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pythonpandasseries

Add item to pandas.Series?


I want to add an integer to my pandas.Series
Here is my code:

import pandas as pd
input = pd.Series([1,2,3,4,5])
input.append(6)

When i run this, i get the following error:

Traceback (most recent call last):
  File "<pyshell#9>", line 1, in <module>
    f.append(6)
  File "C:\Python33\lib\site-packages\pandas\core\series.py", line 2047, in append
    verify_integrity=verify_integrity)
  File "C:\Python33\lib\site-packages\pandas\tools\merge.py", line 878, in concat
    verify_integrity=verify_integrity)
  File "C:\Python33\lib\site-packages\pandas\tools\merge.py", line 954, in __init__
    self.new_axes = self._get_new_axes()
  File "C:\Python33\lib\site-packages\pandas\tools\merge.py", line 1146, in _get_new_axes
    concat_axis = self._get_concat_axis()
  File "C:\Python33\lib\site-packages\pandas\tools\merge.py", line 1163, in _get_concat_axis
    indexes = [x.index for x in self.objs]
  File "C:\Python33\lib\site-packages\pandas\tools\merge.py", line 1163, in <listcomp>
    indexes = [x.index for x in self.objs]
AttributeError: 'int' object has no attribute 'index'

How can I fix that?


Solution

  • Convert appended item to Series:

    >>> ds = pd.Series([1,2,3,4,5]) 
    >>> ds.append(pd.Series([6]))
    0    1
    1    2
    2    3
    3    4
    4    5
    0    6
    dtype: int64
    

    or use DataFrame:

    >>> df = pd.DataFrame(ds)
    >>> df.append([6], ignore_index=True)
       0
    0  1
    1  2
    2  3
    3  4
    4  5
    5  6
    

    and last option if your index is without gaps,

    >>> ds.set_value(max(ds.index) + 1,  6)
    0    1
    1    2
    2    3
    3    4
    4    5
    5    6
    dtype: int64
    

    And you can use numpy as a last resort:

    >>> import numpy as np
    >>> pd.Series(np.concatenate((ds.values, [6])))
    

    Update

    In most recent versions, pandas.Series.append method has been deprecated, a decision that sparked some controversy. Consequently this answer is no longer valid, as well as the question. Consider using pandas.concat as a partial substitute.