I would like to append to each value of a column in a pyspark dataframe a word( for example from a list of words). I though to just convert it to pandas framework because it is easier but I need to do it on pyspark. Any Ideas? Thank you :)
you can do it easily with concat function:
from pyspark.sql import functions as F
for col in df.columns:
df.withColumn(col, F.concat(F.col(col), F.lit("new_word"))
Related
I want to compare dataframe column after trim and convert it into lower case in pyspark.
is below code is wrong ?
if f.trim(Loc_Country_df.LOC_NAME.lower) == f.trim(sdf.location_name.lower):
print('y')
else:
print('N')
No you can't do like this because df columns are not just variable , they are collection of values(Iterable).
The best way is you can perform join.
join_df=join_df.withColumn("LOC_NAME",trim(col("LOC_NAME")))
sdf=sdf.withColumn("location_name",trim(col("location_name")))
join_df=Loc_Country_df.join(sdf,Loc_Country_df.LOC_NAME==sdf.location_name,"left")
from pyspark.sql import functions as f
join_df.withColumn('Result', f.when(f.col('LOC_NAME') == 0, "N").otherwise("Y")).show()
I have a pyspark dataframe:
Now, I want to add a new column called "countryAndState", where, for example for the first row, the value would be "USA_CA". I have tried several approaches, the last one was the following:
df_2 = df.withColumn("countryAndState", '{}_{}'.format(df.country, df.state))
I have tried with "country" and "state" instead, or with simply country and state,and also using col() but nothing seems to work. Can anyone help me solve this?
You can't use Python format strings in Spark. Use concat instead:
import pyspark.sql.functions as F
df_2 = df.withColumn("countryAndState", F.concat(F.col('country'), F.lit('_'), F.col('state')))
or concat_ws, if you need to chain many columns together with a given separator:
import pyspark.sql.functions as F
df_2 = df.withColumn("countryAndState", F.concat_ws('_', F.col('country'), F.col('state')))
I am having the following python/pandas command:
df.groupby('Column_Name').agg(lambda x: x.value_counts().max()
where I am getting the value counts for ALL columns in a DataFrameGroupBy object.
How do I do this action in PySpark?
It's more or less the same:
spark_df.groupBy('column_name').count().orderBy('count')
In the groupBy you can have multiple columns delimited by a ,
For example groupBy('column_1', 'column_2')
try this when you want to control the order:
data.groupBy('col_name').count().orderBy('count', ascending=False).show()
Try this:
spark_df.groupBy('column_name').count().show()
from pyspark.sql import SparkSession
from pyspark.sql.functions import count, desc
spark = SparkSession.builder.appName('whatever_name').getOrCreate()
spark_sc = spark.read.option('header', True).csv(your_file)
value_counts=spark_sc.select('Column_Name').groupBy('Column_Name').agg(count('Column_Name').alias('counts')).orderBy(desc('counts'))
value_counts.show()
but spark is much slower than pandas value_counts() on a single machine
df.groupBy('column_name').count().orderBy('count').show()
In pandas Dataframe/Series there's a .isnull() method. Is there something similar in the syntax of where= filter of the select method of HDFStore?
WORKAROUND SOLUTION:
The /meta section of a data column inside hdf5 can be used as a hack solution:
import pandas as pd
store = pd.HDFStore('store.h5')
print(store.groups)
non_null = list(store.select("/df/meta/my_data_column/meta"))
df = store.select('df', where='my_data_column == non_null')
Let us consider a pandas DataFrame (df) like the one shown above.
How do I convert it to a pandas Series?
Just select the single column of your frame
df['Count']
result = pd.Series(df['Count'])