Adding new column to existing DataFrame in Python pandas

Question

I have the following indexed DataFrame with named columns and rows not- continuous numbers:

          a         b         c         d
2  0.671399  0.101208 -         


        

Answer 1

It seems that in recent Pandas versions the way to go is to use df.assign:

df1 = df1.assign(e=np.random.randn(sLength))

It doesn't produce SettingWithCopyWarning.

Answer 2

Use the original df1 indexes to create the series:

df1['e'] = pd.Series(np.random.randn(sLength), index=df1.index)

---

Edit 2015
Some reported getting the SettingWithCopyWarning with this code.
However, the code still runs perfectly with the current pandas version 0.16.1.

>>> sLength = len(df1['a'])
>>> df1
          a         b         c         d
6 -0.269221 -0.026476  0.997517  1.294385
8  0.917438  0.847941  0.034235 -0.448948

>>> df1['e'] = pd.Series(np.random.randn(sLength), index=df1.index)
>>> df1
          a         b         c         d         e
6 -0.269221 -0.026476  0.997517  1.294385  1.757167
8  0.917438  0.847941  0.034235 -0.448948  2.228131

>>> p.version.short_version
'0.16.1'

The SettingWithCopyWarning aims to inform of a possibly invalid assignment on a copy of the Dataframe. It doesn't necessarily say you did it wrong (it can trigger false positives) but from 0.13.0 it let you know there are more adequate methods for the same purpose. Then, if you get the warning, just follow its advise: Try using .loc[row_index,col_indexer] = value instead

>>> df1.loc[:,'f'] = pd.Series(np.random.randn(sLength), index=df1.index)
>>> df1
          a         b         c         d         e         f
6 -0.269221 -0.026476  0.997517  1.294385  1.757167 -0.050927
8  0.917438  0.847941  0.034235 -0.448948  2.228131  0.006109
>>> 

In fact, this is currently the more efficient method as described in pandas docs

---

Edit 2017

As indicated in the comments and by @Alexander, currently the best method to add the values of a Series as a new column of a DataFrame could be using assign:

df1 = df1.assign(e=pd.Series(np.random.randn(sLength)).values)

Answer 3

Before assigning a new column, if you have indexed data, you need to sort the index. At least in my case I had to:

data.set_index(['index_column'], inplace=True)
"if index is unsorted, assignment of a new column will fail"        
data.sort_index(inplace = True)
data.loc['index_value1', 'column_y'] = np.random.randn(data.loc['index_value1', 'column_x'].shape[0])

Answer 4

For the sake of completeness - yet another solution using DataFrame.eval() method:

Data:

In [44]: e
Out[44]:
0    1.225506
1   -1.033944
2   -0.498953
3   -0.373332
4    0.615030
5   -0.622436
dtype: float64

In [45]: df1
Out[45]:
          a         b         c         d
0 -0.634222 -0.103264  0.745069  0.801288
4  0.782387 -0.090279  0.757662 -0.602408
5 -0.117456  2.124496  1.057301  0.765466
7  0.767532  0.104304 -0.586850  1.051297
8 -0.103272  0.958334  1.163092  1.182315
9 -0.616254  0.296678 -0.112027  0.679112

Solution:

In [46]: df1.eval("e = @e.values", inplace=True)

In [47]: df1
Out[47]:
          a         b         c         d         e
0 -0.634222 -0.103264  0.745069  0.801288  1.225506
4  0.782387 -0.090279  0.757662 -0.602408 -1.033944
5 -0.117456  2.124496  1.057301  0.765466 -0.498953
7  0.767532  0.104304 -0.586850  1.051297 -0.373332
8 -0.103272  0.958334  1.163092  1.182315  0.615030
9 -0.616254  0.296678 -0.112027  0.679112 -0.622436

Answer 5

If the column you are trying to add is a series variable then just :

df["new_columns_name"]=series_variable_name #this will do it for you

This works well even if you are replacing an existing column.just type the new_columns_name same as the column you want to replace.It will just overwrite the existing column data with the new series data.

Answer 6

to insert a new column at a given location (0 <= loc <= amount of columns) in a data frame, just use Dataframe.insert:

DataFrame.insert(loc, column, value)

Therefore, if you want to add the column e at the end of a data frame called df, you can use:

e = [-0.335485, -1.166658, -0.385571]    
DataFrame.insert(loc=len(df.columns), column='e', value=e)

value can be a Series, an integer (in which case all cells get filled with this one value), or an array-like structure

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.insert.html