Pass Entire Contents Of A Dataframe To A Function In Pandas

I'm trying to rework much of my analysis code for signal processing using Dataframes instead of numpy arrays. However, I'm having a hard time figuring out how to pass the entire m

Solution 1:

You can get the raw numpy array version of a DataFrame with df.values. However, in many cases you can just pass the DataFrame itself, since it still allows use of the normal numpy API (i.e., it has all the right methods).

Solution 2:

http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.apply.html this will allow you to perform operations on a row (or column, or the entire dataframe).

import random
signal=pd.DataFrame([[10*random.random() for _ in range(3)]for _ in range(5)])

def testm(frame, average=0):
    return frame-average  

signal.apply(testm,average=signal.mean(),axis=1)

results:

signal  

Out[57]: 
      0         1         2
0  5.566445  7.612070  8.554966
1  0.869158  2.382429  6.197272
2  5.933192  3.564527  9.805669
3  9.676292  1.707944  2.731479
4  5.319629  3.348337  6.476631

signal.mean()

Out[59]: 
0    5.472943
1    3.723062
2    6.753203
dtype: float64

signal.apply(testm,average=signal.mean(),axis=1)

Out[58]: 
          0         1         2
0  0.093502  3.889008  1.801763
1 -4.603785 -1.340632 -0.555932
2  0.460249 -0.158534  3.052466
3  4.203349 -2.015117 -4.021724
4 -0.153314 -0.374724 -0.276572

This will take the mean of each column, and subtract it from each value in that column.