diff --git a/doc/python/box-plots.md b/doc/python/box-plots.md index 342c6da47e7..80f1d29bfcc 100644 --- a/doc/python/box-plots.md +++ b/doc/python/box-plots.md @@ -5,10 +5,10 @@ jupyter: text_representation: extension: .md format_name: markdown - format_version: '1.2' - jupytext_version: 1.4.2 + format_version: '1.3' + jupytext_version: 1.13.7 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 language_info: @@ -20,7 +20,7 @@ jupyter: name: python nbconvert_exporter: python pygments_lexer: ipython3 - version: 3.7.7 + version: 3.9.0 plotly: description: How to make Box Plots in Python with Plotly. display_as: statistical @@ -90,7 +90,7 @@ fig.show() ### Choosing The Algorithm For Computing Quartiles -By default, quartiles for box plots are computed using the `linear` method (for more about linear interpolation, see #10 listed on [http://www.amstat.org/publications/jse/v14n3/langford.html](http://www.amstat.org/publications/jse/v14n3/langford.html) and [https://en.wikipedia.org/wiki/Quartile](https://en.wikipedia.org/wiki/Quartile) for more details). +By default, quartiles for box plots are computed using the `linear` method (for more about linear interpolation, see #10 listed on [http://jse.amstat.org/v14n3/langford.html](http://jse.amstat.org/v14n3/langford.html) and [https://en.wikipedia.org/wiki/Quartile](https://en.wikipedia.org/wiki/Quartile) for more details). However, you can also choose to use an `exclusive` or an `inclusive` algorithm to compute quartiles. diff --git a/doc/python/indicator.md b/doc/python/indicator.md index 24dce1932ec..379cbbfdc5e 100644 --- a/doc/python/indicator.md +++ b/doc/python/indicator.md @@ -5,10 +5,10 @@ jupyter: text_representation: extension: .md format_name: markdown - format_version: '1.1' - jupytext_version: 1.2.1 + format_version: '1.3' + jupytext_version: 1.13.7 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 language_info: @@ -20,7 +20,7 @@ jupyter: name: python nbconvert_exporter: python pygments_lexer: ipython3 - version: 3.7.3 + version: 3.9.0 plotly: description: How to make gauge charts in Python with Plotly. display_as: financial @@ -61,6 +61,8 @@ In this tutorial we introduce a new trace named "Indicator". The purpose of "ind
  • (increasing|decreasing).symbol: symbol displayed on the left of the delta
  • font.(family|size): to control the font
  • position: position relative to `number` (either top, left, bottom, right)
    • +
  • prefix: a string to appear before the delta +
  • suffix: a string to appear after the delta Finally, we can have a simple title for the indicator via `title` with 'text' attribute which is a string, and 'align' which can be set to left, center, and right. There are two gauge types: [angular](https://plotly.com/python/gauge-charts/) and [bullet](https://plotly.com/python/bullet-charts/). Here is a combination of both shapes (angular, bullet), and different modes (gauge, delta, and value): @@ -201,6 +203,31 @@ fig.add_trace(go.Indicator( fig.show() ``` +#### Adding a Prefix and Suffix + + +On both a `number` and a `delta`, you can add a string to appear before the value using `prefix`. You can add a string to appear after the value using `suffix`. In the following example, we add '$' as a `prefix` and 'm' as `suffix` for both the `number` and `delta`. + +Note: `suffix` and `prefix` on `delta` are new in 5.10 + +```python +import plotly.graph_objects as go + +fig = go.Figure(go.Indicator( + mode = "number+delta", + value = 492, + number = {"prefix": "$", "suffix": "m"}, + delta = {"reference": 512, "valueformat": ".0f", "prefix": "$", "suffix": "m"}, + title = {"text": "Profit"}, + domain = {'y': [0, 1], 'x': [0.25, 0.75]})) + +fig.add_trace(go.Scatter( + y = [325, 324, 405, 400, 424, 404, 417, 432, 419, 394, 410, 426, 413, 419, 404, 408, 401, 377, 368, 361, 356, 359, 375, 397, 394, 418, 437, 450, 430, 442, 424, 443, 420, 418, 423, 423, 426, 440, 437, 436, 447, 460, 478, 472, 450, 456, 436, 418, 429, 412, 429, 442, 464, 447, 434, 457, 474, 480, 499, 497, 480, 502, 512, 492])) + +fig.update_layout(xaxis = {'range': [0, 62]}) +fig.show() +``` + #### Reference See https://plotly.com/python/reference/indicator/ for more information and chart attribute options! diff --git a/doc/python/parallel-coordinates-plot.md b/doc/python/parallel-coordinates-plot.md index 9a6e38a382a..ee0c58bd999 100644 --- a/doc/python/parallel-coordinates-plot.md +++ b/doc/python/parallel-coordinates-plot.md @@ -5,12 +5,12 @@ jupyter: text_representation: extension: .md format_name: markdown - format_version: '1.1' - jupytext_version: 1.1.1 + format_version: '1.3' + jupytext_version: 1.13.7 kernel_info: name: python2 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 language_info: @@ -22,7 +22,7 @@ jupyter: name: python nbconvert_exporter: python pygments_lexer: ipython3 - version: 3.7.3 + version: 3.9.0 plotly: description: How to make parallel coordinates plots in Python with Plotly. display_as: scientific @@ -171,6 +171,39 @@ fig = go.Figure(data= fig.show() ``` +### Unselected Line Color and Opacity + + +*New in 5.10* + +The color and opacity of unselected lines can be set with `unselected`. By setting `opacity=0`, you can hide the unselected lines. Here, we set the color to `lightgray` and the opacity to `0.5`. + +```python +import plotly.graph_objects as go + +fig = go.Figure(data= + go.Parcoords( + line_color='blue', + dimensions = list([ + dict(range = [1,5], + constraintrange = [1,2], # change this range by dragging the pink line + label = 'A', values = [1,4]), + dict(range = [1.5,5], + tickvals = [1.5,3,4.5], + label = 'B', values = [3,1.5]), + dict(range = [1,5], + tickvals = [1,2,4,5], + label = 'C', values = [2,4], + ticktext = ['text 1', 'text 2', 'text 3', 'text 4']), + dict(range = [1,5], + label = 'D', values = [4,2]) + ]), + unselected = dict(line = dict(color = 'green', opacity = 0.5)) + ) +) +fig.show() +``` + #### Reference See [function reference for `px.(parallel_coordinates)`](https://plotly.com/python-api-reference/generated/plotly.express.parallel_coordinates) or https://plotly.com/python/reference/parcoords/ for more information and chart attribute options! diff --git a/doc/python/selections.md b/doc/python/selections.md new file mode 100644 index 00000000000..de61ed4490e --- /dev/null +++ b/doc/python/selections.md @@ -0,0 +1,315 @@ +--- +jupyter: + jupytext: + notebook_metadata_filter: all + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.13.7 + kernelspec: + display_name: Python 3 (ipykernel) + language: python + 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: 3.9.0 + plotly: + description: How to use selections in Python. Examples of adding and styling selections. + display_as: file_settings + language: python + layout: base + name: Selections + order: 38 + permalink: python/selections/ + thumbnail: thumbnail/make_selection.jpg +--- + +## Adding Selections to Cartesian Subplots + +*New in 5.10* + +You can add persistent selections to a rendered figure using the **Box Select** and **Lasso Select** tools in the mode bar. +To add multiple selections, select **Shift** when making new selections. +To clear a selection, double-click it. On a subplot you can clear all selections by double-clicking any unselected area of the subplot. + + + +You can also add selections to a figure that displays when it renders using `fig.add_selection`. +Here, we add a rectangular selection with a region between `3.0` and `6.5` on the x axis and between `3.5` and `5.5` on the y axis. + + +```python +import plotly.express as px + +df = px.data.iris() + +fig = px.scatter(df, x="sepal_width", y="sepal_length") +fig.add_selection(x0=3.0, y0=6.5, x1=3.5, y1=5.5) + +fig.show() +``` + +## Selections Using a Custom SVG + + +In the above example, we added a rectangular selection. You can also render a custom SVG for a selection by defining a `path` that can include single or multiple polygons. Here, we create a selection with a single polygon path "M2,6.5L4,7.5L4,6Z". + +Please note that multiple polygons e.g. "M0,0L0,10L10,10,L10,0Z M2,2L2,8L8,8,L8,2Z" could be used to subtract certain regions from the selection. + +```python +import plotly.express as px + +df = px.data.iris() + +fig = px.scatter(df, x="sepal_width", y="sepal_length") +fig.add_selection(path="M2,6.5L4,7.5L4,6Z") + +fig.show() +``` + +## Styling Selections + + +In the above example, we added a selection to the figure that is displayed when the figure renders. +`fig.add_selection` accepts additional properties that you can use to style the selection. Here, we add a `color`, `width`, and specify the `dash` type for the selection. + + +```python +import plotly.express as px + +df = px.data.iris() + +fig = px.scatter(df, x="sepal_width", y="sepal_length") +fig.add_selection( + x0=2.5, y0=6.5, x1=3.5, y1=5.5, + line=dict( + color="Crimson", + width=2, + dash="dash", + )) + +fig.show() + +``` + +## Styling New Selections + +You can style new selections made on the figure by setting properties on `newselection`. +Try making a new selection on the figure to try it out. + +```python +import plotly.express as px + +df = px.data.iris() + +fig = px.scatter(df, x="sepal_width", y="sepal_length") + +fig.update_layout(dragmode='select', + newselection=dict(line=dict(color='blue'))) + +fig.show() +``` + +## Fill Color for Active Selections + +You can style the active selection with `activeselection`. In this example, we set active selections (when created or clicked) to appear with a `fillcolor` of `yellow`. + +```python +import plotly.express as px + +df = px.data.iris() + +fig = px.scatter(df, x="sepal_width", y="sepal_length") +fig.add_selection(x0=3.0, y0=6.5, x1=3.5, y1=5.5) + +fig.update_layout(dragmode='select', + activeselection=dict(fillcolor='yellow')) + +fig.show() +``` + +## Selections with Time Series + +Selections are also supported on time series figures. Here, we add a rectangular selection with a region between the dates `2019-01-01"` and `"2019-10-01"` on the x axis and between `0.95` and `1.15` on the y axis. + + +```python +import plotly.express as px + +df = px.data.stocks() +fig = px.line(df, x='date', y="GOOG") +fig.add_selection(x0="2019-01-01", y0=0.95, x1="2019-10-01", y1=1.15) +fig.show() +``` + +## Referencing Selections on Multiple Cartesian Subplots + + +You can add selections to multiple Cartesian subplots by specifying `xref` and/or `yref`. Here, we add one selection on the plot with axis ids `x` and `y2` and two selections to the the plot with axis ids `x` and `y`. + +```python +import plotly.graph_objects as go + +import numpy as np + +np.random.seed(0) +t = np.linspace(-1, 1.2, 2000) +x = (t**3) + (0.3 * np.random.randn(2000)) +y = (t**6) + (0.3 * np.random.randn(2000)) + +fig = go.Figure() +fig.add_trace(go.Histogram2dContour( + x = x, + y = y, + colorscale = 'Blues', + reversescale = True, + xaxis = 'x', + yaxis = 'y' + )) +fig.add_trace(go.Scatter( + x = x, + y = y, + xaxis = 'x', + yaxis = 'y', + mode = 'markers', + marker = dict( + color = 'rgba(0,0,0,0.3)', + size = 3 + ) + )) +fig.add_trace(go.Histogram( + y = y, + xaxis = 'x2', + marker = dict( + color = 'rgba(0,0,0,1)' + ) + )) +fig.add_trace(go.Histogram( + x = x, + yaxis = 'y2', + marker = dict( + color = 'rgba(0,0,0,1)' + ) + )) + +fig.update_layout( + autosize = False, + xaxis = dict( + zeroline = False, + domain = [0,0.85], + showgrid = False + ), + yaxis = dict( + zeroline = False, + domain = [0,0.85], + showgrid = False + ), + xaxis2 = dict( + zeroline = False, + domain = [0.85,1], + showgrid = False + ), + yaxis2 = dict( + zeroline = False, + domain = [0.85,1], + showgrid = False + ), + height = 600, + width = 600, + bargap = 0, + hovermode = 'closest', + showlegend = False, + selections = [ + dict( + x0 = 0.5, + x1 = -0.5, + xref = "x", + y0 = 190, + y1= 0, + yref = "y2", + line = dict( + color="yellow" + ) + ), + dict( + x0 = -0.2, + x1 = -1.5, + xref = "x", + y0 = 2, + y1= -1, + yref = "y", + line = dict( + color="yellow" + ) + ), + dict( + path= "M0.75,2.39L0.98,3.38L1.46,3.68L1.80,3.35L2.01,2.51L1.67,1.15L1.18,0.50L0.65,0.66L0.54,0.83L0.49,1.56Z", + xref= 'x', + yref = 'y', + line = dict( + color='yellow' + ) + ) + ] +) + + +fig.show() +``` + +## Referencing Selections on a Scatterplot Matrix + + +You can add selections to a scatterplot matrix by specifying `xref` and/or `yref`. Here, we add one selection on the plot with axis ids `x2` and `y2` and another on the plot with ids `x3` and `y`. + +```python +import plotly.express as px + +df = px.data.iris() + +fig = px.scatter_matrix(df, + dimensions=["sepal_length", "sepal_width", "petal_length", "petal_width"], + color="species") + +fig.update_layout( + xaxis = {"matches": "y"}, + xaxis2 = {"matches": "y2"}, + xaxis3 = {"matches": "y3"}, + xaxis4 = {"matches": "y4"}, + height = 900, + width = 750, + dragmode = 'select', + selections = [ + dict( + x0 = 3, + x1 = 4, + xref = "x2", + y0 = 8, + y1= 6, + yref = "y" + ), + dict( + x0 = 5, + x1 = 1, + xref = "x3", + y0 = 5, + y1= 4, + yref = "y", + ) + ] +) + +fig.show() +``` + +## More on Selections + +For more on selections, see the [selections section of the `dcc.Graph` page](https://dash.plotly.com/dash-core-components/graph#selections) in the Dash docs. diff --git a/doc/python/setting-graph-size.md b/doc/python/setting-graph-size.md index 56ae8ef621d..d89e4f16f03 100644 --- a/doc/python/setting-graph-size.md +++ b/doc/python/setting-graph-size.md @@ -5,10 +5,10 @@ jupyter: text_representation: extension: .md format_name: markdown - format_version: '1.2' - jupytext_version: 1.6.0 + format_version: '1.3' + jupytext_version: 1.13.7 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 language_info: @@ -20,7 +20,7 @@ jupyter: name: python nbconvert_exporter: python pygments_lexer: ipython3 - version: 3.7.6 + version: 3.9.0 plotly: description: How to manipulate the graph size, margins and background color. display_as: file_settings @@ -127,6 +127,41 @@ fig.update_yaxes(automargin=True) fig.show() ``` +### Automatically Adjust Specific Margins + +*New in 5.10* + +You can also set `automargin` for specific sides of the figure. Here, we set `automargin` on the `left` and `top` of the figure. + +```python +import plotly.graph_objects as go + + +fig = go.Figure() + +fig.add_trace(go.Bar( + x=["Apples", "Oranges", "Watermelon", "Pears"], + y=[3, 2, 1, 4] +)) + +fig.update_layout( + autosize=False, + width=500, + height=500, + yaxis=dict( + title_text="Y-axis Title", + ticktext=["Very long label", "long label", "3", "label"], + tickvals=[1, 2, 3, 4], + tickmode="array", + titlefont=dict(size=30), + ) +) + +fig.update_yaxes(automargin='left+top') + +fig.show() +``` + #### Reference See https://plotly.com/python/reference/layout/ for more information and chart attribute options! diff --git a/doc/python/violin.md b/doc/python/violin.md index 8e9d2a4e4a7..0e0e86cdd1b 100644 --- a/doc/python/violin.md +++ b/doc/python/violin.md @@ -5,10 +5,10 @@ jupyter: text_representation: extension: .md format_name: markdown - format_version: '1.2' - jupytext_version: 1.4.2 + format_version: '1.3' + jupytext_version: 1.13.7 kernelspec: - display_name: Python 3 + display_name: Python 3 (ipykernel) language: python name: python3 language_info: @@ -20,7 +20,7 @@ jupyter: name: python nbconvert_exporter: python pygments_lexer: ipython3 - version: 3.7.7 + version: 3.9.0 plotly: description: How to make violin plots in Python with Plotly. display_as: statistical @@ -274,6 +274,28 @@ fig = px.strip(df, x='day', y='tip') fig.show() ``` +### Choosing The Algorithm For Computing Quartiles + +*New in 5.10* + +By default, quartiles for violin plots are computed using the `linear` method (for more about linear interpolation, see #10 listed on [http://jse.amstat.org/v14n3/langford.html](http://jse.amstat.org/v14n3/langford.html) and [https://en.wikipedia.org/wiki/Quartile](https://en.wikipedia.org/wiki/Quartile) for more details). + +However, you can also choose to use an `exclusive` or an `inclusive` algorithm to compute quartiles. + +The _exclusive_ algorithm uses the median to divide the ordered dataset into two halves. If the sample is odd, it does not include the median in either half. Q1 is then the median of the lower half and Q3 is the median of the upper half. + +The _inclusive_ algorithm also uses the median to divide the ordered dataset into two halves, but if the sample is odd, it includes the median in both halves. Q1 is then the median of the lower half and Q3 the median of the upper half. + +```python +import plotly.express as px + +df = px.data.tips() +fig = px.violin(df, y="total_bill") +fig.update_traces(quartilemethod="exclusive") # or "inclusive", or "linear" by default + +fig.show() +``` + #### Reference See [function reference for `px.violin()`](https://plotly.com/python-api-reference/generated/plotly.express.violin) or https://plotly.com/python/reference/violin/ for more information and chart attribute options!