Visualizations
Databricks supports various types of visualizations out of the box.
All notebooks support DataFrame visualizations using the display function. The display function also supports rendering image data types and various machine learning visualizations.
Databricks programming language notebooks (Python, Scala, R) support HTML graphics using the displayHTML function; you can pass it any HTML, CSS, or JavaScript code. This function support interactive graphics using JavaScript libraries such as D3.
display function
The easiest way to create a DataFrame visualization in Databricks is to call display(<dataframe-name>). For example, if you have a Spark DataFrame diamonds_df of a diamonds dataset grouped by diamond color, computing the average price, and you call
from pyspark.sql.functions import avg
diamonds_df = spark.read.csv("/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv", header="true", inferSchema="true")
display(diamonds_df.select("color","price").groupBy("color").agg(avg("price")))
A table of diamond color versus average price displays.
Note
If you see OK with no rendering after calling the display function, mostly likely the DataFrame or collection you passed in is empty.
If you register a Spark DataFrame as a table, you can also query it with SQL to create SQL visualizations.
pandas DataFrames
In Databricks Runtime 5.1 and above, display supports pandas DataFrames.
Note
If you call a pandas or koalas DataFrame without display, the table is rendered as it would be in Jupyter.
Visualization types
Click the bar chart icon 
to display a chart of tabular information:
You can click the down arrow next to the bar chart to choose another chart type and click Plot Options… to configure the chart.
Series coloring
Databricks supports two kinds of color consistency across charts: series set and global.
Series set color consistency assigns the same color to the same value if you have series with the
same values but in different orders (for example, A = ["Apple", "Orange", "Banana"] and B =
["Orange", "Banana", "Apple"]). The values are sorted before plotting, so both legends are sorted
the same way (["Apple", "Banana", "Orange"]), and the same values are given the same colors. However,
if you have a series C = ["Orange", "Banana"], it would not be color consistent with set
A because the set isn’t the same. The sorting algorithm would assign the first color to “Banana” in
set C but the second color to “Banana” in set A. If you want these series to be color consistent,
you can specify that charts should have global color consistency.
In global color consistency, each value is always mapped to the same color no matter what values the series have. To enable this for each chart, select the Global color consistency checkbox.
Note
To do achieve this consistency, Databricks hashes directly from values to colors. To avoid collisions (where two values go to the exact same color), the hash is to a large set of colors, which has the side effect that nice-looking or easily distinguishable colors cannot be guaranteed; with many colors there are bound to be some that are very similar looking.
Images types in DataFrames
display renders columns containing image data types as rich HTML. For clusters running Databricks Runtime 4.1 and above, display attempts to render image thumbnails for DataFrame columns matching
Spark’s ImageSchema. Thumbnail rendering works for any images successfully read in through the
readImages:org.apache.spark.sql.DataFrame)
function. For image values generated through other means, Databricks supports the rendering of 1, 3, or 4 channel
images (where each channel consists of a single byte), with the following constraints:
- One-channel images:
modefield must be equal to 0.height,width, andnChannelsfields must accurately describe the binary image data in thedatafield - Three-channel images:
modefield must be equal to 16.height,width, andnChannelsfields must accurately describe the binary image data in thedatafield. Thedatafield must contain pixel data in three-byte chunks, with the channel ordering(blue, green, red)for each pixel. - Four-channel images:
modefield must be equal to 24.height,width, andnChannelsfields must accurately describe the binary image data in thedatafield. Thedatafield must contain pixel data in four-byte chunks, with the channel ordering(blue, green, red, alpha)for each pixel.
Example
Suppose you have a folder containing some images:
If you read the images into a DataFrame with ImageSchema.readImages and then display the DataFrame, display renders thumbnails of the images:
from pyspark.ml.image import ImageSchema
image_df = ImageSchema.readImages(sample_img_dir)
display(image_df)
Machine learning visualizations
The display function supports various machine learning algorithm visualizations.
Residuals
For linear and logistic regressions, display supports rendering a fitted versus residuals plot. To obtain this plot, you supply the model and DataFrame.
The following example runs a linear regression on city population to house sale price data and then displays the residuals versus the fitted data.
# Load data
pop_df = spark.read.csv("/databricks-datasets/samples/population-vs-price/data_geo.csv", header="true", inferSchema="true")
# Drop rows with missing values and rename the feature and label columns, replacing spaces with _
pop_df = data.dropna() # drop rows with missing values
exprs = [col(column).alias(column.replace(' ', '_')) for column in data.columns]
# Register a UDF to convert the feature (2014_Population_estimate) column vector to a VectorUDT type and apply it to the column.
from pyspark.ml.linalg import Vectors, VectorUDT
spark.udf.register("oneElementVec", lambda d: Vectors.dense([d]), returnType=VectorUDT())
tdata = data.select(*exprs).selectExpr("oneElementVec(2014_Population_estimate) as features", "2015_median_sales_price as label")
# Run a linear regression
from pyspark.ml.regression import LinearRegression
lr = LinearRegression()
modelA = lr.fit(tdata, {lr.regParam:0.0})
# Plot residuals versus fitted data
display(modelA, tdata)
ROC curves
For logistic regressions, display supports rendering an ROC curve. To obtain this plot, you supply the model, the prepped data that is input to the fit method, and the parameter "ROC".
The following example develops a classifier that predicts if an individual earns <=50K or >50k a year from various attributes of the individual. The Adult dataset derives from census data, and consists of information about 48842 individuals and their annual income.
CREATE TABLE adult (
age DOUBLE,
workclass STRING,
fnlwgt DOUBLE,
education STRING,
education_num DOUBLE,
marital_status STRING,
occupation STRING,
relationship STRING,
race STRING,
sex STRING,
capital_gain DOUBLE,
capital_loss DOUBLE,
hours_per_week DOUBLE,
native_country STRING,
income STRING)
USING CSV
OPTIONS (path "/databricks-datasets/adult/adult.data", header "true")
dataset = spark.table("adult")
# Use One-Hot Encoding to convert all categorical variables into binary vectors.
from pyspark.ml import Pipeline
from pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, VectorAssembler
categoricalColumns = ["workclass", "education", "marital_status", "occupation", "relationship", "race", "sex", "native_country"]
stages = [] # stages in our Pipeline
for categoricalCol in categoricalColumns:
# Category Indexing with StringIndexer
stringIndexer = StringIndexer(inputCol=categoricalCol, outputCol=categoricalCol + "Index")
# Use OneHotEncoder to convert categorical variables into binary SparseVectors
# encoder = OneHotEncoderEstimator(inputCol=categoricalCol + "Index", outputCol=categoricalCol + "classVec")
encoder = OneHotEncoderEstimator(inputCols=[stringIndexer.getOutputCol()], outputCols=[categoricalCol + "classVec"])
# Add stages. These are not run here, but will run all at once later on.
stages += [stringIndexer, encoder]
# Convert label into label indices using the StringIndexer
label_stringIdx = StringIndexer(inputCol="income", outputCol="label")
stages += [label_stringIdx]
# Transform all features into a vector using VectorAssembler
numericCols = ["age", "fnlwgt", "education_num", "capital_gain", "capital_loss", "hours_per_week"]
assemblerInputs = [c + "classVec" for c in categoricalColumns] + numericCols
assembler = VectorAssembler(inputCols=assemblerInputs, outputCol="features")
stages += [assembler]
# Run the stages as a Pipeline. This puts the data through all of the feature transformations in a single call.
partialPipeline = Pipeline().setStages(stages)
pipelineModel = partialPipeline.fit(dataset)
preppedDataDF = pipelineModel.transform(dataset)
# Fit logistic regression model
from pyspark.ml.classification import LogisticRegression
lrModel = LogisticRegression().fit(preppedDataDF)
# ROC for data
display(lrModel, preppedDataDF, "ROC")
To display the residuals, omit the "ROC" parameter:
display(lrModel, preppedDataDF)
Decision trees
The display function supports rendering a decision tree. This is supported for Scala in Databricks Runtime 4.1 and above and for Python in Databricks Runtime 4.3 and above.
To obtain this visualization, you supply the decision tree model.
The following examples train a tree to recognize digits (0 - 9) from the MNIST dataset of images of handwritten digits and then displays the tree.
Scala
val trainingDF = spark.read.format("libsvm").load("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt").cache
val testDF = spark.read.format("libsvm").load("/databricks-datasets/mnist-digits/data-001/mnist-digits-test.txt").cache
import org.apache.spark.ml.classification.{DecisionTreeClassifier, DecisionTreeClassificationModel}
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.Pipeline
val indexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel")
val dtc = new DecisionTreeClassifier().setLabelCol("indexedLabel")
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
val model = pipeline.fit(trainingDF)
val tree = model.stages.last.asInstanceOf[DecisionTreeClassificationModel]
display(tree)
Python
trainingDF = spark.read.format("libsvm").load("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt").cache()
testDF = spark.read.format("libsvm").load("/databricks-datasets/mnist-digits/data-001/mnist-digits-test.txt").cache()
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import StringIndexer
from pyspark.ml import Pipeline
indexer = StringIndexer().setInputCol("label").setOutputCol("indexedLabel")
dtc = DecisionTreeClassifier().setLabelCol("indexedLabel")
# Chain indexer + dtc together into a single ML Pipeline.
pipeline = Pipeline().setStages([indexer, dtc])
model = pipeline.fit(trainingDF)
display(model.stages[-1])
Visualizations in Python
To create a visualization in Python, call display(<dataframe-name>).
diamonds_df = spark.read.csv("/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv", header="true", inferSchema="true")
display(diamonds_df.groupBy("color").avg("price").orderBy("color"))
For a deep dive into Python visualizations using display, see Visualization Deep Dive in Python.
For visualizations specific to machine learning, see Machine learning visualizations.
You can also use other Python libraries to generate visualizations. The Databricks Runtime includes the seaborn visualization library so it’s easy to create a seaborn plot. For example:
import seaborn as sns
sns.set(style="white")
df = sns.load_dataset("iris")
g = sns.PairGrid(df, diag_sharey=False)
g.map_lower(sns.kdeplot)
g.map_diag(sns.kdeplot, lw=3)
g.map_upper(sns.regplot)
display(g.fig)
For other libraries and examples, see Matplotlib and ggplot in Python Notebooks, Bokeh in Python Notebooks, and Plotly in Python and R Notebooks.
Visualizations in R
In addition to the Databricks visualizations, R notebooks can use any R visualization package. The R notebook will capture the resulting plot as a .png and display it inline.
display function
library(SparkR)
diamonds_df <- read.df("/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv", source = "csv", header="true", inferSchema = "true")
display(arrange(agg(groupBy(diamonds_df, "color"), "price" = "avg"), "color"))
Default library
fit <- lm(Petal.Length ~., data = iris)
layout(matrix(c(1,2,3,4),2,2)) # optional 4 graphs/page
plot(fit)
ggplot2
library(ggplot2)
ggplot(diamonds, aes(carat, price, color = color, group = 1)) + geom_point(alpha = 0.3) + stat_smooth()
Lattice
library(lattice)
xyplot(price ~ carat | cut, diamonds, scales = list(log = TRUE), type = c("p", "g", "smooth"), ylab = "Log price")
You can also install and use other plotting libraries.
install.packages("DandEFA", repos = "https://cran.us.r-project.org")
library(DandEFA)
data(timss2011)
timss2011 <- na.omit(timss2011)
dandpal <- rev(rainbow(100, start = 0, end = 0.2))
facl <- factload(timss2011,nfac=5,method="prax",cormeth="spearman")
dandelion(facl,bound=0,mcex=c(1,1.2),palet=dandpal)
facl <- factload(timss2011,nfac=8,method="mle",cormeth="pearson")
dandelion(facl,bound=0,mcex=c(1,1.2),palet=dandpal)
Also see htmlwidgets in R Notebooks and Plotly in Python and R Notebooks.
Visualizations in Scala
The easiest way to perform plotting in Scala is to use the display method. For example:
val diamonds_df = spark.read.format("csv").option("header","true").option("inferSchema","true").load("/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv")
display(diamonds_df.groupBy("color").avg("price").orderBy("color"))
For a deep dive into Scala visualizations using display, see Visualization Deep Dive in Scala.
For visualizations specific to machine learning, see Machine learning visualizations.
displayHTML function
The displayHTML iframe is served from the domain databricksusercontent.com, and the iframe sandbox includes the allow-same-origin attribute. databricksusercontent.com must be accessible from your browser. If it is currently blocked by your corporate network, it will need to be whitelisted by IT.
See Embed static images in notebooks and HTML, D3, and SVG in Notebooks for examples of using displayHTML.