I would like to create a spark dataframe in pyspark from a text file, that has different number of rows and columns and map it to key/value pair, the key is the first 4 characters from the first column of the text file. I want to do that in order to remove the redundant rows and to be able group them later by the key value. I know how to do that on pandas but still confused where to start doing that in pyspark.
My input is a text file that has the following:
1234567,micheal,male,usa
891011,sara,femal,germany
I want to be able to group every row by the first six characters in the first column
Create a new column that contains only the first six characters of the first column, and then group by that:
from pyspark.sql.functions import col
df2 = df.withColumn("key", col("first_col")[:6])
df2.groupBy("key").agg(...)
Related
I have a df1 based on disticnt values containing two columns, date and value. There is df2 that has multiple column but contains the date and value column. For each distinct value from df1, i want to filter the df2 such that the records before the date from df1 are dropped. It would be rather easy for a single disticnt value, i can use something like filter by vlaue and then gt(lit(date), however i have over 500 such distinct pairs in df1. For single operation, it takes around 20 minute so if i use a loop then it is computationally not feasible. Perhaps some body can advice me on a better methodology here.
have tried multiple methodlogies but nothing has worked yet.
I am working with the OpenFoodFacts dataset using PySpark. There's quite a lot of columns which are entirely made up of missing values and I want to drop said columns. I have been looking up ways to retrieve the number of missing values on each column, but they are displayed in a table format instead of actually giving me the numeric value of the total null values.
The following code shows the number of missing values in a column but displays it in a table format:
from pyspark.sql.functions import col, isnan, when, count
data.select([count(when(isnan("column") | col("column").isNull(), "column")]).show()
I have tried the following codes:
This one does not work as intended as it doesn't drop any columns (as expected)
for c in data.columns:
if(data.select([count(when(isnan(c) | col(c).isNull(), c)]) == data.count()):
data = data.drop(c)
data.show()
This one I am currently trying but takes ages to execute
for c in data.columns:
if(data.filter(data[c].isNull()).count() == data.count()):
data = data.drop(c)
data.show()
Is there a way to get ONLY the number? Thanks
If you need the number instead of showing in the table format, you need to use the .collect(), which is:
list_of_values = data.select([count(when(isnan("column") | col("column").isNull(), "column")]).collect()
What you get is a list of Row, which contain all the information in the table.
i have as solution which goes like
df1 -->dataframe 1 with having 50 columns of data
df2 --->datarame 2 having footer/trailer 3 columns of data like Trailer,count of rows,date
so i added the remaining 47 columns as "","",""..... so on
so that i can union 2 dataframe:
df3=df1.union(df2)
now if i want to save
df3.coalesce(1).write.format("com.databricks.spark.csv")\
.option("header","true").mode("overwrite")\
.save(output_blob_path);
so now i am getting the footer as well
like this Trailer,400,20210805,"","","","","","","".. and so on
if any one can suggest how to remove ,"","","",.. these double quotes from the last row
where i want to save this file in blob container.
it would be very helpful
You can try to define structure of data frame to treat entire row as single column for both the files and then perform union. This way you no need to add extra columns on data frame 2 and then struck in to tricky situation to remove extra columns after union.
I have a use-case where I need to select certain columns from a dataframe containing atleast 30 columns and millions of rows.
I'm loading this data from a cassandra table using scala and apache-spark.
I selected the required columns using: df.select("col1","col2","col3","col4")
Now I have to perform a basic groupBy operation to group the data according to src_ip,src_port,dst_ip,dst_port and I also want to have the latest value from a received_time column of the original dataframe.
I want a dataframe with distinct src_ip values with their count and latest received_time in a new column as last_seen.
I know how to use .withColumn and also, I think that .map() can be used here.
Since I'm relatively new in this field, I really don't know how to proceed further. I could really use your help to get done with this task.
Assuming you have a dataframe df with src_ip,src_port,dst_ip,dst_port and received_time, you can try:
val mydf = df.groupBy(col("src_ip"),col("src_port"),col("dst_ip"),col("dst_port")).agg(count("received_time").as("row_count"),max(col("received_time")).as("max_received_time"))
The above line calculates the count of timestamp received against the group by columns as well as the max timestamp for that group by columns.
I have a Spark (1.4) dataframe where the data in a column is like "1-2-3-4-5-6-7-8-9-10-11-12". I want to split the data into multiple columns. Please note that the number of fields can vary from 1 to 12, its not fixed.
P.S. we are using Scala API.
Edit:
Editing over the original question. I have the delimited string as below:
"ABC-DEF-PQR-XYZ"
From this string I need to create delimited strings in separate columns as below. Please note that this string is in a column in DF.
Original column: ABC-DEF-PQR-XYZ
New col1 : ABC
New col2 : ABC-DEF
New col3 : ABC-DEF-PQR
New col4 : ABC-DEF-PQR-XYZ
Please note that there can be 12 such new columns which needs to get derived from original field. Also, the string in original column might vary i.e. some times 1 column, some time 2 but max can be 12.
Hope I have articulated the problem statement clearly.
Thanks!
You can use explode and pivot. Here is some sample data:
df=sc.parallelize([["1-2-3-4-5-6-7-8-9-10-11-12"], ["1-2-3-4"], ["1-2-3-4-5-6-7-8-9-10"]]).toDF(schema=["col"])
Now add a unique id to rows so that we can keep track of which row the data belongs to:
df=df.withColumn("id", f.monotonically_increasing_id())
Then split the columns by delimiter - and then explode to get a long-form dataset:
df=df.withColumn("col_split", f.explode(f.split("col", "\-")))
Finally pivot on id to get back to wide form:
df.groupby("id")
.pivot("col_split")
.agg(f.max("col_split"))
.drop("id").show()