Data Visualization using Plotly
Plotly is an interactive, open-source visualization library that supports over 40 chart types. It provides high-level APIs for Python (Plotly Express) and low-level graph objects (Plotly Graph Objects) for more customization.
Table of Contents
Basic Charts
Scatter Plot
Syntax (Plotly Express):
px.scatter(data_frame, x, y, color, size, hover_name, trendline)
Example:
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length",
color="species", size="petal_length",
hover_name="species", trendline="ols")
fig.show()
Syntax (Graph Objects):
go.Scatter(x, y, mode, name, marker)
Example:
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(
x=df["sepal_width"],
y=df["sepal_length"],
mode="markers",
marker=dict(
size=df["petal_length"]*2,
color=df["species_id"],
showscale=True
)
))
fig.show()
Line Plot
Syntax:
px.line(data_frame, x, y, color, line_group)
Example:
df = px.data.gapminder().query("country=='Canada'")
fig = px.line(df, x="year", y="lifeExp",
title="Life expectancy in Canada")
fig.show()
Bar Chart
Syntax:
px.bar(data_frame, x, y, color, barmode)
Example:
df = px.data.tips()
fig = px.bar(df, x="day", y="total_bill",
color="sex", barmode="group")
fig.show()
Pie Chart
Syntax:
px.pie(data_frame, names, values, color_discrete_sequence)
Example:
df = px.data.tips()
fig = px.pie(df, names="day", values="total_bill",
color_discrete_sequence=px.colors.sequential.RdBu)
fig.show()
Statistical Charts
Histogram
Syntax:
px.histogram(data_frame, x, y, color, marginal, nbins)
Example:
df = px.data.tips()
fig = px.histogram(df, x="total_bill", color="sex",
marginal="rug", nbins=30)
fig.show()
Box Plot
Syntax:
px.box(data_frame, x, y, color, points)
Example:
fig = px.box(df, x="day", y="total_bill",
color="smoker", points="all")
fig.show()
Violin Plot
Syntax:
px.violin(data_frame, x, y, color, box)
Example:
fig = px.violin(df, x="day", y="total_bill",
color="sex", box=True)
fig.show()
Density Heatmap
Syntax:
px.density_heatmap(data_frame, x, y, z, nbinsx, nbinsy)
Example:
fig = px.density_heatmap(df, x="total_bill", y="tip",
nbinsx=20, nbinsy=20)
fig.show()
Financial Charts
Candlestick Chart
Syntax:
go.Candlestick(x, open, high, low, close)
Example:
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
fig = go.Figure(go.Candlestick(
x=df['Date'],
open=df['AAPL.Open'],
high=df['AAPL.High'],
low=df['AAPL.Low'],
close=df['AAPL.Close']
))
fig.show()
OHLC Chart
Syntax:
go.Ohlc(x, open, high, low, close)
Example:
fig = go.Figure(go.Ohlc(
x=df['Date'],
open=df['AAPL.Open'],
high=df['AAPL.High'],
low=df['AAPL.Low'],
close=df['AAPL.Close']
))
fig.show()
Maps
Scatter Map
Syntax:
px.scatter_geo(data_frame, lat, lon, color, size)
Example:
df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, locations="iso_alpha",
color="continent",
size="pop",
projection="natural earth")
fig.show()
Choropleth Map
Syntax:
px.choropleth(data_frame, locations, color, locationmode)
Example:
fig = px.choropleth(df, locations="iso_alpha",
color="lifeExp",
hover_name="country",
color_continuous_scale=px.colors.sequential.Plasma)
fig.show()
3D Charts
3D Scatter Plot
Syntax:
px.scatter_3d(data_frame, x, y, z, color)
Example:
df = px.data.iris()
fig = px.scatter_3d(df, x='sepal_length',
y='sepal_width', z='petal_width',
color='species')
fig.show()
Surface Plot
Syntax:
go.Surface(z, colorscale)
Example:
import numpy as np
z = np.random.rand(10,10)
fig = go.Figure(go.Surface(z=z, colorscale="Viridis"))
fig.show()
Subplots
Make Subplots
Syntax:
make_subplots(rows, cols, subplot_titles)
Example:
from plotly.subplots import make_subplots
fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Scatter(x=[1,2,3], y=[4,5,6]), row=1, col=1)
fig.add_trace(go.Bar(x=[1,2,3], y=[3,2,1]), row=1, col=2)
fig.update_layout(height=400, width=800, title_text="Subplots")
fig.show()
Animation
Animated Charts
Syntax:
px.scatter(data_frame, animation_frame, animation_group)
Example:
df = px.data.gapminder()
fig = px.scatter(df, x="gdpPercap", y="lifeExp",
size="pop", color="continent",
hover_name="country",
animation_frame="year",
animation_group="country",
range_x=[100,100000], range_y=[25,90])
fig.show()
Customization
Update Layout
Syntax:
fig.update_layout(title, xaxis_title, yaxis_title)
Example:
fig.update_layout(
title="Customized Chart",
xaxis_title="X Axis Title",
yaxis_title="Y Axis Title",
font=dict(
family="Courier New, monospace",
size=18,
color="#7f7f7f"
)
)
Update Traces
Syntax:
fig.update_traces(marker_size, line_width)
Example:
fig.update_traces(
marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')),
selector=dict(mode='markers')
)
Key Plotly Features
- Interactivity: Hover tooltips, zoom, pan, and click events
- Themes: Built-in templates like
plotly,plotly_white,plotly_dark - Export: Save as PNG, JPEG, SVG, or PDF
- Dash Integration: Build interactive web applications
- Web Support: Embed charts in websites and notebooks
To use Plotly in Jupyter notebooks, you may need to install the required extensions:
pip install jupyter-dash
Plotly offers both a high-level API (Plotly Express) for quick visualization and a low-level API (Graph Objects) for detailed customization. The library works seamlessly with Pandas DataFrames and integrates well with Dash for building analytical web applications.