如何在熊猫数据框上应用Scipy功能

Question

I have the following data frame:

import pandas as pd
import io
from scipy import stats

temp=u"""probegenes,sample1,sample2,sample3
1415777_at Pnliprp1,20,0.00,11
1415805_at Clps,17,0.00,55
1415884_at Cela3b,47,0.00,100"""
df = pd.read_csv(io.StringIO(temp),index_col='probegenes')
df

It looks like this

                     sample1  sample2  sample3
probegenes
1415777_at Pnliprp1       20        0       11
1415805_at Clps           17        0       55
1415884_at Cela3b         47        0      100

What I want to do is too perform row-zscore calculation using SCIPY . Using this code I get:

In [98]: stats.zscore(df,axis=1)
Out[98]:
array([[ 1.18195176, -1.26346568,  0.08151391],
       [-0.30444376, -1.04380717,  1.34825093],
       [-0.04896043, -1.19953047,  1.2484909 ]])

How can I conveniently attached the columns and index name back again to that result?

At the end of the day. It'll look like:

                               sample1  sample2  sample3
probegenes
1415777_at Pnliprp1      1.18195176, -1.26346568,  0.08151391
1415805_at Clps         -0.30444376, -1.04380717,  1.34825093
1415884_at Cela3b        -0.04896043, -1.19953047,  1.2484909

Answer 1

The documentation for pd.DataFrame has:

data : numpy ndarray (structured or homogeneous), dict, or DataFrame Dict can contain Series, arrays, constants, or list-like objects index : Index or array-like Index to use for resulting frame. Will default to np.arange(n) if no indexing information part of input data and no index provided columns : Index or array-like Column labels to use for resulting frame. Will default to np.arange(n) if no column labels are provided

So,

pd.DataFrame(
    stats.zscore(df,axis=1),
    index=df.index,
    columns=df.columns)

should do the job.

Answer 2

You don't need scipy. You can do it using a lambda function:

>>> df.apply(lambda row: (row - row.mean()) / row.std(ddof=0), axis=1) 
                      sample1   sample2   sample3
probegenes                                       
1415777_at Pnliprp1  1.181952 -1.263466  0.081514
1415805_at Clps     -0.304444 -1.043807  1.348251
1415884_at Cela3b   -0.048960 -1.199530  1.248491