对熊猫数据框求和

Question

I am running Python 3.6 and Pandas 0.19.2 in PyCharm Community Edition 2016.3.2 and am trying to ensure a set of rows in my dataframe adds up to 1.

Initially my dataframe looks as follows:

 hello     world     label0    label1    label2
 abc       def       1.0       0.0       0.0
 why       not       0.33      0.34      0.33
 hello     you       0.33      0.38      0.15

I proceed as follows:

# get list of label columns (all column headers that contain the string 'label')
label_list = df.filter(like='label').columns

# ensure every row adds to 1
if (df[label_list].sum(axis=1) != 1).any():
    print('ERROR')

Unfortunately this code does not work for me. What seems to be happening is that instead of summing my rows, I just get the value of the first column in my filtered data. In other words: df[label_list].sum(axis=1) returns:

0     1.0
1     0.33
2     0.33

This should be trivial, but I just can't figure out what I'm doing wrong. Thanks up front for the help!

UPDATE:

This is an excerpt from my original data after I have filtered for label columns:

    label0 label1 label2 label3 label4 label5 label6 label7 label8
1    0.34    0.1    0.1    0.1    0.2    0.4    0.1    0.1    1.2
2    0.34    0.1    0.1    0.1    0.2    0.4    0.1    0.1    1.2
3    0.34    0.1    0.1    0.1    0.2    0.4    0.1    0.1    1.2
4    0.34    0.1    0.1    0.1    0.2    0.4    0.1    0.1    1.2
5    0.34    0.1    0.1    0.1    0.2    0.4    0.1    0.1    1.2
6    0.34    0.1    0.1    0.1    0.2    0.4    0.1    0.1    1.2
7    0.34    0.1    0.1    0.1    0.2    0.4    0.1    0.1    1.2
8    0.34    0.1    0.1    0.1    0.2    0.4    0.1    0.1    1.2
9    0.34    0.1    0.1    0.1    0.2    0.4    0.1    0.1    1.2

My code from above still does not work, and I still have absolutely no idea why. When I run my code in python console everything works perfectly fine, but when I run my code in Pycharm 2016.3.2, label_data.sum(axis=1) just returns the values of the first column.

Answer 1

With your sample data for me it works. Just try to reproduce your sample adding a new column check to control the sum:

In [3]: df
Out[3]: 
   hello world  label0  label1  label2
0    abc   def    1.00    0.00    0.00
1    why   not    0.33    0.34    0.33
2  hello   you    0.33    0.38    0.15

In [4]: df['check'] = df.sum(axis=1)

In [5]: df
Out[5]: 
   hello world  label0  label1  label2  check
0    abc   def    1.00    0.00    0.00   1.00
1    why   not    0.33    0.34    0.33   1.00
2  hello   you    0.33    0.38    0.15   0.86

In [6]: label_list = df.filter(like='label').columns

In [7]: label_list
Out[7]: Index([u'label0', u'label1', u'label2'], dtype='object')

In [8]: df[label_list].sum(axis=1)
Out[8]: 
0    1.00
1    1.00
2    0.86
dtype: float64

In [9]: if (df[label_list].sum(axis=1) != 1).any():
   ...:     print('ERROR')
   ...:     
ERROR

Answer 2

Turns out my data type was not consistent. I used astype(float) and things worked out.