Gallery Basic1

Point Scatter

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import plotly.express as px
fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
fig.show()

X, Y Scatter Plot

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# 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()

Bubble Chart

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import plotly.express as px

df = px.data.iris()
df.head()
fig = px.scatter(df, x="sepal_width", 
                     y="sepal_length", 
                     color="species",
                     size='petal_length', 
                     hover_data=['petal_width'])
fig.show()

Function Draw

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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()

Multline Plot

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

Dot line Plot

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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()

Multiple Line Style

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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()

# Add traces
fig.add_trace(go.Scatter(x=random_x, y=random_y0,
                    mode='markers',
                    name='markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y1,
                    mode='lines+markers',
                    name='lines+markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y2,
                    mode='lines',
                    name='lines'))

fig.show()

Use size and color

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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()

Update Figure

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import plotly.graph_objects as go
import numpy as np


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

fig = go.Figure()

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

fig.add_trace(go.Scatter(
    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()

Gradually changed color with y-value

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import plotly.graph_objects as go
import pandas as pd

# 1. Point color is accordence with y-value

data= pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv")

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()

Plotly colorscale

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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()

Plot Large Dataset

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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()

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import plotly.graph_objects as go
import numpy as np

N = 100000
r = np.random.uniform(0, 1, N)
theta = np.random.uniform(0, 2*np.pi, N)

fig = go.Figure(data=go.Scattergl(
    x = r * np.cos(theta), # non-uniform distribution
    y = r * np.sin(theta), # zoom to see more points at the center
    mode='markers',
    marker=dict(
        color=np.random.randn(N),
        colorscale='Viridis',
        line_width=1
    )
))

fig.show()

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