How to select column and rows in pandas without column or row names?

If your DataFrame does not have column/row labels and you want to select some specific columns then you should use iloc method.

example if you want to select first column and all rows:

df = dataset.iloc[:,0] 

Here the df variable will contain the value stored in the first column of your dataframe.

Do remember that

type(df) -> pandas.core.series.Series

Hope it helps

To select filter column by index:

In [19]: df
Out[19]: 
                 Date     Close   Close.1   Close.2  Close.3   Close.4
0  2000-01-0300:00:00       NaN       NaN       NaN      NaN -0.033944
1  2000-01-0400:00:00       NaN       NaN       NaN      NaN  0.035137
2  2000-01-0500:00:00 -0.033944       NaN       NaN      NaN -0.017241
3  2000-01-0600:00:00  0.035137 -0.033944       NaN      NaN -0.004386
4  2000-01-0700:00:00 -0.017241  0.035137 -0.033944      NaN  0.039648

In [20]: df.ix[:, 5]
Out[20]: 
0   -0.033944
1    0.035137
2   -0.017241
3   -0.004386
4    0.039648
Name: Close.4, dtype: float64

In [21]: df.icol(5)
/usr/bin/ipython:1: FutureWarning: icol(i) is deprecated. Please use .iloc[:,i]
  #!/usr/bin/python2
Out[21]: 
0   -0.033944
1    0.035137
2   -0.017241
3   -0.004386
4    0.039648
Name: Close.4, dtype: float64

In [22]: df.iloc[:, 5]
Out[22]: 
0   -0.033944
1    0.035137
2   -0.017241
3   -0.004386
4    0.039648
Name: Close.4, dtype: float64

To select all columns except index:

In [29]: df[[df.columns[i] for i in range(len(df.columns)) if i != 5]]
Out[29]: 
                 Date     Close   Close.1   Close.2  Close.3
0  2000-01-0300:00:00       NaN       NaN       NaN      NaN
1  2000-01-0400:00:00       NaN       NaN       NaN      NaN
2  2000-01-0500:00:00 -0.033944       NaN       NaN      NaN
3  2000-01-0600:00:00  0.035137 -0.033944       NaN      NaN
4  2000-01-0700:00:00 -0.017241  0.035137 -0.033944      NaN

If you want the fifth column:

df.ix[:,4]

Stick the colon in there to take all the rows for that column.

To exclude a fifth column you could try:

df.ix[:, (x for x in range(0, len(df.columns)) if x != 4)]

Use iloc. It is explicitly a position based indexer. ix can be both and will get confused if an index is integer based.

df.iloc[:, [4]]

For all but the fifth column

slc = list(range(df.shape[1]))
slc.remove(4)

df.iloc[:, slc]

or equivalently

df.iloc[:, [i for i in range(df.shape[1]) if i != 4]]