Pyspark: display a spark data frame in a table format

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

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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.

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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 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. If this is the case, the following configuration will help when converting a large spark dataframe to a pandas one:

spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")

For more details you can refer to my blog post Speeding up the conversion between PySpark and Pandas DataFrames

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

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If you are using Jupyter, this is what worked for me:

[1] df= spark.read.parquet("s3://df/*")

[2] dsp = users

[3] %%display dsp

This shows well-formated HTML table, you can also draw some simple charts on it straight away. For more documentation of %%display, type %%help.

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By default show() function prints 20 records of DataFrame. You can define number of rows you want to print by providing argument to show() function. You never know, what will be the total number of rows DataFrame will have. So, we can pass df.count() as argument to show function, which will print all records of DataFrame.

df.show()           --> prints 20 records by default
df.show(30)         --> prints 30 records according to argument
df.show(df.count()) --> get total row count and pass it as argument to show

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Maybe something like this is a tad more elegant:

df.display()
# OR
df.select('column1').display()

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