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Pandas: Split the specified dataframe into groups and count unique values of 'value' column

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-15 with Solution

Write a Pandas program to split the following dataframe into groups and count unique values of 'value' column.

Test Data:

   id value
0   1     a
1   1     a
2   2     b
3   3  None
4   3     a
5   4     a
6   4  None
7   4     b

Sample Solution:

Python Code :

import pandas as pd
df = pd.DataFrame({
    'id': [1, 1, 2, 3, 3, 4, 4, 4],
    'value': ['a', 'a', 'b', None, 'a', 'a', None, 'b']
})
print("Original DataFrame:")
print(df)
print("Count unique values:")
print (df.groupby('value')['id'].nunique())

Sample Output:

Original DataFrame:
   id value
0   1     a
1   1     a
2   2     b
3   3  None
4   3     a
5   4     a
6   4  None
7   4     b
Count unique values:
value
a    3
b    2
Name: id, dtype: int64

Python Code Editor:


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Previous: Write a Pandas program to split the following dataframe into groups based on all columns and calculate Groupby value counts on the dataframe.
Next: Write a Pandas program to split a given dataframe into groups and list all the keys from the GroupBy object.

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Python: Tips of the Day

Python: Time library

Time library provides lots of time related functions and methods and is good to know whether you're developing a website or apps and games or working with data science or trading financial markets. Time is essential in most development pursuits and Python's standard time library comes very handy for that.

Let's check out a few simple examples:

moment=time.strftime("%Y-%b-%d__%H_%M_%S",time.localtime())

import time
time_now=time.strftime("%H:%M:%S",time.localtime())
print(time_now)
date_now=time.strftime("%Y-%b-%d",time.localtime())
print(date_now)

Output:

11:36:34
2020-Nov-30