Consider this small example:
data={"X":[1, 2, 3, 4, 5], "Y":[6, 7, 8, 9, 10], "Z": [11, 12, 13, 14, 15])
frame=pd.DataFrame(data,columns=["X","Y","Z"],index=["A","A","A","B","B"])
I want to group frame
with
grouped=frame.groupby(frame.index)
Then I want to loop over the groups by:
for group in grouped:
But I'm stuck on the next step: How can I extract the group
in each loop as a pandas DataFrame so I can further process it?
groupby() to Iterate over Data frame Groups. DataFrame. groupby() function in Python is used to split the data into groups based on some criteria.
DataFrame Looping (iteration) with a for statement. You can loop over a pandas dataframe, for each column row by row.
What is the GroupBy function? Pandas' GroupBy is a powerful and versatile function in Python. It allows you to split your data into separate groups to perform computations for better analysis.
df.groupby
returns an iterable of 2-tuples: the index, and the group. You can iterate over each group like this:
for _, g in frame.groupby(frame.index):
.... # do something with `g`
However, if you want to perform some operation on the groups, there are probably better ways than iteration.
Here is an example:
groups = frame.groupby(level=0)
for n,g in groups:
print('This is group '+ str(n)+'.')
print(g)
print('\n')
Output:
This is group A.
X Y Z
A 1 6 11
A 2 7 12
A 3 8 13
This is group B.
X Y Z
B 4 9 14
B 5 10 15
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