How to add a single item to a Pandas Series

Question

How Do I add a single item to a serialized panda series  I know it s not the most efficient way memory wise  but i still need to do that   Something along    gt  gt  x   Series    gt  gt  N   4  gt  gt  for i in xrange N    gt  gt      x some appending function i  2       gt  gt  print x  0   0 1   1 2   4 3   9   also  how can i add a single row to a pandas DataFrame

User · Answer

If you have an index and value  Then you can add to Series as   obj   Series  4 7 -5 3   obj index   a    b    c    d    obj  e     181   this will add a new value to Series  at the end of Series

User · Answer

As far as  joaqin s solution is deprecated  because set value method will be removed in a future pandas release  I would mention the other option to add a single item to pandas series  using  at   accessor    gt  gt  gt  import pandas as pd  gt  gt  gt  x   pd Series    gt  gt  gt  N   4  gt  gt  gt  for i in range N           x at i    i  2   It produces the same output    gt  gt  gt  print x  0    0 1    1 2    4 3    9

User · Answer

How to add single item  This is not very effective but follows what you are asking for   x   p Series   N   4 for i in xrange N      x   x set value i  i  2    produces x   0    0 1    1 2    4 3    9   Obviously there are better ways to generate this series in only one shot     For your second question check answer and references of SO question add one row in a pandas DataFrame

User · Answer

TLDR  do not append items to a series one by one  better extend with an ordered collection  I think the question in its current form is a bit tricky  And the accepted answer does answer the question  But the more I use pandas  the more I understand that it s a bad idea to append items to a Series one by one  I ll try to explain why for pandas beginners   You might think that appending data to a given Series might allow you to reuse some resources  but in reality a Series is just a container that stores a relation between an index and a values array  Each is a numpy array under the hood  and the index is immutable  When you add to Series an item with a label that is missing in the index  a new index with size n 1 is created  and a new values values array of the same size  That means that when you append items one by one  you create two more arrays of the n 1 size on each step   By the way  you can not append a new item by position  you will get an IndexError  and the label in an index does not have to be unique  that is when you assign a value with a label  you assign the value to all existing items with the the label  and a new row is not appended in this case  This might lead to subtle bugs   The moral of the story is that you should not append data one by one  you should better extend with an ordered collection  The problem is that you can not extend a Series inplace  That is why it is better to organize your code so that you don t need to update a specific instance of a Series by reference   If you create labels yourself and they are increasing  the easiest way is to add new items to a dictionary  then create a new Series from the dictionary  it sorts the keys  and append the Series to an old one  If the keys are not increasing  then you will need to create two separate lists for the new labels and the new values   Below are some code samples   In  1   import pandas as pd In  2   import numpy as np  In  3   s   pd Series np arange 4   2  index np arange 4    In  4   s Out 4   0    0 1    1 2    4 3    9 dtype  int64  In  6   id s index   id s values  Out 6    4470549648  4470593296    When we update an existing item  the index and the values array stay the same  if you do not change the type of the value   In  7   s 2    14    In  8   id s index   id s values  Out 8    4470549648  4470593296    But when you add a new item  a new index and a new values array is generated   In  9   s 4    16  In  10   s Out 10   0     0 1     1 2    14 3     9 4    16 dtype  int64  In  11   id s index   id s values  Out 11    4470548560  4470595056    That is if you are going to append several items  collect them in a dictionary  create a Series  append it to the old one and save the result   In  13   new items    item  item  2 for item in range 5  7    In  14   s2   pd Series new items   In  15   s2    keys are guaranteed to be sorted  Out 15   5    25 6    36 dtype  int64  In  16   s   s append s2   s Out 16   0     0 1     1 2    14 3     9 4    16 5    25 6    36 dtype  int64

User · Answer

ser1   pd Sereis np linspace 1  10  2   element   np nan ser1   ser1 append pd Series element

User · Answer

Here is another thought n for appending multiple items in one line without changing the name of series  However  this may be not as efficient as the other answer    gt  gt  gt  df   pd Series np random random 5   name  random    gt  gt  gt  df  0    0 363885 1    0 402623 2    0 450449 3    0 172917 4    0 983481 Name  random  dtype  float64    gt  gt  gt  df to frame   T assign a 3  b 2  c 5  squeeze    0    0 363885 1    0 402623 2    0 450449 3    0 172917 4    0 983481 a    3 000000 b    2 000000 c    5 000000 Name  random  dtype  float64

User · Answer

You can use the append function to add another element to it  Only  make a series of the new element  before you append it   test   test append pd Series 200  index  101

User · Answer

Adding to joquin s answer the following form might be a bit cleaner  at least nicer to read    x   p Series   N   4 for i in xrange N      x i    i  2   which would produce the same output  also  a bit less orthodox but if you wanted to simply add a single element to the end   x p Series   value to append 5 x len x   value to append

[python] How to add a single item to a Pandas Series

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