
import plotly.graph_objects as go
import plotly.io as pio
import plotly 

# pio.renderers.default = "notebook"
pio.templates.default = "plotly"

fig = go.Figure(layout=go.Layout(height=600, width=800,margin=dict(l=15, r=15, b=15, t=50)))
templated_fig = pio.to_templated(fig)
pio.templates['slide'] = templated_fig.layout.template
pio.templates.default = 'slide'


import plotly.graph_objects as go

fig = go.Figure(
    data=[go.Bar(x=[1, 2, 3], y=[1, 3, 2])],
)
fig


fig.layout.template = None # to slim down the output
print("Dictionary Representation of A Graph Object:\n\n" + str(fig.to_dict()))
print("\n\n")
print("JSON Representation of A Graph Object:\n\n" + str(fig.to_json()))
print("\n\n")

Dictionary Representation of A Graph Object:

{'data': [{'x': [1, 2, 3], 'y': [1, 3, 2], 'type': 'bar'}], 'layout': {}}


JSON Representation of A Graph Object:

{"data":[{"type":"bar","x":[1,2,3],"y":[1,3,2]}],"layout":{}}


import plotly.graph_objects as go

dict_of_fig = dict({
    "data": [{"type": "bar",
              "x": [1, 2, 3],
              "y": [1, 3, 2]}],
    "layout": {"title": {"text": "A Figure Specified By A Graph Object With A Dictionary"}}
})

fig = go.Figure(dict_of_fig)

fig.show()


import plotly.graph_objects as go

dict_of_fig = dict({
    "data": [{"type": "bar",
              "x": [1, 2, 3],
              "y": [1, 3, 2]}],
    "layout": {"title": {"text": "A Figure Specified By A Graph Object With A Dictionary"}}
})

fig = go.Figure(dict_of_fig)

fig.show()


help(plotly.plot)

Help on function plot in module plotly:

plot(data_frame, kind, **kwargs)
    Pandas plotting backend function, not meant to be called directly.
    To activate, set pandas.options.plotting.backend="plotly"
    See https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/__init__.py


import pandas as pd
pd.options.plotting.backend="plotly"
df = pd.DataFrame({'x': [1, 2, 3], 'y': [5, 2, 7]})
df.plot(kind='line', x='x',y='y')


import plotly.graph_objects as go
import numpy as np

x = np.arange(10)

data=go.Scatter(x=x, y=x**2)
fig = go.Figure(data=data, 
                layout=go.Layout(title="A line chart"))
fig.show()


# sankey data
data = go.Sankey(
    node = dict(
        pad = 100,
        thickness = 10,
        line = dict(color = "black", width = 0.5),
        label = ["A1", "A2", "B1", "B2", "C1", "C2"],
        color = "blue"
    ),
    link = dict(
        source = [0, 1, 0, 2, 3, 3, 0], # indices correspond to labels, eg A1, A2, A2, B1, ...
        target = [2, 3, 3, 4, 4, 5, 5],
        value = [8, 4, 2, 8, 4, 2, 3]
    ))

fig = go.Figure(data=data)

# update layout
fig.update_layout(title_text="桑基图", font_size=10, autosize=False)
fig


# 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()


titanic_df = pd.read_csv("../data/titanic.csv")

# Create dimensions
class_dim = go.parcats.Dimension(
    values=titanic_df.Pclass,
    categoryorder='category ascending', label="Class"
)

gender_dim = go.parcats.Dimension(values=titanic_df.Sex, label="Gender")

survival_dim = go.parcats.Dimension(
    values=titanic_df.Survived, label="Outcome", categoryarray=[0, 1],
    ticktext=['perished', 'survived']
)



# Create parcats trace
color = titanic_df.Survived
colorscale = [[0, 'lightsteelblue'], [1, 'mediumseagreen']];

fig = go.Figure(data = [go.Parcats(dimensions=[class_dim, gender_dim, survival_dim],
        line={'color': color, 'colorscale': colorscale},
        hoveron='color', hoverinfo='count+probability',
        labelfont={'size': 18, 'family': 'Times'},
        tickfont={'size': 16, 'family': 'Times'},
        arrangement='freeform')])

fig.show()


df = pd.read_csv("../data/iris.csv")

fig = go.Figure(data=
    go.Parcoords(
        line = dict(color = df['species_id'],
                   colorscale = [[0,'purple'],[0.5,'lightseagreen'],[1,'gold']]),
        dimensions = list([
            dict(range = [0,8],
                constraintrange = [4,8],
                label = 'Sepal Length', values = df['sepal_length']),
            dict(range = [0,8],
                label = 'Sepal Width', values = df['sepal_width']),
            dict(range = [0,8],
                label = 'Petal Length', values = df['petal_length']),
            dict(range = [0,8],
                label = 'Petal Width', values = df['petal_width'])
        ])
    )
)

fig.update_layout(
    plot_bgcolor = 'white',
    paper_bgcolor = 'white'
)

fig.show()


fig = go.Figure(go.Waterfall(
    name = "20", orientation = "v",
    measure = ["relative", "relative", "total", "relative", "relative", "total"],
    x = ["Sales", "Consulting", "Net revenue", "Purchases", "Other expenses", "Profit before tax"],
    textposition = "outside",
    text = ["+60", "+80", "", "-40", "-20", "Total"],
    y = [60, 80, 0, -40, -20, 0],
    connector = {"line":{"color":"rgb(63, 63, 63)"}},
))

fig.update_layout(
        title = "Profit and loss statement 2018",
        showlegend = True
)

fig.show()


contour_raw_data = pd.read_json('../data/contour_data.json')
contour_dict = contour_raw_data['Data']

def clean_data(data_in):
    """
    Cleans data in a format which can be conveniently
    used for drawing traces. Takes a dictionary as the
    input, and returns a list in the following format:

    input = {'key': ['a b c']}
    output = [key, [a, b, c]]
    """
    key = list(data_in.keys())[0]
    data_out = [key]
    for i in data_in[key]:
        data_out.append(list(map(float, i.split(' '))))

    return data_out

# Defining a colormap:
colors = ['#8dd3c7','#ffffb3','#bebada',
          '#fb8072','#80b1d3','#fdb462',
          '#b3de69','#fccde5','#d9d9d9',
          '#bc80bd']
colors_iterator = iter(colors)

fig = go.Figure()

for raw_data in contour_dict:
    data = clean_data(raw_data)

    a = [inner_data[0] for inner_data in data[1:]]
    a.append(data[1][0]) # Closing the loop

    b = [inner_data[1] for inner_data in data[1:]]
    b.append(data[1][1]) # Closing the loop

    c = [inner_data[2] for inner_data in data[1:]]
    c.append(data[1][2]) # Closing the loop

    fig.add_trace(go.Scatterternary(
        text = data[0],
        a=a, b=b, c=c, mode='lines',
        line=dict(color='#444', shape='spline'),
        fill='toself',
        fillcolor = colors_iterator.__next__()
    ))

fig.update_layout(title = 'Ternary Contour Plot')
fig.show()


df = pd.read_csv("../data/covid19-06-28-2020.csv")
df.head().T


mean = df['Confirmed'].mean()
normlised_data_C = [np.sqrt(value/df['Confirmed'].mean())+5 for value in  df['Confirmed']]

hoverdata1 = df['Combined_Key'] + " - "+ ['Confirmed cases: ' + str(v) for v in df['Confirmed'].tolist()]

fig = go.Figure(data=go.Scattergeo(
        lon = df['Long_'],
        lat = df['Lat'],
    name = 'Confirmed cases',
        hovertext = hoverdata1,
        marker = dict(
            size =  normlised_data_C,
            opacity = 0.5,
            color = 'blue',
            line = dict(
                width=0,
                color='rgba(102, 102, 102)'
            ),
        ),
        ))

fig.update_layout(
        title = 'The global impact of COVID-19',
    legend=dict(
        itemsizing = "constant",
        font=dict(
            family="sans-serif",
            size=20,
            color="black"
        )
    )
)
fig.show()


df_cr = df.groupby('Country_Region').sum()
df_cr.reset_index(inplace=True)
df_cr.head(4).T


fig = go.Figure(data=go.Choropleth(
    locationmode = 'country names',
    locations = df_cr['Country_Region'],
    z = df_cr['Confirmed'],
    text = df_cr['Country_Region'],
    colorscale = 'Oranges',
    autocolorscale=False,
    marker_line_color='darkgray',
    marker_line_width=0.5,
    colorbar_title = 'Conifrmed',
))

fig.update_layout(
    title_text='Covid19 Confirmed Cases 06-28-2020',
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    annotations = [dict(
        x=0.55,
        y=0.0,
        xref='paper',
        yref='paper',
        text='Source: <a href="https://github.com/CSSEGISandData/COVID-19">\
            CSSEGISandData/COVID-19</a>',
        showarrow = False
    )]
)
fig


# Read data from a csv
z_data = pd.read_csv('../data/mt_bruno_elevation.csv')

fig = go.Figure(data=[go.Surface(z=z_data.values)])

fig.update_layout(title='Mt Bruno Elevation',
                  margin=dict(l=15, r=15, b=15, t=50))

fig.show()


from plotly.subplots import make_subplots

# read in volcano database data
df = pd.read_csv(
    "../data/volcano_db.csv",
    encoding="iso-8859-1",
)

# frequency of Country
freq = df
freq = freq.Country.value_counts().reset_index().rename(columns={"index": "x"})

# read in 3d volcano surface data
df_v = pd.read_csv("../data/volcano.csv")

# Initialize figure with subplots
fig = make_subplots(
    rows=2, cols=2,
    column_widths=[0.6, 0.4],
    row_heights=[0.4, 0.6],
    specs=[[{"type": "scattergeo", "rowspan": 2}, {"type": "bar"}],
           [            None                    , {"type": "surface"}]])

# Add scattergeo globe map of volcano locations
fig.add_trace(
    go.Scattergeo(lat=df["Latitude"],
                  lon=df["Longitude"],
                  mode="markers",
                  hoverinfo="text",
                  showlegend=False,
                  marker=dict(color="crimson", size=4, opacity=0.8)),
    row=1, col=1
)

# Add locations bar chart
fig.add_trace(
    go.Bar(x=freq["x"][0:10],y=freq["Country"][0:10], marker=dict(color="crimson"), showlegend=False),
    row=1, col=2
)

# Add 3d surface of volcano
fig.add_trace(
    go.Surface(z=df_v.values.tolist(), showscale=False),
    row=2, col=2
)

# Update geo subplot properties
fig.update_geos(
    projection_type="orthographic",
    landcolor="white",
    oceancolor="MidnightBlue",
    showocean=True,
    lakecolor="LightBlue"
)

# Rotate x-axis labels
fig.update_xaxes(tickangle=45)

# Set theme, margin, and annotation in layout
fig.update_layout(
    template="plotly_dark",
    width=800,
    height=600,
    margin=dict(r=10, t=25, b=40, l=60),
    annotations=[
        dict(
            text="Source: NOAA",
            showarrow=False,
            xref="paper",
            yref="paper",
            x=0,
            y=0)
    ]
)

fig.show()


fig = make_subplots(rows=2, cols=2,
                    specs=[[{"type": "xy"}, {"type": "polar"}],
                           [{"type": "domain"}, {"type": "scene"}]])
fig.add_bar(row=1, col=1, y=[2, 3, 1], )
fig.add_pie(row=2, col=1, values=[2, 3, 1])
fig.add_barpolar(row=1, col=2, theta=[0, 45, 90], r=[2, 3, 1])
fig.add_scatter3d(row=2, col=2, x=[2, 3], y=[0, 0], z=[0.5, 1])
fig.update_layout(height=700, showlegend=False)
fig.show()


# Generate dataset
import numpy as np
np.random.seed(1)

x0 = np.random.normal(2, 0.4, 400)
y0 = np.random.normal(2, 0.4, 400)
x1 = np.random.normal(3, 0.6, 600)
y1 = np.random.normal(6, 0.4, 400)
x2 = np.random.normal(4, 0.2, 200)
y2 = np.random.normal(4, 0.4, 200)

# Create figure
fig = go.Figure()

# Add traces
fig.add_trace(
    go.Scatter(
        x=x0,
        y=y0,
        mode="markers",
        marker=dict(color="DarkOrange")
    )
)

fig.add_trace(
    go.Scatter(
        x=x1,
        y=y1,
        mode="markers",
        marker=dict(color="Crimson")
    )
)

fig.add_trace(
    go.Scatter(
        x=x2,
        y=y2,
        mode="markers",
        marker=dict(color="RebeccaPurple")
    )
)

# Add buttons that add shapes
cluster0 = [dict(type="circle",
                            xref="x", yref="y",
                            x0=min(x0), y0=min(y0),
                            x1=max(x0), y1=max(y0),
                            line=dict(color="DarkOrange"))]
cluster1 = [dict(type="circle",
                            xref="x", yref="y",
                            x0=min(x1), y0=min(y1),
                            x1=max(x1), y1=max(y1),
                            line=dict(color="Crimson"))]
cluster2 = [dict(type="circle",
                            xref="x", yref="y",
                            x0=min(x2), y0=min(y2),
                            x1=max(x2), y1=max(y2),
                            line=dict(color="RebeccaPurple"))]

fig.update_layout(
    updatemenus=[
        dict(buttons=list([
            dict(label="None",
                 method="relayout",
                 args=["shapes", []]),
            dict(label="Cluster 0",
                 method="relayout",
                 args=["shapes", cluster0]),
            dict(label="Cluster 1",
                 method="relayout",
                 args=["shapes", cluster1]),
            dict(label="Cluster 2",
                 method="relayout",
                 args=["shapes", cluster2]),
            dict(label="All",
                 method="relayout",
                 args=["shapes", cluster0 + cluster1 + cluster2])
        ]),
        )
    ]
)

# Update remaining layout properties
fig.update_layout(
    title_text="Highlight Clusters",
    showlegend=False,
)

fig.show()


import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", marginal_y="violin",
           marginal_x="box", trendline="ols", template="simple_white")
fig.update_layout(
    width=900,
    height=600)
fig.show()


df_cr["world"] = "world" # in order to have a single root node

df_cr["Case-Fatality_Ratio"] = df_cr["Deaths"]/df_cr['Confirmed']*100

fig = px.treemap(df_cr, path=['world', 'Country_Region'], values='Confirmed',
                  color='Case-Fatality_Ratio',
                  color_continuous_scale='Blues',range_color=[0,df_cr['Case-Fatality_Ratio'].max()],
                 )
fig.show()


df = px.data.gapminder()
px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country",
           size="pop", color="continent", hover_name="country",
           log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])


import pandas as pd
import cufflinks as cf
import numpy as np
print(cf.__version__)
# 使用离线模式
cf.set_config_file(world_readable=True,
                   theme='pearl',
                   offline=True)

0.17.3


# 随机生成bar 条形图
df1=pd.DataFrame(np.random.rand(12, 4), columns=['a', 'b', 'c', 'd'])	
df1.iplot(kind='bar',barmode='stack')


# 随机生成histogram直方图
cf.datagen.histogram(3).iplot(kind='histogram')


#随机scatter matrix 散点矩阵图
df2 = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])	
df2.scatter_matrix()


# 随机数绘图,'DataFrame' object has no attribute 'lines'
cf.datagen.lines(1,2000).ta_plot(study='sma',periods=[13,21,55])

# 1）cufflinks使用datagen生成随机数；

# 2）figure定义为lines形式，数据为(1,2000)；

# 3）然后再用ta_plot绘制这一组时间序列，参数设置SMA展现三个不同周期的时序分析。


#随机subplots 子图
df3=cf.datagen.lines(4)	
df3.iplot(subplots=True,shape=(4,1),shared_xaxes=True,vertical_spacing=.02,fill=True)


import plotly.express as px

fig =px.scatter(x=range(10), y=range(10))
fig.write_html("./plotly_fig.html")

from IPython.display import IFrame

IFrame(src="./plotly_fig.html", width=810, height=520)


import plotly.io as pio
import plotly.graph_objects as go
fig = go.Figure()
pio.to_html(fig, include_plotlyjs='cdn', full_html=False)

'<div>\n        \n                <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: \'local\'};</script>\n        <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>    \n            <div id="e6c6f1f3-e815-4bc8-86e2-c8ac466f4d1c" class="plotly-graph-div" style="height:600px; width:800px;"></div>\n            <script type="text/javascript">\n                \n                    window.PLOTLYENV=window.PLOTLYENV || {};\n                    \n                if (document.getElementById("e6c6f1f3-e815-4bc8-86e2-c8ac466f4d1c")) {\n                    Plotly.newPlot(\n                        \'e6c6f1f3-e815-4bc8-86e2-c8ac466f4d1c\',\n                        [],\n                        {"template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "barpolar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "histogram": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "histogram"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "height": 600, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "margin": {"b": 15, "l": 15, "r": 15, "t": 50}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": 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"zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}},\n                        {"responsive": true}\n                    )\n                };\n                \n            </script>\n        </div>'


# bubble气泡图
import cufflinks as cf
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
fig = df.iplot(kind='bubble',x='a',y='b',size='b',asFigure=True)
print(type(fig))
fig.write_image("plotly_bubble.png")

from IPython.display import Image
Image(filename="plotly_bubble.png")

<class 'plotly.graph_objs._figure.Figure'>

	0	1	2	3	4
FIPS	45001	22001	51001	16001	19001
Admin2	Abbeville	Acadia	Accomack	Ada	Adair
Province_State	South Carolina	Louisiana	Virginia	Idaho	Iowa
Country_Region	US	US	US	US	US
Last_Update	2020-06-29 04:33:44	2020-06-29 04:33:44	2020-06-29 04:33:44	2020-06-29 04:33:44	2020-06-29 04:33:44
Lat	34.2233	30.2951	37.7671	43.4527	41.3308
Long_	-82.4617	-92.4142	-75.6323	-116.242	-94.4711
Confirmed	103	812	1039	1841	15
Deaths	0	36	14	23	0
Recovered	0	0	0	0	0
Active	103	776	1025	1818	15
Combined_Key	Abbeville, South Carolina, US	Acadia, Louisiana, US	Accomack, Virginia, US	Ada, Idaho, US	Adair, Iowa, US
Incidence_Rate	419.945	1308.73	3215.13	382.278	209.732
Case-Fatality_Ratio	0	4.4335	1.34745	1.24932	0

	0	1	2	3
Country_Region	Afghanistan	Albania	Algeria	Andorra
FIPS	0	0	0	0
Lat	33.9391	41.1533	28.0339	42.5063
Long_	67.71	20.1683	1.6596	1.5218
Confirmed	30967	2402	13273	855
Deaths	721	55	897	52
Recovered	12604	1384	9371	799
Active	17642	963	3005	4
Incidence_Rate	79.5487	83.4665	30.2684	1106.58
Case-Fatality_Ratio	2.32828	2.28976	6.75808	6.08187

Plotly与交互式作图¶

张统帅 清华大学¶

2020.07.04¶

目录¶

背景简介¶

Plotly基本用法¶

Plotly Express使用¶

Cufflinks使用¶

背景介绍¶

Plotly简介¶

Plotly生态¶

依赖安装¶

导入设置¶

Plotly绘图机制¶

Plotly Figure构成¶

Plotly Figure 对象¶

Plotly 交互方式¶

Plotly直接作图¶

面向对象的作图方法¶

Plotly 绘制基本图表¶

Plotly 绘制桑基图¶

绘制多个Traces¶

Plotly 绘制统计图表¶

Plotly绘制平行集合图¶

Plotly绘制平行坐标图¶

Plotly绘制金融图表¶

Plotly绘制瀑布图¶

Plotly 绘制科学图表¶

Plotly绘制三元轮廓图¶

Plotly绘制地图¶

Plotly绘制气泡图¶

Plotly绘制分组统计图¶

Plotly绘制3D图表¶

Plotly 绘制3D地形图¶

Plotly绘制混合子图¶

定制化控件¶

Plotly Express介绍¶

Plotly Express使用¶

Plotly Express图表说明¶

Plotly Express绘制统计图表¶

Plotly绘制矩形树图¶

Plotly Express绘制动画¶

Cufflinks介绍¶

Cufflinks使用示例¶

Plotly图表导出¶

导出html¶

导出div¶

导出静态图片¶

总结¶

张统帅清华大学¶