I'm trying to convert Pandas DF into Spark one. DF head:
10000001,1,0,1,12:35,OK,10002,1,0,9,f,NA,24,24,0,3,9,0,0,1,1,0,0,4,543
10000001,2,0,1,12:36,OK,10002,1,0,9,f,NA,24,24,0,3,9,2,1,1,3,1,3,2,611
10000002,1,0,4,12:19,PA,10003,1,1,7,f,NA,74,74,0,2,15,2,0,2,3,1,2,2,691
Code:
dataset = pd.read_csv("data/AS/test_v2.csv")
sc = SparkContext(conf=conf)
sqlCtx = SQLContext(sc)
sdf = sqlCtx.createDataFrame(dataset)
And I got an error:
TypeError: Can not merge type <class 'pyspark.sql.types.StringType'> and <class 'pyspark.sql.types.DoubleType'>
I made this script, It worked for my 10 pandas Data frames
from pyspark.sql.types import *
# Auxiliar functions
def equivalent_type(f):
if f == 'datetime64[ns]': return TimestampType()
elif f == 'int64': return LongType()
elif f == 'int32': return IntegerType()
elif f == 'float64': return FloatType()
else: return StringType()
def define_structure(string, format_type):
try: typo = equivalent_type(format_type)
except: typo = StringType()
return StructField(string, typo)
# Given pandas dataframe, it will return a spark's dataframe.
def pandas_to_spark(pandas_df):
columns = list(pandas_df.columns)
types = list(pandas_df.dtypes)
struct_list = []
for column, typo in zip(columns, types):
struct_list.append(define_structure(column, typo))
p_schema = StructType(struct_list)
return sqlContext.createDataFrame(pandas_df, p_schema)
You can see it also in this gist
With this you just have to call spark_df = pandas_to_spark(pandas_df)