How to convert column with string type to int form in pyspark data frame?

Question

I have dataframe in pyspark. Some of its numerical columns contain nan so when I am reading the data and checking for the schema of dataframe, those columns will have string type.

How I can change them to int type. I replaced the nan values with 0 and again checked the schema, but then also it's showing the string type for those columns.I am following the below code:

data_df = sqlContext.read.format("csv").load('data.csv',header=True, inferSchema="true")
data_df.printSchema()
data_df = data_df.fillna(0)
data_df.printSchema()

my data looks like this:

here columns Plays and drafts containing integer values but because of nan present in these columns, they are treated as string type.

have a look at https://stackoverflow.com/questions/32284620/how-to-change-a-dataframe-column-from-string-type-to-double-type-in-pyspark — Chetan_Vasudevan, Oct 26 '17 at 13:48
Is there any way by which while reading the data only we can replace the nan so that in schema those columns will be treated as int type. — neha, Oct 26 '17 at 13:59
you will have to make the whole column to be a integer in your case I believe. — Chetan_Vasudevan, Oct 26 '17 at 14:01

Sahil Desai · Answer 1 · 2019-06-21T12:22:21.207

158

from pyspark.sql.types import IntegerType
data_df = data_df.withColumn("Plays", data_df["Plays"].cast(IntegerType()))
data_df = data_df.withColumn("drafts", data_df["drafts"].cast(IntegerType()))

You can run loop for each column but this is the simplest way to convert string column into integer.

edited Jun 21 '19 at 12:22

answered Oct 26 '17 at 20:27

Sahil Desai

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Hi @sahil-desai it's giving me null value. However, while printing schema gives me Integer. Could you justify it why? – Mohit Sharma Jan 15 '21 at 11:59
@Moi if value is non numeric and you are going to cast it then it's converted in to null values. What is previous datatype of your data? – Sahil Desai Jan 16 '21 at 13:12
Thank you - I had the cast on the outside with the `withColumn` and I couldn't understand why it was giving an error. – GenDemo Jan 11 '23 at 23:21

score 19 · Answer 2 · answered Feb 21 '18 at 20:14

19

You could use cast(as int) after replacing NaN with 0,

data_df = df.withColumn("Plays", df.call_time.cast('float'))

answered Feb 21 '18 at 20:14

Ani Menon

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score 8 · Answer 3 · answered Nov 04 '18 at 06:02

Another way to do it is using the StructField if you have multiple fields that needs to be modified.

Ex:

from pyspark.sql.types import StructField,IntegerType, StructType,StringType
newDF=[StructField('CLICK_FLG',IntegerType(),True),
       StructField('OPEN_FLG',IntegerType(),True),
       StructField('I1_GNDR_CODE',StringType(),True),
       StructField('TRW_INCOME_CD_V4',StringType(),True),
       StructField('ASIAN_CD',IntegerType(),True),
       StructField('I1_INDIV_HHLD_STATUS_CODE',IntegerType(),True)
       ]
finalStruct=StructType(fields=newDF)
df=spark.read.csv('ctor.csv',schema=finalStruct)

Output:

Before

root
 |-- CLICK_FLG: string (nullable = true)
 |-- OPEN_FLG: string (nullable = true)
 |-- I1_GNDR_CODE: string (nullable = true)
 |-- TRW_INCOME_CD_V4: string (nullable = true)
 |-- ASIAN_CD: integer (nullable = true)
 |-- I1_INDIV_HHLD_STATUS_CODE: string (nullable = true)

After:

root
 |-- CLICK_FLG: integer (nullable = true)
 |-- OPEN_FLG: integer (nullable = true)
 |-- I1_GNDR_CODE: string (nullable = true)
 |-- TRW_INCOME_CD_V4: string (nullable = true)
 |-- ASIAN_CD: integer (nullable = true)
 |-- I1_INDIV_HHLD_STATUS_CODE: integer (nullable = true)

This is slightly a long procedure to cast , but the advantage is that all the required fields can be done.

It is to be noted that if only the required fields are assigned the data type, then the resultant dataframe will contain only those fields which are changed.

How to convert column with string type to int form in pyspark data frame?

3 Answers3

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