diff --git a/notebooks/splom.md b/notebooks/splom.md
index 43877cb11..4a743b963 100644
--- a/notebooks/splom.md
+++ b/notebooks/splom.md
@@ -8,9 +8,19 @@ jupyter:
format_version: '1.1'
jupytext_version: 1.1.1
kernelspec:
- display_name: Python 2
+ display_name: Python 3
language: python
- name: python2
+ name: python3
+ language_info:
+ codemirror_mode:
+ name: ipython
+ version: 3
+ file_extension: .py
+ mimetype: text/x-python
+ name: python
+ nbconvert_exporter: python
+ pygments_lexer: ipython3
+ version: 3.6.7
plotly:
description: How to make scatterplot matrices or sploms natively in Python with
Plotly.
@@ -25,27 +35,55 @@ jupyter:
redirect_from: python/scatterplot-matrix/
thumbnail: thumbnail/splom_image.jpg
title: Python Scatterplot Matrix | Plotly
+ v4upgrade: true
---
-#### New to Plotly?
-Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
- You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
- We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!
+### Scatter matrix with plotly express
+A scatterplot matrix is a matrix associated to n numerical arrays (data variables), $X_1,X_2,…,X_n$ , of the same length. The cell (i,j) of such a matrix displays the scatter plot of the variable Xi versus Xj.
-#### Version Check
-Plotly's python package is updated frequently. Run `pip install plotly --upgrade` to use the latest version.
+For data available as a tidy pandas dataframe, the plotly express function `px.scatter_matrix` plots the scatter matrix for the columns of the dataframe. By default, all columns are considered.
```python
-import plotly
-plotly.__version__
+import plotly.express as px
+iris = px.data.iris()
+fig = px.scatter_matrix(iris)
+fig.show()
+```
+
+Specify the columns to be represented with the `dimensions` argument, and set colors using a column of the dataframe:
+
+```python
+import plotly.express as px
+iris = px.data.iris()
+fig = px.scatter_matrix(iris,
+ dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"],
+ color="species")
+fig.show()
+```
+
+#### Styled Scatter Matrix with plotly express
+
+The scatter matrix plot can be configured thanks to the parameters of `px.scatter_matrix`, but also thanks to `fig.update_traces` for fine tuning (see the next section to learn more about the options).
+
+```python
+import plotly.express as px
+iris = px.data.iris()
+fig = px.scatter_matrix(iris,
+ dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"],
+ color="species", symbol="species",
+ title="Scatter matrix of iris data set",
+ labels={col:col.replace('_', ' ') for col in iris.columns}) # remove underscore
+fig.update_traces(diagonal_visible=False)
+fig.show()
```
-#### Splom trace
-A scaterplot matrix is a matrix associated to n numerical arrays (data variables), $X_1,X_2,…,X_n$ , of the same length. The cell (i,j) of such a matrix displays the scater plot of the variable Xi versus Xj ,
+### Scatter matrix (splom) with go.Splom
+
+When data are not available as a tidy dataframe, it is possible to use the more generic `go.Splom` function. All its parameters are documented in the reference page https://plot.ly/python/reference/#splom.
-The Plotly splom trace implementation for the scaterplot matrix does not require to set $x=Xi$ , and $y=Xj$, for each scatter plot. All arrays, $X_1,X_2,…,X_n$ , are passed once, through a list of dicts called dimensions, i.e. each array/variable represents a dimension.
+The Plotly splom trace implementation for the scatterplot matrix does not require to set $x=Xi$ , and $y=Xj$, for each scatter plot. All arrays, $X_1,X_2,…,X_n$ , are passed once, through a list of dicts called dimensions, i.e. each array/variable represents a dimension.
A trace of type `splom` is defined as follows:
@@ -59,79 +97,101 @@ trace=go.Splom(dimensions=[dict(label='string-1',
.
dict(label='string-n',
values=Xn)],
- text=text,
- marker=dict(...)
+ ....
)
```
The label in each dimension is assigned to the axes titles of the corresponding matrix cell.
-
-text is either a unique string assigned to all points displayed by splom or a list of strings of the same length as the dimensions, $X_i$. The `text[k]` is the tooltip for the $k^{th}$ point in each cell.
-
-marker sets the markers attributes in all scatter plots.
#### Splom of the Iris data set
```python
-import plotly.graph_objs as go
-import plotly.plotly as py
-import plotly.tools as tls
-import plotly.figure_factory as ff
-
-import copy
-import numpy as np
+import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv')
-df_table = ff.create_table(df.head())
-py.iplot(df_table, filename='iris-data-head')
-```
+# The Iris dataset contains four data variables, sepal length, sepal width, petal length,
+# petal width, for 150 iris flowers. The flowers are labeled as `Iris-setosa`,
+# `Iris-versicolor`, `Iris-virginica`.
+
+# Define indices corresponding to flower categories, using pandas label encoding
+index_vals = df['class'].astype('category').cat.codes
-The Iris dataset contains four data variables, sepal length, sepal width, petal length petal width, for 150 iris flowers. The flowers are labeled as `Iris-setosa`, `Iris-versicolor`, `Iris-virginica`.
+fig = go.Figure(data=go.Splom(
+ dimensions=[dict(label='sepal length',
+ values=df['sepal length']),
+ dict(label='sepal width',
+ values=df['sepal width']),
+ dict(label='petal length',
+ values=df['petal length']),
+ dict(label='petal width',
+ values=df['petal width'])],
+ text=df['class'],
+ marker=dict(color=index_vals,
+ showscale=False, # colors encode categorical variables
+ line_color='white', line_width=0.5)
+ ))
-Extract out the flower class:
-```python
-classes=np.unique(df['class'].values).tolist()
-classes
+fig.update_layout(
+ title='Iris Data set',
+ dragmode='select',
+ width=600,
+ height=600,
+ hovermode='closest',
+)
+
+fig.show()
```
-Encode the three classes by 0, 1, 2:
+The scatter plots on the principal diagonal can be removed by setting `diagonal_visible=False`:
```python
-class_code={classes[k]: k for k in range(3)}
-class_code
-```
+import plotly.graph_objects as go
+import pandas as pd
-The splom associated to the four data variables is intended to illustrate the patterns of the relationship between pairs of variables for each class.
+df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv')
+index_vals = df['class'].astype('category').cat.codes
-The points displayed in splom are colored according to their class:
+fig = go.Figure(data=go.Splom(
+ dimensions=[dict(label='sepal length',
+ values=df['sepal length']),
+ dict(label='sepal width',
+ values=df['sepal width']),
+ dict(label='petal length',
+ values=df['petal length']),
+ dict(label='petal width',
+ values=df['petal width'])],
+ diagonal_visible=False, # remove plots on diagonal
+ text=df['class'],
+ marker=dict(color=index_vals,
+ showscale=False, # colors encode categorical variables
+ line_color='white', line_width=0.5)
+ ))
-```python
-color_vals=[class_code[cl] for cl in df['class']]
-```
-Define a discrete colorscale with three colors corresponding to the three flower classes:
+fig.update_layout(
+ title='Iris Data set',
+ width=600,
+ height=600,
+)
-```python
-pl_colorscale=[[0.0, '#19d3f3'],
- [0.333, '#19d3f3'],
- [0.333, '#e763fa'],
- [0.666, '#e763fa'],
- [0.666, '#636efa'],
- [1, '#636efa']]
+fig.show()
```
-On hover over a particular point in a splom cell is displayed the corresponding iris class:
+To plot only the lower/upper half of the splom we switch the default `showlowerhalf=True`/`showupperhalf=True` to `False`:
```python
-text=[df.loc[ k, 'class'] for k in range(len(df))]
-```
+import plotly.graph_objects as go
+import pandas as pd
-```python
-trace1 = go.Splom(dimensions=[dict(label='sepal length',
+df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv')
+index_vals = df['class'].astype('category').cat.codes
+
+fig = go.Figure(data=go.Splom(
+ dimensions=[dict(label='sepal length',
values=df['sepal length']),
dict(label='sepal width',
values=df['sepal width']),
@@ -139,104 +199,71 @@ trace1 = go.Splom(dimensions=[dict(label='sepal length',
values=df['petal length']),
dict(label='petal width',
values=df['petal width'])],
- text=text,
- #default axes name assignment :
- #xaxes= ['x1','x2', 'x3'],
- #yaxes= ['y1', 'y2', 'y3'],
- marker=dict(color=color_vals,
- size=7,
- colorscale=pl_colorscale,
- showscale=False,
- line=dict(width=0.5,
- color='rgb(230,230,230)'))
- )
-```
+ showupperhalf=False, # remove plots on diagonal
+ text=df['class'],
+ marker=dict(color=index_vals,
+ showscale=False, # colors encode categorical variables
+ line_color='white', line_width=0.5)
+ ))
-```python
-axis = dict(showline=True,
- zeroline=False,
- gridcolor='#fff',
- ticklen=4)
-layout = go.Layout(
+fig.update_layout(
title='Iris Data set',
- dragmode='select',
width=600,
height=600,
- autosize=False,
- hovermode='closest',
- plot_bgcolor='rgba(240,240,240, 0.95)',
- xaxis1=dict(axis),
- xaxis2=dict(axis),
- xaxis3=dict(axis),
- xaxis4=dict(axis),
- yaxis1=dict(axis),
- yaxis2=dict(axis),
- yaxis3=dict(axis),
- yaxis4=dict(axis)
)
-fig1 = dict(data=[trace1], layout=layout)
-py.iplot(fig1, filename='splom-iris1')
+fig.show()
```
-The scatter plots on the principal diagonal can be removed by setting diagonal=dict(visible=False):
+Each dict in the list dimensions has a key, visible, set by default on True. We can choose to remove a variable from splom, by setting `visible=False` in its corresponding dimension. In this case the default grid associated to the scatterplot matrix keeps its number of cells, but the cells in the row and column corresponding to the visible false dimension are empty:
```python
-trace2 = copy.copy(trace1)
-trace2['diagonal'].update(visible=False)
-fig2 = dict(data=[trace2], layout=layout)
-py.iplot(fig2, filename='splom-invisible-diagonal')
-```
+import plotly.graph_objects as go
+import pandas as pd
-To plot only the lower/upper half of the splom we switch the default showlowerhalf=True/showupperhalf=True to False:
+df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv')
+index_vals = df['class'].astype('category').cat.codes
-```python
-trace3 = copy.deepcopy(trace1)
-trace3['showupperhalf']=False
+fig = go.Figure(data=go.Splom(
+ dimensions=[dict(label='sepal length',
+ values=df['sepal length']),
+ dict(label='sepal width',
+ values=df['sepal width'],
+ visible=False),
+ dict(label='petal length',
+ values=df['petal length']),
+ dict(label='petal width',
+ values=df['petal width'])],
+ text=df['class'],
+ marker=dict(color=index_vals,
+ showscale=False, # colors encode categorical variables
+ line_color='white', line_width=0.5)
+ ))
-fig3 = dict(data=[trace3], layout=layout)
-py.iplot(fig3, filename='splom-showupperhalf')
-```
-Each dict in the list dimensions has a key, visible, set by default on True. We can choose to remove a variable from splom, by setting `visible=False` in its corresponding dimension. In this case the default grid associated to the scatterplot matrix keeps its number of cells, but the cells in the row and column corresponding to the visible false dimension are empty:
+fig.update_layout(
+ title='Iris Data set',
+ width=600,
+ height=600,
+)
-```python
-trace4 = copy.deepcopy(trace1)
-trace4['dimensions'][2].update(visible=False)
-fig4 = dict(data=[trace4], layout=layout)
-py.iplot(fig4, filename='splom-invisible-custom-dimensions')
+fig.show()
```
#### Splom for the diabetes dataset
-Diabetes dataset is downloaded from [kaggle](https://www.kaggle.com/uciml/pima-indians-diabetes-database/data). It is used to predict the onset of diabetes based on 8 diagnostic measures.
+Diabetes dataset is downloaded from [kaggle](https://www.kaggle.com/uciml/pima-indians-diabetes-database/data). It is used to predict the onset of diabetes based on 8 diagnostic measures. The diabetes file contains the diagnostic measures for 768 patients, that are labeled as non-diabetic (Outcome=0), respectively diabetic (Outcome=1). The splom associated to the 8 variables can illustrate the strength of the relationship between pairs of measures for diabetic/nondiabetic patients.
```python
-dfd = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv')
-
-df_table = ff.create_table(dfd.head())
-py.iplot(df_table, filename='diabetes-head')
-```
-
-The diabetes file contains the diagnostic measures for 768 patients, that are labeled as non-diabetic (Outcome=0), respectively diabetic (Outcome=1). The splom associated to the 8 variables can illustrate the strength of the relationship between pairs of measures for diabetic/nondiabetic patients.
+import plotly.graph_objs as go
+import pandas as pd
-```python
+dfd = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv')
textd = ['non-diabetic' if cl==0 else 'diabetic' for cl in dfd['Outcome']]
-color_vals = [0 if cl==0 else 1 for cl in dfd['Outcome']]
-```
-
-We define again a discrete colorscale with two colors: blue for non-diabetics and red for diabetics:
-
-```python
-pl_colorscaled = [[0., '#119dff'],
- [0.5, '#119dff'],
- [0.5, '#ef553b'],
- [1, '#ef553b']]
-```
-```python
-traced = go.Splom(dimensions=[dict(label='Pregnancies', values=dfd['Pregnancies']),
+fig = go.Figure(data=go.Splom(
+ dimensions=[dict(label='Pregnancies', values=dfd['Pregnancies']),
dict(label='Glucose', values=dfd['Glucose']),
dict(label='BloodPressure', values=dfd['BloodPressure']),
dict(label='SkinThickness', values=dfd['SkinThickness']),
@@ -244,71 +271,21 @@ traced = go.Splom(dimensions=[dict(label='Pregnancies', values=dfd['Pregnancies'
dict(label='BMI', values=dfd['BMI']),
dict(label='DiabPedigreeFun', values=dfd['DiabetesPedigreeFunction']),
dict(label='Age', values=dfd['Age'])],
- marker=dict(color=color_vals,
+ marker=dict(color=dfd['Outcome'],
size=5,
- colorscale=pl_colorscaled,
+ colorscale='Bluered',
line=dict(width=0.5,
- color='rgb(230,230,230)') ),
+ color='rgb(230,230,230)')),
text=textd,
- diagonal=dict(visible=False))
-```
+ diagonal=dict(visible=False)))
-```python
-axisd = dict(showline=False,
- zeroline=False,
- gridcolor='#fff',
- ticklen=4,
- titlefont=dict(size=13))
title = "Scatterplot Matrix (SPLOM) for Diabetes Dataset Data source:"+\
" [1]"
+fig.update_layout(title=title,
+ dragmode='select',
+ width=1000,
+ height=1000,
+ hovermode='closest')
-layout = go.Layout(title=title,
- dragmode='select',
- width=1000,
- height=1000,
- autosize=False,
- hovermode='closest',
- plot_bgcolor='rgba(240,240,240, 0.95)',
- xaxis1=dict(axisd),
- xaxis2=dict(axisd),
- xaxis3=dict(axisd),
- xaxis4=dict(axisd),
- xaxis5=dict(axisd),
- xaxis6=dict(axisd),
- xaxis7=dict(axisd),
- xaxis8=dict(axisd),
- yaxis1=dict(axisd),
- yaxis2=dict(axisd),
- yaxis3=dict(axisd),
- yaxis4=dict(axisd),
- yaxis5=dict(axisd),
- yaxis6=dict(axisd),
- yaxis7=dict(axisd),
- yaxis8=dict(axisd))
-
-fig = dict(data=[traced], layout=layout)
-py.iplot(fig, filename='large')
-```
-
-```python
-from IPython.display import display, HTML
-
-display(HTML(''))
-display(HTML(''))
-
-! pip install git+https://github.com/plotly/publisher.git --upgrade
-import publisher
-publisher.publish(
- 'splom.ipynb', 'python/splom/', 'Scatterplot Matrix',
- 'How to make scatterplot matrices or sploms natively in Python with Plotly.',
- title = 'Python Scatterplot Matrix | Plotly',
- has_thumbnail='true', thumbnail='thumbnail/splom_image.jpg',
- redirect_from ='python/scatterplot-matrix/',
- language='python',
- display_as='statistical', order=10.2,
- uses_plotly_offline=False)
-```
-
-```python
-
+fig.show()
```