I want to maintain the date sort-order, using collect_list for multiple columns, all with the same date order. I'll need them in the same dataframe so I can utilize to create a time series model input. Below is a sample of the "train_data":
I'm using a Window with PartitionBy to ensure sort order by tuning_evnt_start_dt for each Syscode_Stn. I can create one column with this code:
from pyspark.sql import functions as F
from pyspark.sql import Window
w = Window.partitionBy('Syscode_Stn').orderBy('tuning_evnt_start_dt')
sorted_list_df = train_data
.withColumn('spp_imp_daily', F.collect_list('spp_imp_daily').over(w)
)\
.groupBy('Syscode_Stn')\
.agg(F.max('spp_imp_daily').alias('spp_imp_daily'))
but how do I create two columns in the same new dataframe?
w = Window.partitionBy('Syscode_Stn').orderBy('tuning_evnt_start_dt')
sorted_list_df = train_data
.withColumn('spp_imp_daily',F.collect_list('spp_imp_daily').over(w))
.withColumn('MarchMadInd', F.collect_list('MarchMadInd').over(w))
.groupBy('Syscode_Stn')
.agg(F.max('spp_imp_daily').alias('spp_imp_daily')))
Note that MarchMadInd is not shown in the screenshot, but is included in train_data. Explanation of how I got to where I am: https://stackoverflow.com/a/49255498/8691976
Yes, the correct way is to add successive .withColumn statements, followed by a .agg statement that removes the duplicates for each array.
w = Window.partitionBy('Syscode_Stn').orderBy('tuning_evnt_start_dt')
sorted_list_df = train_data.withColumn('spp_imp_daily',
F.collect_list('spp_imp_daily').over(w)
)\
.withColumn('MarchMadInd', F.collect_list('MarchMadInd').over(w))\
.groupBy('Syscode_Stn')\
.agg(F.max('spp_imp_daily').alias('spp_imp_daily'),
F.max('MarchMadInd').alias('MarchMadInd')
)
Related
I received help with following PySpark to prevent errors when doing a Merge in Databricks, see here
Databricks Error: Cannot perform Merge as multiple source rows matched and attempted to modify the same target row in the Delta table conflicting way
I was wondering if I could get help to modify the code to drop NULLs.
from pyspark.sql.window import Window
from pyspark.sql.functions import row_number
df2 = partdf.withColumn("rn", row_number().over(Window.partitionBy("P_key").orderBy("Id")))
df3 = df2.filter("rn = 1").drop("rn")
Thanks
The code that you are using does not completely delete the rows where P_key is null. It is applying the row number for null values and where row number value is 1 where P_key is null, that row is not getting deleted.
You can instead use the df.na.drop instead to get the required result.
df.na.drop(subset=["P_key"]).show(truncate=False)
To make your approach work, you can use the following approach. Add a row with least possible unique id value. Store this id in a variable, use the same code and add additional condition in filter as shown below.
from pyspark.sql.window import Window
from pyspark.sql.functions import row_number,when,col
df = spark.read.option("header",True).csv("dbfs:/FileStore/sample1.csv")
#adding row with least possible id value.
dup_id = '0'
new_row = spark.createDataFrame([[dup_id,'','x','x']], schema = ['id','P_key','c1','c2'])
#replacing empty string with null for P_Key
new_row = new_row.withColumn('P_key',when(col('P_key')=='',None).otherwise(col('P_key')))
df = df.union(new_row) #row added
#code to remove duplicates
df2 = df.withColumn("rn", row_number().over(Window.partitionBy("P_key").orderBy("id")))
df2.show(truncate=False)
#additional condition to remove added id row.
df3 = df2.filter((df2.rn == 1) & (df2.P_key!=dup_id)).drop("rn")
df3.show()
I have dataframe which has 100+ numerical columns. I want to descretize some columns from it and then apply groupby function and crosstab function on these discretized columns.
Currently, I am using a loop to iterate over all selected numerical columns. But it is very time-consuming. is there any better and cleaner solution? My code looks like below:
from pyspark.ml.feature import QuantileDiscretizer
df_num = spark.createDataFrame(data = [],schema = StructType([]))
for name in number_columns:
steps = QuantileDiscretizer(numBuckets=10,inputCol=name,outputCol=name+'Bin')
Selected_data=steps.fit(Selected_data).transform(Selected_data)
tmp=Selected_data.groupBy(name+'Bin').agg(mean("ABC"),mean("XYZ"),count("ABC"),count("XYZ")).withColumnRenamed(name+'Bin','Category')
temp=Selected_data.crosstab(name+'Bin', 'code').withColumnRenamed(name+'Bin_code','Category')
temp=temp.join(tmp, 'Category','inner')
df_num=df_num.unionByName(temp,allowMissingColumns=True)
I am counting rows by a condition on pyspark
df.agg(count(when((col("my_value")==0),True))).show()
It works as I expected. Then how can I extract the value showed in the table to store to a Python variable?
If you just want to count the Trues (ceros), you should better do this:
from pyspark.sql import functions as F
pythonVariable = df.where(F.col('my_value') == 0).collect[0][0]
As you can see, there is no need to change the ceros to True to count them.
I need to iterate rows of a pyspark.sql.dataframe.DataFrame.DataFrame.
I have done it in pandas in the past with the function iterrows() but I need to find something similar for pyspark without using pandas.
If I do for row in myDF: it iterates columns.DataFrame
Thanks
You can use select method to operate on your dataframe using a user defined function something like this :
columns = header.columns
my_udf = F.udf(lambda data: "do what ever you want here " , StringType())
myDF.select(*[my_udf(col(c)) for c in columns])
then inside the select you can choose what you want to do with each column .
I have a spark dataframe containing 1 million rows and 560 columns. I need to find the count of unique items in each column of the dataframe.
I have written the following code to achieve this but it is getting stuck and taking too much time to execute:
count_unique_items=[]
for j in range(len(cat_col)):
var=cat_col[j]
count_unique_items.append(data.select(var).distinct().rdd.map(lambda r:r[0]).count())
cat_col contains the column names of all the categorical variables
Is there any way to optimize this?
Try using approxCountDistinct or countDistinct:
from pyspark.sql.functions import approxCountDistinct, countDistinct
counts = df.agg(approxCountDistinct("col1"), approxCountDistinct("col2")).first()
but counting distinct elements is expensive.
You can do something like this, but as stated above, distinct element counting is expensive. The single * passes in each value as an argument, so the return value will be 1 row X N columns. I frequently do a .toPandas() call to make it easier to manipulate later down the road.
from pyspark.sql.functions import col, approxCountDistinct
distvals = df.agg(*(approxCountDistinct(col(c), rsd = 0.01).alias(c) for c in
df.columns))
You can use get every different element of each column with
df.stats.freqItems([list with column names], [percentage of frequency (default = 1%)])
This returns you a dataframe with the different values, but if you want a dataframe with just the count distinct of each column, use this:
from pyspark.sql.functions import countDistinct
df.select( [ countDistinct(cn).alias("c_{0}".format(cn)) for cn in df.columns ] ).show()
The part of the count, taken from here: check number of unique values in each column of a matrix in spark