def create_scatterplotmatrix(df, index=None, endpts=None, diag='scatter',
height=500, width=500, size=6,
title='Scatterplot Matrix', colormap=None,
colormap_type='cat', dataframe=None,
headers=None, index_vals=None, **kwargs):
"""
Returns data for a scatterplot matrix.
:param (array) df: array of the data with column headers
:param (str) index: name of the index column in data array
:param (list|tuple) endpts: takes an increasing sequece of numbers
that defines intervals on the real line. They are used to group
the entries in an index of numbers into their corresponding
interval and therefore can be treated as categorical data
:param (str) diag: sets the chart type for the main diagonal plots.
The options are 'scatter', 'histogram' and 'box'.
:param (int|float) height: sets the height of the chart
:param (int|float) width: sets the width of the chart
:param (float) size: sets the marker size (in px)
:param (str) title: the title label of the scatterplot matrix
:param (str|tuple|list|dict) colormap: either a plotly scale name,
an rgb or hex color, a color tuple, a list of colors or a
dictionary. An rgb color is of the form 'rgb(x, y, z)' where
x, y and z belong to the interval [0, 255] and a color tuple is a
tuple of the form (a, b, c) where a, b and c belong to [0, 1].
If colormap is a list, it must contain valid color types as its
members.
If colormap is a dictionary, all the string entries in
the index column must be a key in colormap. In this case, the
colormap_type is forced to 'cat' or categorical
:param (str) colormap_type: determines how colormap is interpreted.
Valid choices are 'seq' (sequential) and 'cat' (categorical). If
'seq' is selected, only the first two colors in colormap will be
considered (when colormap is a list) and the index values will be
linearly interpolated between those two colors. This option is
forced if all index values are numeric.
If 'cat' is selected, a color from colormap will be assigned to
each category from index, including the intervals if endpts is
being used
:param (dict) **kwargs: a dictionary of scatterplot arguments
The only forbidden parameters are 'size', 'color' and
'colorscale' in 'marker'
Example 1: Vanilla Scatterplot Matrix
```
import plotly.plotly as py
from plotly.graph_objs import graph_objs
from plotly.figure_factory import create_scatterplotmatrix
import numpy as np
import pandas as pd
# Create dataframe
df = pd.DataFrame(np.random.randn(10, 2),
columns=['Column 1', 'Column 2'])
# Create scatterplot matrix
fig = create_scatterplotmatrix(df)
# Plot
py.iplot(fig, filename='Vanilla Scatterplot Matrix')
```
Example 2: Indexing a Column
```
import plotly.plotly as py
from plotly.graph_objs import graph_objs
from plotly.figure_factory import create_scatterplotmatrix
import numpy as np
import pandas as pd
# Create dataframe with index
df = pd.DataFrame(np.random.randn(10, 2),
columns=['A', 'B'])
# Add another column of strings to the dataframe
df['Fruit'] = pd.Series(['apple', 'apple', 'grape', 'apple', 'apple',
'grape', 'pear', 'pear', 'apple', 'pear'])
# Create scatterplot matrix
fig = create_scatterplotmatrix(df, index='Fruit', size=10)
# Plot
py.iplot(fig, filename = 'Scatterplot Matrix with Index')
```
Example 3: Styling the Diagonal Subplots
```
import plotly.plotly as py
from plotly.graph_objs import graph_objs
from plotly.figure_factory import create_scatterplotmatrix
import numpy as np
import pandas as pd
# Create dataframe with index
df = pd.DataFrame(np.random.randn(10, 4),
columns=['A', 'B', 'C', 'D'])
# Add another column of strings to the dataframe
df['Fruit'] = pd.Series(['apple', 'apple', 'grape', 'apple', 'apple',
'grape', 'pear', 'pear', 'apple', 'pear'])
# Create scatterplot matrix
fig = create_scatterplotmatrix(df, diag='box', index='Fruit', height=1000,
width=1000)
# Plot
py.iplot(fig, filename = 'Scatterplot Matrix - Diagonal Styling')
```
Example 4: Use a Theme to Style the Subplots
```
import plotly.plotly as py
from plotly.graph_objs import graph_objs
from plotly.figure_factory import create_scatterplotmatrix
import numpy as np
import pandas as pd
# Create dataframe with random data
df = pd.DataFrame(np.random.randn(100, 3),
columns=['A', 'B', 'C'])
# Create scatterplot matrix using a built-in
# Plotly palette scale and indexing column 'A'
fig = create_scatterplotmatrix(df, diag='histogram', index='A',
colormap='Blues', height=800, width=800)
# Plot
py.iplot(fig, filename = 'Scatterplot Matrix - Colormap Theme')
```
Example 5: Example 4 with Interval Factoring
```
import plotly.plotly as py
from plotly.graph_objs import graph_objs
from plotly.figure_factory import create_scatterplotmatrix
import numpy as np
import pandas as pd
# Create dataframe with random data
df = pd.DataFrame(np.random.randn(100, 3),
columns=['A', 'B', 'C'])
# Create scatterplot matrix using a list of 2 rgb tuples
# and endpoints at -1, 0 and 1
fig = create_scatterplotmatrix(df, diag='histogram', index='A',
colormap=['rgb(140, 255, 50)',
'rgb(170, 60, 115)', '#6c4774',
(0.5, 0.1, 0.8)],
endpts=[-1, 0, 1], height=800, width=800)
# Plot
py.iplot(fig, filename = 'Scatterplot Matrix - Intervals')
```
Example 6: Using the colormap as a Dictionary
```
import plotly.plotly as py
from plotly.graph_objs import graph_objs
from plotly.figure_factory import create_scatterplotmatrix
import numpy as np
import pandas as pd
import random
# Create dataframe with random data
df = pd.DataFrame(np.random.randn(100, 3),
columns=['Column A',
'Column B',
'Column C'])
# Add new color column to dataframe
new_column = []
strange_colors = ['turquoise', 'limegreen', 'goldenrod']
for j in range(100):
new_column.append(random.choice(strange_colors))
df['Colors'] = pd.Series(new_column, index=df.index)
# Create scatterplot matrix using a dictionary of hex color values
# which correspond to actual color names in 'Colors' column
fig = create_scatterplotmatrix(
df, diag='box', index='Colors',
colormap= dict(
turquoise = '#00F5FF',
limegreen = '#32CD32',
goldenrod = '#DAA520'
),
colormap_type='cat',
height=800, width=800
)
# Plot
py.iplot(fig, filename = 'Scatterplot Matrix - colormap dictionary ')
```
"""
# TODO: protected until #282
if dataframe is None:
dataframe = []
if headers is None:
headers = []
if index_vals is None:
index_vals = []
validate_scatterplotmatrix(df, index, diag, colormap_type, **kwargs)
# Validate colormap
if isinstance(colormap, dict):
colormap = clrs.validate_colors_dict(colormap, 'rgb')
elif isinstance(colormap, six.string_types) and 'rgb' not in colormap and '#' not in colormap:
if colormap not in clrs.PLOTLY_SCALES.keys():
raise exceptions.PlotlyError(
"If 'colormap' is a string, it must be the name "
"of a Plotly Colorscale. The available colorscale "
"names are {}".format(clrs.PLOTLY_SCALES.keys())
)
else:
# TODO change below to allow the correct Plotly colorscale
colormap = clrs.colorscale_to_colors(clrs.PLOTLY_SCALES[colormap])
# keep only first and last item - fix later
colormap = [colormap[0]] + [colormap[-1]]
colormap = clrs.validate_colors(colormap, 'rgb')
else:
colormap = clrs.validate_colors(colormap, 'rgb')
if not index:
for name in df:
headers.append(name)
for name in headers:
dataframe.append(df[name].values.tolist())
# Check for same data-type in df columns
utils.validate_dataframe(dataframe)
figure = scatterplot(dataframe, headers, diag, size, height, width,
title, **kwargs)
return figure
else:
# Validate index selection
if index not in df:
raise exceptions.PlotlyError("Make sure you set the index "
"input variable to one of the "
"column names of your "
"dataframe.")
index_vals = df[index].values.tolist()
for name in df:
if name != index:
headers.append(name)
for name in headers:
dataframe.append(df[name].values.tolist())
# check for same data-type in each df column
utils.validate_dataframe(dataframe)
utils.validate_index(index_vals)
# check if all colormap keys are in the index
# if colormap is a dictionary
if isinstance(colormap, dict):
for key in colormap:
if not all(index in colormap for index in index_vals):
raise exceptions.PlotlyError("If colormap is a "
"dictionary, all the "
"names in the index "
"must be keys.")
figure = scatterplot_dict(
dataframe, headers, diag, size, height, width, title,
index, index_vals, endpts, colormap, colormap_type,
**kwargs
)
return figure
else:
figure = scatterplot_theme(
dataframe, headers, diag, size, height, width, title,
index, index_vals, endpts, colormap, colormap_type,
**kwargs
)
return figure
def 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.\n
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://plot.ly/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://plot.ly/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://plot.ly/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')
```
"""
# ensure optional modules imported
if not _plotly_geo:
raise ValueError("""
The create_choropleth figure factory requires the plotly-geo package.
Install using pip with:
$ pip install plotly-geo
Or, install using conda with
$ conda install -c plotly plotly-geo
""")
if not gp or not shapefile or not shapely:
raise ImportError(
"geopandas, pyshp and shapely must be installed for this figure "
"factory.\n\nRun the following commands to install the correct "
"versions of the following modules:\n\n"
"```\n"
"$ pip install geopandas==0.3.0\n"
"$ pip install pyshp==1.2.10\n"
"$ pip install shapely==1.6.3\n"
"```\n"
"If you are using Windows, follow this post to properly "
"install geopandas and dependencies:"
"http://geoffboeing.com/2014/09/using-geopandas-windows/\n\n"
"If you are using Anaconda, do not use PIP to install the "
"packages above. Instead use conda to install them:\n\n"
"```\n"
"$ conda install plotly\n"
"$ conda install geopandas\n"
"```")
df, df_state = _create_us_counties_df(st_to_state_name_dict,
state_to_st_dict)
fips_polygon_map = dict(zip(df["FIPS"].tolist(), df["geometry"].tolist()))
if not state_outline:
state_outline = {"color": "rgb(240, 240, 240)", "width": 1}
if not county_outline:
county_outline = {"color": "rgb(0, 0, 0)", "width": 0}
if not centroid_marker:
centroid_marker = {"size": 3, "color": "white", "opacity": 1}
# ensure centroid markers appear on selection
if "opacity" not in centroid_marker:
centroid_marker.update({"opacity": 1})
if len(fips) != len(values):
raise PlotlyError("fips and values must be the same length")
# make fips, values into lists
if isinstance(fips, pd.core.series.Series):
fips = fips.tolist()
if isinstance(values, pd.core.series.Series):
values = values.tolist()
# make fips numeric
fips = map(lambda x: int(x), fips)
if binning_endpoints:
intervals = utils.endpts_to_intervals(binning_endpoints)
LEVELS = _intervals_as_labels(intervals, round_legend_values,
exponent_format)
else:
if not order:
LEVELS = sorted(list(set(values)))
else:
# check if order is permutation
# of unique color col values
same_sets = sorted(list(set(values))) == set(order)
no_duplicates = not any(order.count(x) > 1 for x in order)
if same_sets and no_duplicates:
LEVELS = order
else:
raise PlotlyError(
"if you are using a custom order of unique values from "
"your color column, you must: have all the unique values "
"in your order and have no duplicate items")
if not colorscale:
colorscale = []
viridis_colors = clrs.colorscale_to_colors(
clrs.PLOTLY_SCALES["Viridis"])
viridis_colors = clrs.color_parser(viridis_colors, clrs.hex_to_rgb)
viridis_colors = clrs.color_parser(viridis_colors, clrs.label_rgb)
viri_len = len(viridis_colors) + 1
viri_intervals = utils.endpts_to_intervals(
list(np.linspace(0, 1, viri_len)))[1:-1]
for L in np.linspace(0, 1, len(LEVELS)):
for idx, inter in enumerate(viri_intervals):
if L == 0:
break
elif inter[0] < L <= inter[1]:
break
intermed = (L - viri_intervals[idx][0]) / (viri_intervals[idx][1] -
viri_intervals[idx][0])
float_color = clrs.find_intermediate_color(viridis_colors[idx],
viridis_colors[idx],
intermed,
colortype="rgb")
# make R,G,B into int values
float_color = clrs.unlabel_rgb(float_color)
float_color = clrs.unconvert_from_RGB_255(float_color)
int_rgb = clrs.convert_to_RGB_255(float_color)
int_rgb = clrs.label_rgb(int_rgb)
colorscale.append(int_rgb)
if len(colorscale) < len(LEVELS):
raise PlotlyError(
"You have {} LEVELS. Your number of colors in 'colorscale' must "
"be at least the number of LEVELS: {}. If you are "
"using 'binning_endpoints' then 'colorscale' must have at "
"least len(binning_endpoints) + 2 colors".format(
len(LEVELS), min(LEVELS, LEVELS[:20])))
color_lookup = dict(zip(LEVELS, colorscale))
x_traces = dict(zip(LEVELS, [[] for i in range(len(LEVELS))]))
y_traces = dict(zip(LEVELS, [[] for i in range(len(LEVELS))]))
# scope
if isinstance(scope, str):
raise PlotlyError("'scope' must be a list/tuple/sequence")
scope_names = []
extra_states = [
"Alaska",
"Commonwealth of the Northern Mariana Islands",
"Puerto Rico",
"Guam",
"United States Virgin Islands",
"American Samoa",
]
for state in scope:
if state.lower() == "usa":
scope_names = df["STATE_NAME"].unique()
scope_names = list(scope_names)
for ex_st in extra_states:
try:
scope_names.remove(ex_st)
except ValueError:
pass
else:
if state in st_to_state_name_dict.keys():
state = st_to_state_name_dict[state]
scope_names.append(state)
df_state = df_state[df_state["STATE_NAME"].isin(scope_names)]
plot_data = []
x_centroids = []
y_centroids = []
centroid_text = []
fips_not_in_shapefile = []
if not binning_endpoints:
for index, f in enumerate(fips):
level = values[index]
try:
fips_polygon_map[f].type
(
x_traces,
y_traces,
x_centroids,
y_centroids,
centroid_text,
) = _calculations(
df,
fips,
values,
index,
f,
simplify_county,
level,
x_centroids,
y_centroids,
centroid_text,
x_traces,
y_traces,
fips_polygon_map,
)
except KeyError:
fips_not_in_shapefile.append(f)
else:
for index, f in enumerate(fips):
for j, inter in enumerate(intervals):
if inter[0] < values[index] <= inter[1]:
break
level = LEVELS[j]
try:
fips_polygon_map[f].type
(
x_traces,
y_traces,
x_centroids,
y_centroids,
centroid_text,
) = _calculations(
df,
fips,
values,
index,
f,
simplify_county,
level,
x_centroids,
y_centroids,
centroid_text,
x_traces,
y_traces,
fips_polygon_map,
)
except KeyError:
fips_not_in_shapefile.append(f)
if len(fips_not_in_shapefile) > 0:
msg = ("Unrecognized FIPS Values\n\nWhoops! It looks like you are "
"trying to pass at least one FIPS value that is not in "
"our shapefile of FIPS and data for the counties. Your "
"choropleth will still show up but these counties cannot "
"be shown.\nUnrecognized FIPS are: {}".format(
fips_not_in_shapefile))
warnings.warn(msg)
x_states = []
y_states = []
for index, row in df_state.iterrows():
if df_state["geometry"][index].type == "Polygon":
x = row.geometry.simplify(simplify_state).exterior.xy[0].tolist()
y = row.geometry.simplify(simplify_state).exterior.xy[1].tolist()
x_states = x_states + x
y_states = y_states + y
elif df_state["geometry"][index].type == "MultiPolygon":
x = [
poly.simplify(simplify_state).exterior.xy[0].tolist()
for poly in df_state["geometry"][index]
]
y = [
poly.simplify(simplify_state).exterior.xy[1].tolist()
for poly in df_state["geometry"][index]
]
for segment in range(len(x)):
x_states = x_states + x[segment]
y_states = y_states + y[segment]
x_states.append(np.nan)
y_states.append(np.nan)
x_states.append(np.nan)
y_states.append(np.nan)
for lev in LEVELS:
county_data = dict(
type="scatter",
mode="lines",
x=x_traces[lev],
y=y_traces[lev],
line=county_outline,
fill="toself",
fillcolor=color_lookup[lev],
name=lev,
hoverinfo="none",
)
plot_data.append(county_data)
if show_hover:
hover_points = dict(
type="scatter",
showlegend=False,
legendgroup="centroids",
x=x_centroids,
y=y_centroids,
text=centroid_text,
name="US Counties",
mode="markers",
marker={
"color": "white",
"opacity": 0
},
hoverinfo="text",
)
centroids_on_select = dict(
selected=dict(marker=centroid_marker),
unselected=dict(marker=dict(opacity=0)),
)
hover_points.update(centroids_on_select)
plot_data.append(hover_points)
if show_state_data:
state_data = dict(
type="scatter",
legendgroup="States",
line=state_outline,
x=x_states,
y=y_states,
hoverinfo="text",
showlegend=False,
mode="lines",
)
plot_data.append(state_data)
DEFAULT_LAYOUT = dict(
hovermode="closest",
xaxis=dict(
autorange=False,
range=USA_XRANGE,
showgrid=False,
zeroline=False,
fixedrange=True,
showticklabels=False,
),
yaxis=dict(
autorange=False,
range=USA_YRANGE,
showgrid=False,
zeroline=False,
fixedrange=True,
showticklabels=False,
),
margin=dict(t=40, b=20, r=20, l=20),
width=900,
height=450,
dragmode="select",
legend=dict(traceorder="reversed",
xanchor="right",
yanchor="top",
x=1,
y=1),
annotations=[],
)
fig = dict(data=plot_data, layout=DEFAULT_LAYOUT)
fig["layout"].update(layout_options)
fig["layout"]["annotations"].append(
dict(
x=1,
y=1.05,
xref="paper",
yref="paper",
xanchor="right",
showarrow=False,
text="<b>" + legend_title + "</b>",
))
if len(scope) == 1 and scope[0].lower() == "usa":
xaxis_range_low = -125.0
xaxis_range_high = -55.0
yaxis_range_low = 25.0
yaxis_range_high = 49.0
else:
xaxis_range_low = float("inf")
xaxis_range_high = float("-inf")
yaxis_range_low = float("inf")
yaxis_range_high = float("-inf")
for trace in fig["data"]:
if all(isinstance(n, Number) for n in trace["x"]):
calc_x_min = min(trace["x"] or [float("inf")])
calc_x_max = max(trace["x"] or [float("-inf")])
if calc_x_min < xaxis_range_low:
xaxis_range_low = calc_x_min
if calc_x_max > xaxis_range_high:
xaxis_range_high = calc_x_max
if all(isinstance(n, Number) for n in trace["y"]):
calc_y_min = min(trace["y"] or [float("inf")])
calc_y_max = max(trace["y"] or [float("-inf")])
if calc_y_min < yaxis_range_low:
yaxis_range_low = calc_y_min
if calc_y_max > yaxis_range_high:
yaxis_range_high = calc_y_max
# camera zoom
fig["layout"]["xaxis"]["range"] = [xaxis_range_low, xaxis_range_high]
fig["layout"]["yaxis"]["range"] = [yaxis_range_low, yaxis_range_high]
# aspect ratio
if asp is None:
usa_x_range = USA_XRANGE[1] - USA_XRANGE[0]
usa_y_range = USA_YRANGE[1] - USA_YRANGE[0]
asp = usa_x_range / usa_y_range
# based on your figure
width = float(fig["layout"]["xaxis"]["range"][1] -
fig["layout"]["xaxis"]["range"][0])
height = float(fig["layout"]["yaxis"]["range"][1] -
fig["layout"]["yaxis"]["range"][0])
center = (
sum(fig["layout"]["xaxis"]["range"]) / 2.0,
sum(fig["layout"]["yaxis"]["range"]) / 2.0,
)
if height / width > (1 / asp):
new_width = asp * height
fig["layout"]["xaxis"]["range"][0] = center[0] - new_width * 0.5
fig["layout"]["xaxis"]["range"][1] = center[0] + new_width * 0.5
else:
new_height = (1 / asp) * width
fig["layout"]["yaxis"]["range"][0] = center[1] - new_height * 0.5
fig["layout"]["yaxis"]["range"][1] = center[1] + new_height * 0.5
return go.Figure(fig)
def 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.\n
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://plot.ly/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://plot.ly/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://plot.ly/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')
```
"""
# ensure optional modules imported
if not gp or not shapefile or not shapely:
raise ImportError(
"geopandas, pyshp and shapely must be installed for this figure "
"factory.\n\nRun the following commands to install the correct "
"versions of the following modules:\n\n"
"```\n"
"pip install geopandas==0.3.0\n"
"pip install pyshp==1.2.10\n"
"pip install shapely==1.6.3\n"
"```\n"
"If you are using Windows, follow this post to properly "
"install geopandas and dependencies:"
"http://geoffboeing.com/2014/09/using-geopandas-windows/\n\n"
"If you are using Anaconda, do not use PIP to install the "
"packages above. Instead use conda to install them:\n\n"
"```\n"
"conda install plotly\n"
"conda install geopandas\n"
"```"
)
df, df_state = _create_us_counties_df(st_to_state_name_dict,
state_to_st_dict)
fips_polygon_map = dict(
zip(
df['FIPS'].tolist(),
df['geometry'].tolist()
)
)
if not state_outline:
state_outline = {'color': 'rgb(240, 240, 240)',
'width': 1}
if not county_outline:
county_outline = {'color': 'rgb(0, 0, 0)',
'width': 0}
if not centroid_marker:
centroid_marker = {'size': 3, 'color': 'white', 'opacity': 1}
# ensure centroid markers appear on selection
if 'opacity' not in centroid_marker:
centroid_marker.update({'opacity': 1})
if len(fips) != len(values):
raise PlotlyError(
'fips and values must be the same length'
)
# make fips, values into lists
if isinstance(fips, pd.core.series.Series):
fips = fips.tolist()
if isinstance(values, pd.core.series.Series):
values = values.tolist()
# make fips numeric
fips = map(lambda x: int(x), fips)
if binning_endpoints:
intervals = utils.endpts_to_intervals(binning_endpoints)
LEVELS = _intervals_as_labels(intervals, round_legend_values,
exponent_format)
else:
if not order:
LEVELS = sorted(list(set(values)))
else:
# check if order is permutation
# of unique color col values
same_sets = sorted(list(set(values))) == set(order)
no_duplicates = not any(order.count(x) > 1 for x in order)
if same_sets and no_duplicates:
LEVELS = order
else:
raise PlotlyError(
'if you are using a custom order of unique values from '
'your color column, you must: have all the unique values '
'in your order and have no duplicate items'
)
if not colorscale:
colorscale = []
viridis_colors = clrs.colorscale_to_colors(
clrs.PLOTLY_SCALES['Viridis']
)
viridis_colors = clrs.color_parser(
viridis_colors, clrs.hex_to_rgb
)
viridis_colors = clrs.color_parser(
viridis_colors, clrs.label_rgb
)
viri_len = len(viridis_colors) + 1
viri_intervals = utils.endpts_to_intervals(
list(np.linspace(0, 1, viri_len))
)[1:-1]
for L in np.linspace(0, 1, len(LEVELS)):
for idx, inter in enumerate(viri_intervals):
if L == 0:
break
elif inter[0] < L <= inter[1]:
break
intermed = ((L - viri_intervals[idx][0]) /
(viri_intervals[idx][1] - viri_intervals[idx][0]))
float_color = clrs.find_intermediate_color(
viridis_colors[idx],
viridis_colors[idx],
intermed,
colortype='rgb'
)
# make R,G,B into int values
float_color = clrs.unlabel_rgb(float_color)
float_color = clrs.unconvert_from_RGB_255(float_color)
int_rgb = clrs.convert_to_RGB_255(float_color)
int_rgb = clrs.label_rgb(int_rgb)
colorscale.append(int_rgb)
if len(colorscale) < len(LEVELS):
raise PlotlyError(
"You have {} LEVELS. Your number of colors in 'colorscale' must "
"be at least the number of LEVELS: {}. If you are "
"using 'binning_endpoints' then 'colorscale' must have at "
"least len(binning_endpoints) + 2 colors".format(
len(LEVELS), min(LEVELS, LEVELS[:20])
)
)
color_lookup = dict(zip(LEVELS, colorscale))
x_traces = dict(zip(LEVELS, [[] for i in range(len(LEVELS))]))
y_traces = dict(zip(LEVELS, [[] for i in range(len(LEVELS))]))
# scope
if isinstance(scope, str):
raise PlotlyError(
"'scope' must be a list/tuple/sequence"
)
scope_names = []
extra_states = ['Alaska', 'Commonwealth of the Northern Mariana Islands',
'Puerto Rico', 'Guam', 'United States Virgin Islands',
'American Samoa']
for state in scope:
if state.lower() == 'usa':
scope_names = df['STATE_NAME'].unique()
scope_names = list(scope_names)
for ex_st in extra_states:
try:
scope_names.remove(ex_st)
except ValueError:
pass
else:
if state in st_to_state_name_dict.keys():
state = st_to_state_name_dict[state]
scope_names.append(state)
df_state = df_state[df_state['STATE_NAME'].isin(scope_names)]
plot_data = []
x_centroids = []
y_centroids = []
centroid_text = []
fips_not_in_shapefile = []
if not binning_endpoints:
for index, f in enumerate(fips):
level = values[index]
try:
fips_polygon_map[f].type
(x_traces, y_traces, x_centroids,
y_centroids, centroid_text) = _calculations(
df, fips, values, index, f, simplify_county, level,
x_centroids, y_centroids, centroid_text, x_traces,
y_traces, fips_polygon_map
)
except KeyError:
fips_not_in_shapefile.append(f)
else:
for index, f in enumerate(fips):
for j, inter in enumerate(intervals):
if inter[0] < values[index] <= inter[1]:
break
level = LEVELS[j]
try:
fips_polygon_map[f].type
(x_traces, y_traces, x_centroids,
y_centroids, centroid_text) = _calculations(
df, fips, values, index, f, simplify_county, level,
x_centroids, y_centroids, centroid_text, x_traces,
y_traces, fips_polygon_map
)
except KeyError:
fips_not_in_shapefile.append(f)
if len(fips_not_in_shapefile) > 0:
msg = (
'Unrecognized FIPS Values\n\nWhoops! It looks like you are '
'trying to pass at least one FIPS value that is not in '
'our shapefile of FIPS and data for the counties. Your '
'choropleth will still show up but these counties cannot '
'be shown.\nUnrecognized FIPS are: {}'.format(
fips_not_in_shapefile
)
)
warnings.warn(msg)
x_states = []
y_states = []
for index, row in df_state.iterrows():
if df_state['geometry'][index].type == 'Polygon':
x = row.geometry.simplify(simplify_state).exterior.xy[0].tolist()
y = row.geometry.simplify(simplify_state).exterior.xy[1].tolist()
x_states = x_states + x
y_states = y_states + y
elif df_state['geometry'][index].type == 'MultiPolygon':
x = ([poly.simplify(simplify_state).exterior.xy[0].tolist() for
poly in df_state['geometry'][index]])
y = ([poly.simplify(simplify_state).exterior.xy[1].tolist() for
poly in df_state['geometry'][index]])
for segment in range(len(x)):
x_states = x_states + x[segment]
y_states = y_states + y[segment]
x_states.append(np.nan)
y_states.append(np.nan)
x_states.append(np.nan)
y_states.append(np.nan)
for lev in LEVELS:
county_data = dict(
type='scatter',
mode='lines',
x=x_traces[lev],
y=y_traces[lev],
line=county_outline,
fill='toself',
fillcolor=color_lookup[lev],
name=lev,
hoverinfo='none',
)
plot_data.append(county_data)
if show_hover:
hover_points = dict(
type='scatter',
showlegend=False,
legendgroup='centroids',
x=x_centroids,
y=y_centroids,
text=centroid_text,
name='US Counties',
mode='markers',
marker={'color': 'white', 'opacity': 0},
hoverinfo='text'
)
centroids_on_select = dict(
selected=dict(marker=centroid_marker),
unselected=dict(marker=dict(opacity=0))
)
hover_points.update(centroids_on_select)
plot_data.append(hover_points)
if show_state_data:
state_data = dict(
type='scatter',
legendgroup='States',
line=state_outline,
x=x_states,
y=y_states,
hoverinfo='text',
showlegend=False,
mode='lines'
)
plot_data.append(state_data)
DEFAULT_LAYOUT = dict(
hovermode='closest',
xaxis=dict(
autorange=False,
range=USA_XRANGE,
showgrid=False,
zeroline=False,
fixedrange=True,
showticklabels=False
),
yaxis=dict(
autorange=False,
range=USA_YRANGE,
showgrid=False,
zeroline=False,
fixedrange=True,
showticklabels=False
),
margin=dict(t=40, b=20, r=20, l=20),
width=900,
height=450,
dragmode='select',
legend=dict(
traceorder='reversed',
xanchor='right',
yanchor='top',
x=1,
y=1
),
annotations=[]
)
fig = dict(data=plot_data, layout=DEFAULT_LAYOUT)
fig['layout'].update(layout_options)
fig['layout']['annotations'].append(
dict(
x=1,
y=1.05,
xref='paper',
yref='paper',
xanchor='right',
showarrow=False,
text='<b>' + legend_title + '</b>'
)
)
if len(scope) == 1 and scope[0].lower() == 'usa':
xaxis_range_low = -125.0
xaxis_range_high = -55.0
yaxis_range_low = 25.0
yaxis_range_high = 49.0
else:
xaxis_range_low = float('inf')
xaxis_range_high = float('-inf')
yaxis_range_low = float('inf')
yaxis_range_high = float('-inf')
for trace in fig['data']:
if all(isinstance(n, Number) for n in trace['x']):
calc_x_min = min(trace['x'] or [float('inf')])
calc_x_max = max(trace['x'] or [float('-inf')])
if calc_x_min < xaxis_range_low:
xaxis_range_low = calc_x_min
if calc_x_max > xaxis_range_high:
xaxis_range_high = calc_x_max
if all(isinstance(n, Number) for n in trace['y']):
calc_y_min = min(trace['y'] or [float('inf')])
calc_y_max = max(trace['y'] or [float('-inf')])
if calc_y_min < yaxis_range_low:
yaxis_range_low = calc_y_min
if calc_y_max > yaxis_range_high:
yaxis_range_high = calc_y_max
# camera zoom
fig['layout']['xaxis']['range'] = [xaxis_range_low, xaxis_range_high]
fig['layout']['yaxis']['range'] = [yaxis_range_low, yaxis_range_high]
# aspect ratio
if asp is None:
usa_x_range = USA_XRANGE[1] - USA_XRANGE[0]
usa_y_range = USA_YRANGE[1] - USA_YRANGE[0]
asp = usa_x_range / usa_y_range
# based on your figure
width = float(fig['layout']['xaxis']['range'][1] -
fig['layout']['xaxis']['range'][0])
height = float(fig['layout']['yaxis']['range'][1] -
fig['layout']['yaxis']['range'][0])
center = (sum(fig['layout']['xaxis']['range']) / 2.,
sum(fig['layout']['yaxis']['range']) / 2.)
if height / width > (1 / asp):
new_width = asp * height
fig['layout']['xaxis']['range'][0] = center[0] - new_width * 0.5
fig['layout']['xaxis']['range'][1] = center[0] + new_width * 0.5
else:
new_height = (1 / asp) * width
fig['layout']['yaxis']['range'][0] = center[1] - new_height * 0.5
fig['layout']['yaxis']['range'][1] = center[1] + new_height * 0.5
return fig
