Pandas Data Series: Find the positions of the values neighboured by smaller values on both sides in a given series

Last update on December 21 2024 07:42:40 (UTC/GMT +8 hours)

Write a Pandas program to find the positions of the values neighboured by smaller values on both sides in a given series.

Pictorial Presentation:

Sample Solution:

Python Code :

import pandas as pd
import numpy as np
nums = pd.Series([1, 8, 7, 5, 6, 5, 3, 4, 7, 1])
print("Original series:")
print(nums)
print("\nPositions of the values surrounded by smaller values on both sides:")
temp = np.diff(np.sign(np.diff(nums)))
result = np.where(temp == -2)[0] + 1
print(result)

Sample Output:

Original series:
0    1
1    8
2    7
3    5
4    6
5    5
6    3
7    4
8    7
9    1
dtype: int64

Positions of the values surrounded by smaller values on both sides:
[1 4 8]

Explanation:

nums = pd.Series([1, 8, 7, 5, 6, 5, 3, 4, 7, 1]): This code creates a Pandas Series object 'nums' containing ten integers.

temp = np.diff(np.sign(np.diff(nums))): This code first computes the second order difference of the 'nums' series using the np.diff() and np.sign() functions from the NumPy library. The diff() function computes the difference between consecutive elements in the 'nums' series, while the sign() function returns the sign of each element in the resulting difference series. The second order difference is obtained by computing the difference between consecutive elements of the resulting sign series.

result = np.where(temp == -2)[0] + 1: This code applies the np.where() function to find the positions in the second order difference series where the value is equal to -2. The where() function returns a tuple containing the indices where this condition is true, which are then added to 1 (since the diff() function reduces the length of the series by 1).

Python-Pandas Code Editor:

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