spark-dataframe

I have a Dataframe A that contains a column of array string. ... |-- browse: array (nullable = true) | |-- element: string (containsNull = true) ... For example three sample rows would be +---------+--------+---------+ | column 1| browse| column n| +---------+--------+---------+ | foo1| [X,Y,Z]| bar1| | foo2| [K,L]| bar2| | foo3| [M]| bar3| And another Dataframe B that contains a column of string |-- browsenodeid: string (nullable = true) Some sample rows for it would be +------------+ |browsenodeid| +------------+ | A| | Z| | M| How can I filter A so that I keep all the rows whose browse

I took some rows from csv file like this pd.DataFrame(CV_data.take(5), columns=CV_data.columns) and performed some functions on it. now i want to save it in csv again but it is giving error module 'pandas' has no attribute 'to_csv' I am trying to save it like this pd.to_csv(CV_data, sep='\t', encoding='utf-8') here is my full code. how can i save my resulting data in csv or excel? # Disable warnings, set Matplotlib inline plotting and load Pandas package import warnings warnings.filterwarnings('ignore') %matplotlib inline import pandas as pd pd.options.display.mpl_style = 'default' CV_data =

How can you do the same thing as df.fillna(method='bfill') for a pandas dataframe with a pyspark.sql.DataFrame ? The pyspark dataframe has the pyspark.sql.DataFrame.fillna method, however there is no support for a method parameter. In pandas you can use the following to backfill a time series: Create data import pandas as pd index = pd.date_range('2017-01-01', '2017-01-05') data = [1, 2, 3, None, 5] df = pd.DataFrame({'data': data}, index=index) Giving Out[1]: data 2017-01-01 1.0 2017-01-02 2.0 2017-01-03 3.0 2017-01-04 NaN 2017-01-05 5.0 Backfill the dataframe df = df.fillna(method='bfill')

I have a dataframe like below. itemName, itemCategory Name1, C0 Name2, C1 Name3, C0 I would like to save this dataframe as partitioned parquet file: df.write.mode("overwrite").partitionBy("itemCategory").parquet(path) For this dataframe, when I read the data back, it will have String the data type for itemCategory . However at times, I have dataframe from other tenants as below. itemName, itemCategory Name1, 0 Name2, 1 Name3, 0 In this case, after being written as partition, when read back, the resulting dataframe will have Int for the data type of itemCategory . Parquet file has the metadata

This question already has an answer here : DataFrame / Dataset groupBy behaviour/optimization (1 answer) Closed last year . I know that in RDD's we were discouraged from using groupByKey, and encouraged to use alternatives such as reduceByKey(), and aggregateByKey() since these other methods would reduce first on each partition, and then perform groupByKey() and thus reduces the amount of data being shuffled. Now, my question is if this still applies to Dataset/Dataframe? I was thinking that since catalyst engine does a lot of optimization, that the catalyst will automatically know that it