In this article, we will be looking at how to calculate the rolling mean of a dataframe by time interval using Pandas in Python.
Pandas dataframe.rolling() is a function that helps us to make calculations on a rolling window. In other words, we take a window of a fixed size and perform some mathematical calculations on it.
Syntax: DataFrame.rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0).mean()
Parameters :
- window : Size of the window. That is how many observations we have to take for the calculation of each window.
- min_periods : Least number of observations in a window required to have a value (otherwise result is NA).
- center : It is used to set the labels at the center of the window.
- win_type : It is used to set the window type.
- on : Datetime column of our dataframe on which we have to calculate rolling mean.
- axis : integer or string, default 0
Dataset Used: Tesla_Stock
Stepwise Implementation
Step 1: Importing Libraries
Python3
# import pandas as pd import pandas as pd |
Step 2: Importing Data
Python3
# importing Data tesla_df = pd.read_csv('Tesla_Stock.csv', index_col='Date', parse_dates=True) # printing the dataFrame tesla_df.head(10) |
Output:
We will be calculating the rolling mean of the column ‘Close’ of the DataFrame.
Step 3: Calculating Rolling Mean
Python3
# Updating the dataFrame with just the # column 'Close' as others columns are # of no use right now we have used .to_frame # which converts Series to a DataFrame. tesla_df = tesla_df['Close'].to_frame() # calculating Rolling mean and storing it # into a new column of existing dataFrame # we have set the window as 30 and rest all # parameters are set to default. tesla_df['MA30'] = tesla_df['Close'].rolling(30).mean() # Rolling mean is also called as Moving Average , # hence we have used the notation MA # and MA30 is the moving average (rolling mean) # of 30 days # printing dataframe tesla_df |
Output:
The First 29 rows of the column MA30 will have a value NULL and the first non NULL value will be at row 30. Now we will be calculating the rolling mean with a window of 200.
Python3
# calculating Rolling mean and storing it into # a new column of existing dataFrame we have set # the window as 200 and rest all parameters are # set to default. tesla_df['MA200'] = tesla_df['Close'].rolling(200).mean() # Rolling mean is also called as Moving Average, hence # we have used the notation MA and MA200 is the moving # average (rolling mean) of 200 days # printing dataframe tesla_df |
Output:
For ‘MA200’ the first non-NULL will be at row 200. Now lets plot ‘MA30’ , ‘MA200’ and ‘Close’ for better visualization
Step 4: Plotting
Python3
# importing matplotlib module import matplotlib.pyplot as plt plt.style.use('default') # %matplotlib inline: only draw static # images in the notebook %matplotlib inline tesla_df[['Close', 'MA30', 'MA200']].plot( label='tesla', figsize=(16, 8)) |
OUTPUT:
