In [14]:

    

import pandas as pd
import numpy as np

def pandas2arff(df,filename,wekaname = "pandasdata",cleanstringdata=True,cleannan=True):
    """
    converts the pandas dataframe to a weka compatible file
    df: dataframe in pandas format
    filename: the filename you want the weka compatible file to be in
    wekaname: the name you want to give to the weka dataset (this will be visible to you when you open it in Weka)
    cleanstringdata: clean up data which may have spaces and replace with "_", special characters etc which seem to annoy Weka. 
                     To suppress this, set this to False
    cleannan: replaces all nan values with "?" which is Weka's standard for missing values. 
              To suppress this, set this to False
    """
    import re
    
    def cleanstring(s):
        if s!="?":
            return re.sub('[^A-Za-z0-9]+', "_", str(s))
        else:
            return "?"
            
    dfcopy = df #all cleaning operations get done on this copy

    
    if cleannan!=False:
        dfcopy = dfcopy.fillna(-999999999) #this is so that we can swap this out for "?"
        #this makes sure that certain numerical columns with missing values don't get stuck with "object" type
 
    f = open(filename,"w")
    arffList = []
    arffList.append("@relation " + wekaname + "\n")
    #look at each column's dtype. If it's an "object", make it "nominal" under Weka for now (can be changed in source for dates.. etc)
    for i in range(df.shape[1]):
        if dfcopy.dtypes[i]=='O' or (df.columns[i] in ["Class","CLASS","class"]):
            if cleannan!=False:
                dfcopy.iloc[:,i] = dfcopy.iloc[:,i].replace(to_replace=-999999999, value="?")
            if cleanstringdata!=False:
                dfcopy.iloc[:,i] = dfcopy.iloc[:,i].apply(cleanstring)
            _uniqueNominalVals = [str(_i) for _i in np.unique(dfcopy.iloc[:,i])]
            _uniqueNominalVals = ",".join(_uniqueNominalVals)
            _uniqueNominalVals = _uniqueNominalVals.replace("[","")
            _uniqueNominalVals = _uniqueNominalVals.replace("]","")
            _uniqueValuesString = "{" + _uniqueNominalVals +"}" 
            arffList.append("@attribute " + df.columns[i] + _uniqueValuesString + "\n")
        else:
            arffList.append("@attribute " + df.columns[i] + " real\n") 
            #even if it is an integer, let's just deal with it as a real number for now
    arffList.append("@data\n")           
    for i in range(dfcopy.shape[0]):#instances
        _instanceString = ""
        for j in range(df.shape[1]):#features
                if dfcopy.dtypes[j]=='O':
                    _instanceString+="\"" + str(dfcopy.iloc[i,j]) + "\""
                else:
                    _instanceString+=str(dfcopy.iloc[i,j])
                if j!=dfcopy.shape[1]-1:#if it's not the last feature, add a comma
                    _instanceString+=","
        _instanceString+="\n"
        if cleannan!=False:
            _instanceString = _instanceString.replace("-999999999.0","?") #for numeric missing values
            _instanceString = _instanceString.replace("\"?\"","?") #for categorical missing values
        arffList.append(_instanceString)
    f.writelines(arffList)
    f.close()
    del dfcopy
    return True


    

In [19]:

    

df = pd.read_csv("test.csv",na_values="NA")

pandas2arff(df,"test.arff")


    

    
Out[19]:





True