I need to create a dictionary from Spark dataframe’s schema of type pyspark.sql.types.StructType. The code needs to go through entire StructType, find only those StructField elements which are of type StructType and, when extracting into dictionary, use the name of parent StructField as key while value would be name of only the first nested/child StructField. Example schema (StructType): Desired result:
Tag: pyspark
PySpark: Performing One-Hot-Encoding
I need to perform classification task on a dataset which consists categorical variables. I performed the one-hot encoding on that data. But I am confused that whether I am doing it right way or not. Step 1: Lets say, for example, this is a dataset: Step 2: After performing one-hot encoding it gives this data: Step 3: Here the fourth
Regexp_replace “,” with “.” every other commas in spark
I have a dataframe that instead of . it has , and separators of numbers are also comma, I need to replace only odd comma to dot. The dataframe is very big but as an example, I have this: I want this df: Answer You can split on all commas , and later you can use for-loop: with range(0, len(splitted_data),
Replicate a function from pandas into pyspark
I am trying to execute the same function on a spark dataframe rather than pandas. Answer A direct translation would require you to do multiple collect for each column calculation. I suggest you do all calculations for columns in the dataframe as a single row and then collect that row. Here’s an example. Calculate percentage of whitespace values and number
Replace Spark array values with values from python dictionary
I have a Spark dataframe column having array values: I want to replace [0,1,2,3,4] with [negative,positive,name,sequel,odd] Answer
PySpark sum all the values of Map column into a new column
I have a dataframe which looks like this I want to sum of all the row wise decimal values and store into a new column My approach This is not working as it says, it can be applied only to int Answer Since, your values are of float type, the initial value passed within the aggregate should match the type
Pyspark find existing set of rows in a dataframe and replace it with values from another dataframe
I have a Pyspark dataframe_Old (dfo) as below: Id neighbor_sid neighbor division a1 1100 Naalehu Hawaii a2 1101 key-west-fl Miami a3 1102 lubbock Texas a10 1202 bay-terraces California I have a Pyspark dataframe_new (dfn) as below: Id neighbor_sid neighbor division a1 1100 Naalehu Hawaii a2 1111 key-largo-fl Miami a3 1103 grapevine Texas a4 1115 meriden-ct Connecticut a12 2002 east-louisville Kentucky
PySpark – Cumulative sum with limits
I have a dataframe as follows: The goal is to calculate a score for the user_id using valor as base, it will start from 3 and increase or decrease by 1 as it goes in the valor column. The main problem here is that my score can’t be under 1 and can’t be over 5, so the sum must always
string split with the value of another clumn PySpark
I have the following data frame i want it to split path column with value of the item column in the same index i’ve used this udf function it worked very well But, i was wondering if there’s another way to do it with pyspark function because i can’t use in anyway the “org” to join with another dataframe or
installed geolocator but ImportError: cannot import name ‘DummyLocator’
I Have installed geolocator and when I use pip install geolocator it returns: Requirement already satisfied: geolocator in /opt/anaconda3/lib/python3.6/site-packages (0.1.1) but when I try to import it with import geolocator raises this error: How can I resolve this error? Answer Problem solved by installing packages with this order: