SQL with Manoj

Data typecasting errors are common when you are working with different DataFrames across different languages, like here in this case I got datatype mixing error between Pandas & Spark dataframe:

import pandas as pd
pd_df = pd.DataFrame([(101, 'abc'), 
                      ('def', 201), 
                      ('xyz', 'pqr')], 
                     columns=['col1', 'col2'])

df = spark.createDataFrame(pd_df)
display(df)

TypeError:
field col1: Can not merge type <class 'pyspark.sql.types.longtype'> and 
<class 'pyspark.sql.types.stringtype'>

While converting the Pandas DataFrame to Spark DataFrame its throwing error as Spark is not able to infer correct data type for the columns due to mix type of data in columns.

In this case you just need to explicitly tell Spark to use a correct datatype by creating a new schema and using it in createDataFrame() definition shown below:

import pandas as pd
pd_df = pd.DataFrame([(101, 'abc'), 
                      ('def', 201), 
                      ('xyz', 'pqr')], 
                     columns=['col1', 'col2'])

from pyspark.sql.types import *
df_schema = StructType([StructField("col1", StringType(), True)\
                       ,StructField("col2", StringType(), True)])

df = spark.createDataFrame(pd_df, schema=df_schema)
display(df)