1. Read into a DataFrame
spark.read builds a DataFrameReader; .csv loads the file into a distributed DataFrame.
spark.read.option("header", true).csv("data.csv")Typorithm · easy
Read & Show
df-read-showsparkreadshowschema
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Step 1 of 8 · intro card
Learn Read & Show
DataFrames
spark.read loads data into a DataFrame; chain .option("header", true) so the first row becomes column names. show() previews rows and printSchema() reveals each column's inferred type.
A DataFrame is a distributed table of rows and named, typed columns — Spark's core abstraction for structured data.
spark.read returns a reader; chaining .option("header", true).csv("data.csv") loads the file and treats the first row as headers.
Once loaded, show() prints the first rows as a table and printSchema() lists every column with its inferred type, so you can confirm the data was parsed correctly.
01
spark.read loads data into a DataFrame.
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show() previews the first rows.
03
printSchema() lists columns and their types.
const df = spark.read.option("header", true).csv("data.csv");
df.show();
df.printSchema();Read the implementation
Connect each part of the code to the concept before you type.
2. Reader option
option("header", true) treats the first row as column names instead of data.
.option("header", true)3. Show rows
show() prints the first 20 rows of the DataFrame in a table for a quick look.
df.show();4. Inspect the schema
printSchema() prints each column with its inferred type, so you can verify the shape.
df.printSchema();A complete learning loop
01
Understand
Read the concept map and follow the code breakdown.
02
Reconstruct
Type the implementation, repair mistakes and work through every recall exercise.
03
Verify
Use the final check without hints; revisit weak concepts in short practice sessions.