How to verify Pyspark dataframe column type?

How to verify Pyspark dataframe column type?

If you want to verify the data type of a specific column in a PySpark DataFrame, you can check the DataFrame's schema. Here's how you can do it:

1. Accessing the DataFrame Schema:

Firstly, you can use the schema attribute of the DataFrame to get the schema:

df_schema = df.schema

2. Verifying the Column Type:

Given a column name, you can extract its data type from the schema:

from pyspark.sql.types import IntegerType column_name = "your_column_name" column_type = [f.dataType for f in df_schema.fields if f.name == column_name][0] # Check if column type is IntegerType if isinstance(column_type, IntegerType): print(f"The column '{column_name}' is of type Integer.") else: print(f"The column '{column_name}' is of type {column_type}.")

You can replace IntegerType with any other PySpark data type (e.g., StringType, DoubleType, etc.) as needed.

3. Example:

Let's say you have the following PySpark DataFrame:

from pyspark.sql import SparkSession from pyspark.sql.types import StructType, StructField, StringType, IntegerType spark = SparkSession.builder.appName("verify_column_type").getOrCreate() # Sample DataFrame schema = StructType([ StructField("name", StringType(), True), StructField("age", IntegerType(), True) ]) data = [("Alice", 25), ("Bob", 30)] df = spark.createDataFrame(data, schema=schema)

You can verify the type of the "age" column as follows:

column_name = "age" column_type = [f.dataType for f in df.schema.fields if f.name == column_name][0] if isinstance(column_type, IntegerType): print(f"The column '{column_name}' is of type Integer.") else: print(f"The column '{column_name}' is of type {column_type}.")

This will print:

The column 'age' is of type Integer.

This method ensures that you're checking the actual data type defined in the DataFrame's schema.

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