# Scatter Plots in Python

How to make scatter plots in Python with Plotly.

New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

## Scatter plots with Plotly Express¶

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

With px.scatter, each data point is represented as a marker point, whose location is given by the x and y columns.

In [1]:
# x and y given as array_like objects
import plotly.express as px
fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
fig.show()

In [2]:
# x and y given as DataFrame columns
import plotly.express as px
df = px.data.iris() # iris is a pandas DataFrame
fig = px.scatter(df, x="sepal_width", y="sepal_length")
fig.show()


#### Setting size and color with column names¶

Scatter plots with variable-sized circular markers are often known as bubble charts. Note that color and size data are added to hover information. You can add other columns to hover data with the hover_data argument of px.scatter.

In [3]:
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_data=['petal_width'])
fig.show()


Color can be continuous as follows, or discrete/categorical as above.

In [4]:
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color='petal_length')
fig.show()


The symbol argument can be mapped to a column as well. A wide variety of symbols are available.

In [5]:
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", symbol="species")
fig.show()


## Scatter plots in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

Out[6]:

### Scatter plots and Categorical Axes¶

Scatter plots can be made using any type of cartesian axis, including linear, logarithmic, categorical or date axes.

Scatter plots where one axis is categorical are often known as dot plots.

In [7]:
import plotly.express as px
df = px.data.medals_long()

fig = px.scatter(df, y="nation", x="count", color="medal", symbol="medal")
fig.update_traces(marker_size=10)
fig.show()


### Error Bars¶

Scatter plots support error bars.

In [8]:
import plotly.express as px
df = px.data.iris()
df["e"] = df["sepal_width"]/100
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
error_x="e", error_y="e")
fig.show()


### Marginal Distribution Plots¶

Scatter plots support marginal distribution plots

In [9]:
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_length", y="sepal_width", marginal_x="histogram", marginal_y="rug")
fig.show()


### Facetting¶

Scatter plots support faceting.

In [10]:
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color="smoker", facet_col="sex", facet_row="time")
fig.show()


### Linear Regression and Other Trendlines¶

Scatter plots support linear and non-linear trendlines.

In [11]:
import plotly.express as px

df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", trendline="ols")
fig.show()


## Line plots with Plotly Express¶

In [12]:
import plotly.express as px
import numpy as np

t = np.linspace(0, 2*np.pi, 100)

fig = px.line(x=t, y=np.cos(t), labels={'x':'t', 'y':'cos(t)'})
fig.show()

In [13]:
import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(df, x='year', y='lifeExp', color='country')
fig.show()


The markers argument can be set to True to show markers on lines.

In [14]:
import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(df, x='year', y='lifeExp', color='country', markers=True)
fig.show()


The symbol argument can be used to map a data field to the marker symbol. A wide variety of symbols are available.

In [15]:
import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(df, x='year', y='lifeExp', color='country', symbol="country")
fig.show()


### Line plots on Date axes¶

Line plots can be made on using any type of cartesian axis, including linear, logarithmic, categorical or date axes. Line plots on date axes are often called time-series charts.

Plotly auto-sets the axis type to a date format when the corresponding data are either ISO-formatted date strings or if they're a date pandas column or datetime NumPy array.

In [16]:
import plotly.express as px

df = px.data.stocks()
fig = px.line(df, x='date', y="GOOG")
fig.show()


### Data Order in Scatter and Line Charts¶

Plotly line charts are implemented as connected scatterplots (see below), meaning that the points are plotted and connected with lines in the order they are provided, with no automatic reordering.

This makes it possible to make charts like the one below, but also means that it may be required to explicitly sort data before passing it to Plotly to avoid lines moving "backwards" across the chart.

In [17]:
import plotly.express as px
import pandas as pd

df = pd.DataFrame(dict(
x = [1, 3, 2, 4],
y = [1, 2, 3, 4]
))
fig = px.line(df, x="x", y="y", title="Unsorted Input")
fig.show()

df = df.sort_values(by="x")
fig = px.line(df, x="x", y="y", title="Sorted Input")
fig.show()


### Connected Scatterplots¶

In a connected scatterplot, two continuous variables are plotted against each other, with a line connecting them in some meaningful order, usually a time variable. In the plot below, we show the "trajectory" of a pair of countries through a space defined by GDP per Capita and Life Expectancy. Botswana's life expectancy

In [18]:
import plotly.express as px

df = px.data.gapminder().query("country in ['Canada', 'Botswana']")

fig = px.line(df, x="lifeExp", y="gdpPercap", color="country", text="year")
fig.update_traces(textposition="bottom right")
fig.show()


## Scatter and line plots with go.Scatter¶

If Plotly Express does not provide a good starting point, it is possible to use the more generic go.Scatter class from plotly.graph_objects. Whereas plotly.express has two functions scatter and line, go.Scatter can be used both for plotting points (makers) or lines, depending on the value of mode. The different options of go.Scatter are documented in its reference page.

#### Simple Scatter Plot¶

In [19]:
import plotly.graph_objects as go
import numpy as np

N = 1000
t = np.linspace(0, 10, 100)
y = np.sin(t)

fig = go.Figure(data=go.Scatter(x=t, y=y, mode='markers'))

fig.show()


#### Line and Scatter Plots¶

Use mode argument to choose between markers, lines, or a combination of both. For more options about line plots, see also the line charts notebook and the filled area plots notebook.

In [20]:
import plotly.graph_objects as go

# Create random data with numpy
import numpy as np
np.random.seed(1)

N = 100
random_x = np.linspace(0, 1, N)
random_y0 = np.random.randn(N) + 5
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N) - 5

fig = go.Figure()

mode='markers',
name='markers'))
mode='lines+markers',
name='lines+markers'))
mode='lines',
name='lines'))

fig.show()


#### Bubble Scatter Plots¶

In bubble charts, a third dimension of the data is shown through the size of markers. For more examples, see the bubble chart notebook

In [21]:
import plotly.graph_objects as go

fig = go.Figure(data=go.Scatter(
x=[1, 2, 3, 4],
y=[10, 11, 12, 13],
mode='markers',
marker=dict(size=[40, 60, 80, 100],
color=[0, 1, 2, 3])
))

fig.show()


#### Style Scatter Plots¶

In [22]:
import plotly.graph_objects as go
import numpy as np

t = np.linspace(0, 10, 100)

fig = go.Figure()

x=t, y=np.sin(t),
name='sin',
mode='markers',
marker_color='rgba(152, 0, 0, .8)'
))

x=t, y=np.cos(t),
name='cos',
marker_color='rgba(255, 182, 193, .9)'
))

# Set options common to all traces with fig.update_traces
fig.update_traces(mode='markers', marker_line_width=2, marker_size=10)
fig.update_layout(title='Styled Scatter',
yaxis_zeroline=False, xaxis_zeroline=False)

fig.show()


#### Data Labels on Hover¶

In [23]:
import plotly.graph_objects as go
import pandas as pd

fig = go.Figure(data=go.Scatter(x=data['Postal'],
y=data['Population'],
mode='markers',
marker_color=data['Population'],
text=data['State'])) # hover text goes here

fig.update_layout(title='Population of USA States')
fig.show()


#### Scatter with a Color Dimension¶

In [24]:
import plotly.graph_objects as go
import numpy as np

fig = go.Figure(data=go.Scatter(
y = np.random.randn(500),
mode='markers',
marker=dict(
size=16,
color=np.random.randn(500), #set color equal to a variable
colorscale='Viridis', # one of plotly colorscales
showscale=True
)
))

fig.show()


#### Large Data Sets¶

Now in Plotly you can implement WebGL with Scattergl() in place of Scatter()
for increased speed, improved interactivity, and the ability to plot even more data!

In [25]:
import plotly.graph_objects as go
import numpy as np

N = 100000
fig = go.Figure(data=go.Scattergl(
x = np.random.randn(N),
y = np.random.randn(N),
mode='markers',
marker=dict(
color=np.random.randn(N),
colorscale='Viridis',
line_width=1
)
))

fig.show()