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 = data.select(list(map(lambda 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()

print(y_test)

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: https://stackoverflow.com/questions/25699439/how-to-iterate-over-consecutive-chunks-of-pandas-dataframe-efficiently
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>.cognitiveservices.azure.com'
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)

display(sentiments_df)

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 = requests.post(sentiment_url, 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)
  
display(sentiments_df)
sentiments_df.count()

Write the results out to mounted storage

sentiments_df.coalesce(1).write.csv("/mnt/textanalytics/sentimentanalysis/")

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
    
get_nonstring_cols(df)

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)])")
        
    else:
        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) + ")])"
    else:
        iter_structfield = "StructField('" + colname +  "', " + coltype + ", " + str(colnull) + "),"
    
    schema_set.append(iter_structfield)

## Convert list to single string
schema_string = ''.join(map(str, schema_set))

## This will execute the generated command string
exec(schema_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")))
display(date_dim)

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)
  
  return(df_unpvt)
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
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