Group Columns
In [5]:
# Grouping by a Single Column
import pandas as pd
df = pd.DataFrame({
'Category': ['A', 'B', 'A', 'B', 'A', 'B'],
'Values': [10, 20, 30, 40, 50, 60]
})
grouped_df = df.groupby('Category').sum()
print(grouped_df)
Values Category A 90 B 120
In [6]:
# Grouping by Multiple Columns
import pandas as pd
df = pd.DataFrame({
'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
'Subcategory': ['X', 'Y', 'X', 'Y', 'X', 'Y'],
'Values': [10, 20, 30, 40, 50, 60]
})
grouped_df = df.groupby(['Category', 'Subcategory']).sum()
print(grouped_df)
Values
Category Subcategory
A X 10
Y 20
B X 30
Y 40
C X 50
Y 60
In [7]:
# Grouping and Applying Multiple Aggregations
import pandas as pd
df = pd.DataFrame({
'Category': ['A', 'B', 'A', 'B', 'A', 'B'],
'Values': [10, 20, 30, 40, 50, 60]
})
grouped_df = df.groupby('Category').agg({'Values': ['sum', 'mean', 'max','std']})
print(grouped_df)
Values
sum mean max std
Category
A 90 30.0 50 20.0
B 120 40.0 60 20.0
In [8]:
# Grouping and Applying a Custom Function
import pandas as pd
df = pd.DataFrame({
'Category': ['A', 'B', 'A', 'B', 'A', 'B'],
'Values': [10, 20, 30, 40, 50, 60]
})
def range_func(x):
return x.max() - x.min()
grouped_df = df.groupby('Category').agg({'Values': range_func})
print(grouped_df)
Values Category A 40 B 40