Pandas Data Series: Calculate the frequency counts of each unique value of a given series

Last update on December 21 2024 07:42:33 (UTC/GMT +8 hours)

Write a Pandas program to calculate the frequency counts of each unique value of a given series.

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
num_series = pd.Series(np.take(list('0123456789'), np.random.randint(10, size=40)))
print("Original Series:")
print(num_series)
print("Frequency of each unique value of the said series.")
result = num_series.value_counts()
print(result)

Sample Output:

Original Series:
0     1
1     7
2     1
3     6
4     9
5     1
6     0
7     0
8     7
9     9
10    6
11    0
12    1
13    6
14    7
15    0
16    2
17    9
18    2
19    0
20    5
21    2
22    3
23    2
24    3
25    0
26    0
27    8
28    8
29    2
30    9
31    1
32    2
33    9
34    2
35    9
36    0
37    0
38    4
39    8
dtype: object
Frequency of each unique value of the said series.
0    9
2    7
9    6
1    5
6    3
8    3
7    3
3    2
4    1
5    1
dtype: int64

Explanation:

num_series = pd.Series(np.take(list('0123456789'), np.random.randint(10, size=40)))

This line generates a Pandas Series object 'num_series' containing 40 random characters from the list of digits '0123456789'.
The characters are selected randomly using the np.random.randint() function with a range of 10, indicating that 10 is the highest integer that can be returned.
The np.take() function is then used to select the characters from the list of digits based on the randomly generated indices.

result = num_series.value_counts(): This line creates a new Pandas Series object 'result' by counting the frequency of each unique character in the original Pandas Series object 'num_series' using the .value_counts() method. The resulting Series object 'result' will have the unique characters from the original Series object 'num_series' as its index and the count of each unique character as its value.

The output of print(result) will depend on the random characters generated by the NumPy function np.take().

Python-Pandas Code Editor:

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