Preparing for a PySpark interview? Whether you’re a beginner in big data or an experienced data engineer, mastering the most essential PySpark commands is crucial for cracking technical interviews. In this guide, we’ll walk through the top 10 PySpark commands every candidate should know—complete with real-world use cases, syntax, and tips. From DataFrame creation to machine learning workflows, these commands cover everything you need to confidently tackle data engineering questions and impress interviewers in 2025.
1. DataFrame Creation and Inspection
spark.createDataFrame(): Create DataFrames from RDDs or structured data like lists and dictionaries.df.show(): Display top rows of the DataFrame.df.printSchema(): Show the schema of the DataFrame.df.describe(): Get statistical summaries.df.columnsanddf.dtypes: View column names and data types.
Use Case: Creating and exploring initial data in your PySpark environment.
2. Data Cleaning and Transformation
df.withColumn(): Add or modify columns.df.drop(): Drop columns.df.filter(),df.where(): Filter rows based on conditions.df.select(),df.selectExpr(): Select specific columns or expressions.df.na.drop(),df.na.fill(),df.fillna(): Handle missing values.
Use Case: Performing essential data preprocessing and transformations.
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3. Data Aggregation and Grouping
df.groupBy().agg(): Grouping and aggregating data.df.groupby().count(),df.groupby().sum(),df.groupby().avg(), etc.: Common aggregation methods.df.crosstab(),df.pivot(): Create cross-tabulations and pivot tables.
Use Case: Aggregating data to derive insights and key metrics.
4. Joining and Merging DataFrames
df.join(): Join DataFrames using different types of joins (inner,left,right,outer).df.union(): Combine DataFrames with identical schemas.
Use Case: Combining multiple datasets to perform integrated analysis.
5. Sorting and Ordering Data
df.orderBy(),df.sort(): Sort DataFrames by specific columns.df.distinct(),df.dropDuplicates(): Remove duplicate rows.
Use Case: Organizing and deduplicating your dataset.
6. Window Functions
Window.partitionBy().orderBy(): Define windows for running aggregates like cumulative sums.F.row_number(),F.rank(),F.dense_rank(),F.lag(),F.lead(): Apply row-based functions.
Use Case: Handling time-series data or performing complex aggregations.
7. Exploding, Splitting, and Complex Transformations
df.withColumn("new_col", F.explode("array_col")): Explode nested or array columns.df.withColumn("split_col", F.split("col", delimiter)): Split strings into arrays.df.withColumn("json_col", F.from_json("json_string_col", schema)): Parse JSON columns.
Use Case: Working with complex or nested data structures.
8. Handling Dates and Timestamps
F.to_date(),F.to_timestamp(),F.date_add(),F.date_sub(),F.datediff(): Date-related functions.F.year(),F.month(),F.dayofweek(): Extract specific components from dates.
Use Case: Performing date-based analysis and calculations (like tenure calculations).
9. Machine Learning (MLlib)
VectorAssembler(),StringIndexer(),OneHotEncoder(),Pipeline(): Common feature transformation tools.LogisticRegression(),DecisionTreeClassifier(),RandomForestClassifier(), etc.: Popular machine learning models.CrossValidator(),TrainValidationSplit(): Model evaluation and tuning.
Use Case: Building and evaluating ML models using PySpark.
10. Saving and Reading Data
df.write.format("parquet").save("path"): Write DataFrame to various formats like Parquet, CSV, JSON, etc.spark.read.format("parquet").load("path"): Read from various data formats.
Use Case: Persisting and retrieving processed datasets efficiently.
Bonus Commands to Remember
df.rdd: Convert a DataFrame to an RDD for low-level transformations.df.cache()anddf.persist(): Improve the efficiency of iterative operations by caching DataFrames.df.repartition(),df.coalesce(): Optimize partitioning for distributed computing.
By revisiting these commands and understanding their use cases, you’ll be prepared to handle typical PySpark interview questions.
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