Pandas - Get first row value of a given column

Question

This seems like a ridiculously easy question    but I m not seeing the easy answer I was expecting   So  how do I get the value at an nth row of a given column in Pandas   I am particularly interested in the first row  but would be interested in a more general practice as well    For example  let s say I want to pull the 1 2 value in Btime as a variable    Whats the right way to do this   df test       ATime   X   Y   Z   Btime  C   D   E 0    1 2  2  15   2    1 2  12  25  12 1    1 4  3  12   1    1 3  13  22  11 2    1 5  1  10   6    1 4  11  20  16 3    1 6  2   9  10    1 7  12  29  12 4    1 9  1   1   9    1 9  11  21  19 5    2 0  0   0   0    2 0   8  10  11 6    2 4  0   0   0    2 4  10  12  15

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In a general way  if you want to pick up the first N rows from the J column from pandas dataframe the best way to do this is   data   dataframe 0 N    J

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Note that the answer from  unutbu will be correct until you want to set the value to something new  then it will not work if your dataframe is a view   In  4   df   pd DataFrame   foo  list  ABC     index  0 2 1   In  5   df  bar     100 In  6   df  bar   iloc 0    99  opt local Library Frameworks Python framework Versions 2 7 lib python2 7 site-packages pandas-0 16 0 19 g8d2818e-py2 7-macosx-10 9-x86 64 egg pandas core indexing py 118  SettingWithCopyWarning  A value is trying to be set on a copy of a slice from a DataFrame  See the the caveats in the documentation  http   pandas pydata org pandas-docs stable indexing html indexing-view-versus-copy   self  setitem with indexer indexer  value    Another approach that will consistently work with both setting and getting is   In  7   df loc df index 0    foo   Out 7    A  In  8   df loc df index 0    bar     99 In  9   df Out 9     foo  bar 0   A   99 2   B  100 1   C  100

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Another way of getting the first row and preserving the index   x   df first  d     Returns the first day   3d  gives first three days

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To select the ith row  use iloc   In  31   df test iloc 0  Out 31    ATime     1 2 X         2 0 Y        15 0 Z         2 0 Btime     1 2 C        12 0 D        25 0 E        12 0 Name  0  dtype  float64   To select the ith value in the Btime column you could use   In  30   df test  Btime   iloc 0  Out 30   1 2     There is a difference between df test  Btime   iloc 0   recommended  and df test iloc 0   Btime     DataFrames store data in column-based blocks  where each block has a single dtype   If you select by column first  a view can be returned  which is quicker than returning a copy  and the original dtype is preserved  In contrast  if you select by row first  and if the DataFrame has columns of different dtypes  then Pandas copies the data into a new Series of object dtype  So selecting columns is a bit faster than selecting rows  Thus  although df test iloc 0   Btime   works  df test  Btime   iloc 0  is a little bit more efficient   There is a big difference between the two when it comes to assignment  df test  Btime   iloc 0    x affects df test  but df test iloc 0   Btime   may not  See below for an explanation of why   Because a subtle difference in the order of indexing makes a big difference in behavior  it is better to use single indexing assignment   df iloc 0  df columns get loc  Btime      x     df iloc 0  df columns get loc  Btime      x  recommended    The recommended way to assign new values to a DataFrame is to avoid chained indexing  and instead use the method shown by andrew   df loc df index n    Btime     x   or   df iloc n  df columns get loc  Btime      x   The latter method is a bit faster  because df loc has to convert the row and column labels to positional indices  so there is a little less conversion necessary if you use df iloc instead     df  Btime   iloc 0    x works  but is not recommended   Although this works  it is taking advantage of the way DataFrames are currently implemented  There is no guarantee that Pandas has to work this way in the future  In particular  it is taking advantage of the fact that  currently  df  Btime   always returns a view  not a copy  so df  Btime   iloc n    x can be used to assign a new value at the nth location of the Btime column of df   Since Pandas makes no explicit guarantees about when indexers return a view versus a copy  assignments that use chained indexing generally always raise a SettingWithCopyWarning even though in this case the assignment succeeds in modifying df   In  22   df   pd DataFrame   foo  list  ABC     index  0 2 1   In  24   df  bar     100 In  25   df  bar   iloc 0    99  home unutbu data binky bin ipython 1  SettingWithCopyWarning   A value is trying to be set on a copy of a slice from a DataFrame  See the caveats in the documentation  http   pandas pydata org pandas-docs stable indexing html indexing-view-versus-copy   self  setitem with indexer indexer  value   In  26   df Out 26      foo  bar 0   A   99   lt -- assignment succeeded 2   B  100 1   C  100     df iloc 0   Btime     x does not work   In contrast  assignment with df iloc 0   bar     123 does not work because df iloc 0  is returning a copy   In  66   df iloc 0   bar     123  home unutbu data binky bin ipython 1  SettingWithCopyWarning   A value is trying to be set on a copy of a slice from a DataFrame  See the caveats in the documentation  http   pandas pydata org pandas-docs stable indexing html indexing-view-versus-copy  In  67   df Out 67      foo  bar 0   A   99   lt -- assignment failed 2   B  100 1   C  100     Warning  I had previously suggested df test ix i   Btime    But this is not guaranteed to give you the ith value since ix tries to index by label before trying to index by position  So if the DataFrame has an integer index which is not in sorted order starting at 0  then using ix i  will return the row labeled i rather than the ith row  For example   In  1   df   pd DataFrame   foo  list  ABC     index  0 2 1    In  2   df Out 2      foo 0   A 2   B 1   C  In  4   df ix 1   foo   Out 4    C

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To get e g the value from column  test  and row 1 it works like  df   test    values 0  0    as only df   test    values 0  gives back a array

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To access a single value you can use the method iat that is much faster than iloc  df  Btime   iat 0   Output  1 2

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df iloc 0  head 1  - First data set only from entire first row  df iloc 0  - Entire First row in column

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Another way to do this   first value   df  Btime   values 0    This way seems to be faster than using  iloc   In  1    timeit -n 1000 df  Btime   values 20  5 82   s    142 ns per loop  mean    std  dev  of 7 runs  1000 loops each   In  2    timeit -n 1000 df  Btime   iloc 20  29 2   s    1 28   s per loop  mean    std  dev  of 7 runs  1000 loops each

[python] Pandas - Get first row value of a given column

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