Pandas: Split the specified dataframe into groups based on all columns and calculate Groupby value counts on the dataframe
Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-14 with Solution
Write a Pandas program to split the following dataframe into groups based on all columns and calculate GroupBy value counts on the dataframe.
Test Data:
id type book 0 1 10 Math 1 2 15 English 2 1 11 Physics 3 1 20 Math 4 2 21 English 5 1 12 Physics 6 2 14 English
Sample Solution:
Python Code :
import pandas as pd
df = pd.DataFrame( {'id' : [1, 2, 1, 1, 2, 1, 2],
'type' : [10, 15, 11, 20, 21, 12, 14],
'book' : ['Math','English','Physics','Math','English','Physics','English']})
print("Original DataFrame:")
print(df)
result = df.groupby(['id', 'type', 'book']).size().unstack(fill_value=0)
print("\nResult:")
print(result)
Sample Output:
Original DataFrame: id type book 0 1 10 Math 1 2 15 English 2 1 11 Physics 3 1 20 Math 4 2 21 English 5 1 12 Physics 6 2 14 English Result: book English Math Physics id type 1 10 0 1 0 11 0 0 1 12 0 0 1 20 0 1 0 2 14 1 0 0 15 1 0 0 21 1 0 0
Python Code Editor:
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Previous: Write a Pandas program to split the following dataframe into groups based on first column and set other column values into a list of values.
Next: Write a Pandas program to split the following dataframe into groups and count unique values of ‘value’ column.
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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
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