I currently have an AWS EMR with a linked notebook to that same cluster. I would like to load a spacy model (en_core_web_sm) but first I need to download the model which is usually done using python -m spacy download en_core_web_sm but I really can’t find how to do it in a PySpark Session. Here is my config : I’m
Tag: pyspark
Removing non-ascii and special character in pyspark dataframe column
I am reading data from csv files which has about 50 columns, few of the columns(4 to 5) contain text data with non-ASCII characters and special characters. I am trying to remove all the non-Ascii and special characters and keep only English characters, and I tried to do it as below There are no spaces in my column name. I
Adjusting incorrect data of a CSV file data in a Pyspark dataframe
I am trying to read CSV file into a dataframe in Pyspark but I have a CSV file which has mixed data. Part of its data belongs to its adjacent column. Is there any way to modify the dataframe in python to get the output dataframe as expected. Sample CSV Expected Output Answer You can do this by making use
Most efficient way of transforming a date column to a timestamp column + an hour
I want to know if there is a better way of transforming a date column into a datetime column + 1 hour than the method I am currently using. Here is my dataframe: My code: Which gives the output: Does anyone know a more efficient way of doing this. Casting to a timestamp twice seems a bit clumsy. Many thanks.
Pyspark groupBy DataFrame without aggregation or count
Can it iterate through the Pyspark groupBy dataframe without aggregation or count? For example code in Pandas: Answer At best you can use .first , .last to get respective values from the groupBy but not all in the way you can get in pandas. ex: Since their is a basic difference between the way the data is handled in pandas
Read avro files in pyspark with PyCharm
I’m quite new to spark, I’ve imported pyspark library to pycharm venv and write below code: , everything seems to be okay but when I want to read avro file I get message: pyspark.sql.utils.AnalysisException: ‘Failed to find data source: avro. Avro is built-in but external data source module since Spark 2.4. Please deploy the application as per the deployment section
PySpark udf returns null when function works in Pandas dataframe
I’m trying to create a user-defined function that takes a cumulative sum of an array and compares the value to another column. Here is a reproducible example: In Pandas, this is the output: In Spark using temp_sdf.withColumn(‘len’, test_function_udf(‘x_ary’, ‘y’)), all of len ends up being null. Would anyone know why this is the case? Also, replacing cumsum_array = np.cumsum(np.flip(x_ary)) fails
join two patrition dataframe pyspark
I have two dataframes with partition level 2. Dataframes are small probably around 100 rows each. df1 : df2: my final df will be join of df1 and df2 based on columnindex. But when I am joining two data frames as per below it looks it is shuffling and giving me the incorrect results. Is there any way I can
I need to append only those who has non null values in pyspark dataframe
I am having the pyspark dataframe (df) having below sample table (table1): id, col1, col2, col3 1, abc, null, def 2, null, def, abc 3, def, abc, null I am trying to get new column (final) by appending the all the columns by ignoring null values. I have tried pyspark code and used f.array(col1, col2, col3). Values are getting appended
Extract multiple words using regexp_extract in PySpark
I have a list which contains some words and I need to extract matching words from a text line, I found this, but it only extracts one word. keys file content this is a keyword part_description file content 32015 this is a keyword hello world Code Outputs Expected output I want to return all matching keyword and their count and