Pandas: access data from dataframe by row and column number

Question

I have a simple program made me confused. I read a 3 * 10 data from a csv file, and I want to access a particular data by its row and column number. But it failed, I doun't know why.

matrix.txt:

1,2,3,4,5,6,7,8,9,10
11,12,13,14,15,16,17,18,19,20
21,22,23,24,25,26,27,28,29,30

Program:

datainput = pd.read_csv('matrix.txt',sep=',', header = None)

inputinfo = datainput.shape ==> 3, 10

print datainput[3][3] => failed,but it should return 23,

I can not access any data if the column number is equal or greater than 3

EdChum · Accepted Answer · 2015-03-29 22:12:39Z

3

Indexing starts from 0:

In [8]:

df
Out[8]:
    0   1   2   3   4   5   6   7   8   9
0   1   2   3   4   5   6   7   8   9  10
1  11  12  13  14  15  16  17  18  19  20
2  21  22  23  24  25  26  27  28  29  30

In [11]:

df[2][2]
Out[11]:
23

As you've not supplied a header one is auto generated and also for the index, the default behaviour is to generate indices starting from 0 as you can see above.

Also your last statement is not true:

In [13]:

df[3][2], df[5][2]
Out[13]:
(24, 26)

Here the first subscript value is the column label followed by the row label.

The following does raise a KeyError:

df[3][3]

edited Mar 29, 2015 at 22:12

answered Mar 29, 2015 at 22:09

EdChum

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4 Comments

lserlohn Over a year ago

thanks for your reply, but I still can not access df[2][5], which should be 25

EdChum Over a year ago

Have you read my answer? I've stated that the first subscript is the column, and the next is the row, so 25 would be df[5][2] not df[2][5]

lserlohn Over a year ago

Thanks. It is odd that the first subscript is column, which is against the custom of most other languages. Is there any particular reason for that?

EdChum Over a year ago

I don't know I thought it might be some convention that numpy also uses but numpy uses row,column as the order, I suspect it follow the same sematic that if your df columns had names such as 'col1', 'col2' etc.. then doing df['col1'][2] would select col1 row 3, I just tested what would happen if you did df[2]['col1'] and this raises a KeyError so it looks like it was a conscious design decision by the pandas devs

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