I need to convert this pyspark SQL code sample: Into a fully dataframe code without SQL expression. I tried: TypeError: condition should be a Column But obviously, it’s not working. What am I doing wrong? Any suggestion will be appreciated! Answer Use isNull to check, not is None:
Tag: apache-spark
Filter expected value from list in df column
I have a data frame with the following column: I want to return a column with single value based on a conditional statement. I wrote the following function: When running the function on the column df.withColumn(“col”, filter_func(“raw_col”)) I have the following error col should be Column What’s wrong here? What should I do? Answer You can use array_contains function: But
Databricks – How to pass accessToken to spark._sc._gateway.jvm.java.sql.DriverManager?
I would like to use databricks to run some custom SQL by below function, May I know how to add the “accessToken” as properties? It return: Thanks! Answer It doesn’t work because DriverManager doesn’t have the function that accepts HashMap that is created from Python dict – it has the function that accepts Properties object. You can create instance of
pyspark – filter rows containing set of special characters
I have a data frame as follow:- Now I want to find the count of total special characters present in each column. So I have used str. contains function to find it, though it is running but it does not find the special characters. Answer You may want to use rlike instead of contains, which allows to search for regular
Spread List of Lists to Sparks DF with PySpark?
I’m currently struggling with following issue: Let’s take following List of Lists: How can I create following Sparks DF out of it with one row per element of each sublist: The only way I’m getting this done is by processing this list to another list with for-loops, which basically then already represents all rows of my DF, which is probably
Col names not detected – AnalysisException: Cannot resolve ‘Name’ given input columns ‘col10’
I’m trying to run a transformation function in a pyspark script: My dataset looks like this: My desired output is something like this: However, the last code line gives me an error similar to this: When I check: I see ‘col1’, ‘col2’ etc in the first row instead of the actual labels ( [“Name”,”Type”] ). Should I separately remove and
PySpark write a function to count non zero values of given columns
I want to have a function that will take as input column names and grouping conditions and based on that for each column it will return the count of non zero values for each column. Something like this, but include non-zero condition as well. Answer You can use a list comprehension to generate the list of aggregation expressions:
how to fill in null values in Pyspark
I have a df that will join calendar date df, Next Step: I am populating dates range of first and last date. Step2: let’s say this is the calendar df that has id, and calendar dates and i want to join with calendar dates I would like to fill in those all null values based on the first non null
converting python code to python spark code
Below code is in Python and i want to convert this code to pyspark, basically i’m not sure what will be the codefor the statement – pd.read_sql(query,connect_to_hive) to convert into pyspark Need to extract from data from the EDL, so making the connection to the EDL using PYODBC and them extract the data using sql query. pyodbc connection to the
TypeError: ‘GroupedData’ object is not iterable in pyspark dataframe
I have a Spark dataframe sdf with GPS points that looks like this: Since the spark dataframe contains different GPS trajectories generated by different users on different days, I want to write a function that loops through this df and feeds the corresponding set of coordinates to the (OSRM) request per date and per user group and not all at