问题
I have a data frame in pyspark with more than 100 columns. What I want to do is for all the column names I would like to add back ticks(`) at the start of the column name and end of column name.
For example:
column name is testing user. I want `testing user`
Is there a method to do this in pyspark/python. when we apply the code it should return a data frame.
回答1:
You can use withColumnRenamed
method of dataframe in combination with na
to create new dataframe
df.na.withColumnRenamed('testing user', '`testing user`')
edit : suppose you have list of columns, you can do like -
old = "First Last Age"
new = ["`"+field+"`" for field in old.split()]
df.rdd.toDF(new)
output :
DataFrame[`First`: string, `Last`: string, `Age`: string]
回答2:
Use list comprehension in python.
from pyspark.sql import functions as F
df = ...
df_new = df.select([F.col(c).alias("`"+c+"`") for c in df.columns])
This method also gives you the option to add custom python logic within the alias() function like: "prefix_"+c+"_suffix" if c in list_of_cols_to_change else c
回答3:
If you would like to add a prefix or suffix to multiple columns in a pyspark dataframe, you could use a for loop and .withColumnRenamed().
As an example, you might like:
def add_prefix(sdf, prefix):
for c in sdf.columns:
sdf = sdf.withColumnRenamed(c, '{}{}'.format(prefix, c))
return sdf
You can amend sdf.columns as you see fit.
回答4:
I had a dataframe that I duplicated twice then joined together. Since both had the same columns names I used :
df = reduce(lambda df, idx: df.withColumnRenamed(list(df.schema.names)[idx],
list(df.schema.names)[idx] + '_prec'),
range(len(list(df.schema.names))),
df)
Every columns in my dataframe then had the '_prec' suffix which allowed me to do sweet stuff
来源:https://stackoverflow.com/questions/43160103/how-to-add-suffix-and-prefix-to-all-columns-in-python-pyspark-dataframe