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Assign pandas dataframe column dtypes

Posted by: admin November 30, 2017 Leave a comment

Questions:

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?

Answers:

You can use convert_objects to infer better dtypes:

In [11]: df
Out[11]: 
   x  y
0  a  1
1  b  2

In [12]: df.dtypes
Out[12]: 
x    object
y    object
dtype: object

In [13]: df.convert_objects(convert_numeric=True)
Out[13]: 
   x  y
0  a  1
1  b  2

In [14]: df.convert_objects(convert_numeric=True).dtypes
Out[14]: 
x    object
y     int64
dtype: object

Magic!

Questions:
Answers:

For those coming from Google (etc.) such as myself:

convert_objects has been deprecated – 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:

Questions:
Answers:

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) 

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

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

Questions:
Answers:

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

Questions:
Answers:

facing similar problem to you. In my case I have 1000’s of files from cisco logs that I need to parse manually.

In order to be flexible with fields and types I have successfully tested using StringIO + read_cvs which indeed does accept a dict for the dtype specification.

I usually get each of the files ( 5k-20k lines) into a buffer and create the dtype dictionaries dynamically.

Eventually I concatenate ( with categorical… thanks to 0.19) these dataframes into a large data frame that I dump into hdf5.

Something along these lines

import pandas as pd
import io 

output = io.StringIO()
output.write('A,1,20,31\n')
output.write('B,2,21,32\n')
output.write('C,3,22,33\n')
output.write('D,4,23,34\n')

output.seek(0)


df=pd.read_csv(output, header=None,
        names=["A","B","C","D"],
        dtype={"A":"category","B":"float32","C":"int32","D":"float64"},
        sep=","
       )

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 4 columns):
A    5 non-null category
B    5 non-null float32
C    5 non-null int32
D    5 non-null float64
dtypes: category(1), float32(1), float64(1), int32(1)
memory usage: 205.0 bytes
None

Not very pythonic…. but does the job

Hope it helps.

JC