Insert a row to pandas dataframe

Question

I have a dataframe   s1   pd Series  5  6  7   s2   pd Series  7  8  9    df   pd DataFrame  list s1   list s2     columns      A    B    C        A  B  C 0  5  6  7 1  7  8  9   2 rows x 3 columns    and I need to add a first row  2  3  4  to get      A  B  C 0  2  3  4 1  5  6  7 2  7  8  9   I ve tried append   and concat   functions but can t find the right way how to do that   How to add insert series to dataframe

User · Answer

The simplest way add a row in a pandas data frame is:

DataFrame.loc[ location of insertion ]= list( )

Example :

DF.loc[ 9 ] = [ ´Pepe’ , 33, ´Japan’ ]

NB: the length of your list should match that of the data frame.

User · Answer

Not sure how you were calling concat   but it should work as long as both objects are of the same type  Maybe the issue is that you need to cast your second vector to a dataframe  Using the df that you defined the following works for me   df2   pd DataFrame   2 3 4    columns   A   B   C    pd concat  df2  df

User · Answer

Just assign row to a particular index  using loc   df loc -1     2  3  4     adding a row  df index   df index   1    shifting index  df   df sort index      sorting by index  And you get  as desired      A  B  C  0  2  3  4  1  5  6  7  2  7  8  9  See in Pandas documentation Indexing  Setting with enlargement

User · Answer

I put together a short function that allows for a little more flexibility when inserting a row   def insert row idx  df  df insert       dfA   df iloc  idx        dfB   df iloc idx          df   dfA append df insert  append dfB  reset index drop   True       return df   which could be further shortened to   def insert row idx  df  df insert       return df iloc  idx    append df insert  append df iloc idx      reset index drop   True    Then you could use something like   df   insert row 2  df  df new    where 2 is the index position in df where you want to insert df new

User · Answer

It just came up to me that maybe T attribute is a valid choice  Transpose  can get away from the somewhat misleading df loc -1     2  3  4  as  flow2k mentioned  and it is suitable for more universal situation such as you want to insert   2  3  4  before arbitrary row  which is hard for concat   append   to achieve  And there s no need to bare the trouble defining and debugging a function  a   df T a insert 0  anyName  value  2 3 4     just give insert   any column name you want  we ll rename it  a rename columns dict zip a columns  i for i in range a shape 1      inplace True    set inplace to a Boolean as you need  df a T df      A   B   C 0   2   3   4 1   5   6   7 2   7   8   9  I guess this can partly explain  MattCochrane  s complaint about why pandas doesn t have a method to insert a row like insert   does

User · Answer

You can simply append the row to the end of the DataFrame  and then adjust the index   For instance   df   df append pd DataFrame   2 3 4   columns df columns  ignore index True  df index    df index   1    len df  df   df sort index     Or use concat as   df   pd concat  pd DataFrame   1 2 3 4 5 6   columns df columns  df  ignore index True

User · Answer

We can use numpy insert  This has the advantage of flexibility  You only need to specify the index you want to insert to   s1   pd Series  5  6  7   s2   pd Series  7  8  9    df   pd DataFrame  list s1   list s2     columns      A    B    C     pd DataFrame np insert df values  0  values  2  3  4   axis 0        0   1   2 0   2   3   4 1   5   6   7 2   7   8   9   For np insert df values  0  values  2  3  4   axis 0   0 tells the function the place index you want to place the new values

User · Answer

Below would be the best way to insert a row into pandas dataframe without sorting and reseting an index   import pandas as pd  df   pd DataFrame columns   a   b   c     def insert df  row       insert loc   df index max        if pd isna insert loc           df loc 0    row     else          df loc insert loc   1    row  insert df  2 3 4   insert df  8 9 0   print df

User · Answer

It is pretty simple to add a row into a pandas DataFrame   Create a regular Python dictionary with the same columns names as your Dataframe   Use pandas append   method and pass in the name of your dictionary  where  append   is a method on DataFrame instances   Add ignore index True right after your dictionary name

User · Answer

One way to achieve this is    gt  gt  gt  pd DataFrame np array   2  3  4     columns   A    B    C    append df  ignore index True  Out 330       A  B  C 0  2  3  4 1  5  6  7 2  7  8  9   Generally  it s easiest to append dataframes  not series  In your case  since you want the new row to be  on top   with starting id   and there is no function pd prepend    I first create the new dataframe and then append your old one   ignore index will ignore the old ongoing index in your dataframe and ensure that the first row actually starts with index 1 instead of restarting with index 0   Typical Disclaimer  Cetero censeo     appending rows is a quite inefficient operation  If you care about performance and can somehow ensure to first create a dataframe with the correct  longer  index and then just inserting the additional row into the dataframe  you should definitely do that  See    gt  gt  gt  index   np array  0  1  2    gt  gt  gt  df2   pd DataFrame columns   A    B    C    index index   gt  gt  gt  df2 loc 0 1     list s1   list s2    gt  gt  gt  df2 Out 336         A    B    C 0    5    6    7 1    7    8    9 2  NaN  NaN  NaN  gt  gt  gt  df2   pd DataFrame columns   A    B    C    index index   gt  gt  gt  df2 loc 1      list s1   list s2     So far  we have what you had as df    gt  gt  gt  df2 Out 339         A    B    C 0  NaN  NaN  NaN 1    5    6    7 2    7    8    9   But now you can easily insert the row as follows  Since the space was preallocated  this is more efficient     gt  gt  gt  df2 loc 0    np array  2  3  4    gt  gt  gt  df2 Out 341       A  B  C 0  2  3  4 1  5  6  7 2  7  8  9

User · Answer

concat   seems to be a bit faster than last row insertion and reindexing  In case someone would wonder about the speed of two top approaches   In  x     timeit           df   pd DataFrame columns   a   b              for i in range 10000                 df loc -1     1 2                df index   df index   1               df   df sort index     17 1 s    705 ms per loop  mean    std  dev  of 7 runs  1 loop each   In  y     timeit           df   pd DataFrame columns   a    b              for i in range 10000                 df   pd concat  pd DataFrame   1 2    columns df columns   df     6 53 s    127 ms per loop  mean    std  dev  of 7 runs  1 loop each

User · Answer

this might seem overly simple but its incredible that a simple insert new row function isn t built in  i ve read a lot about appending a new df to the original  but i m wondering if this would be faster    df loc 0     row1data  blah     i   len df    1 df loc i     row2data  blah

[python] Insert a row to pandas dataframe

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