Other Common Tasks

Split Data into Training and Test Datasets

train, test = dataset.randomSplit([0.75, 0.25], seed = 1337)

Rename all columns

column_list = data.columns
prefix = "my_prefix"
new_column_list = [prefix + s for s in column_list]
#new_column_list = [prefix + s if s != "ID" else s for s in column_list] ## Use if you plan on joining on an ID later
column_mapping = [[o, n] for o, n in zip(column_list, new_column_list)]
# print(column_mapping)
data = old, new: col(old).alias(new),*zip(*column_mapping))))

Convert PySpark DataFrame to NumPy array

## Convert `train` DataFrame to NumPy
pdtrain = train.toPandas()
trainseries = pdtrain['features'].apply(lambda x : np.array(x.toArray())).as_matrix().reshape(-1,1)
X_train = np.apply_along_axis(lambda x : x[0], 1, trainseries)
y_train = pdtrain['label'].values.reshape(-1,1).ravel()
## Convert `test` DataFrame to NumPy
pdtest = test.toPandas()
testseries = pdtest['features'].apply(lambda x : np.array(x.toArray())).as_matrix().reshape(-1,1)
X_test = np.apply_along_axis(lambda x : x[0], 1, testseries)
y_test = pdtest['label'].values.reshape(-1,1).ravel()

Call Cognitive Service API using PySpark

Create `chunker` function

The cognitive service APIs can only take a limited number of observations at a time (1,000, to be exact) or a limited amount of data in a single call. So, we can create a chunker function that we will use to split the dataset up into smaller chunks.
## Define Chunking Logic
import pandas as pd
import numpy as np
# Based on:
def chunker(seq, size):
return (seq[pos:pos + size] for pos in range(0, len(seq), size))

Convert Spark DataFrame to Pandas

## sentiment_df_pd = sentiment_df.toPandas()

Set up API requirements

# pprint is used to format the JSON response
from pprint import pprint
import json
import requests
subscription_key = '<SUBSCRIPTIONKEY>'
endpoint = 'https://<SERVICENAME>'
sentiment_url = endpoint + "/text/analytics/v2.1/sentiment"
headers = {"Ocp-Apim-Subscription-Key": subscription_key}

Create DataFrame for incoming scored data

from pyspark.sql.types import *
sentiment_schema = StructType([StructField("id", IntegerType(), True),
StructField("score", FloatType(), True)])
sentiments_df = spark.createDataFrame([], sentiment_schema)

Loop through chunks of the data and call the API

for chunk in chunker(sentiment_df_pd, 1000):
print("Scoring", len(chunk), "rows.")
sentiment_df_json = json.loads('{"documents":' + chunk.to_json(orient='records') + '}')
response =, headers = headers, json = sentiment_df_json)
sentiments = response.json()
# pprint(sentiments)
sentiments_pd = pd.read_json(json.dumps(sentiments['documents']))
sentiments_df_chunk = spark.createDataFrame(sentiments_pd)
sentiments_df = sentiments_df.unionAll(sentiments_df_chunk)

Write the results out to mounted storage


Find All Columns of a Certain Type

import pandas as pd
def get_nonstring_cols(df):
types = spark.createDataFrame(pd.DataFrame({'Column': df.schema.names, 'Type': [str(f.dataType) for f in df.schema.fields]}))
result = types.filter(col('Type') != 'StringType').select('Column').rdd.flatMap(lambda x: x).collect()
return result

Change a Column's Type

from pyspark.sql.types import *
from pyspark.sql.functions import col
df = df.withColumn('col1', col('col1').cast(IntegerType()))

Generate StructType Schema Printout (Manual Execution)

## Fill in list with your desired column names
cols = ["col1", "col2", "col3"]
i = 1
for col in cols:
if i == 1:
print("schema = StructType([")
print("\tStructField('" + col + "', StringType(), True),")
elif i == len(cols):
print("\tStructField('" + col + "', StringType(), True)])")
print("\tStructField('" + col + "', StringType(), True),")
i += 1
## Once the output has printed, copy and paste into a new cell
## and change column types and nullability

Generate StructType Schema from List (Automatic Execution)

Struct Schema Creator for PySpark
[<Column Name>, <Column Type>, <Column Nullable>]
Types: binary, boolean, byte, date,
double, integer, long, null,
short, string, timestamp, unknown
from pyspark.sql.types import *
## Fill in with your desired column names, types, and nullability
cols = [["col1", "string", False],
["col2", "date", True],
["col3", "integer", True]]
## Loop to build list of StructFields
schema_set = ["schema = StructType(["]
for i, col in enumerate(cols):
colname = col[0]
coltype = col[1].title() + "Type()"
colnull = col[2]
if i == len(cols)-1:
iter_structfield = "StructField('" + colname + "', " + coltype + ", " + str(colnull) + ")])"
iter_structfield = "StructField('" + colname + "', " + coltype + ", " + str(colnull) + "),"
## Convert list to single string
schema_string = ''.join(map(str, schema_set))
## This will execute the generated command string

Make a DataFrame of Consecutive Dates

from pyspark.sql.functions import sequence, to_date, explode, col
date_dim = spark.sql("SELECT sequence(to_date('2018-01-01'), to_date('2019-12-31'), interval 1 day) as DATE").withColumn("DATE", explode(col("DATE")))

Unpivot a DataFrame Dynamically (Longer)

Pivot a wide dataset into a longer form. (Similar to the pivot_longer() function from the tidyr R package or the .wide_to_long method from pandas.)
## UnpivotDF Function
def UnpivotDF(df, columns, pivotCol, unpivotColName, valueColName):
columnsValue = list(map(lambda x: str("'") + str(x) + str("',") + str(x), columns))
stackCols = ','.join(x for x in columnsValue)
df_unpvt = df.selectExpr(pivotCol, f"stack({str(len(columns))}, {stackCols}) as ({unpivotColName}, {valueColName})")\
.select(pivotCol, unpivotColName, valueColName)
df_unpvt = UnpivotDF(df = df,
columns = df.columns[1:], ## The columns to transpose into a single, longer column
pivotCol = "ID", ## The column to leave in place (usually an ID)
unpivotColName = "Category", ## The name of the new column
valueColName = "value") ## The name of the column of values