Visualize data

Glean insights from your data, visually.

Create beautiful visualizations using our in-app Plotly support. Plotly support works just as you're used to in Python, displaying your chart straight to the spreadsheet.

Getting started

Building charts in Quadratic is centered around Python charting libraries, starting with Plotly. Building charts in Plotly is broken down into 3 simple steps:

1. Create and display a chart

2. Style your chart

3. Chart controls

1. Create and display a chart

Line charts

# import plotly
import plotly.express as px

# replace this df with your data
df = px.data.gapminder().query("country=='Canada'")

# create your chart type, for more chart types: https://plotly.com/python/
fig = px.line(df, x="year", y="lifeExp", title='Life expectancy in Canada')

# make chart prettier
fig.update_layout(
    plot_bgcolor="White",
)

# display chart 
fig.show()

Bar charts

import plotly.express as px

# replace this df with your data
df = px.data.gapminder().query("country == 'Canada'")

# create your chart type, for more chart types: https://plotly.com/python/
fig = px.bar(df, x='year', y='pop')

# make chart prettier
fig.update_layout(
    plot_bgcolor="White",
)

# display chart
fig.show()

Histograms

# Import Plotly
import plotly.express as px

# Create figure - replace df with your data
fig = px.histogram(df, x = 'output')

# Display to sheet 
fig.show()

Scatter plots

import plotly.express as px

# replace df, x, and y and color with your data
fig = px.scatter(df, x="col1", y="col2", color="col3")
fig.update_traces(marker_size=10)
fig.update_layout(scattermode="group")
fig.show()

Heatmaps

# Import library
import plotly.express as px

# Assumes 2d array Z
fig = px.imshow(Z, text_auto=True)

# Display chart
fig.show()

More chart types

For more chart types, explore the Plotly docs: https://plotly.com/python/

2. Styling

For more styling, explore the Plotly styling docs: https://plotly.com/python/styling-plotly-express/

# Example chart styling options to get started
fig.update_layout(
    xaxis=dict(
        showline=True,
        showgrid=False,
        showticklabels=True,
        linecolor='rgb(204, 204, 204)',
        linewidth=2,
        ticks='outside',
        tickfont=dict(
            family='Arial',
            size=12,
            color='rgb(82, 82, 82)',
        ),
    ),
    yaxis=dict(
        showgrid=False,
        zeroline=False,
        showline=False,
        showticklabels=True,
    ),
    autosize=False,
    showlegend=False,
    plot_bgcolor='white',
    title='Historical power usage by month (1985-2018)'
)

3. Chart controls

Resize by dragging the edges of the chart.