h2o frame from pandas casting

Question

I am using h2o to perform predictive modeling from python. I have loaded some data from a csv using pandas, specifying some column types:

dtype_dict = {'SIT_SSICCOMP':'object',
              'SIT_CAPACC':'object',
              'PTT_SSIRMPOL':'object',
              'PTT_SPTCLVEI':'object',
              'cap_pad':'object',
              'SIT_SADNS_RESP_PERC':'object',
              'SIT_GEOCODE':'object',
              'SIT_TIPOFIRMA':'object',
              'SIT_TPFRODESI':'object',
              'SIT_CITTAACC':'object',
              'SIT_INDIRACC':'object',
              'SIT_NUMCIVACC':'object'
              }
date_cols = ["SIT_SSIDTSIN","SIT_SSIDTDEN","PTT_SPTDTEFF","PTT_SPTDTSCA","SIT_DTANTIFRODE","PTT_DTELABOR"]


columns_to_drop = ['SIT_TPFRODESI','SIT_CITTAACC',
       'SIT_INDIRACC', 'SIT_NUMCIVACC', 'SIT_CAPACC', 'SIT_LONGITACC',
       'SIT_LATITACC','cap_pad','SIT_DTANTIFRODE']


comp='mycomp'

file_completo = os.path.join(dataDir,"db4modelrisk_"+comp+".csv")
db4scoring = pd.read_csv(filepath_or_buffer=file_completo,sep=";", encoding='latin1',
                          header=0,infer_datetime_format =True,na_values=[''], keep_default_na =False,
                          parse_dates=date_cols,dtype=dtype_dict,nrows=500e3)
db4scoring.drop(labels=columns_to_drop,axis=1,inplace =True)

Then, after I set up a h2o cluster I import it in h2o using db4scoring_h2o = H2OFrame(db4scoring) and I convert categorical predictors in factor for example:

db4scoring_h2o["SIT_SADTPROV"]=db4scoring_h2o["SIT_SADTPROV"].asfactor()
db4scoring_h2o["PTT_SPTFRAZ"]=db4scoring_h2o["PTT_SPTFRAZ"].asfactor()

When I check data types using db4scoring.dtypes I notice that they are properly set but when I import it in h2o I notice that h2oframe performs some unwanted conversions to enum (eg from float or from int). I wonder if is is a way to specify the variable format in H2OFrame.

Answer 1

Yes, there is. See the H2OFrame doc here: http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/frame.html#h2oframe

You just need to use the column_types argument when you cast.

Here's a short example:

# imports
import h2o
import numpy as np
import pandas as pd

# create small random pandas df
df = pd.DataFrame(np.random.randint(0,10,size=(10, 2)), 
columns=list('AB'))
print(df)

#   A  B
#0  5  0
#1  1  3
#2  4  8
#3  3  9
# ...

# start h2o, convert pandas frame to H2OFrame
# use column_types dict to set data types
h2o.init()
h2o_df = h2o.H2OFrame(df, column_types={'A':'numeric', 'B':'enum'})
h2o_df.describe() # you should now see the desired data types 

#       A   B
# type int enum
# ...