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Use Dataframe. dtypes to get Data types of columns in Dataframe. In Python's pandas module Dataframe class provides an attribute to get the data type information of each columns i.e. It returns a series object containing data type information of each column.
You can get the column names from pandas DataFrame using df. columns. values , and pass this to python list() function to get it as list, once you have the data you can print it using print() statement.
To access the names of a Pandas dataframe, we can the method columns(). For example, if our dataframe is called df we just type print(df. columns) to get all the columns of the Pandas dataframe.
Use the tolist() Method to Convert a Dataframe Column to a List. A column in the Pandas dataframe is a Pandas Series . So if we need to convert a column to a list, we can use the tolist() method in the Series . tolist() converts the Series of pandas data-frame to a list.
If you want a list of columns of a certain type, you can use groupby:
>>> df = pd.DataFrame([[1, 2.3456, 'c', 'd', 78]], columns=list("ABCDE"))
>>> df
A B C D E
0 1 2.3456 c d 78
[1 rows x 5 columns]
>>> df.dtypes
A int64
B float64
C object
D object
E int64
dtype: object
>>> g = df.columns.to_series().groupby(df.dtypes).groups
>>> g
{dtype('int64'): ['A', 'E'], dtype('float64'): ['B'], dtype('O'): ['C', 'D']}
>>> {k.name: v for k, v in g.items()}
{'object': ['C', 'D'], 'int64': ['A', 'E'], 'float64': ['B']}
As of pandas v0.14.1, you can utilize select_dtypes() to select columns by dtype
In [2]: df = pd.DataFrame({'NAME': list('abcdef'),
'On_Time': [True, False] * 3,
'On_Budget': [False, True] * 3})
In [3]: df.select_dtypes(include=['bool'])
Out[3]:
On_Budget On_Time
0 False True
1 True False
2 False True
3 True False
4 False True
5 True False
In [4]: mylist = list(df.select_dtypes(include=['bool']).columns)
In [5]: mylist
Out[5]: ['On_Budget', 'On_Time']
Using dtype will give you desired column's data type:
dataframe['column1'].dtype
if you want to know data types of all the column at once, you can use plural of dtype as dtypes:
dataframe.dtypes
list(df.select_dtypes(['object']).columns)
This should do the trick
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