How to get pandas.DataFrame columns containing specific dtype

There's a new feature in 0.14.1, select_dtypes to select columns by dtype, by providing a list of dtypes to include or exclude.

For example:

df = pd.DataFrame({'a': np.random.randn(1000),
                   'b': range(1000),
                   'c': ['a'] * 1000,
                   'd': pd.date_range('2000-1-1', periods=1000)})


df.select_dtypes(['float64','int64'])

Out[129]: 
            a    b
0    0.153070    0
1    0.887256    1
2   -1.456037    2
3   -1.147014    3
...

dtypes is a Pandas Series. That means it contains index & values attributes. If you only need the column names:

headers = df.dtypes.index

it will return a list containing the column names of "df" dataframe.

Someone will give you a better answe than this possibly, but one thing I tend to do is if all my numeric data are int64 or float64 objects, then you can create a dict of the column data types and then use the values to create your list of columns.

So for example, in a dataframe where I have columns of type float64, int64 and object firstly you can look at the data types as so:

DF.dtypes

and if they conform to the standard whereby the non-numeric columns of data are all object types (as they are in my dataframes), then you can do the following to get a list of the numeric columns:

[key for key in dict(DF.dtypes) if dict(DF.dtypes)[key] in ['float64', 'int64']]

Its just a simple list comprehension. Nothing fancy. Again, though whether this works for you will depend upon how you set up you dataframe...