Plotly Express Kwargs¶

All keyword arguments are passed directly to Plotly Express.

In [1]:

import plotly.express as px
import xarray as xr

from xarray_plotly import config, xpx

config.notebook()

import plotly.express as px import xarray as xr from xarray_plotly import config, xpx config.notebook()

In [2]:

df = px.data.stocks().set_index("date")
df.index = df.index.astype("datetime64[ns]")

stocks = xr.DataArray(
    df.values,
    dims=["date", "company"],
    coords={"date": df.index, "company": df.columns.tolist()},
    name="price",
)

df = px.data.stocks().set_index("date") df.index = df.index.astype("datetime64[ns]") stocks = xr.DataArray( df.values, dims=["date", "company"], coords={"date": df.index, "company": df.columns.tolist()}, name="price", )

title¶

In [3]:

xpx(stocks).line(title="Stock Prices")

xpx(stocks).line(title="Stock Prices")

template¶

See Plotly templates.

In [4]:

xpx(stocks).line(template="plotly_dark")

xpx(stocks).line(template="plotly_dark")

In [5]:

xpx(stocks).line(template="seaborn")

xpx(stocks).line(template="seaborn")

labels¶

Override axis and legend labels:

In [6]:

xpx(stocks).line(labels={"price": "Normalized Price", "date": "Date", "company": "Ticker"})

xpx(stocks).line(labels={"price": "Normalized Price", "date": "Date", "company": "Ticker"})

color_discrete_sequence¶

See Plotly color sequences.

In [7]:

xpx(stocks).line(color_discrete_sequence=px.colors.qualitative.Set2)

xpx(stocks).line(color_discrete_sequence=px.colors.qualitative.Set2)

In [8]:

xpx(stocks).line(
    color_discrete_sequence=["#E63946", "#457B9D", "#2A9D8F", "#E9C46A", "#F4A261", "#264653"]
)

xpx(stocks).line( color_discrete_sequence=["#E63946", "#457B9D", "#2A9D8F", "#E9C46A", "#F4A261", "#264653"] )

color_continuous_scale¶

See Plotly colorscales.

In [9]:

xpx(stocks).imshow(color_continuous_scale="Viridis")

xpx(stocks).imshow(color_continuous_scale="Viridis")

In [10]:

# Diverging colorscale with midpoint
change = stocks - stocks.isel(date=0)
xpx(change).imshow(color_continuous_scale="RdBu_r", color_continuous_midpoint=0)

# Diverging colorscale with midpoint change = stocks - stocks.isel(date=0) xpx(change).imshow(color_continuous_scale="RdBu_r", color_continuous_midpoint=0)

colors (unified parameter)¶

The colors parameter provides a simpler way to set colors without remembering the exact Plotly parameter name. It automatically maps to the correct parameter based on the input type:

Input	Maps To
"Viridis" (continuous scale name)	color_continuous_scale
"D3" (qualitative palette name)	color_discrete_sequence
["red", "blue"] (list)	color_discrete_sequence
{"A": "red"} (dict)	color_discrete_map

In [11]:

# Named qualitative palette
xpx(stocks).line(colors="D3")

# Named qualitative palette xpx(stocks).line(colors="D3")

In [12]:

# List of custom colors
xpx(stocks).line(colors=["#E63946", "#457B9D", "#2A9D8F", "#E9C46A", "#F4A261"])

# List of custom colors xpx(stocks).line(colors=["#E63946", "#457B9D", "#2A9D8F", "#E9C46A", "#F4A261"])

In [13]:

# Dict for explicit mapping
xpx(stocks).line(
    colors={
        "GOOG": "red",
        "AAPL": "blue",
        "AMZN": "green",
        "FB": "purple",
        "NFLX": "orange",
        "MSFT": "brown",
    }
)

# Dict for explicit mapping xpx(stocks).line( colors={ "GOOG": "red", "AAPL": "blue", "AMZN": "green", "FB": "purple", "NFLX": "orange", "MSFT": "brown", } )

In [14]:

# Continuous scale for heatmaps
xpx(stocks).imshow(colors="Plasma")

# Continuous scale for heatmaps xpx(stocks).imshow(colors="Plasma")

markers¶

In [15]:

xpx(stocks.isel(date=slice(0, 30))).line(markers=True)

xpx(stocks.isel(date=slice(0, 30))).line(markers=True)

barmode¶

See Plotly bar charts.

In [16]:

xpx(stocks.isel(date=[0, 50, 100])).bar(barmode="group")

xpx(stocks.isel(date=[0, 50, 100])).bar(barmode="group")

text_auto¶

In [17]:

xpx(stocks.isel(date=[0, 50, 100])).bar(barmode="group", text_auto=".2f")

xpx(stocks.isel(date=[0, 50, 100])).bar(barmode="group", text_auto=".2f")

range_x, range_y¶

In [18]:

xpx(stocks).line(range_y=[0.5, 2.0])

xpx(stocks).line(range_y=[0.5, 2.0])

More¶

See the full Plotly Express API reference for all available kwargs.