Assign pandas dataframe column dtypes

I want to set the dtypes of multiple columns in pd.Dataframe (I have a file that I've had to manually parse into a list of lists, as the file was not amenable for pd.read_csv)

import pandas as pd
print pd.DataFrame([['a','1'],['b','2']],
                   dtype={'x':'object','y':'int'},
                   columns=['x','y'])

I get

ValueError: entry not a 2- or 3- tuple

The only way I can set them is by looping through each column variable and recasting with astype.

dtypes = {'x':'object','y':'int'}
mydata = pd.DataFrame([['a','1'],['b','2']],
                      columns=['x','y'])
for c in mydata.columns:
    mydata[c] = mydata[c].astype(dtypes[c])
print mydata['y'].dtype   #=> int64

Is there a better way?

Solution 1:

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.)

Solution 2:

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"])

Solution 3:

you can set the types explicitly with pandas DataFrame.astype(dtype, copy=True, raise_on_error=True, **kwargs) and pass in a dictionary with the dtypes you want to dtype

here's an example:

import pandas as pd
wheel_number = 5
car_name = 'jeep'
minutes_spent = 4.5

# set the columns
data_columns = ['wheel_number', 'car_name', 'minutes_spent']

# create an empty dataframe
data_df = pd.DataFrame(columns = data_columns)
df_temp = pd.DataFrame([[wheel_number, car_name, minutes_spent]],columns = data_columns)
data_df = data_df.append(df_temp, ignore_index=True) 

you get

In [11]: data_df.dtypes
Out[11]:
wheel_number     float64
car_name          object
minutes_spent    float64
dtype: object

with

data_df = data_df.astype(dtype= {"wheel_number":"int64",
        "car_name":"object","minutes_spent":"float64"})

now you can see that it's changed

In [18]: data_df.dtypes
Out[18]:
wheel_number       int64
car_name          object
minutes_spent    float64

Solution 4:

Another way to set the column types is to first construct a numpy record array with your desired types, fill it out and then pass it to a DataFrame constructor.

import pandas as pd
import numpy as np    

x = np.empty((10,), dtype=[('x', np.uint8), ('y', np.float64)])
df = pd.DataFrame(x)

df.dtypes ->

x      uint8
y    float64