Pandas Data Series: Compute the autocorrelations of a given numeric series

Pandas: Data Series Exercise-34 with Solution

Write a Pandas program to compute the autocorrelations of a given numeric series.

From Wikipedia:
Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations as a function of the time lag between them.

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
num_series = pd.Series(np.arange(15) + np.random.normal(1, 10, 15))
print("Original series:")
print(num_series)
autocorrelations = [num_series.autocorr(i).round(2) for i in range(11)]
print("\nAutocorrelations of the said series:")
print(autocorrelations[1:])

Sample Output:

Original series:
0     13.207262
1      4.098685
2     -1.435534
3     13.626760
4     -1.435962
5     28.823612
6     -3.299048
7     14.048354
8      6.991233
9     13.289209
10    23.032654
11     7.080452
12    -2.453857
13    -2.346193
14    17.873884
dtype: float64

Autocorrelations of the said series:
[-0.38, 0.1, -0.43, 0.03, 0.35, -0.2, 0.04, -0.59, 0.34, 0.11]

Explanation:

num_series = pd.Series(np.arange(15) + np.random.normal(1, 10, 15)): This line creates a Pandas Series object 'num_series' containing 15 elements, where each element is the sum of a corresponding element in a range of 15 numbers and a random number drawn from a normal distribution with a mean of 1 and standard deviation of 10.

autocorrelations = [num_series.autocorr(i).round(2) for i in range(11)]: This code computes the autocorrelations of the 'num_series' series up to a lag of 10, using the autocorr() function of the series. The resulting autocorrelations are stored in the 'autocorrelations' list, with each element rounded to 2 decimal places. print(autocorrelations[1:]): Finally, the code prints the autocorrelations from the first lag to the 10th lag using list slicing ([1:]).

Python-Pandas Code Editor:

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