Merge branch 'master' into nocolor

Author: nicolaskruchten

CHANGELOG.md

@@ -2,6 +2,7 @@
 All notable changes to this project will be documented in this file.
 This project adheres to [Semantic Versioning](http://semver.org/).
 
+
 ## [4.9.0] - unreleased
 
 ### Added
@@ -13,6 +14,12 @@ This project adheres to [Semantic Versioning](http://semver.org/).
 
 - trendline traces are now of type `scattergl` when `render_mode="webgl"` in Plotly Express ([#2614](https://github.com/plotly/plotly.py/pull/2614))
 
+### Updated
+
+- Added all cartesian-2d Plotly Express functions, plus `imshow` to Pandas backend with `kind` option
+- `plotly.express.imshow` now uses data frame index and columns names and values to populate axis parameters by default ([#2539](https://github.com/plotly/plotly.py/pull/2539))
+
+
 
 ## [4.8.2] - 2020-06-26
 
@@ -21,6 +28,7 @@ This project adheres to [Semantic Versioning](http://semver.org/).
 - Updated Plotly.js to version 1.54.5. See the [plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/v1.54.5/CHANGELOG.md) for more information.
 - `add_traces()` now accepts bare `int`-like values for `rows`/`cols` as well as lists thereof ([#2546](https://github.com/plotly/plotly.py/pull/2546)), with thanks to [@MCBoarder289](https://github.com/MCBoarder289) for the contribution!
 
+
 ### Fixed
 
 - `row`/`col` now accept `int`-like values, not strictly `int` values ([#2451](https://github.com/plotly/plotly.py/pull/2451)), with thanks to [@MCBoarder289](https://github.com/MCBoarder289) for the contribution!

doc/python/dropdowns.md

@@ -147,7 +147,7 @@ fig.update_scenes(
     aspectmode="manual"
 )
 
-# Add drowdowns
+# Add dropdowns
 button_layer_1_height = 1.08
 fig.update_layout(
     updatemenus=[

doc/python/heatmaps.md

@@ -36,7 +36,7 @@ jupyter:
 
 ### Heatmap with `plotly.express` and `px.imshow`
 
-[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/). With `px.imshow`, each value of the input array is represented as a heatmap pixel.
+[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/). With `px.imshow`, each value of the input array or data frame is represented as a heatmap pixel.
 
 For more examples using `px.imshow`, see the [tutorial on displaying image data with plotly](/python/imshow).
 
@@ -49,6 +49,14 @@ fig = px.imshow([[1, 20, 30],
 fig.show()
 ```
 
+```python
+import plotly.express as px
+
+df = px.data.medals_wide(indexed=True)
+fig = px.imshow(df)
+fig.show()
+```
+
 ### Customizing the axes and labels on a heatmap
 
 You can use the `x`, `y` and `labels` arguments to customize the display of a heatmap, and use `.update_xaxes()` to move the x axis tick labels to the top:
@@ -182,4 +190,4 @@ Arrays of rasterized values build by datashader can be visualized using
 plotly's heatmaps, as shown in the [plotly and datashader tutorial](/python/datashader/).
 
 #### Reference
-See https://plotly.com/python/reference/#heatmap for more information and chart attribute options!
+See https://plotly.com/python/reference/#heatmap for more information and chart attribute options!

doc/python/hover-text-and-formatting.md

@@ -228,7 +228,7 @@ df_2007 = px.data.gapminder().query("year==2007")
 fig = px.scatter(df_2007, x="gdpPercap", y="lifeExp", log_x=True, color='continent'
                 )
 print("plotly express hovertemplate:", fig.data[0].hovertemplate)
-fig.update_traces(hovertemplate='GDP: %{x} <br>Life Expectany: %{y}') # 
+fig.update_traces(hovertemplate='GDP: %{x} <br>Life Expectancy: %{y}') # 
 fig.update_traces(hovertemplate=None, selector={'name':'Europe'}) # revert to default hover
 print("user_defined hovertemplate:", fig.data[0].hovertemplate)
 fig.show()
@@ -404,4 +404,4 @@ fig.show()
 
 #### Reference
 
-See https://plotly.com/python/reference/ for more information and chart attribute options!
+See https://plotly.com/python/reference/ for more information and chart attribute options!

doc/python/legend.md

@@ -428,10 +428,10 @@ fig.add_trace(go.Streamtube(
 ))
 # Update all traces together
 fig.update_traces(showlegend=True, showscale=False)
-fig.update_layout(width=600, title_text='Exporation of a vector field using several traces')
+fig.update_layout(width=600, title_text='Exploration of a vector field using several traces')
 fig.show()
 ```
 
 #### Reference
 
-See https://plotly.com/python/reference/#layout-legend for more information!
+See https://plotly.com/python/reference/#layout-legend for more information!

doc/python/pandas-backend.md

@@ -98,7 +98,7 @@ fig.show()
 
 ### Supported Methods
 
-The Plotly backend supports the following `kind`s of Pandas plots: `scatter`, `line`, `area`, `bar`, `barh`, `hist` and `box`, via the call pattern `df.plot(kind='scatter')` or `df.plot.scatter()`. These delegate to the corresponding Plotly Express functions.
+The Plotly backend supports the following `kind`s of Pandas plots: `scatter`, `line`, `area`, `bar`, `barh`, `hist` and `box`, via the call pattern `df.plot(kind='scatter')` or `df.plot.scatter()`. These delegate to the corresponding Plotly Express functions. In addition, the following are valid options to the `kind` argument of `df.plot()`: `violin`, `strip`, `funnel`, `density_heatmap`, `density_contour` and `imshow`, even though the call pattern `df.plot.violin()` is not supported for these kinds of charts, per the Pandas API.
 
 ```python
 import pandas as pd
@@ -198,4 +198,4 @@ fig.show()
 
 ### What about Cufflinks?
 
-There also exists an independent third-party wrapper library around Plotly called [Cufflinks](https://github.com/santosjorge/cufflinks), which provides similar functionality (with an API closer to that of Pandas' default `matplotlib` backend) by adding a `.iplot()` method to Pandas dataframes, as it was developed before Pandas supported configurable backends. Issues and questions regarding Cufflinks should be [raised in the Cufflinks repository](https://github.com/santosjorge/cufflinks/issues/new).
+There also exists an independent third-party wrapper library around Plotly called [Cufflinks](https://github.com/santosjorge/cufflinks), which provides similar functionality (with an API closer to that of Pandas' default `matplotlib` backend) by adding a `.iplot()` method to Pandas dataframes, as it was developed before Pandas supported configurable backends. Issues and questions regarding Cufflinks should be [raised in the Cufflinks repository](https://github.com/santosjorge/cufflinks/issues/new).

doc/python/px-arguments.md

@@ -49,7 +49,7 @@ There are three common conventions for storing column-oriented data, usually in
 * **wide-form data** has one row per value of one of the first variable, and one column per value of the second variable. This is suitable for storing and displaying 2-dimensional data.
 * **mixed-form data** is a hybrid of long-form and wide-form data, with one row per value of one variable, and some columns representing values of another, and some columns representing more variables. See the [wide-form documentation](/python/wide-form/) for examples of how to use Plotly Express to visualize this kind of data.
 
-Every Plotly Express function other than `imshow` can operate on long-form data, and in addition, the following 2D-Cartesian functions can operate on wide-form and mixed-form data: `px.scatter`, `px.line`, `px.area`, `px.bar`, `px.histogram`, `px.violin`, `px.box`, `px.strip`, `px.funnel`, `px.density_heatmap` and `px.density_contour`.
+Every Plotly Express function can operate on long-form data (other than `px.imshow` which operates only on wide-form input), and in addition, the following 2D-Cartesian functions can operate on wide-form and mixed-form data: `px.scatter`, `px.line`, `px.area`, `px.bar`, `px.histogram`, `px.violin`, `px.box`, `px.strip`, `px.funnel`, `px.density_heatmap` and `px.density_contour`.
 
 By way of example here is the same data, represented in long-form first, and then in wide-form:
 
@@ -241,4 +241,4 @@ fig = px.bar(df, x='year', y=gdp, color='continent', labels={'y':'log gdp'},
              hover_data=['country'],
              title='Evolution of world GDP')
 fig.show()
-```
+```

doc/python/wide-form.md

@@ -48,7 +48,7 @@ There are three common conventions for storing column-oriented data, usually in
 * **wide-form data** has one row per value of one of the first variable, and one column per value of the second variable. This is suitable for storing and displaying 2-dimensional data.
 * **mixed-form data** is a hybrid of long-form and wide-form data, with one row per value of one variable, and some columns representing values of another, and some columns representing more variables.
 
-Every Plotly Express function other than `imshow` can operate on long-form data, and in addition, the following 2D-Cartesian functions can operate on wide-form and mixed-form data: `px.scatter`, `px.line`, `px.area`, `px.bar`, `px.histogram`, `px.violin`, `px.box`, `px.strip`, `px.funnel`, `px.density_heatmap` and `px.density_contour`.
+Every Plotly Express function can operate on long-form data (other than `px.imshow` which operates only on wide-form input), and in addition, the following 2D-Cartesian functions can operate on wide-form and mixed-form data: `px.scatter`, `px.line`, `px.area`, `px.bar`, `px.histogram`, `px.violin`, `px.box`, `px.strip`, `px.funnel`, `px.density_heatmap` and `px.density_contour`.
 
 By way of example here is the same data, represented in long-form first, and then in wide-form:
 

packages/python/plotly/plotly/__init__.py

@@ -80,7 +80,20 @@ def plot(data_frame, kind, **kwargs):
     To activate, set pandas.options.plotting.backend="plotly"
     See https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/__init__.py
     """
-    from .express import scatter, line, area, bar, box, histogram
+    from .express import (
+        scatter,
+        line,
+        area,
+        bar,
+        box,
+        histogram,
+        violin,
+        strip,
+        funnel,
+        density_contour,
+        density_heatmap,
+        imshow,
+    )
 
     if kind == "scatter":
         new_kwargs = {k: kwargs[k] for k in kwargs if k not in ["s", "c"]}
@@ -96,9 +109,27 @@ def plot(data_frame, kind, **kwargs):
     if kind == "box":
         new_kwargs = {k: kwargs[k] for k in kwargs if k not in ["by"]}
         return box(data_frame, **new_kwargs)
-    if kind in "hist":
+    if kind in ["hist", "histogram"]:
         new_kwargs = {k: kwargs[k] for k in kwargs if k not in ["by", "bins"]}
         return histogram(data_frame, **new_kwargs)
+    if kind == "violin":
+        return violin(data_frame, **kwargs)
+    if kind == "strip":
+        return strip(data_frame, **kwargs)
+    if kind == "funnel":
+        return funnel(data_frame, **kwargs)
+    if kind == "density_contour":
+        return density_contour(data_frame, **kwargs)
+    if kind == "density_heatmap":
+        return density_heatmap(data_frame, **kwargs)
+    if kind == "imshow":
+        return imshow(data_frame, **kwargs)
+    if kind == "heatmap":
+        raise ValueError(
+            "kind='heatmap' not supported plotting.backend='plotly'. "
+            "Please use kind='imshow' or kind='density_heatmap'."
+        )
+
     raise NotImplementedError(
         "kind='%s' not yet supported for plotting.backend='plotly'" % kind
     )

packages/python/plotly/plotly/express/_imshow.py

@@ -196,6 +196,17 @@ def imshow(
             labels["color"] = xarray.plot.utils.label_from_attrs(img)
             labels["color"] = labels["color"].replace("\n", "<br>")
     else:
+        if hasattr(img, "columns") and hasattr(img.columns, "__len__"):
+            if x is None:
+                x = img.columns
+            if labels.get("x", None) is None and hasattr(img.columns, "name"):
+                labels["x"] = img.columns.name or ""
+        if hasattr(img, "index") and hasattr(img.index, "__len__"):
+            if y is None:
+                y = img.index
+            if labels.get("y", None) is None and hasattr(img.index, "name"):
+                labels["y"] = img.index.name or ""
+
         if labels.get("x", None) is None:
             labels["x"] = ""
         if labels.get("y", None) is None: