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Examples

This is a collection of rich examples supported by Hydrogen. Please share your favorite snippets with us and add them to this page.

Interactive plots using Plotly

{% codetabs name="Python", type="py" -%} from plotly import offline offline.init_notebook_mode()

offline.iplot([{"y": [1, 2, 1]}]) {%- language name="Python using matplotlib", type="py" -%} import numpy as np import matplotlib.pyplot as plt from plotly import offline as py py.init_notebook_mode()

t = np.linspace(0, 20, 500) plt.plot(t, np.sin(t))

py.iplot_mpl(plt.gcf()) {%- language name="R", type="r" -%} library(IRdisplay)

data <- list(list(x=c(1999, 2000, 2001, 2002), y=c(10, 15, 13, 17), type='scatter')) figure <- list(data=data)

mimebundle <- list('application/vnd.plotly.v1+json'=figure) IRdisplay::publish_mimebundle(mimebundle) {%- endcodetabs %}

Interactive plots using Matplotlib

Interactive plots via PyQt/Pyside (creates separate window).

{% codetabs name="Python", type="py" -%} import matplotlib matplotlib.use('Qt5Agg')

This should be done before import matplotlib.pyplot

'Qt4Agg' for PyQt4 or PySide, 'Qt5Agg' for PyQt5

import matplotlib.pyplot as plt import numpy as np

t = np.linspace(0, 20, 500) plt.plot(t, np.sin(t)) plt.show() {%- endcodetabs %}

Interactive JSON Objects

{% codetabs name="Python", type="py" -%} from IPython.display import JSON

data = {"foo": {"bar": "baz"}, "a": 1} JSON(data) {%- endcodetabs %}

Static plots

With support for svg, png, jpeg and gif

{% codetabs name="Python using matplotlib", type="py" -%} import matplotlib.pyplot as plt import numpy as np

%matplotlib inline %config InlineBackend.figure_format = 'svg' t = np.linspace(0, 20, 500)

plt.plot(t, np.sin(t)) plt.show() {%- language name="Python using altair >= 2.0", type="py" -%} import altair as alt from vega_datasets import data

iris = data.iris()

alt.Chart(iris).mark_point().encode( x='petalLength', y='petalWidth', color='species' ) {%- language name="Python using altair >= v1.3 < 2.0", type="py" -%} from altair import Chart, load_dataset, enable_mime_rendering enable_mime_rendering()

cars = load_dataset('cars') spec = Chart(cars).mark_point().encode( x='Horsepower', y='Miles_per_Gallon', color='Origin', ) spec {%- language name="Python using altair < v1.3", type="py" -%} from IPython.display import display from altair import Chart, load_dataset def vegify(spec): display({ 'application/vnd.vegalite.v1+json': spec.to_dict() }, raw=True)

cars = load_dataset('cars') spec = Chart(cars).mark_point().encode( x='Horsepower', y='Miles_per_Gallon', color='Origin', ) vegify(spec) {%- endcodetabs %}

LaTeX

{% codetabs name="Python using sympy", type="py" -%} import sympy as sp sp.init_printing(use_latex='mathjax')

x, y, z = sp.symbols('x y z') f = sp.sin(x _ y) + sp.cos(y _ z) sp.integrate(f, x) {%- language name="Python using Math", type="py" -%} from IPython.display import Math

Math(r'i\hbar \frac{dA}{dt}=[A(t),H(t)]+i\hbar \frac{\partial A}{\partial t}.') {%- language name="Python using Latex", type="py" -%} from IPython.display import Latex Latex('''The mass-energy equivalence is described by the famous equation

$$E=mc^2$$

discovered in 1905 by Albert Einstein. In natural units ($c$ = 1), the formula expresses the identity

\begin{equation} E=m \end{equation}''') {%- endcodetabs %}

Data frames

{% codetabs name="Python using pandas", type="py" -%} import numpy as np import pandas as pd

df = pd.DataFrame({'A': 1., 'B': pd.Timestamp('20130102'), 'C': pd.Series(1, index=list(range(4)), dtype='float32'), 'D': np.array([3] * 4, dtype='int32'), 'E': pd.Categorical(["test", "train", "test", "train"]), 'F': 'foo'})

df {%- language name="Python using numpy", type="py" -%} import numpy as np

t = np.linspace(0, 20, 500) t {%- endcodetabs %}

Images

{% codetabs name="Python", type="py" -%} from IPython.display import Image Image('https://cloud.githubusercontent.com/assets/836375/15271096/98e4c102-19fe-11e6-999a-a74ffe6e2000.gif') {%- endcodetabs %}

HTML

{% codetabs name="Python", type="py" -%} from IPython.display import IFrame IFrame('https://nteract.io/', width='900', height='490') {%- endcodetabs %}

Plain Text

{% codetabs name="Python", type="py" -%} print("Hello World!") {%- language name="JavaScript", type="js" -%} console.log("Hello World!"); {%- endcodetabs %}

Automatic visualization with the nteract Data Explorer

{% codetabs name="Python", type="py" -%} import pandas as pd

pd.options.display.html.table_schema = True pd.options.display.max_rows = None

iris_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"

df1 = pd.read_csv(iris_url)

df1 {%- endcodetabs %} (https://blog.nteract.io/designing-the-nteract-data-explorer-f4476d53f897)