Prerequisites: Pandas
Pandas GroupBy is very powerful function. This function is capable of splitting a dataset into various groups for analysis.
Syntax:
dataframe.groupby([column names])
Along with groupby function we can use agg() function of pandas library. Agg() function aggregates the data that is being used for finding minimum value, maximum value, mean, sum in dataset.
Syntax:
dataframe.agg(dictionary with keys as column name)
Approach:
- Import module
- Create or Load data
- Use GroupBy function on column that you want
- Then use agg() function on Date column.
- Display result
Data frame in Use:
Program:
Python3
import pandas as pdimport numpy as np # Creating Dataframedataset = {'Group': ['G-2', 'G-3', 'G-3', 'G-2', 'G-2', 'G-2', 'G-3', 'G-1', 'G-1', 'G-2'], 'Date': ['2019-11-04', '2020-05-17', '2020-12-12', '2019-10-15', '2019-01-31', '2019-02-13', '2020-12-25', '2018-06-01', '2018-07-15', '2019-09-14']} dataset = pd.DataFrame(dataset, columns=['Group', 'Date']) # using groupby() function on Group columndf = dataset.groupby(['Group']) # using agg() function on Date columndf2 = df.agg(Minimum_Date=('Date', np.min), Maximum_Date=('Date', np.max)) # Displaying resultdisplay(df2) |
Output:
