If I have a dataframe with the following columns:
1. NAME object
2. On_Time object
3. On_Budget object
4. %actual_hr float64
5. Baseline Start Date datetime64[ns]
6. Forecast Start Date datetime64[ns]
I would like to be able to say: here is a dataframe, give me a list of the columns which are of type Object or of type DateTime?
I have a function which converts numbers (Float64) to two decimal places, and I would like to use this list of dataframe columns, of a particular type, and run it through this function to convert them all to 2dp.
Maybe:
For c in col_list: if c.dtype = "Something"
list[]
List.append(c)?
list(df.select_dtypes(['object']).columns)
This should do the trick
If you want a list of only the object columns you could do:
non_numerics = [x for x in df.columns \
if not (df[x].dtype == np.float64 \
or df[x].dtype == np.int64)]
and then if you want to get another list of only the numerics:
numerics = [x for x in df.columns if x not in non_numerics]
I use infer_objects()
Docstring: Attempt to infer better dtypes for object columns.
Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.
df.infer_objects().dtypes
use df.info(verbose=True)
where df
is a pandas datafarme, by default verbose=False
The most direct way to get a list of columns of certain dtype e.g. 'object':
df.select_dtypes(include='object').columns
For example:
>>df = pd.DataFrame([[1, 2.3456, 'c', 'd', 78]], columns=list("ABCDE"))
>>df.dtypes
A int64
B float64
C object
D object
E int64
dtype: object
To get all 'object' dtype columns:
>>df.select_dtypes(include='object').columns
Index(['C', 'D'], dtype='object')
For just the list:
>>list(df.select_dtypes(include='object').columns)
['C', 'D']
You can use boolean mask on the dtypes attribute:
In [11]: df = pd.DataFrame([[1, 2.3456, 'c']])
In [12]: df.dtypes
Out[12]:
0 int64
1 float64
2 object
dtype: object
In [13]: msk = df.dtypes == np.float64 # or object, etc.
In [14]: msk
Out[14]:
0 False
1 True
2 False
dtype: bool
You can look at just those columns with the desired dtype:
In [15]: df.loc[:, msk]
Out[15]:
1
0 2.3456
Now you can use round (or whatever) and assign it back:
In [16]: np.round(df.loc[:, msk], 2)
Out[16]:
1
0 2.35
In [17]: df.loc[:, msk] = np.round(df.loc[:, msk], 2)
In [18]: df
Out[18]:
0 1 2
0 1 2.35 c
I came up with this three liner.
Essentially, here's what it does:
inp = pd.read_csv('filename.csv') # read input. Add read_csv arguments as needed
columns = pd.DataFrame({'column_names': inp.columns, 'datatypes': inp.dtypes})
columns.to_csv(inp+'columns_list.csv', encoding='utf-8') # encoding is optional
This made my life much easier in trying to generate schemas on the fly. Hope this helps
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']
If after 6 years you still have the issue, this should solve it :)
cols = [c for c in df.columns if df[c].dtype in ['object', 'datetime64[ns]']]
for yoshiserry;
def col_types(x,pd):
dtypes=x.dtypes
dtypes_col=dtypes.index
dtypes_type=dtypes.value
column_types=dict(zip(dtypes_col,dtypes_type))
return column_types
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
Source: Stackoverflow.com