create pyspark dataframe from list with code examples

Apache Spark is an open-source, distributed computing system that provides an interface for programming entire clusters with programming languages like Python, Java or Scala. Spark provides a built-in framework for large-scale data processing based on RDDs – Resilient Distributed Datasets. PySpark, the Python API for Apache Spark, extends the capabilities of Spark with a programming interface compatible with Python.

Working with large datasets in PySpark requires handling distributed data structures like RDDs and DataFrames. DataFrames are distributed collections of data, organized into named columns similar to a database table. The PySpark DataFrames offer a rich API for data manipulation and querying, and are particularly useful for SQL-like operations and parallelized data processing.

In this tutorial, we will discuss how to create a PySpark DataFrame from a Python list, and perform basic operations on it.

Step 1 – Set up the PySpark Environment
Before we begin, we need to set up a PySpark environment. To get started, you can install the PySpark package using pip. Make sure that you have a compatible version of Apache Spark installed on your machine.

pip install pyspark

Once installed, you can start a PySpark session by importing the PySpark module and creating a SparkSession object.

from pyspark.sql import SparkSession

# Create a SparkSession
spark = SparkSession.builder \
    .appName("Pyspark Example") \

Step 2 – Create a List of Data
We begin by creating a Python list of data that we want to convert to a PySpark DataFrame. Here is an example of a list of employee records.

employee_list = [
    (1, "John", "Smith", "", 25, 2500),
    (2, "Jane", "Doe", "", 30, 3000),
    (3, "Mike", "Johnson", "", 40, 4000),
    (4, "Sarah", "Jones", "", 45, 4500),
    (5, "Tom", "Brown", "", 50, 5000)

The list above contains records of employee data like the employee ID, first name, last name, email, age, and salary.

Step 3 – Create a PySpark DataFrame from a Python List
To create a PySpark DataFrame from a Python list, we can use the PySpark API method createDataFrame().

# Define the schema for our DataFrame
from pyspark.sql.types import *

fields = [
    StructField("emp_id", IntegerType(), True),
    StructField("first_name", StringType(), True),
    StructField("last_name", StringType(), True),
    StructField("email", StringType(), True),
    StructField("age", IntegerType(), True),
    StructField("salary", IntegerType(), True)

emp_schema = StructType(fields)

# Convert the Python list to a PySpark DataFrame
employee_df = spark.createDataFrame(employee_list, schema=emp_schema)

We begin by defining the schema for our PySpark DataFrame. The schema defines the structure of the DataFrame by specifying the field names, data types, and whether the field can be nullable or not.

The createDataFrame() method accepts two arguments; the first being the Python list of data we want to convert to a PySpark DataFrame, and the second the schema that we defined earlier.

Step 4 – Displaying the PySpark DataFrame
We can use the show() method to display the contents of the PySpark DataFrame.

The output should look like this:

|emp_id|first_name|last_name|email |age|salary|
|1 |John |Smith | |25 |2500 |
|2 |Jane |Doe | |30 |3000 |
|3 |Mike |Johnson ||40 |4000 |
|4 |Sarah |Jones | |45 |4500 |
|5 |Tom |Brown | |50 |5000 |

The output shows the contents of our PySpark DataFrame, which is organized in columns.

Step 5 – Performing Basic Operations on the PySpark DataFrame
Now that we have created a PySpark DataFrame from a Python list, we can perform basic operations on the DataFrame like filtering, sorting, and aggregating.

Here is an example of how to filter the PySpark DataFrame to select only employees with a salary greater than 4000.

filtered_employee_df = employee_df.filter(employee_df.salary > 4000)

The output should look like this:

|emp_id|first_name|last_name|email |age|salary|
|4 |Sarah |Jones ||45 |4500 |
|5 |Tom |Brown | |50 |5000 |

We can also sort the PySpark DataFrame by salary in descending order.

sorted_employee_df = employee_df.sort("salary", ascending=False)

The output should look like this:

|emp_id|first_name|last_name|email |age|salary|
|5 |Tom |Brown | |50 |5000 |
|4 |Sarah |Jones | |45 |4500 |
|3 |Mike |Johnson ||40 |4000 |
|2 |Jane |Doe | |30 |3000 |
|1 |John |Smith | |25 |2500 |

In this tutorial, we learned how to create a PySpark DataFrame from a Python list, perform basic operations on it, and display the DataFrame contents. DataFrames provide a powerful API for big data processing in PySpark, and the combination of PySpark with Python provides users with a flexible and intuitive programming environment. With the knowledge gained in this tutorial, you can confidently start working with DataFrames in PySpark, and analyze large datasets with ease.

Sure! Here are some more details about the previous topics covered in the article.

PySpark DataFrame
PySpark DataFrames provide a structured and high-level distributed API for working with data. DataFrames are organized into named columns, resembling a table in a relational database. The PySpark DataFrame API provides a wide range of built-in functions to manipulate data, making it easier for data scientists and developers to build scalable and robust data-intensive applications. DataFrames are also optimized for parallel processing, allowing users to analyze massive amounts of data quickly and efficiently.

Creating PySpark DataFrame from List
Creating a PySpark DataFrame from a Python list is a common use case when working with data in PySpark. The createDataFrame() method accepts data in several different formats, including a list of tuples, a list of dictionaries, or a list of rows. When creating a PySpark DataFrame from a list, it is important to first define the schema of the DataFrame. The schema specifies the data types of each column, the column names, and whether columns can be null or not. Once the schema is defined, the createDataFrame() method is used to create a PySpark DataFrame from the list.

Basic Operations on PySpark DataFrame
PySpark DataFrame API provides a variety of inbuilt functions to perform basic operations on dataFrames, like filtering, sorting, aggregating, transforming, etc. One of the most common operations is filtering, which allows you to return a subset of rows based on a particular condition. Sorting is also commonly used in data analysis, and PySpark provides the sort() method to sort the data based on one or more columns. Aggregating is a process of summarizing data by grouping it based on one or more columns. PySpark provides the groupBy() method to group the data based on one or more columns and apply various aggregate functions like sum(), count() etc.

In conclusion, PySpark is a powerful and easy-to-use big data processing framework that makes it possible to analyze huge datasets using Python. PySpark DataFrames provide a high-level API for working with structured data, allowing users to perform SQL-like operations on large datasets. PySpark lets users scale up their data processing capabilities seamlessly and provides a powerful tool for data scientists and developers who work with large datasets. By using the PySpark DataFrame API, you can easily manipulate, transform and analyze your data, even if it is massive in size.

Popular questions

  1. What is a PySpark DataFrame?
    Answer: A PySpark DataFrame is a distributed collection of data organized in named columns, similar to a table in a relational database. It is a high-level API provided by PySpark that allows users to work with structured data using SQL-like syntax.

  2. How can you create a PySpark DataFrame from a Python list?
    Answer: To create a PySpark DataFrame from a Python list, first, you need to define the schema by specifying the data types for each column. Next, you need to use the createDataFrame() method to create the DataFrame from the list, passing the defined schema in the schema parameter.

  3. What is the purpose of the StructType function in PySpark?
    Answer: The StructType function in PySpark is used to define the schema of a PySpark DataFrame. It specifies the data types for each column in the DataFrame, along with column names and whether the column can be nullable or not.

  4. What are some common operations that can be performed on a PySpark DataFrame?
    Answer: Some common operations that can be performed on a PySpark DataFrame include filtering, sorting, aggregating, transforming and joining data. PySpark provides a wide range of built-in functions to perform these operations.

  5. Why is PySpark an important tool for data scientists and developers?
    Answer: PySpark is an important tool for data scientists and developers because it provides a scalable and distributed computing environment for handling big data. PySpark provides a high-level API that allows users to work with structured data in a way that is similar to SQL queries, making it easier to perform data analysis on large and complex datasets. Additionally, PySpark supports Python, which is a popular and widely-used language among data scientists and developers.



Cloud Computing and DevOps Engineering have always been my driving passions, energizing me with enthusiasm and a desire to stay at the forefront of technological innovation. I take great pleasure in innovating and devising workarounds for complex problems. Drawing on over 8 years of professional experience in the IT industry, with a focus on Cloud Computing and DevOps Engineering, I have a track record of success in designing and implementing complex infrastructure projects from diverse perspectives, and devising strategies that have significantly increased revenue. I am currently seeking a challenging position where I can leverage my competencies in a professional manner that maximizes productivity and exceeds expectations.
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