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11A: Databricks – Spark ML – Pandas Dataframe & Matplotlib

Prerequisite: Extends 11: Databricks – Spark ML – Multivariate Linear Regression. How do you convert Pyspark dataframe to Pndas Dataframe? df.toPandas() converts Pyspark Dataframe to Pandas Dataframe.

from pyspark.sql.types import *

input_data = (
 (400,3,5,480000.00),
 (500,3,20,400000.00,), 
 (600,5,None,850000.00),
 (700,4,10,770000.00),
 (800,4,15,800000.00),
 (1000,6,20,1100000.00),
 (1200,7,2,1600000.00) 
)
 
data_schema = [StructField('land_area', IntegerType(), False), StructField('bedrooms', IntegerType(), True), StructField('age', IntegerType(), True), StructField('house_price',DoubleType(),False)]
final_struc = StructType(fields=data_schema)
 
df = spark.sparkContext.parallelize(input_data)
 
df_input= spark.createDataFrame(df, final_struc)
 
pandas_df = df_input.toPandas()

print(pandas_df)

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from pyspark.sql.types import *

input_data = (

(400,3,5,480000.00),

(500,3,20,400000.00,),

(600,5,None,850000.00),

(700,4,10,770000.00),

(800,4,15,800000.00),

(1000,6,20,1100000.00),

(1200,7,2,1600000.00)

)

data_schema = [StructField('land_area', IntegerType(), False), StructField('bedrooms', IntegerType(), True), StructField('age', IntegerType(), True), StructField('house_price',DoubleType(),False)]

final_struc = StructType(fields=data_schema)

df = spark.sparkContext.parallelize(input_data)

df_input= spark.createDataFrame(df, final_struc)

pandas_df = df_input.toPandas()

print(pandas_df)

Output:

df_input:pyspark.sql.dataframe.DataFrame
land_area:integer
bedrooms:integer
age:integer
house_price:double

   land_area  bedrooms   age  house_price
0        400         3   5.0     480000.0
1        500         3  20.0     400000.0
2        600         5   NaN     850000.0
3        700         4  10.0     770000.0
4        800         4  15.0     800000.0
5       1000         6  20.0    1100000.0
6       1200         7   2.0    1600000.0

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df_input:pyspark.sql.dataframe.DataFrame

land_area:integer

bedrooms:integer

age:integer

house_price:double

land_area bedrooms age house_price

0 400 3 5.0 480000.0

1 500 3 20.0 400000.0

2 600 5 NaN 850000.0

3 700 4 10.0 770000.0

4 800 4 15.0 800000.0

5 1000 6 20.0 1100000.0

6 1200 7 2.0 1600000.0

How to perform statistical data exploration?… Read more ›...

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12: Databricks – Spark ML – Categorical Features

Prerequisite: Extends Databricks – Spark ML – Linear Regression. Problem statement: Predict the house prices by land area in square feet, house condition as in “Bad”, “Average”, and “Good”, and house locality as in “Sydney”, … Read more ›...

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13: Databricks – Spark ML – Dummy Variables

Prerequisite: Extends Databricks – Spark ML – Categorical Features – Linear Regression. Problem statement: Predict the house prices by land area in square feet, house color as in “White”, “Grey”, and “Cream”, and house locality as in “Eastwood”, … Read more ›...

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14: Databricks – Spark ML – StringIndexer & OneHotEncoder

Prerequisite: Extends Databricks – Spark ML – Linear Regression Categorical Features. Problem statement: Predict the house prices by land area in square feet, house condition as in “Bad”, “Average”, and “Good”, and house locality as in “Sydney”, … Read more ›...

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15: Databricks – Spark ML – Classification with Logistic Regression

Prerequisite: Extends Spark ML – StringIndexer & OneHotEncoder – LinearRegression. Q. What is a Classification type prediction? How does it differ from Linear Regression? A. Classification type predictions are 1) Is Email spam or not? … Read more ›...

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16: Databricks – Spark ML Multiclass Logistic Regression & Pipeline

Prerequisite: Extends 15: Databricks – Spark ML – Classification with Logistic Regression & Databricks – Spark ML – StringIndexer & OneHotEncoder. Problem statement: Predict the likelihood of leaning towards a political party based on age & … Read more ›...

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17: Databricks – Spark ML K-Folds Cross Validation

Prerequisite: Extends Databricks – Spark ML Multiclass Logistic Regression & Pipeline. Q: What is K-Folds Cross Validation in ML? How does it differ from the train-split validation? A: Once we are done with training our model, … Read more ›...

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18: Databricks – Spark ML Decision Tree Classifier

Prerequisite: Extends Databricks – Spark ML Multiclass Logistic Regression & Pipeline. Q: What is a Decision Tree Classifier? A: A Decision Tree a Supervised Machine Learning where the data is continuously split into Root Node, … Read more ›...

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19: Databricks – Spark ML Random Forrest Classifier

Prerequisite: Extends Databricks – Spark ML Decision Tree Classifier. Q. What is the difference between a decision tree classifier and a random forrest classifier? A. As is implied by the names “Tree” … Read more ›...

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20: Databricks – Spark ML K-Means

Prerequisite: Extends Databricks – Spark ML Random Forrest Classifier. Q. What is K-Means algorithm? A. K-Means is an unsupervised algorithm, hence it does only have “features“, … Read more ›...

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21: Databricks – Spark ML Naive Bayes

Prerequisite: Extends 16: Databricks – Spark ML Multiclass Logistic Regression & Pipeline. Problem statement: Predict the iris flower species based on its sepal & petal length & width. Independent Variables (aka features): sepal length in cm, … Read more ›...

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22: Writing Tests in Databricks Notebooks in Python

Databricks writing tests with assert

Prerequisite: Extends 03: Databricks – Spark SCD Type 1. Note: databricks-connect let’s you connect your favourite IDE to your Databricks cluster. This allows you to lint, test, and package the code that you want to run on Databricks as explained in Enhance Your Databricks Workflow. … Read more ›...

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23: PySpark Delta Lake on Databricks notebook

Prerequisite: Databricks – Spark SCD Type 1. Delta Lake bring features like ACID Transactions, Scalable metadata, time travel, etc to a Data Lake. Databricks notebook has delta-spark package preinstalled. … Read more ›...

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