How to print a groupby object

Question

I want to print the result of grouping with Pandas   I have a dataframe   import pandas as pd df   pd DataFrame   A     one    one    two    three    three    one     B   range 6    print df          A  B 0    one  0 1    one  1 2    two  2 3  three  3 4  three  4 5    one  5   When printing after grouping by  A  I have the following   print df groupby  A      lt pandas core groupby DataFrameGroupBy object at 0x05416E90 gt    How can I print the dataframe grouped   If I do   print df groupby  A   head      I obtain the dataframe as if it was not grouped                A  B A                 one   0    one  0       1    one  1 two   2    two  2 three 3  three  3       4  three  4 one   5    one  5   I was expecting something like                A  B A                 one   0    one  0       1    one  1       5    one  5 two   2    two  2 three 3  three  3       4  three  4

User · Answer

df groupby  A   apply display   gives neat formatting

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If you re simply looking for a way to display it  you could use describe     grp   df groupby  colName   grp describe     This gives you a neat table

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Call list   on the GroupBy object  print list df groupby  A       gives you      one        A  B 0  one  0 1  one  1 5  one  5     three          A  B 3  three  3 4  three  4     two        A  B 2  two  2

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to print all  or arbitrarily many  lines of the grouped df    import pandas as pd pd set option  display max rows   500   grouped df   df group   var1    var2    print grouped df

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In addition to previous answers   Taking your example   df   pd DataFrame   A     one    one    two    three    three    one     B   range 6      Then simple 1 line code  df groupby  A   apply print

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Another simple alternative   for name of the group  group in grouped dataframe     print  name of the group     print  group

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Also  other simple alternative could be   gb   df groupby  A   gb count     or  gb get group your key

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In Jupyter Notebook  if you do the following  it prints a nice grouped version of the object  The apply method helps in creation of a multiindex dataframe  by    A     groupby  by  argument df groupby by  apply lambda a  a      Output               A  B A                 one   0    one  0       1    one  1       5    one  5 three 3  three  3       4  three  4 two   2    two  2  If you want the by column s  to not appear in the output  just drop the column s   like so  df groupby by  apply lambda a  a drop by  axis 1       Output           B A          one   0  0       1  1       5  5 three 3  3       4  4 two   2  2  Here  I am not sure as to why  iloc    does not work instead of     at the end  So  if there are some issues in future due to updates  or at present    iloc  len a   also works

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I confirmed that the behavior of head   changes between version 0 12 and 0 13  That looks like a bug to me  I created an issue   But a groupby operation doesn t actually return a DataFrame sorted by group  The  head   method is a little misleading here -- it s just a convenience feature to let you re-examine the object  in this case  df  that you grouped  The result of groupby is separate kind of object  a GroupBy object  You must apply  transform  or filter to get back to a DataFrame or Series   If all you wanted to do was sort by the values in columns A  you should use df sort  A

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you cannot see the groupBy data directly by print statement but you can see by iterating over the group using for loop  try this code to see the group by data  group   df groupby  A    group variable contains groupby data for A A df in group    A is your column and A df is group of one kind at a time   print A    print A df    you will get an output after trying this as a groupby result  I hope it helps

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I found a tricky way  just for brainstorm  see the code   df  a     df  A      create a shadow column for MultiIndexing df sort values  A   inplace True  df set index   A   a    inplace True  print df    the output                B A     a one   one    0       one    1       one    5 three three  3       three  4 two   two    2   The pros is so easy to print  as it returns a dataframe  instead of Groupby Object  And the output looks nice  While the con is that it create a series of redundant data

User · Answer

df groupby  key you want to group by   apply print  As mentioned by an other member  this is the easiest and simplest solution to visualize a groupby object

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Thanks to Surya for good insights  I d clean up his solution and simply do   for key  value in df groupby  A        print key  value

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Simply do   grouped df   df groupby  A    for key  item in grouped df      print grouped df get group key     n n     This also works   grouped df   df groupby  A       gb   grouped df groups  for key  values in gb iteritems        print df ix values     n n     For selective key grouping  Insert the keys you want inside the key list from gb  in following  using gb keys    For Example   gb   grouped df groups gb keys    key list from gb    key1  key2  key3   for key  values in gb items        if key in key list from gb          print df ix values     n

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In python 3  k   None for name of the group  group in dict df group       if k    name of the group           print    n   name of the group          print                n       print  group      k   name of the group   In more interactive way

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This is a better general purpose answer   This function will print all group names and values  or optionally selects one or more groups for display  def print pd groupby X  grp None          Display contents of a Panda groupby object      param X  Pandas groupby object      param grp  a list with one or more group names             if grp is None          for k i in X              print  quot group  quot   k              print i      else          for j in grp              print  quot group  quot   j              print X get group j    In your example case  here s session output In  116   df   pd DataFrame   A     one    one    two    three    three    one     B   range 6     In  117   dfg   df groupby  A    In  118   print pd groupby dfg  group  one      A  B 0  one  0 1  one  1 5  one  5 group  three        A  B 3  three  3 4  three  4 group  two      A  B 2  two  2  In  119   print pd groupby dfg  grp     quot one quot    quot two quot    group  one      A  B 0  one  0 1  one  1 5  one  5 group  two      A  B 2  two  2  This is a better answer because a function is re-usable content  put it in your package or function collection and never re-write that  quot scriptish quot  approach again  IMHO  something like this should be a built in method in Pandas groupby

[python] How to print a groupby object

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