databyjp

# ========== (c) JP Hwang 22/8/20  ==========

import logging

# ===== START LOGGER =====
logger = logging.getLogger(__name__)
root_logger = logging.getLogger()
root_logger.setLevel(logging.INFO)
sh = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

databyjp

>>> per_gm_df
                      name   g     mp  ...    pf    pts                  link
0         Dallas Mavericks  75  242.3  ...  19.5  117.0  /teams/DAL/2020.html
1          Milwaukee Bucks  73  241.0  ...  19.6  118.7  /teams/MIL/2020.html
2   Portland Trail Blazers  74  241.0  ...  21.7  115.0  /teams/POR/2020.html
3          Houston Rockets  72  241.4  ...  21.8  117.8  /teams/HOU/2020.html
4     Los Angeles Clippers  72  241.4  ...  22.1  116.3  /teams/LAC/2020.html
5     New Orleans Pelicans  72  242.1  ...  21.2  115.8  /teams/NOP/2020.html
6             Phoenix Suns  73  241.0  ...  22.0  113.6  /teams/PHO/2020.html
7       Washington Wizards  72  241.0  ...  22.7  114.4  /teams/WAS/2020.html

databyjp

# ========== (c) JP Hwang 22/8/20  ==========

import logging

# ===== START LOGGER =====
logger = logging.getLogger(__name__)
root_logger = logging.getLogger()
root_logger.setLevel(logging.INFO)
sh = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

databyjp

data_rows = table.find_all("tr")  # Includes the header row!
parsed_data = list()
team_attr = {"data-stat" : "team_name"}
stat_keys = [col.attrs["data-stat"] for col in data_rows[0].find_all("th")]

for row in data_rows:
    tmp_data = dict()
    if row.find(attrs=team_attr).find("a") is not None:
        team_link = row.find(attrs=team_attr).find("a").attrs["href"]
        tmp_data["name"] = row.find(attrs=team_attr).find("a").text

databyjp

# ========== (c) JP Hwang 27/7/20  ==========
from shared_funcs import load_fig
fig = load_fig()

import streamlit as st

st.header("Houston Rockets Shot Chart - 2020 Season")
st.write("This chart shows the shot distribution of the NBA team Houston Rockets, based on particular areas of the court, and their expected points per shot (i.e. efficiency) from each area.")
st.write("Use the dropdown menu to change the color scale for the shot chart.")
choice = st.selectbox(

databyjp

# ========== (c) JP Hwang 27/7/20  ==========
from shared_funcs import load_fig
fig = load_fig()

import dash
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import Input, Output

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

databyjp

bins = [0, 10000, 25000, 65000, np.inf]
names = ['<10k', '10k-25k', '25k-65k', '65k+']
x = pd.cut(tmp_df["POP_ESTIMATE_2018"], bins=bins, labels=names)
tmp_df = data_df.assign(pop_bin=x)

bins = [0, 40000, 50000, 60000, 70000, 80000, np.inf]
names = ['<40k', '40k-50k', '50k-60k', '60k-70k', '70k-80k', '80k+']
x = pd.cut(tmp_df["Median_Household_Income_2018"], bins=bins, labels=names)
tmp_df = tmp_df.assign(income_bin=x)

databyjp

# Map showing counties with typical income < 40k
tmp_df = data_df[data_df["Median_Household_Income_2018"] < 40000]
tmp_df = tmp_df.assign(tmp=1)
fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=tmp_df.fips, z=tmp_df.tmp,
                                    colorscale=px.colors.sequential.Teal, marker_line_width=0))
fig.update_layout(mapbox_style="carto-positron",
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129},
                  margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

databyjp

fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=data_df.fips, z=data_df["Median_Household_Income_2018"],
                                    colorscale="Greens", marker_line_width=0))
fig.update_layout(mapbox_style="carto-positron",
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129},
                  margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

databyjp

>>> data_df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 3223 entries, 0 to 3222
Data columns (total 12 columns):
 #   Column                           Non-Null Count  Dtype  
---  ------                           --------------  -----  
 0   fips                             3223 non-null   int64