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    description: How to make 2D Histogram Contour plots in Python with Plotly.
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## 2D Histogram Contours or Density Contours

A 2D histogram contour plot, also known as a density contour plot, is a 2-dimensional generalization of a [histogram](/python/histograms/) which resembles a [contour plot](/python/contour-plots/) but is computed by grouping a set of points specified by their `x` and `y` coordinates into bins, and applying an aggregation function such as `count` or `sum` (if `z` is provided) to compute the value to be used to compute contours. This kind of visualization (and the related [2D histogram, or density heatmap](/python/2D-Histogram/)) is often used to manage over-plotting, or situations where showing large data sets as [scatter plots](/python/line-and-scatter/) would result in points overlapping each other and hiding patterns.

## Density Contours with Plotly Express

[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/). The Plotly Express function `density_contour()` can be used to produce density contours.

```python
import plotly.express as px
df = px.data.tips()

fig = px.density_contour(df, x="total_bill", y="tip")
fig.show()
```

Marginal plots can be added to visualize the 1-dimensional distributions of the two variables. Here we use a marginal [`histogram`](/python/histograms/). Other allowable values are `violin`, `box` and `rug`.

```python
import plotly.express as px
df = px.data.tips()

fig = px.density_contour(df, x="total_bill", y="tip", marginal_x="histogram", marginal_y="histogram")
fig.show()
```

Density contours can also be [faceted](/python/facet-plots/) and [discretely colored](/python/discrete-color/):

```python
import plotly.express as px
df = px.data.tips()

fig = px.density_contour(df, x="total_bill", y="tip", facet_col="sex", color="smoker")
fig.show()
```

Plotly Express density contours can be [continuously-colored](/python/colorscales/) and labeled:

```python
import plotly.express as px
df = px.data.tips()

fig = px.density_contour(df, x="total_bill", y="tip")
fig.update_traces(contours_coloring="fill", contours_showlabels = True)
fig.show()
```

### Other aggregation functions than `count`

By passing in a `z` value and a `histfunc`, density contours can perform basic aggregation operations. Here we show average Sepal Length grouped by Petal Length and Petal Width for the Iris dataset.

```python
import plotly.express as px
df = px.data.iris()

fig = px.density_contour(df, x="petal_length", y="petal_width", z="sepal_length", histfunc="avg")
fig.show()
```

### 2D Histograms with Graph Objects

To build this kind of figure with [graph objects](/python/graph-objects/) without using Plotly Express, we can use the `go.Histogram2d` class.

#### Basic 2D Histogram Contour

```python
import plotly.graph_objects as go

import numpy as np
np.random.seed(1)

x = np.random.uniform(-1, 1, size=500)
y = np.random.uniform(-1, 1, size=500)

fig = go.Figure(go.Histogram2dContour(
        x = x,
        y = y
))

fig.show()
```

#### 2D Histogram Contour Colorscale

```python
import plotly.graph_objects as go

import numpy as np

x = np.random.uniform(-1, 1, size=500)
y = np.random.uniform(-1, 1, size=500)

fig = go.Figure(go.Histogram2dContour(
        x = x,
        y = y,
        colorscale = 'Blues'
))

fig.show()
```

#### 2D Histogram Contour Styled

```python
import plotly.graph_objects as go

import numpy as np

x = np.random.uniform(-1, 1, size=500)
y = np.random.uniform(-1, 1, size=500)

fig = go.Figure(go.Histogram2dContour(
        x = x,
        y = y,
        colorscale = 'Jet',
        contours = dict(
            showlabels = True,
            labelfont = dict(
                family = 'Raleway',
                color = 'white'
            )
        ),
        hoverlabel = dict(
            bgcolor = 'white',
            bordercolor = 'black',
            font = dict(
                family = 'Raleway',
                color = 'black'
            )
        )

))

fig.show()
```

#### 2D Histogram Contour Subplot

```python
import plotly.graph_objects as go

import numpy as np

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
)

fig.show()
```

#### Reference
See https://plotly.com/python/reference/histogram2dcontour/ for more information and chart attribute options!
