analysis: The moving average can be used to analyze timeseries data, such as temperature readings, stock prices, and sales data, to determine longterm trends and patterns.

Forecasting: The moving average can be used to forecast future values based on past data. By calculating the moving average over a set of historical data, it is possible to make predictions about future values.
Types of Moving Averages
There are several types of moving averages, including simple moving average (SMA), weighted moving average (WMA), and exponential moving average (EMA).

Simple Moving Average (SMA): SMA is the most basic form of moving average. It is calculated as the average of a set of data points over a specified window of time.

Weighted Moving Average (WMA): WMA gives more weight to recent data points, which makes it more responsive to changes in the data.

Exponential Moving Average (EMA): EMA gives more weight to the most recent data points and decreases the weight of older data points. This makes it more responsive to changes in the data than SMA and WMA.
Window Size Selection
The window size, also known as the lag, is an important parameter in the calculation of the moving average. The window size determines the number of data points that are included in each calculation of the moving average.
A smaller window size will result in a more sensitive calculation that responds quickly to changes in the data. On the other hand, a larger window size will result in a smoother and less sensitive calculation.
The selection of the window size depends on the specific application and the characteristics of the data. It is often determined through trial and error, or by using statistical methods.
In conclusion, the moving average is a simple and useful tool for analyzing and forecasting timeseries data. By using the traditional method or the NumPy library, it is possible to calculate the moving average in Python. The choice of the type of moving average and the window size will depend on the specific application and the characteristics of the data.
Popular questions
Sure! Here are five questions and answers about "python moving average of list with code examples".
 What is a moving average and why is it used?
A moving average is a statistical calculation that is used to analyze timeseries data. It is calculated by taking the average of a set of data points over a specified window of time, with the goal of smoothing out fluctuations in the data and identifying trends and patterns.
 What is the simplest way to calculate the moving average in Python?
The simplest way to calculate the moving average in Python is by using a for loop and manually iterating through the list of data points. For each iteration, the average of the current data point and the previous n1 data points (where n is the window size) is calculated and appended to a new list.
 Can the moving average be calculated using the NumPy library in Python?
Yes, the moving average can be calculated using the NumPy library in Python. The numpy.convolve
function can be used to calculate the moving average by convolving the data with a uniform window of size equal to the window size.
 What is the difference between a simple moving average, a weighted moving average, and an exponential moving average?
A simple moving average (SMA) is the most basic form of moving average and is calculated as the average of a set of data points over a specified window of time. A weighted moving average (WMA) gives more weight to recent data points, making it more responsive to changes in the data. An exponential moving average (EMA) gives more weight to the most recent data points and decreases the weight of older data points, making it even more responsive to changes in the data than SMA and WMA.
 What is the window size and why is it important in the calculation of the moving average?
The window size, also known as the lag, is an important parameter in the calculation of the moving average. It determines the number of data points that are included in each calculation of the moving average. A smaller window size will result in a more sensitive calculation that responds quickly to changes in the data, while a larger window size will result in a smoother and less sensitive calculation. The selection of the window size depends on the specific application and the characteristics of the data.
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Statistics.