databyjp

import plotly.express as px
fig = px.line(df, x="date", y="close", template="plotly_white",
              title=f"Stock price history for {symbol}")
fig.show()

databyjp

    resp = get_prices(symbol, iex_tkn, date_param=date_range)
    if resp is not None:
        prices_obj = json.loads(resp.text)
    df = pd.DataFrame(prices_obj)

databyjp

with open("../../tokens/iex_token.txt", "r") as f:
iex_tkn = f.read().strip()

date_range = "3m"
symbol = "MSFT"
resp = get_prices(symbol, iex_tkn, date_param=date_range)

databyjp

def get_prices(symbol, key, date_param='5d'):

    url_prefix = "https://cloud.iexapis.com/stable/"

    path = f'stock/{symbol}/chart/{date_param}?chartCloseOnly=True&&token={key}'
    print(f"Fetching {date_param} data for {symbol}")
    full_url = requests.compat.urljoin(url_prefix, path)

    try:
        resp = requests.get(full_url)

databyjp

# Add column for cold/neutral/hot start - based on (n_streak-margin) / n_streak makes or misses)
shots_df = shots_df.assign(start="Unknown")
for pl in players:
    logger.info(f"Processing data for {pl}")
    dates = shots_df[shots_df.player == pl].date.unique()
    for date in dates:
        day_filter = ((shots_df.player == pl) & (shots_df.date == date))
        day_df = shots_df[day_filter].sort_values("tot_time")
        if len(day_df) > n_streak:
            if day_df[:n_streak]["shot_made"].sum() >= n_streak - margin:

databyjp

# ===== Group dataframe by player & game
players = shots_df.groupby("player").count()["date"].sort_values()[-200:].index
pl_df = shots_df[shots_df.player.isin(players)]
grp_pl_df = pl_df.groupby(["player", "date"]).shot_made.sum().reset_index()
grp_pl_df = grp_pl_df.assign(shot_count=pl_df.groupby(["player", "date"]).shot_made.count().values)
grp_pl_df = grp_pl_df.assign(shot_acc=grp_pl_df["shot_made"]/grp_pl_df["shot_count"])

databyjp

# Merge income data & plot
county_df = county_df.join(data_df[["fips", "Median_Household_Income_2018", "POP_ESTIMATE_2018"]].set_index("fips"), on="fips", how="left")
county_df["POP_ESTIMATE_2018"].fillna(0, inplace=True)

fig = px.scatter_mapbox(county_df, lat="lat", lon="lon", color="Median_Household_Income_2018", size="POP_ESTIMATE_2018",
                        range_color=[40000, 70000], size_max=40,
                        color_continuous_scale=px.colors.diverging.RdYlBu, labels=labels
                        )
fig.update_layout(mapbox_style="carto-positron",
                  mapbox_zoom=3.9, mapbox_center={"lat": 37.0902, "lon": -95.7129},

databyjp

>>> county_df.head()
    fips        lon        lat
0  01001 -86.664383  32.524095
1  01009 -86.633437  34.012316
2  01017 -85.358299  32.918304
3  01021 -86.697066  32.866796
4  01033 -87.778236  34.729641

databyjp

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()

databyjp

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]