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

Pandas: Data Series Exercise-19 with Solution

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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