I want to use PySpark to efficiently remove Emoji (e.g., :-)) from 1 billion records. How could I achieve this using pyspark syntax?
use regexp_replace pyspark function
Related
I need to replace only null values in selected columns in a dataframe. I know we have df.na.fill option . How can we implement it only on selected columns or is there any better option other than using df.na.fill
Reading spark documentation here we can see that fill is well suited for your need. You can do something like:
df.na.fill(0, Seq("colA", "colB"))
I am doing the AES Encryption for pyspark dataframe column.
I am iterating the column data, and replacing the column value with encrypted value using df.withcolumn, But it is too slow
I am looking for the alternative approach, But I did not get any
'''
for i in column_data:
obj= AES.new(key, AES.MODE_CBC,v)
ciphertext= obj.encrypt(i)
df=df.withColumn(col,F.when(df[col]==i,str(ciphertext)).otherwise(df[col])) return df
'''
But it's taking long time.
Could you please suggest the other alternative
Your code is slow because of your for-loop, as it forces Spark to run only on one thread.
Please provide an example of input and expected output and someone might be able to help you with rewriting your code.
I have spark dataframe
Here it is
I would like to fetch the values of a column one by one and need to assign it to some variable?How can it be done in pyspark.Sorry I am a newbie to spark as well as stackoverflow.Please forgive the lack of clarity in question
col1=df.select(df.column_of_df).collect()
list1=[str(i[0]) for i in col1]
#after this we can iterate through list (list1 in this case)
I don't understand exactly what you are asking, but if you want to store them in a variable outside of the dataframes that spark offers, the best option is to select the column you want and store it as a panda series (if they are not a lot, because your memory is limited).
from pyspark.sql import functions as F
var = df.select(F.col('column_you_want')).toPandas()
Then you can iterate on it like a normal pandas series.
I am new to pyspark and am trying to recreate a code I wrote in python. I am trying to create a new dataframe that has the averages of every 60 observations from the old dataframe. Here is the code I implemented in old python:
new_df=old_df.groupby(old_df.index // 60).mean()
I am struggling with how to do this same thing in databricks using pyspark.
I think if you have an index column in your dataframe you can do something similar as you proposed:
new_df=old_df.withColumn("new_index", col(index)/60).groupBy("new_index").agg(avg(YOUR_COLUMN_FOR_AVERAGE))
Best Regards,
I have many json string lines in many files and they are very similar in schema but there are a few different in some cases.
I made a DataFrame from them and want to see only rows which have a specific column like
DF.filter("myColumn" is present).show
How can I do this?
You can use isNotNull in filter()
import org.apache.spark.sql.functions.isNotNull
df.filter($"myColumn".isNotNull)