Here I have used PySpark map transformation to read the values of properties (MapType column) Let’s see how to extract the key and values from the PySpark DataFrame Dictionary column. | |- value: string (valueContainsNull = true) ('James',),ĭf = spark.createDataFrame(data=dataDictionary, schema = schema)ĭf.printSchema() yields the Schema and df.show() yields the DataFrame output. Now let’s create a DataFrame by using above StructType schema. StructField('properties', MapType(StringType(),StringType()),True) Let’s see how to create a MapType by using PySpark StructType & StructField, StructType() constructor takes list of StructField, StructField takes a fieldname and type of the value.įrom import StructField, StructType, StringType, MapType PySpark provides several SQL functions to work with MapType.The key of the map won’t accept None/Null values.Third parm valueContainsNull is an optional boolean type that is used to specify if the value of the second param can accept Null/None values.The Second param valueType is used to specify the type of the value in the map.The First param keyType is used to specify the type of the key in the map.MapCol = MapType(StringType(),StringType(),False) In order to use MapType data type first, you need to import it from and use MapType() constructor to create a map object.įrom import StringType, MapType for e.g StringType, IntegerType, ArrayType, MapType, StructType (struct) e.t.c. keyType and valueType can be any type that extends the DataType class. Want to get better at using Pandas for data science-ing? Check out Byte Sized Pandas 101 tutorials.PySpark MapType is used to represent map key-value pair similar to python Dictionary (Dict), it extends DataType class which is a superclass of all types in PySpark and takes two mandatory arguments keyType and valueType of type DataType and one optional boolean argument valueContainsNull. Therefore, we select the column we need from the “big” dictionary. This creates a dictionary for all columns in the dataframe. Pd.Series(df.name.values,index=df.state).to_dict()Īnother approach to convert two column values into a dictionary is to first set the column values we need as keys to be index for the dataframe and then use Pandas’ to_dict() function to convert it a dictionary. And then we can apply Pandas’ to_dict() function to get dictionary. To create a dictionary from two column values, we first create a Pandas series with the column for keys as index and the other column as values. However, our purpose is slightly different, with one of the columns being keys for dictionary and the other column being values. It creates a dictionary for column values using the index as keys. It uses column names as keys and the column values as values. In most use cases, Pandas’ to_dict() function creates dictionary of dictionaries. It is a versatile function to convert a Pandas dataframe or Series into a dictionary. Recently came across Pandas’ to_dict() function. Pandas Columns to Dictionary with Pandas’ to_dict() function We can use list() function on the results from zip() function to see the list of tuples.Īpplying dict() function on the zip object with two iterables gives us the dictionary we need. In Python 3+, zip() function takes iterables as its argument and returns iterator. I have been using zip() function in Python to create list of tuples and then use dict() function to conver the list of tuples into a dictionary. Our goal is to create a dictionary with state code as keys and state names as values. The data is available at ‘s github page.įor our examples, let us subset the data and our data looks like this. We will use the US states data set containing two letter codes and state names. Pandas Convert Two Columns to a Dictionary And this will help understanding the Basics of the Pandas Dictionary with Pandas. Next, we will see two ways to use to_dict() functions to convert two columns into a dictionary. Just recently, came across a function pandas to_dict() function. This is one of the common situations, we will first see the solution that I have used for a while using zip() function and dict(). In this tutorial, we will learn how to convert two columns from dataframe into a dictionary.
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