Selecting a row of pandas series dataframe by integer index

Question

I am curious as to why df 2  is not supported  while df ix 2  and df 2 3  both work    In  26   df ix 2  Out 26    A    1 027680 B    1 514210 C   -1 466963 D   -0 162339 Name  2000-01-03 00 00 00  In  27   df 2 3  Out 27                      A        B         C         D 2000-01-03  1 02768  1 51421 -1 466963 -0 162339   I would expect df 2  to work the same way as df 2 3  to be consistent with Python indexing convention  Is there a design reason for not supporting indexing row by single integer

User · Answer

You can think DataFrame as a dict of Series. df[key] try to select the column index by key and returns a Series object.

However slicing inside of [] slices the rows, because it's a very common operation.

You can read the document for detail:

http://pandas.pydata.org/pandas-docs/stable/indexing.html#basics

User · Answer

I would normally go for  loc  iloc as suggested by Ted  but one may also select a row by tranposing the DataFrame  To stay in the example above  df T 2  gives you row 2 of df

User · Answer

You can take a look at the source code    DataFrame has a private function  slice   to slice the DataFrame  and it allows the parameter axis to determine which axis to slice  The   getitem     for DataFrame doesn t set the axis while invoking  slice    So the  slice   slice it by default axis 0   You can take a simple experiment  that might help you   print df  slice slice 0  2   print df  slice slice 0  2   0  print df  slice slice 0  2   1

User · Answer

you can loop through the data frame like this    for ad in range 1 dataframe c size       print dataframe c values ad

User · Answer

echoing  HYRY  see the new docs in 0 11  http   pandas pydata org pandas-docs stable indexing html  Here we have new operators   iloc to explicity support only integer indexing  and  loc to explicity support only label indexing  e g  imagine this scenario  In  1   df   pd DataFrame np random rand 5 2  index range 0 10 2  columns list  AB     In  2   df Out 2              A         B 0  1 068932 -0 794307 2 -0 470056  1 192211 4 -0 284561  0 756029 6  1 037563 -0 267820 8 -0 538478 -0 800654  In  5   df iloc  2   Out 5              A         B 4 -0 284561  0 756029  In  6   df loc  2   Out 6              A         B 2 -0 470056  1 192211      slices the rows  by label location  only

User · Answer

The primary purpose of the DataFrame indexing operator     is to select columns   When the indexing operator is passed a string or integer  it attempts to find a column with that particular name and return it as a Series   So  in the question above  df 2  searches for a column name matching the integer value 2  This column does not exist and a KeyError is raised     The DataFrame indexing operator completely changes behavior to select rows when slice notation is used  Strangely  when given a slice  the DataFrame indexing operator selects rows and can do so by integer location or by index label    df 2 3    This will slice beginning from the row with integer location 2 up to 3  exclusive of the last element  So  just a single row  The following selects rows beginning at integer location 6 up to but not including 20 by every third row   df 6 20 3    You can also use slices consisting of string labels if your DataFrame index has strings in it  For more details  see this solution on  iloc vs  loc   I almost never use this slice notation with the indexing operator as its not explicit and hardly ever used  When slicing by rows  stick with  loc  iloc

User · Answer

To index-based access to the pandas table  one can also consider numpy as array option to convert the table to Numpy array as  np df   df as matrix     and then  np df i     would work

[python] Selecting a row of pandas series/dataframe by integer index

Examples related to python

Examples related to pandas

Examples related to dataframe

Examples related to indexing