Pandas Data Series: Compute the minimum, 25th percentile, median, 75th, and maximum of a given series

Pandas: Data Series Exercise-18 with Solution

Write a Pandas program to compute the minimum, 25th percentile, median, 75th, and maximum of a given series.

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
num_state = np.random.RandomState(100)
num_series = pd.Series(num_state.normal(10, 4, 20))
print("Original Series:")
print(num_series)
result = np.percentile(num_series, q=[0, 25, 50, 75, 100])
print("\nMinimum, 25th percentile, median, 75th, and maximum of a given series:")
print(result)

Sample Output:

Original Series:
0      3.000938
1     11.370722
2     14.612143
3      8.990256
4     13.925283
5     12.056875
6     10.884719
7      5.719827
8      9.242017
9     11.020006
10     8.167892
11    11.740654
12     7.665620
13    13.267388
14    12.690883
15     9.582355
16     7.874878
17    14.118931
18     8.247458
19     5.526727
dtype: float64

Minimum, 25th percentile, median, 75th, and maximum of a given series:
[ 3.00093811  8.09463867 10.23353705 12.21537733 14.61214321]         

Explanation:

In the above exercise -

num_state = np.random.RandomState(100): This code creates a NumPy RandomState object 'num_state' with a seed value of 100.

num_series = pd.Series(num_state.normal(10, 4, 20)): This code creates a Pandas Series object 'num_series' containing 20 random values generated from a normal distribution with a mean of 10 and a standard deviation of 4 using the num_state.normal() method.

result = np.percentile(num_series, q=[0, 25, 50, 75, 100]): This code calculates the percentiles of the values in the Pandas Series object 'num_series' using the np.percentile() function. The 'q' parameter specifies the percentiles to calculate, with the values [0, 25, 50, 75, 100] indicating the minimum value, the lower quartile (25th percentile), the median (50th percentile), the upper quartile (75th percentile), and the maximum value, respectively.

Python-Pandas Code Editor:

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