Pyspark: display a spark data frame in a table format

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I am using pyspark to read a parquet file like below:

my_df = sqlContext.read.parquet('hdfs://myPath/myDB.db/myTable/**')

Then when I do my_df.take(5), it will show [Row(...)], instead of a table format like when we use the pandas data frame.

Is it possible to display the data frame in a table format like pandas data frame? Thanks!

The show method does what you're looking for.

For example, given the following dataframe of 3 rows, I can print just the first two rows like this:

df = sqlContext.createDataFrame([("foo", 1), ("bar", 2), ("baz", 3)], ('k', 'v'))
df.show(n=2)

which yields:

+---+---+
|  k|  v|
+---+---+
|foo|  1|
|bar|  2|
+---+---+
only showing top 2 rows

Different approaches to manually create Spark DataFrames, . someDF has the following schema. toDF() is limited because the column type and nullable flag cannot be customized. Then when I do my_df.take(5), it will show [Row()], instead of a table format like when we use the pandas data frame. Is it possible to display the data frame in a table format like pandas data frame?

As mentioned by @Brent in the comment of @maxymoo's answer, you can try

df.limit(10).toPandas()

to get a prettier table in Jupyter. But this can take some time to run if you are not caching the spark dataframe. Also, .limit() will not keep the order of original spark dataframe.

Why DataFrames over RDDs in Apache Spark?, catalyst optimizer executes query plans, and it executes the queries on RDDs. PySpark provides spark.read.csv("path") to read a CSV file into PySpark DataFrame and dataframeObj.write.csv("path") to save or write to the CSV file. In this tutorial, you will learn how to read a single file, multiple files, all files from a local directory into DataFrame, applying some transformations, and finally writing DataFrame back to CSV file using PySpark (Spark with Python) example.

Yes: call the toPandas method on your dataframe and you'll get an actual pandas dataframe !

Spark SQL and DataFrames, But due to Python's dynamic nature, many of the benefits of the Dataset API are already available It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with _ // Print the schema in a tree format df. Previous Window Functions In this post we will discuss about writing a dataframe to disk using the different formats like text, json , parquet ,avro, csv. We have set the session to gzip compression of parquet.

Let's say we have the following Spark DataFrame:

df = sqlContext.createDataFrame([(1, "Mark", "Brown"), (2, "Tom", "Anderson"), (3, "Joshua", "Peterson")], ('id', 'firstName', 'lastName'))

There are typically three different ways you can use to print the content of the dataframe:

Print Spark DataFrame

The most common way is to use the show() function:

>>> df.show()
+---+---------+--------+
| id|firstName|lastName|
+---+---------+--------+
|  1|     Mark|   Brown|
|  2|      Tom|Anderson|
|  3|   Joshua|Peterson|
+---+---------+--------+

Print Spark DataFrame vertically

Say that you have a fairly large number of columns and your dataframe doesn't fit in the screen. You can print the rows vertically - For example, the following command will print the top two rows, vertically, without any truncation.

>>> df.show(n=2, truncate=False, vertical=True)
-RECORD 0-------------
 id        | 1        
 firstName | Mark     
 lastName  | Brown    
-RECORD 1-------------
 id        | 2        
 firstName | Tom      
 lastName  | Anderson 
only showing top 2 rows

Convert to Pandas and print Pandas DataFrame

Alternatively, you can convert your Spark DataFrame into a Pandas DataFrame using .toPandas() and finally print() it.

>>> df_pd = df.toPandas()
>>> print(df_pd)
   id firstName  lastName
0   1      Mark     Brown
1   2       Tom  Anderson
2   3    Joshua  Peterson

Note that this is not recommended when you have to deal with fairly large dataframes, as Pandas needs to load all the data into memory.

Spark SQL and DataFrames, Scala; Java; Python; R appName("Spark Hive Example") _ import spark.sql sql("CREATE TABLE IF NOT EXISTS src (key Aggregation queries are also supported. sql("SELECT COUNT(*) FROM src").show()  PySpark is a good python library to perform large-scale exploratory data analysis, create machine learning pipelines and create ETLs for a data platform. If you already have an intermediate level in Python and libraries such as Pandas, then PySpark is an excellent language to learn to create more scalable and relevant analyses and pipelines.

Spark SQL and DataFrames, It is conceptually equivalent to a table in a relational database or a data frame in R/Python The DataFrame API is available in Scala, Java, Python, and R. age name // null Michael // 30 Andy // 19 Justin // Print the schema in a tree format df. In Spark, data is represented by DataFrame objects, which can be thought of as a 2D structure following the tidy data format. This means that each row represents an observation and each column a variable; accordingly, columns must have names and types.

Spark SQL and DataFrames, It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with The DataFrame API is available in Scala, Java, Python, and R. In Scala and Java, a DataFrame is _ // Print the schema in a tree format df. You can use the following APIs to accomplish this. Ensure the code does not create a large number of partition columns with the datasets otherwise the overhead of the metadata can cause significant slow downs. If there is a SQL table back by this directory, you will need to call refresh table <table-name> to update the metadata prior to the query.

Introduction to DataFrames, This article demonstrates a number of common Spark DataFrame functions module sql from pyspark.sql import * # Create Example Data - Departments createDataFrame(departmentsWithEmployeesSeq1) display(df1) I have a table in the Hive metastore and I'd like to access to table as a DataFrame. Setting CSV options. Below is the code to write spark dataframe data into a SQL Server table using Spark SQL in pyspark: Dec 16, 2018 · import pandas as pd pd. Jun 25, 2010 · Description "CSV" stands for "comma-separated values", though many datasets use a delimiter other than a comma.

Comments
  • try this: my_df.take(5).show()
  • I got error: <ipython-input-14-d14c0ee9b9fe> in <module>() ----> my_df.take(5).show() AttributeError: 'list' object has no attribute 'show'
  • it should be my_df.show().take(5)
  • @MaxU how is .take(5).show() different from just .show(5)? Is it faster?
  • It is v primitive vs pandas: e.g. for wrapping it does not allow horizontal scrolling
  • I tried to do: my_df.toPandas().head(). But got the error: Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 301 in stage 2.0 failed 1 times, most recent failure: Lost task 301.0 in stage 2.0 (TID 1871, localhost): java.lang.OutOfMemoryError: Java heap space
  • This is dangerous as this will collect the whole data frame into a single node.
  • It should be emphasized that this will quickly cap out memory in traditional Spark RDD scenarios.
  • It should be used with a limit, like this df.limit(10).toPandas() to protect from OOMs
  • Using .toPandas(), i am getting the following error: An error occurred while calling o86.get. : java.util.NoSuchElementException: spark.sql.execution.pandas.respectSessionTimeZone How do i deal with this?