Apply function to each row of pandas dataframe to create two new columns


I have a pandas DataFrame, st containing multiple columns:

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 53732 entries, 1993-01-07 12:23:58 to 2012-12-02 20:06:23
Data columns:
Date(dd-mm-yy)_Time(hh-mm-ss)       53732  non-null values
Julian_Day                          53732  non-null values
AOT_1020                            53716  non-null values
AOT_870                             53732  non-null values
AOT_675                             53188  non-null values
AOT_500                             51687  non-null values
AOT_440                             53727  non-null values
AOT_380                             51864  non-null values
AOT_340                             52852  non-null values
Water(cm)                           51687  non-null values
%TripletVar_1020                    53710  non-null values
%TripletVar_870                     53726  non-null values
%TripletVar_675                     53182  non-null values
%TripletVar_500                     51683  non-null values
%TripletVar_440                     53721  non-null values
%TripletVar_380                     51860  non-null values
%TripletVar_340                     52846  non-null values
440-870Angstrom                     53732  non-null values
380-500Angstrom                     52253  non-null values
440-675Angstrom                     53732  non-null values
500-870Angstrom                     53732  non-null values
340-440Angstrom                     53277  non-null values
Last_Processing_Date(dd/mm/yyyy)    53732  non-null values
Solar_Zenith_Angle                  53732  non-null values
dtypes: datetime64[ns](1), float64(22), object(1)

I want to create two new columns for this dataframe based on applying a function to each row of the dataframe. I don't want to have to call the function multiple times (eg. by doing two separate apply calls) as it is rather computationally intensive. I have tried doing this in two ways, and neither of them work:

Using apply:

I have written a function which takes a Series and returns a tuple of the values I want:

def calculate(s):
    a = s['path'] + 2*s['row'] # Simple calc for example
    b = s['path'] * 0.153
    return (a, b)

Trying to apply this to the DataFrame gives an error:

st.apply(calculate, axis=1)
AssertionError                            Traceback (most recent call last)
<ipython-input-248-acb7a44054a7> in <module>()
----> 1 st.apply(calculate, axis=1)

C:\Python27\lib\site-packages\pandas\core\frame.pyc in apply(self, func, axis, broadcast, raw, args, **kwds)
   4191                     return self._apply_raw(f, axis)
   4192                 else:
-> 4193                     return self._apply_standard(f, axis)
   4194             else:
   4195                 return self._apply_broadcast(f, axis)

C:\Python27\lib\site-packages\pandas\core\frame.pyc in _apply_standard(self, func, axis, ignore_failures)
   4274                 index = None
-> 4276             result = self._constructor(data=results, index=index)
   4277             result.rename(columns=dict(zip(range(len(res_index)), res_index)),
   4278                           inplace=True)

C:\Python27\lib\site-packages\pandas\core\frame.pyc in __init__(self, data, index, columns, dtype, copy)
    390             mgr = self._init_mgr(data, index, columns, dtype=dtype, copy=copy)
    391         elif isinstance(data, dict):
--> 392             mgr = self._init_dict(data, index, columns, dtype=dtype)
    393         elif isinstance(data, ma.MaskedArray):
    394             mask = ma.getmaskarray(data)

C:\Python27\lib\site-packages\pandas\core\frame.pyc in _init_dict(self, data, index, columns, dtype)
    522         return _arrays_to_mgr(arrays, data_names, index, columns,
--> 523                               dtype=dtype)
    525     def _init_ndarray(self, values, index, columns, dtype=None,

C:\Python27\lib\site-packages\pandas\core\frame.pyc in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
   5412     # consolidate for now
-> 5413     mgr = BlockManager(blocks, axes)
   5414     return mgr.consolidate()

C:\Python27\lib\site-packages\pandas\core\internals.pyc in __init__(self, blocks, axes, do_integrity_check)
    803         if do_integrity_check:
--> 804             self._verify_integrity()
    806         self._consolidate_check()

C:\Python27\lib\site-packages\pandas\core\internals.pyc in _verify_integrity(self)
    892                                      "items")
    893             if block.values.shape[1:] != mgr_shape[1:]:
--> 894                 raise AssertionError('Block shape incompatible with manager')
    895         tot_items = sum(len(x.items) for x in self.blocks)
    896         if len(self.items) != tot_items:

AssertionError: Block shape incompatible with manager

I was then going to assign the values returned from apply to two new columns using the method shown in this question. However, I can't even get to this point! This all works fine if I just return one value.

Using a loop:

I first created two new columns of the dataframe and set them to None:

st['a'] = None
st['b'] = None

Then looped over all of the indices and tried to modify these None values that I'd got in there, but the modifications I did didn't seem to work. That is, no error was generated, but the DataFrame didn't seem to be modified.

for i in st.index:
    # do calc here
    st.ix[i]['a'] = a
    st.ix[i]['b'] = b

I thought that both of these methods would work, but neither of them did. So, what am I doing wrong here? And what is the best, most 'pythonic' and 'pandaonic' way to do this?

5/23/2017 10:31:27 AM

Accepted Answer

To make the first approach work, try returning a Series instead of a tuple (apply is throwing an exception because it doesn't know how to glue the rows back together as the number of columns doesn't match the original frame).

def calculate(s):
    a = s['path'] + 2*s['row'] # Simple calc for example
    b = s['path'] * 0.153
    return pd.Series(dict(col1=a, col2=b))

The second approach should work if you replace:

st.ix[i]['a'] = a


st.ix[i, 'a'] = a
2/28/2013 1:21:16 AM

I always use lambdas and the built-in map() function to create new rows by combining other rows:

st['a'] = map(lambda path, row: path + 2 * row, st['path'], st['row'])

It might be slightly more complicated than necessary for doing linear combinations of numerical columns. On the other hand, I feel it's good to adopt as a convention as it can be used with more complicated combinations of rows (e.g. working with strings) or filling missing data in a column using functions of the other columns.

For example, lets say you have a table with columns gender, and title, and some of the titles are missing. You can fill them with a function as follows:

title_dict = {'male': 'mr.', 'female': 'ms.'}
table['title'] = map(lambda title,
    gender: title if title != None else title_dict[gender],
    table['title'], table['gender'])

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