The draw time for this plot will be slow for clients without much RAM.

/home/michael/.virtualenvs/plot2py2/local/lib/python2.7/site-packages/plotly/api/v1/clientresp.py:40: UserWarning:

Estimated Draw Time Slow

Help on function create_choropleth in module plotly.figure_factory._county_choropleth:

create_choropleth(fips, values, scope=['usa'], binning_endpoints=None, colorscale=None, order=None, simplify_county=0.02, simplify_state=0.02, asp=None, show_hover=True, show_state_data=True, state_outline=None, county_outline=None, centroid_marker=None, round_legend_values=False, exponent_format=False, legend_title='', **layout_options)
    Returns figure for county choropleth. Uses data from package_data.
    
    :param (list) fips: list of FIPS values which correspond to the con
        catination of state and county ids. An example is '01001'.
    :param (list) values: list of numbers/strings which correspond to the
        fips list. These are the values that will determine how the counties
        are colored.
    :param (list) scope: list of states and/or states abbreviations. Fits
        all states in the camera tightly. Selecting ['usa'] is the equivalent
        of appending all 50 states into your scope list. Selecting only 'usa'
        does not include 'Alaska', 'Puerto Rico', 'American Samoa',
        'Commonwealth of the Northern Mariana Islands', 'Guam',
        'United States Virgin Islands'. These must be added manually to the
        list.
        Default = ['usa']
    :param (list) binning_endpoints: ascending numbers which implicitly define
        real number intervals which are used as bins. The colorscale used must
        have the same number of colors as the number of bins and this will
        result in a categorical colormap.
    :param (list) colorscale: a list of colors with length equal to the
        number of categories of colors. The length must match either all
        unique numbers in the 'values' list or if endpoints is being used, the
        number of categories created by the endpoints.
    
        For example, if binning_endpoints = [4, 6, 8], then there are 4 bins:
        [-inf, 4), [4, 6), [6, 8), [8, inf)
    :param (list) order: a list of the unique categories (numbers/bins) in any
        desired order. This is helpful if you want to order string values to
        a chosen colorscale.
    :param (float) simplify_county: determines the simplification factor
        for the counties. The larger the number, the fewer vertices and edges
        each polygon has. See
        http://toblerity.org/shapely/manual.html#object.simplify for more
        information.
        Default = 0.02
    :param (float) simplify_state: simplifies the state outline polygon.
        See http://toblerity.org/shapely/manual.html#object.simplify for more
        information.
        Default = 0.02
    :param (float) asp: the width-to-height aspect ratio for the camera.
        Default = 2.5
    :param (bool) show_hover: show county hover and centroid info
    :param (bool) show_state_data: reveals state boundary lines
    :param (dict) state_outline: dict of attributes of the state outline
        including width and color. See
        https://plotly.com/python/reference/#scatter-marker-line for all valid
        params
    :param (dict) county_outline: dict of attributes of the county outline
        including width and color. See
        https://plotly.com/python/reference/#scatter-marker-line for all valid
        params
    :param (dict) centroid_marker: dict of attributes of the centroid marker.
        The centroid markers are invisible by default and appear visible on
        selection. See https://plotly.com/python/reference/#scatter-marker for
        all valid params
    :param (bool) round_legend_values: automatically round the numbers that
        appear in the legend to the nearest integer.
        Default = False
    :param (bool) exponent_format: if set to True, puts numbers in the K, M,
        B number format. For example 4000.0 becomes 4.0K
        Default = False
    :param (str) legend_title: title that appears above the legend
    :param **layout_options: a **kwargs argument for all layout parameters
    
    
    Example 1: Florida
    ```
    import plotly.plotly as py
    import plotly.figure_factory as ff
    
    import numpy as np
    import pandas as pd
    
    df_sample = pd.read_csv(
        'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
    )
    df_sample_r = df_sample[df_sample['STNAME'] == 'Florida']
    
    values = df_sample_r['TOT_POP'].tolist()
    fips = df_sample_r['FIPS'].tolist()
    
    binning_endpoints = list(np.mgrid[min(values):max(values):4j])
    colorscale = ["#030512","#1d1d3b","#323268","#3d4b94","#3e6ab0",
                  "#4989bc","#60a7c7","#85c5d3","#b7e0e4","#eafcfd"]
    fig = ff.create_choropleth(
        fips=fips, values=values, scope=['Florida'], show_state_data=True,
        colorscale=colorscale, binning_endpoints=binning_endpoints,
        round_legend_values=True, plot_bgcolor='rgb(229,229,229)',
        paper_bgcolor='rgb(229,229,229)', legend_title='Florida Population',
        county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
        exponent_format=True,
    )
    py.iplot(fig, filename='choropleth_florida')
    ```
    
    Example 2: New England
    ```
    import plotly.plotly as py
    import plotly.figure_factory as ff
    
    import pandas as pd
    
    NE_states = ['Connecticut', 'Maine', 'Massachusetts',
                 'New Hampshire', 'Rhode Island']
    df_sample = pd.read_csv(
        'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
    )
    df_sample_r = df_sample[df_sample['STNAME'].isin(NE_states)]
    colorscale = ['rgb(68.0, 1.0, 84.0)',
     'rgb(66.0, 64.0, 134.0)',
     'rgb(38.0, 130.0, 142.0)',
     'rgb(63.0, 188.0, 115.0)',
     'rgb(216.0, 226.0, 25.0)']
    
    values = df_sample_r['TOT_POP'].tolist()
    fips = df_sample_r['FIPS'].tolist()
    fig = ff.create_choropleth(
        fips=fips, values=values, scope=NE_states, show_state_data=True
    )
    py.iplot(fig, filename='choropleth_new_england')
    ```
    
    Example 3: California and Surrounding States
    ```
    import plotly.plotly as py
    import plotly.figure_factory as ff
    
    import pandas as pd
    
    df_sample = pd.read_csv(
        'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
    )
    df_sample_r = df_sample[df_sample['STNAME'] == 'California']
    
    values = df_sample_r['TOT_POP'].tolist()
    fips = df_sample_r['FIPS'].tolist()
    
    colorscale = [
        'rgb(193, 193, 193)',
        'rgb(239,239,239)',
        'rgb(195, 196, 222)',
        'rgb(144,148,194)',
        'rgb(101,104,168)',
        'rgb(65, 53, 132)'
    ]
    
    fig = ff.create_choropleth(
        fips=fips, values=values, colorscale=colorscale,
        scope=['CA', 'AZ', 'Nevada', 'Oregon', ' Idaho'],
        binning_endpoints=[14348, 63983, 134827, 426762, 2081313],
        county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
        legend_title='California Counties',
        title='California and Nearby States'
    )
    py.iplot(fig, filename='choropleth_california_and_surr_states_outlines')
    ```
    
    Example 4: USA
    ```
    import plotly.plotly as py
    import plotly.figure_factory as ff
    
    import numpy as np
    import pandas as pd
    
    df_sample = pd.read_csv(
        'https://raw.githubusercontent.com/plotly/datasets/master/laucnty16.csv'
    )
    df_sample['State FIPS Code'] = df_sample['State FIPS Code'].apply(
        lambda x: str(x).zfill(2)
    )
    df_sample['County FIPS Code'] = df_sample['County FIPS Code'].apply(
        lambda x: str(x).zfill(3)
    )
    df_sample['FIPS'] = (
        df_sample['State FIPS Code'] + df_sample['County FIPS Code']
    )
    
    binning_endpoints = list(np.linspace(1, 12, len(colorscale) - 1))
    colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef",
                  "#b3d2e9", "#9ecae1", "#85bcdb", "#6baed6", "#57a0ce",
                  "#4292c6", "#3082be", "#2171b5", "#1361a9", "#08519c",
                  "#0b4083","#08306b"]
    fips = df_sample['FIPS']
    values = df_sample['Unemployment Rate (%)']
    fig = ff.create_choropleth(
        fips=fips, values=values, scope=['usa'],
        binning_endpoints=binning_endpoints, colorscale=colorscale,
        show_hover=True, centroid_marker={'opacity': 0},
        asp=2.9, title='USA by Unemployment %',
        legend_title='Unemployment %'
    )
    
    py.iplot(fig, filename='choropleth_full_usa')
    ```

Collecting git+https://github.com/plotly/publisher.git
  Cloning https://github.com/plotly/publisher.git to /tmp/pip-req-build-h71Kdm
Building wheels for collected packages: publisher
  Running setup.py bdist_wheel for publisher ... done
  Stored in directory: /tmp/pip-ephem-wheel-cache-37a0oz/wheels/99/3e/a0/fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966
Successfully built publisher
Installing collected packages: publisher
Successfully installed publisher-0.11
You are using pip version 10.0.1, however version 18.0 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.

/home/michael/.virtualenvs/plot2py2/local/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning:

The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead.

/home/michael/.virtualenvs/plot2py2/local/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning:

Did you "Save" this notebook before running this command? Remember to save, always save.