Assign pandas dataframe column dtypes
For those coming from Google (etc.) such as myself:
convert_objects
has been deprecated since 0.17 - if you use it, you get a warning like this one:
FutureWarning: convert_objects is deprecated. Use the data-type specific converters
pd.to_datetime, pd.to_timedelta and pd.to_numeric.
You should do something like the following:
df =
df.astype(np.float)
df["A"] =
pd.to_numeric(df["A"])
Since 0.17, you have to use the explicit conversions:
pd.to_datetime, pd.to_timedelta and pd.to_numeric
(As mentioned below, no more "magic", convert_objects
has been deprecated in 0.17)
df = pd.DataFrame({'x': {0: 'a', 1: 'b'}, 'y': {0: '1', 1: '2'}, 'z': {0: '2018-05-01', 1: '2018-05-02'}})
df.dtypes
x object
y object
z object
dtype: object
df
x y z
0 a 1 2018-05-01
1 b 2 2018-05-02
You can apply these to each column you want to convert:
df["y"] = pd.to_numeric(df["y"])
df["z"] = pd.to_datetime(df["z"])
df
x y z
0 a 1 2018-05-01
1 b 2 2018-05-02
df.dtypes
x object
y int64
z datetime64[ns]
dtype: object
and confirm the dtype is updated.
OLD/DEPRECATED ANSWER for pandas 0.12 - 0.16: You can use convert_objects
to infer better dtypes:
In [21]: df
Out[21]:
x y
0 a 1
1 b 2
In [22]: df.dtypes
Out[22]:
x object
y object
dtype: object
In [23]: df.convert_objects(convert_numeric=True)
Out[23]:
x y
0 a 1
1 b 2
In [24]: df.convert_objects(convert_numeric=True).dtypes
Out[24]:
x object
y int64
dtype: object
Magic! (Sad to see it deprecated.)