Spark DataFrame and overview
DataFrame definition is very well explained by Databricks hence I do not want to define it again and confuse you. Below is the definition I took it from Databricks.
DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as structured data files, tables in Hive, external databases, or existing RDDs.
The simplest way to create a DataFrame is from a Python list of data. DataFrame can also be created from an RDD and by reading files from several sources.
createDataFrame() function of the SparkSession you can create a DataFrame.
data = [('James','','Smith','1991-04-01','M',3000), ('Michael','Rose','','2000-05-19','M',4000), ('Robert','','Williams','1978-09-05','M',4000), ('Maria','Anne','Jones','1967-12-01','F',4000), ('Jen','Mary','Brown','1980-02-17','F',-1) ] columns = ["firstname","middlename","lastname","dob","gender","salary"] df = spark.createDataFrame(data=data, schema = columns)
DataFrame from external data sources
In real-time applications, DataFrames are created from external sources like files from the local system, HDFS, S3 Azure, HBase, MySQL table e.t.c. Below is an example of how to read a CSV file from a local system.
df = spark.read.csv("/tmp/resources/zipcodes.csv") df.printSchema()
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