PySpark DataFrame
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.
– Databricks
If you are coming from a Python background I would assume you already know what Pandas DataFrame is; PySpark DataFrame is mostly similar to Pandas DataFrame with exception PySpark DataFrames are distributed in the cluster (meaning the data in DataFrame’s are stored in different machines in a cluster) and any operations in PySpark executes in parallel on all machines whereas Panda Dataframe stores and operates on a single machine.
If you have no Python background, I would recommend you learn some basics on Python before you proceeding this Spark tutorial. For now, just know that data in PySpark DataFrame’s are stored in different machines in a cluster.
Is PySpark faster than pandas?
Due to parallel execution on all cores on multiple machines, PySpark runs operations faster then pandas. In other words, pandas DataFrames run operations on a single node whereas PySpark runs on multiple machines. To know more read at pandas DataFrame vs PySpark Differences with Examples.
DataFrame creation
Simplest way to create an DataFrame is from a Python list of data. DataFrame can also be created from an RDD and by reading a files from several sources.
using createDataFrame()
By using 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)
Since DataFrame’s are structure format which contains names and column, we can get the schema of the DataFrame using df.printSchema()
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)
df.show() shows the 20 elements from the DataFrame.
+---------+----------+--------+----------+------+------+
|firstname|middlename|lastname|dob |gender|salary|
+---------+----------+--------+----------+------+------+
|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 |
+---------+----------+--------+----------+------+------+
DataFrame operations
Like RDD, DataFrame also has operations like Transformations and Actions.
DataFrame from external data sources
In realtime applications, DataFrame’s 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()
Supported file formats
DataFrame has a rich set of API which supports reading and writing several file formats
- csv
- text
- Avro
- Parquet
- tsv
- xml and many more
