Apply vs transform on a group object

Question

Consider the following dataframe  columns     A    B    C    D   records           foo    one   0 162003  0 087469         bar    one   -1 156319  -1 5262719999999999         foo    two   0 833892  -1 666304              bar    three   -2 026673  -0 32205700000000004         foo    two   0 41145200000000004  -0 9543709999999999         bar    two   0 765878  -0 095968         foo    one   -0 65489  0 678091         foo    three   -1 789842  -1 130922    df   pd DataFrame from records records  columns columns    quot  quot  quot       A      B         C         D 0  foo    one  0 162003  0 087469 1  bar    one -1 156319 -1 526272 2  foo    two  0 833892 -1 666304 3  bar  three -2 026673 -0 322057 4  foo    two  0 411452 -0 954371 5  bar    two  0 765878 -0 095968 6  foo    one -0 654890  0 678091 7  foo  three -1 789842 -1 130922  quot  quot  quot   The following commands work  df groupby  A   apply lambda x   x  C   - x  D     df groupby  A   apply lambda x   x  C   - x  D    mean     but none of the following work  df groupby  A   transform lambda x   x  C   - x  D       KeyError or ValueError  could not broadcast input array from shape  5  into shape  5 3   df groupby  A   transform lambda x   x  C   - x  D    mean      KeyError or TypeError  cannot concatenate a non-NDFrame object  Why  The example on the documentation seems to suggest that calling transform on a group allows one to do row-wise operation processing    Note that the following suggests row-wise operation  x mean is the column mean  zscore   lambda x   x - x mean      x std   transformed   ts groupby key  transform zscore   In other words  I thought that transform is essentially a specific type of apply  the one that does not aggregate   Where am I wrong  For reference  below is the construction of the original dataframe above  df   pd DataFrame   A      foo    bar    foo    bar                              foo    bar    foo    foo                        B      one    one    two    three                             two    two    one    three                        C    randn 8    D    randn 8

User · Answer

As I felt similarly confused with  transform operation vs   apply I found a few answers shedding some light on the issue  This answer for example was very helpful   My takeout so far is that  transform will work  or deal  with Series  columns  in isolation from each other  What this means is that in your last two calls   df groupby  A   transform lambda x   x  C   - x  D     df groupby  A   transform lambda x   x  C   - x  D    mean      You asked  transform to take values from two columns and  it  actually does not  see  both of them at the same time  so to speak   transform will look at the dataframe columns one by one and return back a series  or group of series   made  of scalars which are repeated len input column  times   So this scalar  that should be used by  transform to make the Series is a result of some reduction function applied on an input Series  and only on ONE series column at a time    Consider this example  on your dataframe    zscore   lambda x   x - x mean      x std     Note that it does not reference anything outside of  x  and for transform  x  is one column  df groupby  A   transform zscore    will yield          C      D 0  0 989  0 128 1 -0 478  0 489 2  0 889 -0 589 3 -0 671 -1 150 4  0 034 -0 285 5  1 149  0 662 6 -1 404 -0 907 7 -0 509  1 653   Which is exactly the same as if you would use it on only on one column at a time   df groupby  A    C   transform zscore    yielding   0    0 989 1   -0 478 2    0 889 3   -0 671 4    0 034 5    1 149 6   -1 404 7   -0 509   Note that  apply in the last example  df groupby  A    C   apply zscore   would work in exactly the same way  but it would fail if you tried using it on a dataframe   df groupby  A   apply zscore    gives error   ValueError  operands could not be broadcast together with shapes  6    2     So where else  is  transform useful  The simplest case is trying to assign results of reduction function back to original dataframe   df  sum C     df groupby  A    C   transform sum  df sort  A     to clearly see the scalar   sum   applies to the whole column of the group   yielding        A      B      C      D  sum C 1  bar    one  1 998  0 593  3 973 3  bar  three  1 287 -0 639  3 973 5  bar    two  0 687 -1 027  3 973 4  foo    two  0 205  1 274  4 373 2  foo    two  0 128  0 924  4 373 6  foo    one  2 113 -0 516  4 373 7  foo  three  0 657 -1 179  4 373 0  foo    one  1 270  0 201  4 373   Trying the same with  apply would give NaNs in sum C  Because  apply would return a reduced Series  which it does not know how to broadcast back   df groupby  A    C   apply sum    giving   A bar    3 973 foo    4 373   There are also cases when  transform is used to filter the data   df df groupby   B     D   transform sum   lt  -1        A      B      C      D 3  bar  three  1 287 -0 639 7  foo  three  0 657 -1 179   I hope this adds a bit more clarity

User · Answer

tmp   df groupby   A     c   transform  mean     is like   tmp1   df groupby   A    agg   c   mean    tmp   df  A   map tmp1  c      or  tmp1   df groupby   A     c   mean   tmp   df  A   map tmp1

User · Answer

Two major differences between apply and transform There are two major differences between the transform and apply groupby methods   Input  apply implicitly passes all the columns for each group as a DataFrame to the custom function  while transform passes each column for each group individually as a Series to the custom function  Output  The custom function passed to apply can return a scalar  or a Series or DataFrame  or numpy array or even list   The custom function passed to transform must return a sequence  a one dimensional Series  array or list  the same length as the group   So  transform works on just one Series at a time and apply works on the entire DataFrame at once  Inspecting the custom function It can help quite a bit to inspect the input to your custom function passed to apply or transform  Examples Let s create some sample data and inspect the groups so that you can see what I am talking about  import pandas as pd import numpy as np df   pd DataFrame   State    Texas    Texas    Florida    Florida                         a   4 5 1 3    b   6 10 3 11          State  a   b 0    Texas  4   6 1    Texas  5  10 2  Florida  1   3 3  Florida  3  11  Let s create a simple custom function that prints out the type of the implicitly passed object and then raised an error so that execution can be stopped  def inspect x       print type x       raise  Now let s pass this function to both the groupby apply and transform methods to see what object is passed to it  df groupby  State   apply inspect    lt class  pandas core frame DataFrame  gt   lt class  pandas core frame DataFrame  gt  RuntimeError  As you can see  a DataFrame is passed into the inspect function  You might be wondering why the type  DataFrame  got printed out twice  Pandas runs the first group twice  It does this to determine if there is a fast way to complete the computation or not  This is a minor detail that you shouldn t worry about  Now  let s do the same thing with transform df groupby  State   transform inspect   lt class  pandas core series Series  gt   lt class  pandas core series Series  gt  RuntimeError  It is passed a Series - a totally different Pandas object  So  transform is only allowed to work with a single Series at a time  It is impossible for it to act on two columns at the same time  So  if we try and subtract column a from b inside of our custom function we would get an error with transform  See below  def subtract two x       return x  a   - x  b    df groupby  State   transform subtract two  KeyError    a    occurred at index a    We get a KeyError as pandas is attempting to find the Series index a which does not exist  You can complete this operation with apply as it has the entire DataFrame  df groupby  State   apply subtract two   State      Florida  2   -2          3   -8 Texas    0   -2          1   -5 dtype  int64  The output is a Series and a little confusing as the original index is kept  but we have access to all columns   Displaying the passed pandas object It can help even more to display the entire pandas object within the custom function  so you can see exactly what you are operating with  You can use print statements by I like to use the display function from the IPython display module so that the DataFrames get nicely outputted in HTML in a jupyter notebook  from IPython display import display def subtract two x       display x      return x  a   - x  b    Screenshot    Transform must return a single dimensional sequence the same size as the group The other difference is that transform must return a single dimensional sequence the same size as the group  In this particular instance  each group has two rows  so transform must return a sequence of two rows  If it does not then an error is raised  def return three x       return np array  1  2  3    df groupby  State   transform return three  ValueError  transform must return a scalar value for each group  The error message is not really descriptive of the problem  You must return a sequence the same length as the group  So  a function like this would work  def rand group len x       return np random rand len x    df groupby  State   transform rand group len             a         b 0  0 962070  0 151440 1  0 440956  0 782176 2  0 642218  0 483257 3  0 056047  0 238208   Returning a single scalar object also works for transform If you return just a single scalar from your custom function  then transform will use it for each of the rows in the group  def group sum x       return x sum    df groupby  State   transform group sum      a   b 0  9  16 1  9  16 2  4  14 3  4  14

User · Answer

I am going to use a very simple snippet to illustrate the difference   test   pd DataFrame   id   1 2 3 1 2 3 1 2 3    price   1 2 3 2 3 1 3 1 2    grouping   test groupby  id    price     The DataFrame looks like this       id  price    0   1   1    1   2   2    2   3   3    3   1   2    4   2   3    5   3   1    6   1   3    7   2   1    8   3   2      There are 3 customer IDs in this table  each customer made three transactions and paid 1 2 3 dollars each time    Now  I want to find the minimum payment made by each customer  There are two ways of doing it    Using apply   grouping min     The return looks like this   id 1    1 2    1 3    1 Name  price  dtype  int64  pandas core series Series   return type Int64Index  1  2  3   dtype  int64   name  id    The returned Series  index   lenght is 3    Using transform   grouping transform min    The return looks like this   0    1 1    1 2    1 3    1 4    1 5    1 6    1 7    1 8    1 Name  price  dtype  int64  pandas core series Series   return type RangeIndex start 0  stop 9  step 1    The returned Series  index   length is 9       Both methods return a Series object  but the length of the first one is 3 and the length of the second one is 9   If you want to answer What is the minimum price paid by each customer  then the apply method is the more suitable one to choose   If you want to answer What is the difference between the amount paid for each transaction vs the minimum payment  then you want to use transform  because   test  minimum     grouping transform min    ceates an extra column filled with minimum payment test price - test minimum   returns the difference for each row   Apply does not work here simply because it returns a Series of size 3  but the original df s length is 9  You cannot integrate it back to the original df easily

User · Answer

you can use zscore to analyze the data in column C and D for outliers  where zscore is the series - series mean   series std      Use apply too create a user defined function for difference between C and D creating a new resulting dataframe    Apply uses the group result set  from scipy stats import zscore  columns     A    B    C    D   records       foo    one   0 162003  0 087469     bar    one   -1 156319  -1 5262719999999999     foo    two   0 833892  -1 666304          bar    three   -2 026673  -0 32205700000000004     foo    two   0 41145200000000004  -0 9543709999999999     bar    two   0 765878  -0 095968     foo    one   -0 65489  0 678091     foo    three   -1 789842  -1 130922    df   pd DataFrame from records records  columns columns  print df   standardize df groupby  A    C   D   transform zscore  print standardize  outliersC   standardize  C    lt -1 1     standardize  C   gt 1 1  outliersD   standardize  D    lt -1 1     standardize  D   gt 1 1   results df outliersC   outliersD  print results       Dataframe results    A      B         C         D    0  foo    one  0 162003  0 087469    1  bar    one -1 156319 -1 526272    2  foo    two  0 833892 -1 666304    3  bar  three -2 026673 -0 322057    4  foo    two  0 411452 -0 954371    5  bar    two  0 765878 -0 095968    6  foo    one -0 654890  0 678091    7  foo  three -1 789842 -1 130922   C and D transformed Z score            C         D  0  0 398046  0 801292  1 -0 300518 -1 398845  2  1 121882 -1 251188  3 -1 046514  0 519353  4  0 666781 -0 417997  5  1 347032  0 879491  6 -0 482004  1 492511  7 -1 704704 -0 624618    filtering using arbitrary ranges -1 and 1 for the z-score       A      B         C         D  1  bar    one -1 156319 -1 526272  2  foo    two  0 833892 -1 666304  5  bar    two  0 765878 -0 095968  6  foo    one -0 654890  0 678091  7  foo  three -1 789842 -1 130922     gt  gt  gt  gt  gt  gt  gt  gt  gt  gt  gt  gt  gt  Part 2   splitting   df groupby  A      look at how the data is grouped  for group name  group in splitting       print group name    def column difference gr         return gr  C  -gr  D     grouped splitting apply column difference   print grouped    A       bar  1    0 369953       3   -1 704616       5    0 861846  foo  0    0 074534       2    2 500196       4    1 365823       6   -1 332981       7   -0 658920

[python] Apply vs transform on a group object

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