pandas Grouping Data Basic grouping
Example
Group by one column
Using the following DataFrame
df = pd.DataFrame({'A': ['a', 'b', 'c', 'a', 'b', 'b'],
'B': [2, 8, 1, 4, 3, 8],
'C': [102, 98, 107, 104, 115, 87]})
df
# Output:
# A B C
# 0 a 2 102
# 1 b 8 98
# 2 c 1 107
# 3 a 4 104
# 4 b 3 115
# 5 b 8 87
Group by column A and get the mean value of other columns:
df.groupby('A').mean()
# Output:
# B C
# A
# a 3.000000 103
# b 6.333333 100
# c 1.000000 107
Group by multiple columns
df.groupby(['A','B']).mean()
# Output:
# C
# A B
# a 2 102.0
# 4 104.0
# b 3 115.0
# 8 92.5
# c 1 107.0
Note how after grouping each row in the resulting DataFrame is indexed by a tuple or MultiIndex (in this case a pair of elements from columns A and B).
To apply several aggregation methods at once, for instance to count the number of items in each group and compute their mean, use the agg function:
df.groupby(['A','B']).agg(['count', 'mean'])
# Output:
# C
# count mean
# A B
# a 2 1 102.0
# 4 1 104.0
# b 3 1 115.0
# 8 2 92.5
# c 1 1 107.0
