Trendline options

CHANGELOG.md

@@ -8,6 +8,9 @@ This project adheres to [Semantic Versioning](http://semver.org/).
 ### Added
   - Extra flags were added to the `gapminder` and `stocks` dataset to facilitate testing, documentation and demos [#3305](https://github.com/plotly/plotly.py/issues/3305)
   - All line-like Plotly Express functions now accept `markers` argument to display markers, and all but `line_mapbox` accept `symbol` to map a field to the symbol attribute, similar to scatter-like functions [#3326](https://github.com/plotly/plotly.py/issues/3326)
+  - `px.scatter` and `px.density_contours` now support new `trendline` types `'rolling'`, `'expanding'` and `'ewm'` [#2997](https://github.com/plotly/plotly.py/pull/2997)
+  - `px.scatter` and `px.density_contours` now support new `trendline_options` argument to parameterize trendlines, with support for constant control and log-scaling in `'ols'` and specification of the fraction used for `'lowess'`, as well as pass-through to Pandas for `'rolling'`, `'expanding'` and `'ewm'` [#2997](https://github.com/plotly/plotly.py/pull/2997)
+  - `px.scatter` and `px.density_contours` now support new `trendline_scope` argument that accepts the value `'overall'` to request a single trendline for all traces, including across facets and animation frames [#2997](https://github.com/plotly/plotly.py/pull/2997)
 
 ### Fixed
   - Fixed regression introduced in version 5.0.0 where pandas/numpy arrays with `dtype` of Object were being converted to `list` values when added to a Figure ([#3292](https://github.com/plotly/plotly.py/issues/3292), [#3293](https://github.com/plotly/plotly.py/pull/3293))

doc/apidoc/plotly.express.rst

@@ -49,6 +49,8 @@ plotly's high-level API for rapid figure generation. ::
     density_heatmap
     density_mapbox
     imshow
+    set_mapbox_access_token
+    get_trendline_results
 
 
 `plotly.express` subpackages
@@ -60,3 +62,4 @@ plotly's high-level API for rapid figure generation. ::
 
    generated/plotly.express.data.rst
    generated/plotly.express.colors.rst
+   generated/plotly.express.trendline_functions.rst

doc/python/linear-fits.md

@@ -5,8 +5,8 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.1
+      format_version: '1.2'
+      jupytext_version: 1.4.2
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,11 +20,12 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.8
+    version: 3.7.7
   plotly:
     description: Add linear Ordinary Least Squares (OLS) regression trendlines or
       non-linear Locally Weighted Scatterplot Smoothing (LOWESS) trendlines to scatterplots
-      in Python.
+      in Python. Options for moving averages (rolling means) as well as exponentially-weighted
+      and expanding functions.
     display_as: statistical
     language: python
     layout: base
@@ -39,7 +40,7 @@ jupyter:
 
 [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
-Plotly Express allows you to add [Ordinary Least](https://en.wikipedia.org/wiki/Ordinary_least_squares) Squares regression trendline to scatterplots with the `trendline` argument. In order to do so, you will need to install `statsmodels` and its dependencies. Hovering over the trendline will show the equation of the line and its R-squared value.
+Plotly Express allows you to add [Ordinary Least Squares](https://en.wikipedia.org/wiki/Ordinary_least_squares) regression trendline to scatterplots with the `trendline` argument. In order to do so, you will need to [install `statsmodels` and its dependencies](https://www.statsmodels.org/stable/install.html). Hovering over the trendline will show the equation of the line and its R-squared value.
 
 ```python
 import plotly.express as px
@@ -66,14 +67,160 @@ print(results)
 results.query("sex == 'Male' and smoker == 'Yes'").px_fit_results.iloc[0].summary()
 ```
 
-### Non-Linear Trendlines
+### Displaying a single trendline with multiple traces
 
-Plotly Express also supports non-linear [LOWESS](https://en.wikipedia.org/wiki/Local_regression) trendlines.
+_new in v5.2_
+
+To display a single trendline using the entire dataset, set the `trendline_scope` argument to `"overall"`. The same trendline will be overlaid on all facets and animation frames. The trendline color can be overridden with `trendline_color_override`.
+
+```python
+import plotly.express as px
+
+df = px.data.tips()
+fig = px.scatter(df, x="total_bill", y="tip", symbol="smoker", color="sex", trendline="ols", trendline_scope="overall")
+fig.show()
+```
+
+```python
+import plotly.express as px
+
+df = px.data.tips()
+fig = px.scatter(df, x="total_bill", y="tip", facet_col="smoker", color="sex", 
+                 trendline="ols", trendline_scope="overall", trendline_color_override="black")
+fig.show()
+```
+
+### OLS Parameters
+
+_new in v5.2_
+
+OLS trendlines can be fit with log transformations to both X or Y data using the `trendline_options` argument, independently of whether or not the plot has [logarithmic axes](https://plotly.com/python/log-plot/).
+
+```python
+import plotly.express as px
+
+df = px.data.gapminder(year=2007)
+fig = px.scatter(df, x="gdpPercap", y="lifeExp", 
+                 trendline="ols", trendline_options=dict(log_x=True),
+                 title="Log-transformed fit on linear axes")
+fig.show()
+```
+
+```python
+import plotly.express as px
+
+df = px.data.gapminder(year=2007)
+fig = px.scatter(df, x="gdpPercap", y="lifeExp", log_x=True, 
+                 trendline="ols", trendline_options=dict(log_x=True),
+                 title="Log-scaled X axis and log-transformed fit")
+fig.show()
+```
+
+### Locally WEighted Scatterplot Smoothing (LOWESS)
+
+Plotly Express also supports non-linear [LOWESS](https://en.wikipedia.org/wiki/Local_regression) trendlines. In order use this feature, you will need to [install `statsmodels` and its dependencies](https://www.statsmodels.org/stable/install.html).
+
+```python
+import plotly.express as px
+
+df = px.data.stocks(datetimes=True)
+fig = px.scatter(df, x="date", y="GOOG", trendline="lowess")
+fig.show()
+```
+
+_new in v5.2_
+
+The level of smoothing can be controlled via the `frac` trendline option, which indicates the fraction of the data that the LOWESS smoother should include. The default is a fairly smooth line with `frac=0.6666` and lowering this fraction will give a line that more closely follows the data.
+
+```python
+import plotly.express as px
+
+df = px.data.stocks(datetimes=True)
+fig = px.scatter(df, x="date", y="GOOG", trendline="lowess", trendline_options=dict(frac=0.1))
+fig.show()
+```
+
+### Moving Averages
+
+_new in v5.2_
+
+Plotly Express can leverage Pandas' [`rolling`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html), [`ewm`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ewm.html) and [`expanding`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.expanding.html) functions in trendlines as well, for example to display moving averages. Values passed to `trendline_options` are passed directly to the underlying Pandas function (with the exception of the `function` and `function_options` keys, see below).
+
+```python
+import plotly.express as px
+
+df = px.data.stocks(datetimes=True)
+fig = px.scatter(df, x="date", y="GOOG", trendline="rolling", trendline_options=dict(window=5),
+                title="5-point moving average")
+fig.show()
+```
+
+```python
+import plotly.express as px
+
+df = px.data.stocks(datetimes=True)
+fig = px.scatter(df, x="date", y="GOOG", trendline="ewm", trendline_options=dict(halflife=2),
+                title="Exponentially-weighted moving average (halflife of 2 points)")
+fig.show()
+```
+
+```python
+import plotly.express as px
+
+df = px.data.stocks(datetimes=True)
+fig = px.scatter(df, x="date", y="GOOG", trendline="expanding", title="Expanding mean")
+fig.show()
+```
+
+### Other Functions
+
+The `rolling`, `expanding` and `ewm` trendlines support other functions than the default `mean`, enabling, for example, a moving-median trendline, or an expanding-max trendline.
 
 ```python
 import plotly.express as px
 
-df = px.data.gapminder().query("year == 2007")
-fig = px.scatter(df, x="gdpPercap", y="lifeExp", color="continent", trendline="lowess")
+df = px.data.stocks(datetimes=True)
+fig = px.scatter(df, x="date", y="GOOG", trendline="rolling", trendline_options=dict(function="median", window=5),
+                title="Rolling Median")
 fig.show()
 ```
+
+```python
+import plotly.express as px
+
+df = px.data.stocks(datetimes=True)
+fig = px.scatter(df, x="date", y="GOOG", trendline="expanding", trendline_options=dict(function="max"),
+                title="Expanding Maximum")
+fig.show()
+```
+
+In some cases, it is necessary to pass options into the underying Pandas function, for example the `std` parameter must be provided if the `win_type` argument to `rolling` is `"gaussian"`. This is possible with the `function_args` trendline option.
+
+```python
+import plotly.express as px
+
+df = px.data.stocks(datetimes=True)
+fig = px.scatter(df, x="date", y="GOOG", trendline="rolling", 
+                 trendline_options=dict(window=5, win_type="gaussian", function_args=dict(std=2)),
+                title="Rolling Mean with Gaussian Window")
+fig.show()
+```
+
+### Displaying only the trendlines
+
+In some cases, it may be desirable to show only the trendlines, by removing the scatter points.
+
+```python
+import plotly.express as px
+
+df = px.data.stocks(indexed=True, datetimes=True)
+fig = px.scatter(df, trendline="rolling", trendline_options=dict(window=5),
+                title="5-point moving average")
+fig.data = [t for t in fig.data if t.mode == "lines"]
+fig.update_traces(showlegend=True) #trendlines have showlegend=False by default
+fig.show()
+```
+
+```python
+
+```

packages/python/plotly/plotly/express/__init__.py

@@ -60,7 +60,7 @@
 
 from ._special_inputs import IdentityMap, Constant, Range  # noqa: F401
 
-from . import data, colors  # noqa: F401
+from . import data, colors, trendline_functions  # noqa: F401
 
 __all__ = [
     "scatter",
@@ -100,6 +100,7 @@
     "imshow",
     "data",
     "colors",
+    "trendline_functions",
     "set_mapbox_access_token",
     "get_trendline_results",
     "IdentityMap",

packages/python/plotly/plotly/express/_chart_types.py

@@ -46,7 +46,9 @@ def scatter(
     marginal_x=None,
     marginal_y=None,
     trendline=None,
+    trendline_options=None,
     trendline_color_override=None,
+    trendline_scope="trace",
     log_x=False,
     log_y=False,
     range_x=None,
@@ -90,7 +92,9 @@ def density_contour(
     marginal_x=None,
     marginal_y=None,
     trendline=None,
+    trendline_options=None,
     trendline_color_override=None,
+    trendline_scope="trace",
     log_x=False,
     log_y=False,
     range_x=None,