how to do moving average in python?

How to Perform Moving Average in Python

The moving average is a popular statistical tool used to estimate the average value of a series of data points over a specific time period. It is particularly useful for smoothing out fluctuations in financial data, market trends, and other time-series data. In this article, we will learn how to perform a moving average in Python, using the pandas library.

Step 1: Import the necessary libraries

Before we can start calculating moving averages, we need to import the pandas library and create a sample data frame.

```python

import pandas as pd

import numpy as np

# Create a sample data frame

data = {

'Date': ['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05'],

'Value': [100, 150, 120, 170, 140]

}

df = pd.DataFrame(data)

```

Step 2: Calculate the moving average

Now, we can calculate the moving average for each value in the data frame. The pandas library provides a built-in function called `rolling()` that makes this task very easy.

```python

# Calculate the moving average for each value

moving_avg = df['Value'].rolling(window=3).mean()

```

Here, `window=3` represents the number of points we want to consider for the moving average. You can change this value based on your requirement.

Step 3: Add the moving average column to the data frame

Now, we need to add the `moving_avg` column to the original data frame.

```python

# Add the moving average column

df['moving_avg'] = moving_avg

```

Step 4: View the result

Finally, we can view the result by printing the data frame.

```python

print(df)

```

You should see a data frame with two additional columns - `Date` and `moving_avg`. The `moving_avg` column contains the moving average values for each date.

In this article, we learned how to perform a moving average in Python using the pandas library. The moving average is a useful tool for analyzing time-series data and making informed decisions. By understanding how to calculate moving averages in Python, you can apply this technique to your own data analysis projects.