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fig = px.choropleth_mapbox(tmp_df, locations="fips", color="Median_Household_Income_2018",
                           range_color=[20000, 40000],
                           geojson=counties, color_continuous_scale=px.colors.sequential.Oranges[::-1], labels=labels)
fig.update_layout(mapbox_style="carto-positron",
                  mapbox_zoom=3.9, mapbox_center={"lat": 37.0902, "lon": -95.7129},
                  margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.update_traces(marker=dict(line=dict(width=0.3, color="gray")))
fig.show()

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county_data_list = list()
for tmp in counties["features"]:
    tmp_fips = tmp["id"]
    if tmp["geometry"]["type"] == 'Polygon':
        tmp_crds = tmp["geometry"]["coordinates"][0]
    elif tmp["geometry"]["type"] == 'MultiPolygon':
        tmp_crds = tmp["geometry"]["coordinates"][0][0]
    else:
        logger.warning("Check geometry type")
    x_crds = [i[0] for i in tmp_crds]

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# Map showing counties with typical income < 40k
tmp_df = data_df[data_df["Median_Household_Income_2018"] < 40000]
fig = px.choropleth_mapbox(tmp_df, locations="fips", color="Median_Household_Income_2018",
                           range_color=[0, 100000],
                           geojson=counties, color_continuous_scale=px.colors.diverging.RdYlBu, labels=labels)
fig.update_layout(coloraxis_colorbar=dict(
    tickvals=[0, 20000, 40000, 60000, 80000, 100000],
    ticktext=["0", "20k", "40k", "60k", "80k", "100k+"]
))
fig.update_layout(mapbox_style="carto-positron",

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fig = px.choropleth_mapbox(data_df, locations="fips", color="Median_Household_Income_2018",
                           range_color=[0, 100000],
                           geojson=counties, color_continuous_scale=px.colors.diverging.RdYlBu, labels=labels)
fig.update_layout(coloraxis_colorbar=dict(
    tickvals=[0, 20000, 40000, 60000, 80000, 100000],
    ticktext=["0", "20k", "40k", "60k", "80k", "100k+"]
))
fig.update_layout(mapbox_style="carto-positron",
                  mapbox_zoom=3.9, mapbox_center={"lat": 37.0902, "lon": -95.7129},
                  margin={"r": 0, "t": 0, "l": 0, "b": 0})

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import pandas as pd
import numpy as np
import plotly.express as px
import streamlit as st
from sklearn import model_selection
from sklearn import preprocessing
from sklearn import linear_model
from sklearn import svm
from sklearn import metrics

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import pandas as pd
import numpy as np
import plotly.express as px
import streamlit as st
from sklearn import model_selection
from sklearn import preprocessing
from sklearn import linear_model
from sklearn import svm
from sklearn import metrics

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import pandas as pd

# ===== START SCRAPING =====
import requests
from bs4 import BeautifulSoup
import re


def scrape_this(uri="/pages/forms/"):

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data_rows = table.find_all("tr", attrs={"class": "team"})  # Includes the header row!
parsed_data = list()
stat_keys = [col.attrs["class"][0] for col in data_rows[0].find_all("td")]

for row in data_rows:
    tmp_data = dict()
    for attr in stat_keys:
        attr_val = row.find(attrs={"class": attr}).text
        tmp_data[attr] = re.sub(r"^\s+|\s+$", "", attr_val)
    parsed_data.append(tmp_data)

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>>> team_elms
<tr class="team">
<td class="name">
                            Boston Bruins
                        </td>
<td class="year">
                            1990
                        </td>
<td class="wins">
                            44

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>>> per_poss_df
                     name   g     mp  ...    pts                  link season
0        Sacramento Kings  82  20080  ...  105.6  /teams/SAC/2001.html   2001
1         Milwaukee Bucks  82  19780  ...  108.8  /teams/MIL/2001.html   2001
2      Los Angeles Lakers  82  19905  ...  108.4  /teams/LAL/2001.html   2001
3        Dallas Mavericks  82  19805  ...  107.1  /teams/DAL/2001.html   2001
4         Toronto Raptors  82  19955  ...  105.9  /teams/TOR/2001.html   2001
..                    ...  ..    ...  ...    ...                   ...    ...
25        New York Knicks  66  15965  ...  106.5  /teams/NYK/2020.html   2020
26    Cleveland Cavaliers  65  15725  ...  107.5  /teams/CLE/2020.html   2020