Dataset Plotting¶

Plot multiple variables from an xarray Dataset with automatic or custom slot assignment.

In [1]:

import numpy as np
import xarray as xr

from xarray_plotly import config, xpx

config.notebook()

import numpy as np import xarray as xr from xarray_plotly import config, xpx config.notebook()

In [2]:

# Create a Dataset with multiple variables
time = np.arange(50)
cities = ["NYC", "LA", "Chicago"]

ds = xr.Dataset(
    {
        "temperature": (["time", "city"], 20 + 5 * np.random.randn(50, 3).cumsum(axis=0) / 10),
        "humidity": (["time", "city"], 50 + 10 * np.random.randn(50, 3).cumsum(axis=0) / 10),
        "pressure": (["time", "city"], 1013 + np.random.randn(50, 3).cumsum(axis=0)),
    },
    coords={"time": time, "city": cities},
)
ds

# Create a Dataset with multiple variables time = np.arange(50) cities = ["NYC", "LA", "Chicago"] ds = xr.Dataset( { "temperature": (["time", "city"], 20 + 5 * np.random.randn(50, 3).cumsum(axis=0) / 10), "humidity": (["time", "city"], 50 + 10 * np.random.randn(50, 3).cumsum(axis=0) / 10), "pressure": (["time", "city"], 1013 + np.random.randn(50, 3).cumsum(axis=0)), }, coords={"time": time, "city": cities}, ) ds

Out[2]:

<xarray.Dataset> Size: 4kB
Dimensions:      (time: 50, city: 3)
Coordinates:
  * time         (time) int64 400B 0 1 2 3 4 5 6 7 8 ... 42 43 44 45 46 47 48 49
  * city         (city) <U7 84B 'NYC' 'LA' 'Chicago'
Data variables:
    temperature  (time, city) float64 1kB 20.32 19.74 20.48 ... 19.11 26.35
    humidity     (time, city) float64 1kB 50.65 49.89 51.17 ... 42.46 63.34 67.0
    pressure     (time, city) float64 1kB 1.012e+03 1.014e+03 ... 1.007e+03

xarray.Dataset

• Dimensions:
  • time: 50
  • city: 3
• Coordinates: (2)
  • time
    (time)
    int64
    0 1 2 3 4 5 6 ... 44 45 46 47 48 49

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])

  • city
    (city)
    <U7
    'NYC' 'LA' 'Chicago'

    array(['NYC', 'LA', 'Chicago'], dtype='<U7')

• Data variables: (3)
  • temperature
    (time, city)
    float64
    20.32 19.74 20.48 ... 19.11 26.35

    array([[20.3190984 , 19.73584962, 20.4813125 ],
           [19.92026105, 19.99970378, 20.66066282],
           [19.75174788, 19.54928466, 20.95296618],
           [19.47933651, 20.47290438, 21.19894726],
           [20.08313005, 20.08650358, 21.74394731],
           [20.52535749, 20.29062   , 21.47297015],
           [18.95583011, 20.61830785, 21.2367295 ],
           [18.67435289, 20.46063722, 20.99891161],
           [18.7538438 , 20.62011075, 21.88633037],
           [18.95449723, 20.1439489 , 20.87686622],
           [18.9169625 , 20.11767182, 21.63404801],
           [18.09784538, 19.52354821, 22.00882738],
           [17.73938751, 19.47658785, 21.89611217],
           [17.43255222, 19.24500462, 22.46100373],
           [17.09368681, 19.38125322, 22.35694253],
           [18.15406864, 19.52860028, 22.51490073],
           [17.44980487, 18.67259018, 22.88242436],
           [18.39684086, 18.31557938, 22.69500486],
           [18.45045773, 17.97857631, 22.24617761],
           [18.24880394, 17.0863461 , 21.84948629],
    ...
           [17.97245869, 18.72803476, 22.32519977],
           [18.62262093, 18.57520439, 22.31830021],
           [18.80167886, 18.13198965, 22.17244186],
           [19.25227188, 18.6684501 , 22.40222282],
           [18.54423318, 19.43559433, 22.90601173],
           [18.38242576, 20.12891839, 22.34435541],
           [18.29732326, 19.7108686 , 22.50757062],
           [18.2481511 , 20.39778774, 23.67524187],
           [18.66676102, 20.34420652, 23.84174133],
           [18.74918641, 21.07203385, 24.108278  ],
           [18.30385654, 20.31798959, 24.97755962],
           [17.90694158, 20.6951146 , 25.34697328],
           [17.3308847 , 19.63468032, 24.75721796],
           [17.70149777, 19.62178636, 25.21726964],
           [18.16497532, 19.4413884 , 25.07779835],
           [17.84361031, 18.95530542, 25.77442377],
           [18.24540638, 18.80839377, 26.24022916],
           [17.60471427, 18.84812876, 26.03270718],
           [18.01360713, 19.56192968, 25.53035729],
           [18.62932896, 19.10993891, 26.3477068 ]])

  • humidity
    (time, city)
    float64
    50.65 49.89 51.17 ... 63.34 67.0

    array([[50.64670209, 49.88860384, 51.16734585],
           [49.98205985, 50.80497445, 52.01848035],
           [50.86038733, 51.00208557, 52.23528308],
           [49.67619136, 52.74627527, 52.80731288],
           [52.55873018, 53.09991592, 54.24260709],
           [53.13931747, 52.71957668, 55.41974695],
           [52.51078047, 53.55995262, 57.42626167],
           [51.584308  , 52.79967754, 58.33496795],
           [50.83918952, 52.90116338, 60.84927083],
           [50.96632592, 52.80530833, 60.59906543],
           [51.69821124, 52.61861745, 60.53412591],
           [52.39213765, 53.08741915, 60.80835272],
           [51.05609058, 51.86886212, 59.97566071],
           [49.96827893, 50.60198545, 59.76471415],
           [49.836287  , 51.50705311, 60.37438891],
           [49.48879114, 52.7372882 , 60.62066853],
           [49.56800327, 54.10396046, 60.26895975],
           [48.90085938, 51.73127752, 61.27902069],
           [48.04114075, 52.74675194, 60.88033829],
           [50.21210683, 53.18223114, 60.06653253],
    ...
           [48.06562551, 56.73426097, 61.08398391],
           [47.58309018, 56.85457416, 62.48500833],
           [47.12650257, 55.09225564, 64.17976635],
           [46.33244717, 56.20155887, 64.17852991],
           [46.51029263, 55.64927557, 64.53080006],
           [44.74863878, 56.79460424, 61.22833671],
           [43.94518025, 56.27041562, 62.71273552],
           [45.04852513, 57.40719287, 63.5441764 ],
           [44.83236885, 56.58697663, 65.05792809],
           [44.53617025, 58.71273448, 63.25253107],
           [44.70635826, 60.54087747, 64.80847159],
           [46.02950327, 61.2519276 , 65.28779953],
           [44.6448823 , 61.58848612, 66.46935554],
           [43.96012831, 61.57396129, 65.83337693],
           [44.96331826, 62.34327677, 66.90081006],
           [44.09045746, 61.93847413, 66.5343618 ],
           [42.74190428, 61.85078826, 66.13892025],
           [43.3679963 , 62.30898004, 66.87036877],
           [42.07277615, 63.41599043, 67.2885217 ],
           [42.46096352, 63.33778324, 67.00235555]])

  • pressure
    (time, city)
    float64
    1.012e+03 1.014e+03 ... 1.007e+03

    array([[1011.73430083, 1013.72425564, 1011.62895858],
           [1011.33818711, 1015.05310321, 1009.82655425],
           [1010.25567444, 1015.29420955, 1009.86397654],
           [1007.90280423, 1015.35253315, 1009.10053996],
           [1007.64833272, 1014.31923039, 1007.7697865 ],
           [1007.65852947, 1013.08281524, 1007.68417058],
           [1008.74322122, 1013.99278953, 1008.46547856],
           [1009.06539088, 1012.86978594, 1006.29018505],
           [1008.45331306, 1013.46818742, 1004.32822856],
           [1007.28676564, 1012.57877956, 1005.51620421],
           [1006.03398955, 1013.07412759, 1005.02527531],
           [1004.74073093, 1013.59587763, 1005.23304446],
           [1006.36812093, 1013.53822384, 1005.05591723],
           [1005.57061418, 1013.14954367, 1005.68859661],
           [1004.7343204 , 1011.95316568, 1005.47965486],
           [1002.96514002, 1012.16494533, 1004.68475504],
           [1002.96628371, 1012.19759014, 1004.21254472],
           [1002.72957505, 1011.37055407, 1004.01018846],
           [1001.61510007, 1010.76065491, 1003.74589795],
           [1000.82150321, 1011.00428273, 1003.91246821],
    ...
           [1004.56332405, 1008.07728766, 1004.13048665],
           [1004.99476433, 1009.13417835, 1002.95978126],
           [1006.64022807, 1008.61869863, 1002.06573954],
           [1006.10753119, 1008.68005897, 1000.96519347],
           [1005.79173187, 1009.71319805,  998.98381705],
           [1005.87241017, 1010.18475747, 1000.79624687],
           [1005.76591096, 1009.46987561, 1001.01344113],
           [1005.68434163, 1010.28203438, 1001.23463269],
           [1004.58108766, 1010.8180047 , 1002.05111746],
           [1003.93590075, 1011.97757579, 1001.57814104],
           [1004.48671539, 1010.64720407, 1003.19164725],
           [1003.34938675, 1010.59967079, 1002.8007188 ],
           [1003.17336589, 1011.08815075, 1003.57614656],
           [1001.83049128, 1012.97090537, 1004.7762378 ],
           [1002.02904978, 1013.79596509, 1006.20848156],
           [1003.05786718, 1013.93131389, 1005.25040031],
           [1003.4061299 , 1013.9438231 , 1004.52478031],
           [1005.73440123, 1013.15516188, 1004.31198573],
           [1003.88453581, 1014.20385136, 1006.13427925],
           [1004.69193718, 1015.49883603, 1006.56561098]])

Plot All Variables¶

When you call a plot method on a Dataset without specifying var, all variables are combined into a single DataArray with a new "variable" dimension:

In [3]:

# All variables: time -> x, variable -> color, city -> line_dash
xpx(ds).line()

# All variables: time -> x, variable -> color, city -> line_dash xpx(ds).line()

Control Where "variable" Goes¶

The "variable" dimension can be assigned to any slot:

In [4]:

# Variables as facet columns
xpx(ds).line(facet_col="variable")

# Variables as facet columns xpx(ds).line(facet_col="variable")

In [5]:

# Variables as rows, cities as columns
xpx(ds).line(facet_row="variable", facet_col="city")

# Variables as rows, cities as columns xpx(ds).line(facet_row="variable", facet_col="city")

Configure Default "variable" Position¶

By default, "variable" is placed as the second dimension so it maps to color. This keeps your first dimension (e.g., time) on the x-axis.

You can change this globally with config.set_options():

In [6]:

# Default: position=1 (second) -> variable goes to color
# Note: to_array() puts "variable" first, but xpx() reorders it to position 1
print("Raw to_array() dims:", ds.to_array().dims)  # (variable, time, city)
print("After xpx reorder:   (time, variable, city)")  # time->x, variable->color
xpx(ds).line(title="Default: variable as color (position=1)")

# Default: position=1 (second) -> variable goes to color # Note: to_array() puts "variable" first, but xpx() reorders it to position 1 print("Raw to_array() dims:", ds.to_array().dims) # (variable, time, city) print("After xpx reorder: (time, variable, city)") # time->x, variable->color xpx(ds).line(title="Default: variable as color (position=1)")

Raw to_array() dims: ('variable', 'time', 'city')
After xpx reorder:   (time, variable, city)

In [7]:

# Position 0: variable goes first (x-axis) - usually not what you want!
with config.set_options(dataset_variable_position=0):
    fig = xpx(ds).line(title="position=0: variable on x-axis (probably not desired)")
fig

# Position 0: variable goes first (x-axis) - usually not what you want! with config.set_options(dataset_variable_position=0): fig = xpx(ds).line(title="position=0: variable on x-axis (probably not desired)") fig

In [8]:

# Position -1: variable goes last -> city gets color, variable gets line_dash
with config.set_options(dataset_variable_position=-1):
    fig = xpx(ds).line(title="position=-1: variable as line_dash")
fig

# Position -1: variable goes last -> city gets color, variable gets line_dash with config.set_options(dataset_variable_position=-1): fig = xpx(ds).line(title="position=-1: variable as line_dash") fig

Plot a Single Variable¶

Use var="name" to plot just one variable:

In [9]:

xpx(ds).line(var="temperature", title="Temperature Only")

xpx(ds).line(var="temperature", title="Temperature Only")

Different Plot Types¶

In [10]:

# Bar chart - latest values by city
xpx(ds.isel(time=-1)).bar(x="city", color="variable", barmode="group")

# Bar chart - latest values by city xpx(ds.isel(time=-1)).bar(x="city", color="variable", barmode="group")

In [11]:

# Box plot - distribution by variable
xpx(ds).box(x="variable", color="city")

# Box plot - distribution by variable xpx(ds).box(x="variable", color="city")

In [12]:

# Area chart
xpx(ds).area(var="humidity", title="Humidity Over Time")

# Area chart xpx(ds).area(var="humidity", title="Humidity Over Time")

In [13]:

# Scatter
xpx(ds).scatter(var="temperature", title="Temperature Scatter")

# Scatter xpx(ds).scatter(var="temperature", title="Temperature Scatter")

In [14]:

# Pie chart - snapshot at one time
xpx(ds.isel(time=-1)).pie(var="temperature", names="city", title="Temperature Distribution")

# Pie chart - snapshot at one time xpx(ds.isel(time=-1)).pie(var="temperature", names="city", title="Temperature Distribution")

Combining Slot Assignments¶

Mix explicit assignments with auto-assignment:

In [15]:

# Explicit: variable -> facet_col, let city auto-assign to color
xpx(ds).line(facet_col="variable", color="city")

# Explicit: variable -> facet_col, let city auto-assign to color xpx(ds).line(facet_col="variable", color="city")

In [16]:

# Skip color slot with None
xpx(ds).line(var="temperature", color=None)

# Skip color slot with None xpx(ds).line(var="temperature", color=None)