Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.autocorr() function compute the lag-N autocorrelation. This method computes the Pearson correlation between the Series and its shifted self.
Syntax: Series.autocorr(lag=1)
Parameter :
lag : Number of lags to apply before performing autocorrelation.Returns : float
Example #1: Use Series.autocorr() function to compute the lag-N auto-correlation of the underlying data for the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None]) # Create the Index index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='H') # set the index sr.index = index_ # Print the series print(sr) |
Output :
Now we will use Series.autocorr() function to compute the lag-n auto-correlation of the underlying data for the given series object.
# return the auto correlation result = sr.autocorr() # Print the result print(result) |
Output :
As we can see in the output, the Series.autocorr() function has successfully returned the auto correlation of the underlying data of the given series object by lag 1.
Example #2 : Use Series.autocorr() function to compute the lag-N auto-correlation of the underlying data for the given series object. Take the lag value equal to 3.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([34, 5, 13, 32, 4, 15]) # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the index sr.index = index_ # Print the series print(sr) |
Output :
Now we will use Series.autocorr() function to compute the lag-n auto-correlation of the underlying data for the given series object.
# return the auto correlation # by lag-3 result = sr.autocorr(lag = 3) # Print the result print(result) |
Output :
As we can see in the output, the Series.autocorr() function has successfully returned the auto correlation of the underlying data of the given series object by lag 1.
