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