How to take column-slices of dataframe in pandas

Question

I load some machine learning data from a CSV file. The first 2 columns are observations and the remaining columns are features.

Currently, I do the following:

data = pandas.read_csv('mydata.csv') 

which gives something like:

data = pandas.DataFrame(np.random.rand(10,5), columns = list('abcde')) 

I'd like to slice this dataframe in two dataframes: one containing the columns a and b and one containing the columns c, d and e.

It is not possible to write something like

observations = data[:'c'] features = data['c':] 

I'm not sure what the best method is. Do I need a pd.Panel?

By the way, I find dataframe indexing pretty inconsistent: data['a'] is permitted, but data[0] is not. On the other side, data['a':] is not permitted but data[0:] is. Is there a practical reason for this? This is really confusing if columns are indexed by Int, given that data[0] != data[0:1]

Answer 1

2017 Answer - pandas 0.20: .ix is deprecated. Use .loc

See the deprecation in the docs

.loc uses label based indexing to select both rows and columns. The labels being the values of the index or the columns. Slicing with .loc includes the last element.

Let's assume we have a DataFrame with the following columns:
foo, bar, quz, ant, cat, sat, dat.

# selects all rows and all columns beginning at 'foo' up to and including 'sat' df.loc[:, 'foo':'sat'] # foo bar quz ant cat sat 

.loc accepts the same slice notation that Python lists do for both row and columns. Slice notation being start:stop:step

# slice from 'foo' to 'cat' by every 2nd column df.loc[:, 'foo':'cat':2] # foo quz cat  # slice from the beginning to 'bar' df.loc[:, :'bar'] # foo bar  # slice from 'quz' to the end by 3 df.loc[:, 'quz'::3] # quz sat  # attempt from 'sat' to 'bar' df.loc[:, 'sat':'bar'] # no columns returned  # slice from 'sat' to 'bar' df.loc[:, 'sat':'bar':-1] sat cat ant quz bar  # slice notation is syntatic sugar for the slice function # slice from 'quz' to the end by 2 with slice function df.loc[:, slice('quz',None, 2)] # quz cat dat  # select specific columns with a list # select columns foo, bar and dat df.loc[:, ['foo','bar','dat']] # foo bar dat 

You can slice by rows and columns. For instance, if you have 5 rows with labels v, w, x, y, z

# slice from 'w' to 'y' and 'foo' to 'ant' by 3 df.loc['w':'y', 'foo':'ant':3] #    foo ant # w # x # y