Selecting multiple columns in a Pandas dataframe

Question

I have data in different columns  but I don t know how to extract it to save it in another variable  index  a   b   c 1      2   3   4 2      3   4   5  How do I select  a    b  and save it in to df1  I tried df1   df  a   b   df1   df ix     a   b    None seem to work

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Starting with 0 21 0  using  loc or    with a list with one or more missing labels is deprecated in favor of  reindex  So  the answer to your question is  df1   df reindex columns   b   c     In prior versions  using  loc list-of-labels  would work as long as at least one of the keys was found  otherwise it would raise a KeyError   This behavior is deprecated and now shows a warning message  The recommended alternative is to use  reindex    Read more at Indexing and Selecting Data

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Assuming your column names  df columns  are   index   a   b   c    then the data you want is in the third and fourth columns  If you don t know their names when your script runs  you can do this newdf   df df columns 2 4     Remember  Python is zero-offset  The  quot third quot  entry is at slot two   As EMS points out in his answer  df ix slices columns a bit more concisely  but the  columns slicing interface might be more natural  because it uses the vanilla one-dimensional Python list indexing slicing syntax  Warning   index  is a bad name for a DataFrame column  That same label is also used for the real df index attribute  an Index array  So your column is returned by df  index   and the real DataFrame index is returned by df index  An Index is a special kind of Series optimized for lookup of its elements  values  For df index it s for looking up rows by their label  That df columns attribute is also a pd Index array  for looking up columns by their labels

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df   a    b       Select all rows of  a  and  b column  df loc 0 10    a    b       Index 0 to 10 select column  a  and  b  df loc 0 10   a   b      Index 0 to 10 select column  a  to  b  df iloc 0 10  3 5     Index 0 to 10 and column 3 to 5 df iloc 3  3 5     Index 3 of column 3 to 5

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The different approaches discussed in the previous answers are based on the assumption that either the user knows column indices to drop or subset on  or the user wishes to subset a dataframe using a range of columns  for instance between  C     E    pandas DataFrame drop   is certainly an option to subset data based on a list of columns defined by user  though you have to be cautious that you always use copy of dataframe and inplace parameters should not be set to True    Another option is to use pandas columns difference    which does a set difference on column names  and returns an index type of array containing desired columns  Following is the solution  df   pd DataFrame   2 3 4    3 4 5    columns   a   b   c    index  1 2   columns for differencing     a   df1   df copy   df columns difference columns for differencing   print df1   The output would be      b   c 1   3   4 2   4   5

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You could provide a list of columns to be dropped and return back the DataFrame with only the columns needed using the drop   function on a Pandas DataFrame   Just saying  colsToDrop     a   df drop colsToDrop  axis 1    would return a DataFrame with just the columns b and c   The drop method is documented here

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In the latest version of Pandas there is an easy way to do exactly this  Column names  which are strings  can be sliced in whatever manner you like  columns     b    c   df1   pd DataFrame df  columns columns

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The column names  which are strings  cannot be sliced in the manner you tried  Here you have a couple of options  If you know from context which variables you want to slice out  you can just return a view of only those columns by passing a list into the   getitem   syntax  the    s   df1   df   a    b     Alternatively  if it matters to index them numerically and not by their name  say your code should automatically do this without knowing the names of the first two columns  then you can do this instead  df1   df iloc    0 2    Remember that Python does not slice inclusive of the ending index   Additionally  you should familiarize yourself with the idea of a view into a Pandas object vs  a copy of that object  The first of the above methods will return a new copy in memory of the desired sub-object  the desired slices   Sometimes  however  there are indexing conventions in Pandas that don t do this and instead give you a new variable that just refers to the same chunk of memory as the sub-object or slice in the original object  This will happen with the second way of indexing  so you can modify it with the copy   function to get a regular copy  When this happens  changing what you think is the sliced object can sometimes alter the original object  Always good to be on the look out for this  df1   df iloc 0  0 2  copy     To avoid the case where changing df1 also changes df  To use iloc  you need to know the column positions  or indices   As the column positions may change  instead of hard-coding indices  you can use iloc along with get loc function of columns method of dataframe object to obtain column indices   df columns get loc c   c for idx  c in enumerate df columns    Now you can use this dictionary to access columns through names and using iloc

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In  39   df Out 39       index  a  b  c 0      1  2  3  4 1      2  3  4  5  In  40   df1   df   b    c     In  41   df1 Out 41       b  c 0  3  4 1  4  5

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One different and easy approach  iterating rows Using iterows  df1   pd DataFrame     Creating an empty dataframe  for index i in df iterrows        df1 loc index   A     df loc index   A       df1 loc index   B     df loc index   B       df1 head

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You can use Pandas  I create the DataFrame  import pandas as pd df   pd DataFrame   1  2 5    5 4  5    7 7  8    7 6 9                      index   Jane    Peter   Alex   Ann                      columns   Test 1    Test 2    Test 3     The DataFrame         Test 1  Test 2  Test 3 Jane        1       2       5 Peter       5       4       5 Alex        7       7       8 Ann         7       6       9  To select one or more columns by name  df   Test 1    Test 3            Test 1  Test 3 Jane        1       5 Peter       5       5 Alex        7       8 Ann         7       9  You can also use  df Test 2  And you get column Test 2  Jane     2 Peter    4 Alex     7 Ann      6  You can also select columns and rows from these rows using  loc    This is called  quot slicing quot   Notice that I take from column Test 1 to Test 3  df loc     Test 1   Test 3    The  quot Slice quot  is         Test 1  Test 2  Test 3 Jane        1       2       5 Peter       5       4       5 Alex        7       7       8 Ann         7       6       9  And if you just want Peter and Ann from columns Test 1 and Test 3  df loc   Peter    Ann      Test 1    Test 3     You get         Test 1  Test 3 Peter       5       5 Ann         7       9

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I ve seen several answers on that  but one remained unclear to me  How would you select those columns of interest  The answer to that is that if you have them gathered in a list  you can just reference the columns using the list  Example print extracted features shape  print extracted features    63     f000004   f000005   f000006   f000014   f000039   f000040   f000043    f000047   f000048   f000049   f000050   f000051   f000052   f000053    f000054   f000055   f000056   f000057   f000058   f000059   f000060    f000061   f000062   f000063   f000064   f000065   f000066   f000067    f000068   f000069   f000070   f000071   f000072   f000073   f000074    f000075   f000076   f000077   f000078   f000079   f000080   f000081    f000082   f000083   f000084   f000085   f000086   f000087   f000088    f000089   f000090   f000091   f000092   f000093   f000094   f000095    f000096   f000097   f000098   f000099   f000100   f000101   f000103    I have the following list NumPy array extracted features  specifying 63 columns  The original dataset has 103 columns  and I would like to extract exactly those  then I would use dataset extracted features   And you will end up with this  This something you would use quite often in machine learning  more specifically  in feature selection   I would like to discuss other ways too  but I think that has already been covered by other Stack Overflower users

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To select multiple columns  extract and view them thereafter  df is previously named data frame  than create new data frame df1  and select the columns A to D which you want to extract and view  df1   pd DataFrame data frame  columns   Column A    Column B    Column C    Column D    df1  All required columns will show up

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With Pandas  wit column names dataframe   column1   column2     to select by iloc and specific columns with index number  dataframe iloc    1 2    with loc column names can be used like dataframe loc     column1   column2

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You can use the pandas DataFrame filter method to either filter or reorder columns like this  df1   df filter   a    b     This is also very useful when you are chaining methods

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Try to use pandas DataFrame get  see the documentation   import pandas as pd import numpy as np  dates   pd date range  20200102   periods 6  df   pd DataFrame np random randn 6  4   index dates  columns list  ABCD    df get   A    C

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As of version 0 11 0  columns can be sliced in the manner you tried using the  loc indexer  df loc     C   E    is equivalent to df   C    D    E       or df loc      C    D    E     and returns columns C through E   A demo on a randomly generated DataFrame  import pandas as pd import numpy as np np random seed 5  df   pd DataFrame np random randint 100  size  100  6                      columns list  ABCDEF                      index   R    format i  for i in range 100    df head    Out       A   B   C   D   E   F R0  99  78  61  16  73   8 R1  62  27  30  80   7  76 R2  15  53  80  27  44  77 R3  75  65  47  30  84  86 R4  18   9  41  62   1  82  To get the columns from C to E  note that unlike integer slicing   E  is included in the columns   df loc     C   E    Out        C   D   E R0   61  16  73 R1   30  80   7 R2   80  27  44 R3   47  30  84 R4   41  62   1 R5    5  58   0      The same works for selecting rows based on labels  Get the rows  R6  to  R10  from those columns  df loc  R6   R10    C   E    Out        C   D   E R6   51  27  31 R7   83  19  18 R8   11  67  65 R9   78  27  29 R10   7  16  94   loc also accepts a Boolean array so you can select the columns whose corresponding entry in the array is True  For example  df columns isin list  BCD    returns array  False   True   True   True  False  False   dtype bool  - True if the column name is in the list   B    C    D    False  otherwise  df loc    df columns isin list  BCD      Out        B   C   D R0   78  61  16 R1   27  30  80 R2   53  80  27 R3   65  47  30 R4    9  41  62 R5   78   5  58

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I found this method to be very useful    iloc row slicing  column slicing  surveys df iloc  0 3  1 4   More details can be found here

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You can also use df pop     gt  gt  gt  df   pd DataFrame    falcon    bird      389 0                            parrot    bird       24 0                            lion      mammal     80 5                            monkey    mammal   np nan                          columns   name    class    max speed     gt  gt  gt  df      name   class  max speed 0  falcon    bird      389 0 1  parrot    bird       24 0 2    lion  mammal       80 5 3  monkey  mammal   gt  gt  gt  df pop  class   0      bird 1      bird 2    mammal 3    mammal Name  class  dtype  object   gt  gt  gt  df      name  max speed 0  falcon      389 0 1  parrot       24 0 2    lion       80 5 3  monkey        NaN  Please use df pop c

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If you want to get one element by row index and column name  you can do it just like df  b   0   It is as simple as you can imagine  Or you can use df ix 0  b   - mixed usage of index and label  Note  Since v0 20  ix has been deprecated in favour of loc   iloc

[python] Selecting multiple columns in a Pandas dataframe

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