add a different random number to every cell in a pandas dataframe

Question

I need to add some 'noise' to my data, so I would like to add a different random number to every cell in my pandas dataframe. This code works, but seems unpythonic. Is there a better way?

import pandas as pd
import numpy as np
df = pd.DataFrame(0.0, index=[1,2,3,4,5], columns=list('ABC') )
print df
for x,line in df.iterrows():
  for col in df:
     line[col] = line[col] + (np.random.rand()-0.5)/1000.0
 print df

Answer 1

df + np.random.rand(*df.shape) / 10000.0

OR

Let's use applymap:

df = pd.DataFrame(1.0, index=[1,2,3,4,5], columns=list('ABC') )

df.applymap(lambda x: x + np.random.rand()/10000.0)

output:

                                                   A  \
1  [[1.00006953418, 1.00009164785, 1.00003177706]...   
2  [[1.00007291245, 1.00004186046, 1.00006935173]...   
3  [[1.00000490127, 1.0000633115, 1.00004117181],...   
4  [[1.00007159622, 1.0000559506, 1.00007038891],...   
5  [[1.00000980335, 1.00004760836, 1.00004214422]...   

                                                   B  \
1  [[1.00000320322, 1.00006981682, 1.00008912557]...   
2  [[1.00007443802, 1.00009270815, 1.00007225764]...   
3  [[1.00001371778, 1.00001512412, 1.00007986851]...   
4  [[1.00005883343, 1.00007936509, 1.00009523334]...   
5  [[1.00009329606, 1.00003174878, 1.00006187704]...   

                                                   C  
1  [[1.00005894836, 1.00006592776, 1.0000171843],...  
2  [[1.00009085391, 1.00006606979, 1.00001755092]...  
3  [[1.00009736701, 1.00007240762, 1.00004558753]...  
4  [[1.00003981393, 1.00007505714, 1.00007209959]...  
5  [[1.0000031608, 1.00009372917, 1.00001960112],...  

Answer 2

For nonzero data:

df + (np.random.rand(df.shape)-0.5)*0.001

OR

df + np.random.uniform(-0.01,0.01,(df.shape)))

For cases where your data frame contains zeros that you wish to keep as zero:

df * (1 + (np.random.rand(df.shape)-0.5)*0.001)

OR

df * (1 + np.random.uniform(-0.01,0.01,(df.shape)))

I think either of these should work, its a case of generating a same size "dataframe" (or perhaps array of arrays) as your existing df and adding it to your existing df (multiplying by 1 + random for cases where you wish zeros to remain zero). With the uniform function you can determine the scale of your noise by altering the 0.01 variable.