Reputation: 7713
I have a dataframe like as shown below
df = pd.DataFrame({
'date':['11/12/2001','11/12/2002','11/12/2003','11/12/2004','11/12/2005','11/12/2006'],
'readings' : ['READ_1','READ_2','READ_1','READ_3','READ_4','READ_5'],
'val_date' :['21/12/2001','22/12/2002','23/12/2003','24/12/2004','25/12/2005','26/12/2006'],
})
spark_df = spark.createDataFrame(df)
spark_df = spark_df.withColumn("date", spark_df["date"].cast(TimestampType()))
spark_df = spark_df.withColumn("val_date", spark_df["val_date"].cast(TimestampType()))
I have a dataframe with column datatypes as shown above
What I would like to do is identify
a) columns that contains the terms date
,time
in their names and convert their datatype from Timestamp/Datetime
to string
and
b) identify columns based on Timestamp
or Datetime
datatypes and convert them to string
type
Though the below works, this isn't elegant and efficient. I have more than 3k columns and cannot do this line by line
spark_df = spark_df.withColumn("date", spark_df["date"].cast(StringType()))
spark_df = spark_df.withColumn("val_date", spark_df["val_date"].cast(StringType()))
I also tried below but doesn't help
selected = [c.cast(StringType()) for c in spark_df.columns if ('date') in c]+['time']
spark_df.select(selected)
Is there anyway to identify columns based on condition a
and b
given above and convert them all at once?
Your inputs to solve this problem with at least one of the approach would be helpful
Upvotes: 4
Views: 1805
Reputation: 7399
You can do the following:
from pyspark.sql.functions import col
schema = {col: col_type for col, col_type in df.dtypes}
time_cols = [col for col, col_type in schema.items() if col_type in "timestamp date".split() or "date" in col or "time" in col]
for column in time_cols:
df = df.withColumn(column, col(column).cast("string"))
Upvotes: 5