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season_tot_df = season_tot_df.assign(
    fan_ppg=(
        (season_tot_df.points+
         (season_tot_df.offensive_rebounds+season_tot_df.defensive_rebounds)*1.2+
         season_tot_df.assists*1.5+season_tot_df['blocks']*2+season_tot_df.steals*2-season_tot_df.turnovers)
        /season_tot_df.games_played
    )
)

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def add_fan_pts(in_df):
    in_df = in_df.assign(
        fan_pts=(
                (in_df.points_scored +
                 (in_df.offensive_rebounds + in_df.defensive_rebounds) * 1.2 +
                 in_df.assists * 1.5 + in_df['blocks'] * 2 + in_df.steals * 2 - in_df.turnovers)
        )
    )
    return in_df

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top_pl_df_list = list()
temp_slugs = season_tot_df[(season_tot_df.fan_ppg > 30) & (season_tot_df.fan_ppg < 40)].slug.unique()
for pl_slug in temp_slugs:
    temp_df = pd.DataFrame(pl_data_dict[pl_slug])
    temp_df = add_fan_pts(temp_df)
    temp_df = temp_df.assign(player=season_tot_df[season_tot_df.slug == pl_slug]['name'].values[0])
    top_pl_df_list.append(temp_df)
top_pl_df = pd.concat(top_pl_df_list, axis=0)
top_pl_df.reset_index(inplace=True, drop=True)
# Box plot

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# Find players with highest stdev / avg ratio:
pl_pts_list = list()
for pl_slug in season_tot_df.slug.values:
    temp_df = pd.DataFrame(pl_data_dict[pl_slug])
    temp_df = add_fan_pts(temp_df)
    fan_pts_mean = temp_df.fan_pts.mean()
    fan_pts_std = temp_df.fan_pts.std()
    temp_dict=dict(
        player=season_tot_df[season_tot_df.slug == pl_slug]['name'].values[0],
        fan_pts_mean=round(fan_pts_mean, 3),

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temp_df_list = list()
temp_slugs = season_tot_df[season_tot_df['name'].isin(['James Harden', 'Chris Paul'])].slug.unique()
for pl_slug in temp_slugs:
    temp_df = pd.DataFrame(pl_data_dict[pl_slug])
    temp_df = add_fan_pts(temp_df)
    temp_df = temp_df.assign(player=season_tot_df[season_tot_df.slug == pl_slug]['name'].values[0])
    temp_df_list.append(temp_df)
comp_df = pd.concat(temp_df_list, axis=0)
comp_df.reset_index(inplace=True, drop=True)

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top_pl_df_list = list()
temp_slugs = season_tot_df[(season_tot_df.fan_ppg > 35) & (season_tot_df.fan_ppg < 40)].slug.unique()
for pl_slug in temp_slugs:
    temp_df = pd.DataFrame(pl_data_dict[pl_slug])
    temp_df = add_fan_pts(temp_df)
    temp_df = temp_df.assign(player=season_tot_df[season_tot_df.slug == pl_slug]['name'].values[0])
    top_pl_df_list.append(temp_df)
top_pl_df = pd.concat(top_pl_df_list, axis=0)
top_pl_df.reset_index(inplace=True, drop=True)
# Box plot

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# ========== GET ARENA LOCATIONS ==========
arena_csv = 'srcdata/arenas_list.csv'
arena_df = pd.read_csv(arena_csv)
arena_df = arena_df.assign(full_loc=arena_df.arena_name + ', ' + arena_df.arena_city)


def get_loc_resp(srch_str):

    import time
    import requests

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# ========== ADD CAPACITY & AGE ==========
from datetime import datetime
cur_yr = datetime.today().year
arena_df = arena_df.assign(age=(cur_yr-arena_df.opened))
arena_df = arena_df.assign(capacity=arena_df.capacity.str.replace(',','').astype(int))
fig = px.scatter_mapbox(
    arena_df, lat="lat", lon="lon", zoom=3, size='capacity', color='age', size_max=15, hover_name='teamname')
fig.update_layout(mapbox_style="light", mapbox_accesstoken=mapbox_key)  # <== Using Mapbox
fig.show()

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# ========== GET TEAMS' TRAVEL DATA ==========
# Load teams' schedules
schedule_df = pd.read_csv('srcdata/2020_nba_schedule.csv', index_col=0)
arena_df = arena_df.assign(teamupper=arena_df.teamname.str.replace(' ', '_').str.upper())


def get_home_arena(teamname):
    rows = arena_df[arena_df.teamupper == teamname]
    return rows

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# ========== ANIMATE TRAVEL PATHS ==========
# FOR ONE TEAM
teamname = 'DALLAS_MAVERICKS'
travel_team_df = travel_df[travel_df.teamname == teamname]
team_col = teamcolor_dict[teamname]

lon_vals = np.append(travel_team_df['orig_lon'].values, travel_team_df['dest_lon'].values[-1])
lat_vals = np.append(travel_team_df['orig_lat'].values, travel_team_df['dest_lat'].values[-1])

frames = list()