Pyspark Foundation for Data Engineering | Beginners

Launch your PySpark foundation by creating a SparkSession, importing essential libraries, and using basic functions to read dataframes and add columns.

Explore the spark data frame as a two-dimensional, table-like structure with columns and rows where each column holds values for a variable and each row a record.

Learn to create a dataframe in PySpark by passing data and columns, and understand column names, data types, and schema.

Explore the print operation in PySpark data engineering by observing how code output appears, how newly created data is printed, and how functional print behavior translates to visible results.

Analyze a small five-row dataset and inspect it quickly using a language of your choice, demonstrating the simplest way to examine the data.

Analyze the data to determine the number of records in a dataframe. Learn practical steps to count records in PySpark for data engineering beginners.

Learn how to inspect a pyspark dataframe's structure by using the columns function to reveal all the columns in your data.

Explore a PySpark dataframe by describing basic statistics and column details, including a district column, and demonstrate how simple data descriptions support data engineering tasks.

Get the schema of a DataFrame in Pyspark, and view the output in a tree format to understand fields like name, offspring, bank, and subject.

Learn how to work with a PySpark dataframe by creating a new column with withColumn, casting data types, and modifying existing columns to reflect updated values.

Learn how to cast a column's data type in PySpark, converting between integers, doubles, and strings, and inspect schema changes as you reconstitute data types.

Create and manipulate a PySpark DataFrame by adding a new column with an integer constant (for example, 300), explore arithmetic and string constant columns, and view the results.

Apply the learnings to compute percentages from scores in data engineering by creating a new percentage column from a total column, using the 100 division formula, illustrated with example scores.

Use bround to round numeric columns to integers or decimals, applying banker’s rounding to the nearest even number and controlling decimal places in PySpark data frames.

Explore how to sort a pyspark data frame using ascending and descending order, compare different sort methods and the order by syntax, and understand how to apply sorting on columns.

Drop single or multiple columns in a dataframe to create a new dataframe excluding total score and percentage columns, using the drop operation and handling missing columns.

Discover how to rename an existing column in Pyspark by passing the current column name and the new column name to the rename function, and preview the resulting data.

Create a column with constant string values to label performers as excellent, very good, good, or average in a PySpark data frame.

Master conditional statements in PySpark to classify student scores into categories like excellent and very good, using if and otherwise logic to create a new dataframe column.

Learn how to change a column's case in PySpark using the upper and lower functions, with practical examples converting values to lowercase and uppercase.

Explore filter operations in pyspark by applying boolean conditions to dataframes, using comparisons like greater than and greater than or equal to, and filtering by strings and column values.

Learn grouping and aggregations in PySpark: group by grade, count students, compute mean and max, handle distinct values, and rename or create columns based on conditions.