Test your knowledge on pandas groupby with this quiz
Quiz on Group By
Refer to this Dataset to solve Quiz
Fill in the blanks by drag and drop operation in right spot
pandas
pd
“Name”
[20, 21, 19, 15, 25, 35, 20]
[‘Tom’, ‘Nick’,’Tom’, ‘John’,’Nick’,’Tom’, ‘John’]
Python
import as
df = pd.DataFrame({'Name': , 'Bill': })
df_grouped = df.groupby( ).sum()
df_grouped
Output:
Bill
Name
John 35
Nick 46
Tom 74Aggregate
“City, Age”
mean
df = pd.DataFrame({'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19], 'City': ['NY', 'NY', 'CH']})
df.groupby([ ])['Age']. ().sum()
Output:
60Pivot
Age
Name
Year
df = pd.DataFrame({'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19], 'City': ['NY', 'NY', 'CH'], 'Year': [2020, 2021, 2022]})
df.pivot_table(values=" ", index=" ", columns=" ")
Output:
Name John Nick Tom
Year
2020 NaN NaN 20.0
2021 NaN 21.0 NaN
2022 19.0 NaN NaNGroup By Multi Columns
“Name”
“City”
df = pd.DataFrame({'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19], 'City': ['NY', 'NY', 'CH'], 'Sales': [100, 200, 300]})
df.groupby([ , ]).sum()
Output:
Age Sales
Name City
John CH 19 300
Nick NY 21 200
Tom NY 20 100Filter Group By
Name
mean
df = pd.DataFrame({'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 17, 19]})
df.groupby(" ").filter(lambda x: x["Age"]. () > 18)
Output:
Name Age
0 Tom 20
2 John 19ANCOVA: Analysis of Covariance with python
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