Modify pandas dataframe values with numpy array

Question

I'm trying to modify the values field of a pandas data frame with a numpy array [same size]. something like this does not work

import pandas as pd
# create 2d numpy array, called arr
df = pd.DataFrame(arr, columns=some_list_of_names)
df.values = myfunction(arr)

any alternatives?

Answer 1

The .values attribute is often a copy - especially for mixed dtypes (so assignment to it is not guaranteed to work - in newer versions of pandas this will raise).

You should assign to the specific columns (note the order is important).

df = pd.DataFrame(arr, columns=some_list_of_names)
df[some_list_of_names] = myfunction(arr)

---

Example (in pandas 0.15.2):

In [11]: df = pd.DataFrame([[1, 2.], [3, 4.]], columns=['a', 'b'])

In [12]: df.values = [[5, 6], [7, 8]]
AttributeError: can't set attribute

In [13]: df[['a', 'b']] = [[5, 6], [7, 8]]

In [14]: df
Out[14]:
   a  b
0  5  6
1  7  8

In [15]: df[['b', 'a']] = [[5, 6], [7, 8]]

In [16]: df
Out[16]:
   a  b
0  6  5
1  8  7

Answer 2

I think this is the method you are looking for:

http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.applymap.html

Apply a function to a DataFrame that is intended to operate elementwise, i.e. like doing map(func, series) for each series in the DataFrame

Example:

>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame(np.random.rand(3,4), columns = list('abcd'))
>>> df
          a         b         c         d
0  0.394819  0.662614  0.752139  0.396745
1  0.802134  0.934494  0.652150  0.698127
2  0.518531  0.582429  0.189880  0.168490
>>> f = lambda x: x*100
>>> df.applymap(f)
           a          b          c          d
0  39.481905  66.261374  75.213857  39.674529
1  80.213437  93.449447  65.215018  69.812667
2  51.853097  58.242895  18.988020  16.849014
>>>