python - Pyspark: show histogram of a data frame column

Question

In pandas data frame, I am using the following code to plot histogram of a column:

my_df.hist(column = 'field_1')

Is there something that can achieve the same goal in pyspark data frame? (I am in Jupyter Notebook) Thanks!

Answer 1

Unfortunately I don't think that there's a clean plot() or hist() function in the PySpark Dataframes API, but I'm hoping that things will eventually go in that direction.

For the time being, you could compute the histogram in Spark, and plot the computed histogram as a bar chart. Example:

import pandas as pd
import pyspark.sql as sparksql

# Let's use UCLA's college admission dataset
file_name = "https://stats.idre.ucla.edu/stat/data/binary.csv"

# Creating a pandas dataframe from Sample Data
df_pd = pd.read_csv(file_name)

sql_context = sparksql.SQLcontext(sc)

# Creating a Spark DataFrame from a pandas dataframe
df_spark = sql_context.createDataFrame(df_pd)

df_spark.show(5)

This is what the data looks like:

Out[]:    +-----+---+----+----+
          |admit|gre| gpa|rank|
          +-----+---+----+----+
          |    0|380|3.61|   3|
          |    1|660|3.67|   3|
          |    1|800| 4.0|   1|
          |    1|640|3.19|   4|
          |    0|520|2.93|   4|
          +-----+---+----+----+
          only showing top 5 rows


# This is what we want
df_pandas.hist('gre');

Histogram when plotted in using df_pandas.hist()

# Doing the heavy lifting in Spark. We could leverage the `histogram` function from the RDD api

gre_histogram = df_spark.select('gre').rdd.flatMap(lambda x: x).histogram(11)

# Loading the Computed Histogram into a Pandas Dataframe for plotting
pd.DataFrame(
    list(zip(*gre_histogram)), 
    columns=['bin', 'frequency']
).set_index(
    'bin'
).plot(kind='bar');

Histogram computed by using RDD.histogram()