Introduction to PySpark substring
PYSPARK SUBSTRING is a function that is used to extract the substring from a DataFrame in PySpark. By the term substring, we mean to refer to a part of a portion of a string. We can provide the position and the length of the string and can extract the relative substring from that.
PySpark SubString returns the substring of the column in PySpark.
We can also extract character from a String with the substring method in PySpark. All the required output from the substring is a subset of another String in a PySpark DataFrame. This function is used in PySpark to work deliberately with string type DataFrame and fetch the required needed pattern for the same.
The syntax for the PYSPARK SUBSTRING function is:-
column name is the name of the column in DataFrame where the operation needs to be done.
S:- The starting Index of the PySpark Application.
L:- The Length to which the Substring needs to be extracted.
Df:- The PySpark DataFrame.
The withColumn function is used in PySpark to introduce New Columns in Spark DataFrame.
a.Name is the name of column name used to work with the DataFrame String whose value needs to be fetched.
Working Of Substring in PySpark
Let us see somehow the SubString function works in PySpark:-
The substring function is a String Class Method. The return type of substring is of the Type String that is basically a substring of the DataFrame string we are working on.
A certain Index is specified starting with the start index and end index, the substring is basically the subtraction of End – Start Index.
String basically is a char having the character of the String with an offset and count. A new string is created with the same char while calling the substring method. A different offset and count is created that basically is dependent on the input variable provided by us for that particular string DataFrame.
The count is the length of the string in which we are working for a given DataFrame.
By This method, the value of the String is extracted using the index and input value in PySpark.
One more method prior to handling memory leakage is the creation of new char every time the method is called and no more offset and count fields in the string.
Let us see some Example of how the PYSPARK SubString function works:-
Let’s start by creating a small DataFrame on which we want our DataFrame substring method to work.
This creates a Data Frame and the type of data in DataFrame is of type String.
Let us see the first example to check how substring normal function works:-
This will create a New Column with the Name of Sub_Name with the SubStr
The output will only contain the substring in a new column from 1 to 3.
Let’s check if we want to take the elements from the last index. The last index of a substring can be fetched by a (-) sign followed by the length of the String.
Let’s work with the same data frame as above and try to observe the scenario.
Creation of Data Frame.
Let’s try to fetch a part of SubString from the last String Element.
This prints out the last two elements from the Python Data Frame.
This will print the last 3 elements from the DataFrame.
The substring can also be used to concatenate the two or more Substring from a Data Frame in PySpark and result in a new substring.
The way to do this with substring is to extract both the substrings from the desired length needed to extract and then use the String concat method on the same.
Let’s check an example for this by creating the same data Frame that was used in the previous example.
Creation of Data Frame.
Now let’s try to concat two sub Strings and put that in a new column in a Python Data Frame.
Since SQL functions Concat or Lit is to be used for concatenation just we need to import a simple SQL function From PYSPARK.
from pyspark.sql.functions import concat, col, lit
This will all the necessary imports needed for concatenation.
This will concatenate the last 3 values of a substring with the first 3 values and display the output in a new Column. If the string length is the same or smaller then all the string will be returned as the output.
From these above examples, we saw how the substring methods are used in PySpark for various Data Related operations.
From the above article, we saw the use of SubString in PySpark. From various examples and classification, we tried to understand how the SubString method works in PySpark and what are is used at the programming level.
We also saw the internal working and the advantages of having SubString in Spark Data Frame and its usage in various programming purpose. Also, the syntax and examples helped us to understand much precisely the function.
This is a guide to PySpark substring. Here we discuss the use of SubString in PySpark along with the various examples and classification. You may also have a look at the following articles to learn more –
- PySpark SQL
- Python Substring
- SQL RANK()
How to check for a substring in a PySpark dataframe ?
In this article, we are going to see how to check for a substring in PySpark dataframe.
Substring is a continuous sequence of characters within a larger string size. For example, “learning pyspark” is a substring of “I am learning pyspark from GeeksForGeeks”. Let us look at different ways in which we can find a substring from one or more columns of a PySpark dataframe.
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Creating Dataframe for demonstration:
In the above dataframe, LicenseNo is composed of 3 information, 2-letter State Code + Year of registration + 8 digit registration number.
Method 1: Using DataFrame.withColumn()
The DataFrame.withColumn(colName, col) can be used for extracting substring from the column data by using pyspark’s substring() function along with it.
Syntax: DataFrame.withColumn(colName, col)
- colName: str,name of the new column
- col: str, a column expression for the new column
Returns a new DataFrame by adding a column or replacing the existing column that has the same name.
We will make use of the pyspark’s substring() function to create a new column “State” by extracting the respective substring from the LicenseNo column.
Syntax: pyspark.sql.functions.substring(str, pos, len)
Example 1: For single columns as substring.
Here, we have created a new column “State” where the substring is taken from “LicenseNo” column. (1, 2) indicates that we need to start from the first character and extract 2 characters from the “LicenseNo” column.
Example 2: For multiple columns as substring
Extracting State Code as ‘State’, Registration Year as ‘RegYear’, Registration ID as ‘RegID’, Expiry Year as ‘ExpYr’, Expiry Date as ‘ExpDt’, Expiry Month as ‘ExpMo’.
The above code demonstrates how withColumn() method can be used multiple times to get multiple substring columns. Each withColumn() method adds a new column in the dataframe. It is worth noting that it also retains the original columns as well.
Method 2: Using substr inplace of substring
Alternatively, we can also use substr fromcolumn typeinstead of using substring.
Returns a Column which is a substring of the column that starts at ‘startPos’ in byte and is of length ‘length’ when ‘str’ is Binary type.
Example: Using substr
The substr() method works in conjunction with the col function from the spark.sql module. However, more or less it is just a syntactical change and the positioning logic remains the same.
Method 3: Using DataFrame.select()
Here we will use the select() function to substring the dataframe.
Example: Using DataFrame.select()
Method 4: Using ‘spark.sql()’
The spark.sql() method helps to run relational SQL queries inside spark itself. It allows the execution of relational queries, including those expressed in SQL using Spark.
Example: Using ‘spark.sql()’
Here, we can see the expression used inside the spark.sql() is a relational SQL query. We can use the same in an SQL query editor as well to fetch the respective output.
Method 5: Using spark.DataFrame.selectExpr()
Using selectExpr() method is a way of providing SQL queries, but it is different from the relational ones’. We can provide one or more SQL expressions inside the method. It takes one or more SQL expressions in a String and returns a new DataFrame
Example: Using spark.DataFrame.selectExpr().
In the above code snippet, we can observe that we have provided multiple SQL expressions inside the selectExpr() method. Each of these expressions resemble a part of the relational SQL query that we write. We also preserved the original columns by mentioning them explicitly.
Get Substring of the column in Pyspark – substr()
In order to get substring of the column in pyspark we will be using substr() Function. We look at an example on how to get substring of the column in pyspark.
- Get substring of the column in pyspark using substring function.
- Get Substring from end of the column in pyspark substr() .
- Extract characters from string column in pyspark
colname- column name
start – starting position
length – number of string from starting position
We will be using the dataframe named df_states
Substring from the start of the column in pyspark – substr() :
df.colname.substr() gets the substring of the column. Extracting first 6 characters of the column in pyspark is achieved as follows.### Get Substring of the column in pyspark df = df_states.withColumn("substring_statename", df_states.state_name.substr(1,6)) df.show()
substr(1,6) returns the first 6 characters from column “state_name”
Get Substring from end of the column in pyspark
df.colname.substr() gets the substring of the column in pyspark . In order to get substring from end we will specifying first parameter with minus(-) sign.### Get Substring from end of the column in pyspark df = df_states.withColumn("substring_from_end", df_states.state_name.substr(-2,2)) df.show()
In our example we will extract substring from end. i.e. last two character of the column. We will specifying first parameter with minus(-) sign, Followed by length as second parameter so the resultant table will be
Extract characters from string columnin pyspark – substr()
Extract characters from string column in pyspark is obtained using substr() function. by passing two values first one represents the starting position of the character and second one represents the length of the substring. In our example we have extracted the two substrings and concatenated them using concat() function as shown below########## Extract N characters from string column in pyspark df_states_new=df_states.withColumn('new_string', concat(df_states.state_name.substr(1, 3), lit('_'), df_states.state_name.substr(6, 2))) df_states_new.show()
so the resultant dataframe will be
Other Related Topics:
PySpark Substring : In this tutorial we will see how to get a substring of a column on PySpark dataframe.
There are several methods to extract a substring from a DataFrame string column:
- The substring() function: This function is available using SPARK SQL in thepyspark.sql.functionsmodule.
- The substr() function: The function is also available through SPARK SQL but in thepyspark.sql.Columnmodule.
In this tutorial, I will show you how to get the substring of the column in pyspark using the substring() and substr() functions and also show you how to get a substring starting towards the end of the string.
Pyspark Substring Using SQL Function substring()
We have seen that the substring() function is available thanks to the pyspark.sql.functions module. The syntax of the function is as follows :
The function takes 3 parameters :
- str : the string whose substring we want to extract
- pos: the position at which the substring starts
- len: the length of the substring to be extracted
The substring starts from the position specified in the parameter pos and is of length len when str is String type.
Note : It is important to note that the index position is not based on 0 but starts from 1.
Pyspark substring() function using withColumn()
Below is an example of the substring() function using withColumn() :
In this example we have extracted the first 4 characters of the string from the Website column.
Pyspark substring() function using select()
We can get the substring of a column by using the select() function. Here is an example of its use :
Get Substring from end of the column
At times, it can be interesting to leave towards the end of the string:
Using substr() from pyspark.sql.Column
We also saw that it was possible to use the substr() function available in the pyspark.sql.Column module:
This produces the same result as the substring() function.
In this tutorial, we learned how to get a substring of a column in a DataFrame. I hope this tutorial interested you and don’t hesitate to leave me a comment if you have any questions about either of these 2 methods!
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Back to the python section
Substring column pyspark
In PySpark, the function is used to extract the substring from a DataFrame string column by providing the position and length of the string you wanted to extract.
In this tutorial, I have explained with an example of getting substring of a column using from pyspark.sql.functions and using from type.
Using SQL function substring()
Using the function of module we can extract a substring or slice of a string from the DataFrame column by providing the position and length of the string you wanted to slice.
Note: Please note that the position is not zero based, but 1 based index.
Below is an example of Pyspark substring() using withColumn().
In above example, we have created a DataFrame with two columns, id and date. Here date is in the form “year month day”. HereI have used substring() on date column to return sub strings of date as year, month, day respectively. Below is the output.
2. Using substring() with select()
In Pyspark we can get substring() of a column using select. Above example can bed written as below.
3.Using substring() with selectExpr()
Sample example using selectExpr to get sub string of column(date) as year,month,day. Below is the code that gives same output as above.
4. Using substr() from Column type
Below is the example of getting substring using function from type in Pyspark.
The above example gives output same as the above mentioned examples.
Complete Example of PySpark substring()
In this session, we have learned different ways of getting substring of a column in PySpark DataFarme. I hope you liked it! Keep practicing. And do comment in the comment section for any kind of questions!!
Pyspark alter column with substring
pyspark.sql.functions.substring(str, pos, len)
Substring starts at pos and is of length len when str is String type or returns the slice of byte array that starts at pos in byte and is of length len when str is Binary type
In your code,
Try this, (with fixed syntax)
where 1 = start position in the string and 10 = number of characters to include from start position (inclusive)
The accepted answer uses a (user defined function), which is usually (much) slower than native spark code. Grant Shannon's answer does use native spark code, but as noted in the comments by citynorman, it is not 100% clear how this works for variable string lengths.
Answer with native spark code (no udf) and variable string length
From the documentation of substr in pyspark, we can see that the arguments: startPos and length can be either or types (both must be the same type). So we just need to create a column that contains the string length and use that as argument.
- We use length - 2 because we start from the second character (and need everything up to the 2nd last).
- We need to use because we cannot add (or subtract) a number to a object. We need to first convert that number into a .
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