# Charts/visualizations
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 ](#create-and-display-a-chart)
2. [Style your chart ](#styling)
3. [Chart controls](#3.-chart-controls)
## 1. Create and display a chart
### Line charts
```python
# 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
```python
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
```python
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
```python
# 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:
## 2. Styling
For more styling, explore the Plotly styling docs:
```python
# 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.
