How to convert grouped/binned dataframe to numpy array?

Question

I was wondering how I would be able to convert my binned dataframe to a binned numpy array that I can use in sklearn's PCA.

Here's my code so far (x is my original unbinned dataframe):

bins=(2,6,10,14,20,26,32,38,44,50,56,62,68,74,80,86,92,98)
binned_data = x.groupby(pd.cut(x.Weight, bins))

I want to convert binned_data to a numpy array. Thanks in advance.

EDIT:

When I try binned_data.values, I receive this error:

AttributeError: Cannot access attribute 'values' of 'DataFrameGroupBy' objects, try using the 'apply' method

No, I have tried that and received this: AttributeError: Cannot access attribute 'values' of 'DataFrameGroupBy' objects, try using the 'apply' method — tooty44
– tooty44, Commented Jun 12, 2014 at 19:06
Please add a short description of x or some code to generate it. — FooBar
– FooBar, Commented Jun 13, 2014 at 6:58

chrisb · Accepted Answer · 2014-06-12 20:16:39Z

1

You need to apply some kind of aggregation to the GroupBy object to return a DataFrame. Once you have that, you can use .values to extract the numpy arrary.

For example, if you wanted the sum or count of the data in each bin you could do:

binned_data.sum().values
binned_data.size().values

Edit: My code wasn't exactly right, because the column (Weight) and the index will have the same name. It can be fixed by renaming the index, as below:

binned_data = x.groupby(pd.cut(x.Weight, bins)).sum()
binned_data.index.name = 'Weight_Bin'
binned_data.reset_index().values

edited Jun 12, 2014 at 20:16

answered Jun 12, 2014 at 19:44

chrisb

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tooty44 Over a year ago

Input: data = binned_data.sum().reset_index().values and then I got the Output: ValueError: cannot insert Weight, already exists. What do you think is causing this?

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