Pandas groupby与csv的行

Question

I have a large CSV file that I am pulling two columns from (Month and Cancelled) and needing to display the results in a dataframe. The months are integer (eg. January is 1 in the csv) and need to convert it to a string.

What I'm having trouble with is setting the correct indices and grouping the data from the months together.

    import pandas as pd
    data = pd.read_csv('data.csv', encoding='latin-1', usecols=['Month','Cancelled'])
    grouped = data.groupby(axis=1)

The expected output I'm looking for is along the lines of:

               Cancelled
    January        19891

But I am currently receiving:

        Month  Cancelled
    0       1          0
    1       1          0
    2       1          0

Answer 1

Since you didn't post a row input data. Let's consider this quick example just to show how to make groupby values in pandas;

After reading your data and puting in a dataframe, you can groupby values based on one of the columns groupby(['month']) , and then apply a function on these values,Pandas includes a number of common ones such as mean(), max(), median(), etc.: you can use sum() for example.

 df.groupby(['month']).sum()

Or pass any other function using aggregate

df.groupby(['month']).aggregate(numpy.sum)

import pandas as pd
from io import StringIO


data="""month  cancelled
0    1      1
1    1      0
2    0      1
3    1      1
4    0      0
5    1      1
6    1      1
7    2      1
8    2      1
9    1      1"""


df= pd.read_csv(StringIO(data.decode('UTF-8')),delim_whitespace=True )
print df.groupby(['month']).sum()

RESULT

       cancelled
month           
0              1
1              5
2              2