Components

All the components you can use to develop and layout your reports

Overview

Components are used to create Reports. Components are compatible with Python objects, such as Pandas DataFrames, visualisations, and Markdown. We are always adding new components, and if you have some ideas on what you would like to use in your reports, please open an issue on GitHub.

Plot

The plot component takes a supported plot object and renders it in your report.

Currently, supported libraries are:

  • Matplotlib and seaborn figures

  • Plotly plots

  • Bokeh plots

  • Altair plots

  • Folium plots

  • Files and images

Altair

Altair is a declarative statistical visualization library for Python, based on Vega and Vega-Lite. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code.

To get started using Altair to make your visualizations, begin with Altair's Documentation

import altair as alt
import datapane as dp
from vega_datasets import data as vega_data
gap = pd.read_json(vega_data.gapminder.url)
select_year = alt.selection_single(
name='select', fields=['year'], init={'year': 1955},
bind=alt.binding_range(min=1955, max=2005, step=5)
)
alt_chart = alt.Chart(gap).mark_point(filled=True).encode(
alt.X('fertility', scale=alt.Scale(zero=False)),
alt.Y('life_expect', scale=alt.Scale(zero=False)),
alt.Size('pop:Q'),
alt.Color('cluster:N'),
alt.Order('pop:Q', sort='descending'),
).add_selection(select_year).transform_filter(select_year)
dp.Report(dp.Plot(alt_chart)).publish(name='time_interval')

Bokeh

Bokeh is an interactive visualization library which provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large datasets.

To get started using Bokeh to make your visualizations, begin with Bokeh's User Guide.

from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.iris import flowers
import datapane as dp
# Create scatter plot with Bokeh
colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}
colors = [colormap[x] for x in flowers['species']]
bohek_chart = figure(title = "Iris Morphology")
bokeh_chart.xaxis.axis_label = 'Petal Length'
bokeh_chart.yaxis.axis_label = 'Petal Width'
bokeh_chart.circle(flowers["petal_length"], flowers["petal_width"],
color=colors, fill_alpha=0.2, size=10)
output_file("iris.html", title="iris.py example")
# View the plot
dp.Report(dp.Plot(bokeh_chart)).preview()
# Publish the report
dp.Report(dp.Plot(bokeh_chart)).publish(name='bokeh_plot')

Plotly

Plotly's Python graphing library makes interactive, publication-quality graphs.

import plotly.express as px
import datapane as dp
df = px.data.gapminder()
plotly_chart = px.scatter(df.query("year==2007"), x="gdpPercap", y="lifeExp",
size="pop", color="continent",
hover_name="country", log_x=True, size_max=60)
plotly_chart.show()
dp.Report(dp.Plot(plotly_chart)).publish(name='bubble')

Folium

Folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map.

The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys.

import folium
import datapane as dp
m = folium.Map(location=[45.5236, -122.6750])
dp.Report(dp.Plot(m)).publish(name='folium_map')
https://datapane.com/khuyentran1401/reports/folium_map/

Markdown

Markdown is a lightweight markup language that allows you to include text in your report.

import datapane as dp
dp.Report(dp.Markdown("My awesome markdown"))

To include more sentences and format the words, use """Some words"""

import datapane as dp
report = dp.Report(dp.Markdown( "# Altair"),
dp.Plot(alt_chart),
dp.Markdown("""
* There is a negative relation between life expectanty and fertility
* The number of population with high life expactancy increases as time increase
""")
report.publish(name = 'results')
Graph with Markdown

Check here for more information on how to format your text with markdown.

Code

You can also share your code using dp.Markdown like below

code = dp.Markdown(f'''
```df = data.reset_index().melt('Date', var_name='symbol', value_name='price')
base_chart = alt.Chart(df).encode(x='Date:T', y='price:Q', color='symbol').interactive()
chart = base_chart.mark_line() if plot_type == 'line' else base_chart.mark_bar()```
''')
dp.Report(code).publish(name='report_with_code')

Table

The Table component takes a pandas DataFrame and renders an interactive, sortable, searchable table in your Datapane report. Viewers can also download the table from the website. Tables can typically render datasets up to 2-3m cells without performance problems.

By default, a Table only displays the first 1000 rows, and gives the user the option to load the entire dataset.

import datapane as dp
import pandas as pd
df = pd.DataFrame({
'A': np.random.normal(-1, 1, 5000),
'B': np.random.normal(1, 2, 5000),
})
table = dp.Table(df)
report = dp.Report(table)
report.publish(name='sample_table')

File and Image

Datapane also enables you to share files and images. For example, to share your image, simply use dp.File like below

dp.File(file=dp.Path('./image.png'))

And your image would appear on Datapane report automatically!

PivotTable

Coming soon!