How to take column-slices of dataframe in pandas
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]
Solution 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
Solution 2:
Note: .ix has been deprecated since Pandas v0.20. You should instead use .loc or .iloc, as appropriate.
The DataFrame.ix index is what you want to be accessing. It's a little confusing (I agree that Pandas indexing is perplexing at times!), but the following seems to do what you want:
>>> df = DataFrame(np.random.rand(4,5), columns = list('abcde'))
>>> df.ix[:,'b':]
b c d e
0 0.418762 0.042369 0.869203 0.972314
1 0.991058 0.510228 0.594784 0.534366
2 0.407472 0.259811 0.396664 0.894202
3 0.726168 0.139531 0.324932 0.906575
where .ix[row slice, column slice] is what is being interpreted. More on Pandas indexing here: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-advanced
Solution 3:
Lets use the titanic dataset from the seaborn package as an example
# Load dataset (pip install seaborn)
>> import seaborn.apionly as sns
>> titanic = sns.load_dataset('titanic')
using the column names
>> titanic.loc[:,['sex','age','fare']]
using the column indices
>> titanic.iloc[:,[2,3,6]]
using ix (Older than Pandas <.20 version)
>> titanic.ix[:,[‘sex’,’age’,’fare’]]
or
>> titanic.ix[:,[2,3,6]]
using the reindex method
>> titanic.reindex(columns=['sex','age','fare'])
Solution 4:
Also, Given a DataFrame
data
as in your example, if you would like to extract column a and d only (e.i. the 1st and the 4th column), iloc mothod from the pandas dataframe is what you need and could be used very effectively. All you need to know is the index of the columns you would like to extract. For example:
>>> data.iloc[:,[0,3]]
will give you
a d
0 0.883283 0.100975
1 0.614313 0.221731
2 0.438963 0.224361
3 0.466078 0.703347
4 0.955285 0.114033
5 0.268443 0.416996
6 0.613241 0.327548
7 0.370784 0.359159
8 0.692708 0.659410
9 0.806624 0.875476
Solution 5:
You can slice along the columns of a DataFrame by referring to the names of each column in a list, like so:
data = pandas.DataFrame(np.random.rand(10,5), columns = list('abcde'))
data_ab = data[list('ab')]
data_cde = data[list('cde')]