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228 changes: 101 additions & 127 deletionsnotebooks/horizontal-bar-charts.md
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -8,9 +8,19 @@ jupyter: | ||
| format_version: '1.1' | ||
| jupytext_version: 1.1.1 | ||
| kernelspec: | ||
| display_name: Python3 | ||
| language: python | ||
| name: python3 | ||
| language_info: | ||
| codemirror_mode: | ||
| name: ipython | ||
| version: 3 | ||
| file_extension: .py | ||
| mimetype: text/x-python | ||
| name: python | ||
| nbconvert_exporter: python | ||
| pygments_lexer: ipython3 | ||
| version: 3.6.7 | ||
| plotly: | ||
| description: How to make horizontal bar charts in Python with Plotly. | ||
| display_as: basic | ||
| @@ -24,80 +34,94 @@ jupyter: | ||
| permalink: python/horizontal-bar-charts/ | ||
| thumbnail: thumbnail/horizontal-bar.jpg | ||
| title: Horizontal Bar Charts | plotly | ||
| v4upgrade: true | ||
| --- | ||
| See more examples of bar charts (including vertical bar charts) and styling options [here](https://plot.ly/python/bar-charts/). | ||
| ### Horizontal Bar Chart with plotly express | ||
| Plotly express functions take as argument a tidy [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html). For a horizontal bar char, use the `px.bar` function with `orientation='h'`. | ||
| #### Basic Horizontal Bar Chart with plotly express | ||
| ```python | ||
| import plotly.express as px | ||
| tips = px.data.tips() | ||
| fig = px.bar(tips, x="total_bill", y="day", orientation='h') | ||
| fig.show() | ||
| ``` | ||
| #### Configure horizontal bar chart | ||
| In this example a column is used to color the bars, and we add the information from other columns to the hover data. | ||
| ```python | ||
| import plotly.express as px | ||
| tips = px.data.tips() | ||
| fig = px.bar(tips, x="total_bill", y="sex", color='day', orientation='h', | ||
| hover_data=["tip", "size"], | ||
| height=400, | ||
| title='Restaurant bills') | ||
| fig.show() | ||
| ``` | ||
| ### Horizontal Bar Chart with go.Bar | ||
| When data are not available as a tidy dataframe, you can use the more generic function `go.Bar` from `plotly.graph_objects`. All the options of `go.Bar` are documented in the reference https://plot.ly/python/reference/#bar | ||
| #### Basic Horizontal Bar Chart | ||
| ```python | ||
emmanuelle marked this conversation as resolved. Show resolvedHide resolvedUh oh!There was an error while loading.Please reload this page. | ||
| import plotly.graph_objects as go | ||
| fig =go.Figure(go.Bar( | ||
| x=[20, 14, 23], | ||
| y=['giraffes', 'orangutans', 'monkeys'], | ||
| orientation='h')) | ||
| fig.show() | ||
| ``` | ||
| ### Colored Horizontal Bar Chart | ||
| ```python | ||
| import plotly.graph_objects as go | ||
| fig = go.Figure() | ||
| fig.add_trace(go.Bar( | ||
| y=['giraffes', 'orangutans', 'monkeys'], | ||
| x=[20, 14, 23], | ||
| name='SF Zoo', | ||
| orientation='h', | ||
| marker=dict( | ||
| color='rgba(246, 78, 139, 0.6)', | ||
| line=dict(color='rgba(246, 78, 139, 1.0)', width=3) | ||
| ) | ||
| )) | ||
| fig.add_trace(go.Bar( | ||
| y=['giraffes', 'orangutans', 'monkeys'], | ||
| x=[12, 18, 29], | ||
| name='LA Zoo', | ||
| orientation='h', | ||
| marker=dict( | ||
| color='rgba(58, 71, 80, 0.6)', | ||
| line=dict(color='rgba(58, 71, 80, 1.0)', width=3) | ||
| ) | ||
| )) | ||
| fig.update_layout(barmode='stack') | ||
| fig.show() | ||
| ``` | ||
| ### Color Palette for Bar Chart | ||
| ```python | ||
| import plotly.graph_objects as go | ||
| top_labels = ['Strongly<br>agree', 'Agree', 'Neutral', 'Disagree', | ||
| 'Strongly<br>disagree'] | ||
| @@ -117,24 +141,20 @@ y_data = ['The course was effectively<br>organized', | ||
| 'my<br>ability to think critically about<br>the subject', | ||
| 'I would recommend this<br>course to a friend'] | ||
| fig = go.Figure() | ||
| for i in range(0, len(x_data[0])): | ||
| for xd, yd in zip(x_data, y_data): | ||
| fig.add_trace(go.Bar( | ||
| x=[xd[i]], y=[yd], | ||
| orientation='h', | ||
| marker=dict( | ||
| color=colors[i], | ||
| line=dict(color='rgb(248, 248, 249)', width=1) | ||
| ) | ||
| )) | ||
| fig.update_layout( | ||
| xaxis=dict( | ||
| showgrid=False, | ||
| showline=False, | ||
| @@ -151,12 +171,7 @@ layout = go.Layout( | ||
| barmode='stack', | ||
| paper_bgcolor='rgb(248, 248, 255)', | ||
| plot_bgcolor='rgb(248, 248, 255)', | ||
| margin=dict(l=120, r=10, t=140, b=80), | ||
| showlegend=False, | ||
| ) | ||
| @@ -205,18 +220,16 @@ for yd, xd in zip(y_data, x_data): | ||
| showarrow=False)) | ||
| space += xd[i] | ||
| fig.update_layout(annotations=annotations) | ||
| fig.show() | ||
| ``` | ||
| ### Bar Chart with Line Plot | ||
| ```python | ||
| import plotly.graph_objects as go | ||
| from plotly.subplots import make_subplots | ||
| import numpy as np | ||
| @@ -226,14 +239,17 @@ y_saving = [1.3586, 2.2623000000000002, 4.9821999999999997, 6.5096999999999996, | ||
| y_net_worth = [93453.919999999998, 81666.570000000007, 69889.619999999995, | ||
| 78381.529999999999, 141395.29999999999, 92969.020000000004, | ||
| 66090.179999999993, 122379.3] | ||
| x = ['Japan', 'United Kingdom', 'Canada', 'Netherlands', | ||
| 'United States', 'Belgium', 'Sweden', 'Switzerland'] | ||
| # Creating two subplots | ||
| fig = make_subplots(rows=1, cols=2, specs=[[{}, {}]], shared_xaxes=True, | ||
| shared_yaxes=False, vertical_spacing=0.001) | ||
| fig.append_trace(go.Bar( | ||
| x=y_saving, | ||
| y=x, | ||
| marker=dict( | ||
| color='rgba(50, 171, 96, 0.6)', | ||
| line=dict( | ||
| @@ -242,16 +258,16 @@ trace0 = go.Bar( | ||
| ), | ||
| name='Household savings, percentage of household disposable income', | ||
| orientation='h', | ||
| ), 1, 1) | ||
| fig.append_trace(go.Scatter( | ||
| x=y_net_worth, y=x, | ||
| mode='lines+markers', | ||
| line_color='rgb(128, 0, 128)', | ||
| name='Household net worth, Million USD/capita', | ||
| ), 1, 2) | ||
| fig.update_layout( | ||
| title='Household savings & net worth for eight OECD countries', | ||
| yaxis=dict( | ||
| showgrid=False, | ||
| @@ -283,19 +299,8 @@ layout = dict( | ||
| side='top', | ||
| dtick=25000, | ||
| ), | ||
| legend=dict(x=0.029, y=1.038, font_size=10), | ||
| margin=dict(l=100, r=20, t=70, b=70), | ||
| paper_bgcolor='rgb(248, 248, 255)', | ||
| plot_bgcolor='rgb(248, 248, 255)', | ||
| ) | ||
| @@ -306,7 +311,7 @@ y_s = np.round(y_saving, decimals=2) | ||
| y_nw = np.rint(y_net_worth) | ||
| # Adding labels | ||
| for ydn, yd, xd in zip(y_nw, y_s,x): | ||
| # labeling the scatter savings | ||
| annotations.append(dict(xref='x2', yref='y2', | ||
| y=xd, x=ydn - 20000, | ||
| @@ -328,44 +333,13 @@ annotations.append(dict(xref='paper', yref='paper', | ||
| '(2015), Household savings (indicator), ' + | ||
| 'Household net worth (indicator). doi: ' + | ||
| '10.1787/cfc6f499-en (Accessed on 05 June 2015)', | ||
| font=dict(family='Arial', size=10, color='rgb(150,150,150)'), | ||
| showarrow=False)) | ||
| fig.update_layout(annotations=annotations) | ||
| fig.show() | ||
| ``` | ||
| ### Reference | ||
| See more examples of bar charts and styling options [here](https://plot.ly/python/bar-charts/).<br> See https://plot.ly/python/reference/#bar for more information and chart attribute options! | ||
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