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graph_objects, update_layout
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‎notebooks/box-plots.md

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@@ -88,12 +88,12 @@ fig.show()
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## Box plot with go.Box
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When data are not available as tidy dataframes, it is also possible to use the more generic `go.Box` function from `plotly.graph_objs`. All available options for `go.Box` are described in the reference page https://plot.ly/python/reference/#box.
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When data are not available as tidy dataframes, it is also possible to use the more generic `go.Box` function from `plotly.graph_objects`. All available options for `go.Box` are described in the reference page https://plot.ly/python/reference/#box.
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### Basic Box Plot ###
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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y0 = np.random.randn(50) - 1
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### Basic Horizontal Box Plot ###
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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x0 = np.random.randn(50)
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### Box Plot That Displays the Underlying Data ###
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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fig = go.Figure(data=[go.Box(y=[0, 1, 1, 2, 3, 5, 8, 13, 21],
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boxpoints='all', # can also be outliers, or suspectedoutliers, or False
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### Colored Box Plot ###
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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y0 = np.random.randn(50)
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### Box Plot Styling Mean & Standard Deviation ###
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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fig = go.Figure()
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fig.add_trace(go.Box(
@@ -182,7 +182,7 @@ fig.show()
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The example below shows how to use the `boxpoints` argument. If "outliers", only the sample points lying outside the whiskers are shown. If "suspectedoutliers", the outlier points are shown and points either less than 4Q1-3Q3 or greater than 4Q3-3Q1 are highlighted (using `outliercolor`). If "all", all sample points are shown. If False, only the boxes are shown with no sample points.
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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fig = go.Figure()
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fig.add_trace(go.Box(
@@ -229,13 +229,14 @@ fig.add_trace(go.Box(
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))
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fig.update(layout_title_text="Box Plot Styling Outliers")
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fig.update_layout(title_text="Box Plot Styling Outliers")
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fig.show()
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```
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### Grouped Box Plots ###
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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x = ['day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 1',
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'day 2', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2']
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marker_color='#FF851B'
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))
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fig.update(layout = go.Layout(
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fig.update_layout(
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yaxis_title='normalized moisture',
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boxmode='group' # group together boxes of the different traces for each value of x
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))
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)
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fig.show()
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```
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### Grouped Horizontal Box Plot ###
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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y = ['day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 1',
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'day 2', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2']
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marker_color='#FF851B'
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))
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fig.update(layout = go.Layout(
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xaxis=dict(
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title='normalized moisture',
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zeroline=False
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),
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fig.update_layout(
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xaxis=dict(title='normalized moisture', zeroline=False),
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boxmode='group'
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))
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)
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fig.update_traces(orientation='h') # horizontal box plots
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fig.show()
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```
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### Rainbow Box Plots ###
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import numpy as np
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N = 30 # Number of boxes
@@ -328,12 +327,12 @@ fig = go.Figure(data=[go.Box(
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) for i in range(int(N))])
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# format the layout
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fig.update(layout = go.Layout(
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fig.update_layout(
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xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
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yaxis=dict(zeroline=False, gridcolor='white'),
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paper_bgcolor='rgb(233,233,233)',
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plot_bgcolor='rgb(233,233,233)',
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))
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)
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fig.show()
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```
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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x_data = ['Carmelo Anthony', 'Dwyane Wade',
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'Deron Williams', 'Brook Lopez',
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line_width=1)
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)
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fig.update(layout = go.Layout(
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fig.update_layout(
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title='Points Scored by the Top 9 Scoring NBA Players in 2012',
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yaxis=dict(
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autorange=True,
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paper_bgcolor='rgb(243, 243, 243)',
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plot_bgcolor='rgb(243, 243, 243)',
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showlegend=False
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))
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)
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fig.show()
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```

‎notebooks/bubble-charts.md

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@@ -48,19 +48,20 @@ We first show a bubble chart example using plotly express. Plotly express functi
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import plotly.express as px
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gapminder = px.data.gapminder()
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51-
px.scatter(gapminder.query("year==2007"), x="gdpPercap", y="lifeExp",
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size="pop", color="continent",
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hover_name="country", log_x=True, size_max=60)
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fig = px.scatter(gapminder.query("year==2007"), x="gdpPercap", y="lifeExp",
52+
size="pop", color="continent",
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hover_name="country", log_x=True, size_max=60)
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fig.show()
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```
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## Bubble Chart with plotly.graph_objs
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## Bubble Chart with plotly.graph_objects
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When data are not available as tidy dataframes, it is also possible to use the more generic `go.Scatter` from `plotly.graph_objs`, and define the size of markers to create a bubble chart. All of the available options are described in the scatter section of the reference page: https://plot.ly/python/reference#scatter.
59+
When data are not available as tidy dataframes, it is also possible to use the more generic `go.Scatter` from `plotly.graph_objects`, and define the size of markers to create a bubble chart. All of the available options are described in the scatter section of the reference page: https://plot.ly/python/reference#scatter.
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### Simple Bubble Chart
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```python
63-
import plotly.graph_objs as go
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import plotly.graph_objects as go
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fig = go.Figure(data=[go.Scatter(
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x=[1, 2, 3, 4], y=[10, 11, 12, 13],
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### Setting Marker Size and Color
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```python
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import plotly.graph_objects as go
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fig = go.Figure(data=[go.Scatter(
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x=[1, 2, 3, 4], y=[10, 11, 12, 13],
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Additionally, we recommend setting the sizemode attribute: https://plot.ly/python/reference/#scatter-marker-sizemode to area.
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```python
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import plotly.graph_objects as go
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size = [20, 40, 60, 80, 100, 80, 60, 40, 20, 40]
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fig = go.Figure(data=[go.Scatter(
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### Hover Text with Bubble Charts
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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fig = go.Figure(data=[go.Scatter(
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x=[1, 2, 3, 4], y=[10, 11, 12, 13],
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### Bubble Charts with Colorscale
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import plotly.graph_objects as go
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fig = go.Figure(data=[go.Scatter(
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x=[1, 3.2, 5.4, 7.6, 9.8, 12.5],
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### Categorical Bubble Charts
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import plotly.express as px
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import pandas as pd
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import math
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203204
fig.update_traces(mode='markers', marker=dict(sizemode='area',
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sizeref=sizeref, line_width=2))
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206-
fig.update(layout=go.Layout(
207+
fig.update_layout(
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title='Life Expectancy v. Per Capita GDP, 2007',
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xaxis=dict(
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title='GDP per capita (2000 dollars)',
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),
219220
paper_bgcolor='rgb(243, 243, 243)',
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plot_bgcolor='rgb(243, 243, 243)',
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))
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)
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fig.show()
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```
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### Reference

‎notebooks/candlestick-charts.md

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#### Simple Candlestick with Pandas
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```python
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import plotly.graph_objs as go
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import plotly.graph_objects as go
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import pandas as pd
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from datetime import datetime
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#### Candlestick without Rangeslider
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```python
67-
import plotly.graph_objs as go
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import plotly.graph_objects as go
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df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
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low=df['AAPL.Low'], close=df['AAPL.Close'])
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])
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77-
fig.update(layout_xaxis_rangeslider_visible=False)
77+
fig.update_layout(xaxis_rangeslider_visible=False)
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fig.show()
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```
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#### Adding Customized Text and Annotations
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```python
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import plotly.graph_objects as go
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import pandas as pd
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low=df['AAPL.Low'], close=df['AAPL.Close'])
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])
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94-
fig.update(layout=go.Layout(
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fig.update_layout(
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title='The Great Recession',
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yaxis_title='AAPL Stock',
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shapes = [dict(
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annotations=[dict(
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x='2016-12-09', y=0.05, xref='x', yref='paper',
102103
showarrow=False, xanchor='left', text='Increase Period Begins')]
103-
))
104+
)
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fig.show()
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```
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#### Custom Candlestick Colors
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import plotly.graph_objects as go
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import pandas as pd
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df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
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#### Simple Example with `datetime` Objects
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```python
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from datetime import datetime
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open_data = [33.0, 33.3, 33.5, 33.0, 34.1]

‎notebooks/distplot.md

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```python
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tips = px.data.tips()
48-
px.histogram(tips, x="total_bill", y="tip", color="sex", marginal="rug",
49-
hover_data=tips.columns)
48+
fig = px.histogram(tips, x="total_bill", y="tip", color="sex", marginal="rug",
49+
hover_data=tips.columns)
50+
fig.show()
5051
```
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```python
5354
import plotly.express as px
5455
tips = px.data.tips()
55-
px.histogram(tips, x="total_bill", y="tip", color="sex", marginal="box", # or violin, rug
56-
hover_data=tips.columns)
56+
fig = px.histogram(tips, x="total_bill", y="tip", color="sex",
57+
marginal="box", # or violin, rug
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hover_data=tips.columns)
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fig.show()
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```
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## Combined statistical representations with distplot figure factory
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[x1, x2], group_labels, bin_size=.2,
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153-
fig.update(layout_title_text='Customized Distplot')
156+
fig.update_layout(title_text='Customized Distplot')
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fig.show()
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```
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173176
colors=colors)
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# Add title
176-
fig.update(layout_title_text='Distplot with Normal Distribution')
179+
fig.update_layout(title_text='Distplot with Normal Distribution')
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fig.show()
178181
```
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# Add title
199-
fig.update(layout_title_text='Curve and Rug Plot')
202+
fig.update_layout(title_text='Curve and Rug Plot')
200203
fig.show()
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```
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show_curve=False)
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# Add title
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fig.update(layout_title_text='Hist and Rug Plot')
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fig.update_layout(title_text='Hist and Rug Plot')
224227
fig.show()
225228
```
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bin_size=.2, show_rug=False)
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# Add title
270-
fig.update(layout_title_text='Hist and Curve Plot')
273+
fig.update_layout(title_text='Hist and Curve Plot')
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fig.show()
272275
```
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