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

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.

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  lastName0   1      Mark     Brown1   2       Tom  Anderson2   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