In this blog, we are going to learn different spark join types. We will also write code and validate data output for each join type to better understand them.
While processing data, working with strings is one of the most used tasks. That is why spark has provided multiple functions that can be used to process string data easily. In this blog, we will be listing most of the string functions in spark.
Table of Contents
We can use "initCap" function to capitalize word in string.
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df_csv = spark.read.format("csv") \ .option("inferSchema", "true") \ .option("header","true") \ .load("data/flights.csv") from pyspark.sql.functions import initcap df_csv.select(initcap("DEST_COUNTRY_NAME")).show(2) +--------------------------+ |initcap(DEST_COUNTRY_NAME)| +--------------------------+ | United States| | United States| +--------------------------+ |
We can use the "upper" function to convert all characters in a string to uppercase.
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from pyspark.sql.functions import upper, lower, col df_csv.select(upper(col("DEST_COUNTRY_NAME"))).show(2) +------------------------+ |upper(DEST_COUNTRY_NAME)| +------------------------+ | UNITED STATES| | UNITED STATES| +------------------------+ |
Similarly, we can use the"lower" function to convert string to lowercase characters.
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df_csv.select(lower(col("ORIGIN_COUNTRY_NAME"))).show(2) |
We can use the trim function to remove leading and trailing white spaces from data in spark.
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from pyspark.sql.functions import ltrim,rtrim,trim df.select(trim(col("DEST_COUNTRY_NAME"))).show(5) |
There are other two functions as well. ltrim and rtrim. These functions can be used to remove leading white spaces and trailing white spaces respectively. If you need detail about these function you can read this blog.
we can use lpad and rpad functions to add padding to data in spark. These function can be used to format data if needed.
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from pyspark.sql.functions import lpad, rpad df_csv.select("DEST_COUNTRY_NAME", \ "count", \ lpad(trim(col("count")), 4, "0").alias("formmated_data") \ ).show(2) |
For more details about padding, you can read this blog.
regexp_replace is a powerful function and can be used for multiple purposes from removing white spaces to replacing the string with something else. In the below example, we are replacing the "United States" with "us".
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from pyspark.sql.functions import regexp_replace reg_exp = "United States" df_csv.select(regexp_replace \ (col("DEST_COUNTRY_NAME"), \ reg_exp,"us") \ .alias("dest"), "DEST_COUNTRY_NAME") \ .show(5) |
Though regexp_replace is a powerful function, it can be difficult to use in many cases. That is why spark has provided some useful functions to deal with strings.
Using translate function we can replace one or more characters to another character.
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from pyspark.sql.functions import translate df_csv.select(translate \ (col("DEST_COUNTRY_NAME"), \ "t", "T") \ .alias("Updated Name") \ ).show(5) +-------------+ | Updated Name| +-------------+ |UniTed STaTes| |UniTed STaTes| |UniTed STaTes| | EgypT| |UniTed STaTes| +-------------+ |
Sometimes we need to check if the string contains some other string or not. For doing this, we can use the instr function.
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from pyspark.sql.functions import instr df_csv.select("DEST_COUNTRY_NAME", \ instr(col("DEST_COUNTRY_NAME"), \ "United")).show(5) |
We have seen multiple useful string functions in spark. This list by no meas is exhaustive. I will keep adding more functions when i encounter them. If you know some function to handle string do let me know. I hope you found this useful. See you in the next blog (Y)
In this blog, we are going to learn different spark join types. We will also write code and validate data output for each join type to better understand them.
In this blog, we are going to integrate spark with jupyter notebook and visual studio code to create easy-to-use development environment.
In this blog, we are going to learn about reading data from SQL tables in Spark. We will create Spark data frames from tables and query results as well.
