Pandas: Combine multiple DataFrame.apply into one

Question

im using pandas to combine some csv files.

I need to create multiple new columns based on one of the rows, in this case, network. Currently I have as u can see a bunch of applies to create the columns, and this is hurting the performance, is there a way that I could create multiple columns with just one apply, or a more performant way to achieve the same result?

dataReader = pd.read_csv('file.csv', usecols=['geoname_id' , 'country_iso_code','country_name','subdivision_1_name','subdivision_2_name','city_name','time_zone'])
rangeReader = pd.read_csv('file2.csv', chunksize = size, usecols=['geoname_id','network'])
start_time = time.time()

output = open("result.csv" , 'w')

#removes countries we dont care about
dataReader = dataReader[(dataReader.country_iso_code.isin(countries))]

addHeader = True
for chunk in rangeReader:
    print("Loop ",i,"took %s seconds" % (time.time() - start_time))
    chunk = pd.merge(chunk, dataReader, on="geoname_id", how="inner")
    chunk['low_ip'] = chunk.apply(lambda row: getLowIp(row), axis=1)
    chunk['high_ip'] = chunk.apply(lambda row: getHighIp(row), axis=1)
    chunk['low_ip_int']= chunk.apply(lambda row: getIpInt(row['low_ip']), axis=1)
    chunk['high_ip_int']= chunk.apply(lambda row: getIpInt(row['high_ip']), axis=1)
    chunk['json'] = chunk.apply(lambda row: toElasticJson(row), axis=1)
    chunk.to_csv(output, header=addHeader, sep='|')
    addHeader = False

Answer 1

After some digging I found out

lambda should return a pd.Series() like

return pd.Series((low , high, int(IPAddress(low)) , int(IPAddress(high))))

and the assign would be

chunk[['low_ip' , 'high_ip' , 'low_ip_int', 'high_ip_int']] = chunk.apply(lambda row: getAllIpFields(row['network']), axis=1)

this way I joined all applies into one, saving some performance.