<reponame>thliang01/streamlit-nba<gh_stars>1-10
# -*- coding: utf-8 -*-
# Copyright 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

# More info: https://github.com/thliang01/streamlit-nba

from datetime import datetime
from math import pi

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
from plotly.subplots import make_subplots

plt.style.use('fivethirtyeight')
st.set_option('deprecation.showPyplotGlobalUse', False)

st.title('NBA Data Visualization')

# ----- Load data -----

st.markdown('## Load data')

games_details = pd.read_csv('data/NBA-games-data/games_details.csv')
if st.checkbox('Show games_details'):
    st.write(games_details.head(10))
# st.dataframe(games_details.head(5))

players = pd.read_csv('data/NBA-games-data/players.csv')
if st.checkbox('Show players'):
    st.write(players.head(10))
# st.dataframe(players.head(5))

teams = pd.read_csv('data/NBA-games-data/teams.csv')
if st.checkbox('Show teams'):
    st.write(teams.head(10))
# st.dataframe(teams.head(5))

ranking = pd.read_csv('data/NBA-games-data/ranking.csv')
if st.checkbox('Show ranking'):
    st.write(ranking.head(10))
# st.dataframe(ranking.head(5))

games = pd.read_csv('data/NBA-games-data/games.csv')
if st.checkbox('Show games'):
    st.write(games.head(10))
# st.dataframe(games.head(5))

data = pd.read_csv('data/NBA2K/nba2k20-full.csv')
if st.checkbox('Show nba2k20'):
    st.write(data.head(10))
# st.write(df.head(5))

# ----- ----- -----

st.title('NBA games Data Visualization')
"""
### Dataset with all NBA games from 2004 season to dec 2020
---
"""

st.header('Who are the players with most games played ?')


def plot_top(df, column, label_col=None, max_plot=5):
    top_df = df.sort_values(column, ascending=False).head(max_plot)

    height = top_df[column]
    x = top_df.index if label_col is None else top_df[label_col]

    gold, silver, bronze, other = ('#FFA400', '#bdc3c7', '#cd7f32', '#3498db')
    colors = [gold if i == 0 else silver if i == 1 else bronze if i ==
              2 else other for i in range(0, len(top_df))]

    fig, ax = plt.subplots(figsize=(18, 7))
    ax.bar(x, height, color=colors)
    plt.xticks(x, x, rotation=60)
    plt.xlabel(label_col)
    plt.ylabel(column)
    plt.title(f'Top {max_plot} of {column}')
    # plt.show()


players_name = games_details['PLAYER_NAME']
val_cnt = players_name.value_counts().to_frame().reset_index()
val_cnt.columns = ['PLAYER_NAME', 'Number of games']

st.pyplot(
    plot_top(
        val_cnt,
        column='Number of games',
        label_col='PLAYER_NAME',
        max_plot=10))

st.markdown('---')

st.header('Is most game played means most time played ?')


def convert_min(x):
    if pd.isna(x):
        return 0
    x = str(x).split(':')
    if len(x) < 2:
        return int(x[0])
    else:
        return int(x[0]) * 60 + int(x[1])


df_tmp = games_details[['PLAYER_NAME', 'MIN']]
df_tmp.loc[:, 'MIN'] = df_tmp['MIN'].apply(convert_min)
agg = df_tmp.groupby('PLAYER_NAME').agg('sum').reset_index()
agg.columns = ['PLAYER_NAME', 'Number of seconds played']

st.pyplot(
    plot_top(
        agg,
        column='Number of seconds played',
        label_col='PLAYER_NAME',
        max_plot=10))

st.markdown('And the answer is yes ! <NAME> is truly a living legend !')
st.markdown('---')

st.header('What are the Lebron James average stats compare to overall stats ?')

stats_cols = {
    'FGM': 'Field Goals Made',
    'FGA': 'Field Goals Attempted',
    'FG_PCT': 'Field Goal Percentage',
    'FG3M': 'Three Pointers Made',
    'FG3A': 'Three Pointers Attempted',
    'FG3_PCT': 'Three Point Percentage',
    'FTM': 'Free Throws Made',
    'FTA': 'Free Throws Attempted',
    'FT_PCT': 'Free Throw Percentage',
    'OREB': 'Offensive Rebounds',
    'DREB': 'Defensive Rebounds',
    'REB': 'Rebounds',
    'AST': 'Assists',
    'TO': 'Turnovers',
    'STL': 'Steals',
    'BLK': 'Blocked Shots',
    'PF': 'Personal Foul',
    'PTS': 'Points',
    'PLUS_MINUS': 'Plus-Minus'
}


def agg_on_columns(df, agg_var, operation=['mean']):
    return df[agg_var].agg(operation)


# Remove players that didn't played at a game
df_tmp = games_details[~games_details['MIN'].isna()]
del df_tmp['MIN']

# Define key statistics columns, one for percentage variable and one for
# other important statistics
prct_var = ['FG_PCT', 'FG3_PCT', 'FT_PCT']
other_var = ['REB', 'AST', 'STL', 'PF', 'BLK']

# Create a specific dataset for <NAME>
lebron_james_df = df_tmp[df_tmp['PLAYER_NAME'] == '<NAME>']

overall_agg_prct = agg_on_columns(
    df=df_tmp,
    agg_var=prct_var,
    operation=['mean'])
overall_agg_other = agg_on_columns(
    df=df_tmp,
    agg_var=other_var,
    operation=['mean'])

lebron_james_stats_prct = agg_on_columns(
    df=lebron_james_df,
    agg_var=prct_var,
    operation=['mean'])
lebron_james_stats_other = agg_on_columns(
    df=lebron_james_df,
    agg_var=other_var,
    operation=['mean'])

stats_prct = pd.concat([lebron_james_stats_prct, overall_agg_prct])
stats_other = pd.concat([lebron_james_stats_other, overall_agg_other])

stats_prct.index = ['<NAME>', 'overall stats']
stats_other.index = ['<NAME>', 'overall stats']


def rename_df(df, col_dict):
    cols = df.columns
    new_cols = [(col_dict[c] if c in col_dict else c) for c in cols]
    df.columns = new_cols
    return df


stats_prct = rename_df(stats_prct, col_dict=stats_cols)
stats_other = rename_df(stats_other, col_dict=stats_cols)


def radar_plot(ax, df, max_val=1):
    # number of variable
    categories = list(df)
    N = len(categories)

    # What will be the angle of each axis in the plot? (we divide the plot /
    # number of variable)
    angles = [n / float(N) * 2 * pi for n in range(N)]
    angles += angles[:1]

    # Draw one axe per variable + add labels labels yet
    plt.xticks(angles[:-1], categories, color='black', size=12)

    # Draw ylabels
    ax.set_rlabel_position(0)
    yticks = [max_val * i / 4 for i in range(1, 4)]
    plt.yticks(yticks, [str(e) for e in yticks], color="grey", size=10)
    plt.ylim(0, max_val)

    # We are going to plot the first line of the data frame.
    # But we need to repeat the first value to close the circular graph:
    colors = ['b', 'r', 'g']
    for i in range(len(df)):
        values = df.values[i].flatten().tolist()
        values += values[:1]
        color = colors[i]

        # Plot data
        ax.plot(
            angles,
            values,
            linewidth=1,
            linestyle='solid',
            color=color,
            label=df.index[i])

        # Fill area
        ax.fill(angles, values, color, alpha=0.1)

    # Add legend
    plt.legend(loc=0, bbox_to_anchor=(0.1, 0.1), prop={'size': 13})


st.markdown('#### Stats comparison between Lebron James and overall statistics')
fig, ax = plt.subplots(figsize=(18, 9))

ax = plt.subplot(121, polar=True)
ax.set_title('Percentage statistics')
radar_plot(ax=ax, df=stats_prct, max_val=1)

ax = plt.subplot(122, polar=True)
ax.set_title('Others statistics')
radar_plot(ax=ax, df=stats_other, max_val=10)

st.pyplot(fig)
st.markdown('As we can see <NAME> is above average ! ')
st.markdown('---')

st.header('Compare players statistics (example: Curry vs Harden)')


def get_players_stats(player_one, player_two):
    # Remove players that didn't played at a game
    df_tmp = games_details[~games_details['MIN'].isna()]
    del df_tmp['MIN']

    # Define key statistics columns, one for percentage variable and one for
    # other important statistics
    prct_var = ['FG_PCT', 'FG3_PCT', 'FT_PCT']
    other_var = ['REB', 'AST', 'STL', 'PF', 'BLK']

    # Create a specific dataset for LeBron James
    player_one_df = df_tmp[df_tmp['PLAYER_NAME'] == player_one]
    player_two_df = df_tmp[df_tmp['PLAYER_NAME'] == player_two]

    player_one_agg_prct = agg_on_columns(
        df=player_one_df, agg_var=prct_var, operation=['mean'])
    player_one_agg_other = agg_on_columns(
        df=player_one_df, agg_var=other_var, operation=['mean'])

    player_two_agg_prct = agg_on_columns(
        df=player_two_df, agg_var=prct_var, operation=['mean'])
    player_two_agg_other = agg_on_columns(
        df=player_two_df, agg_var=other_var, operation=['mean'])

    stats_prct = pd.concat([player_one_agg_prct, player_two_agg_prct])
    stats_other = pd.concat([player_one_agg_other, player_two_agg_other])

    stats_prct.index = [player_one, player_two]
    stats_other.index = [player_one, player_two]

    stats_prct = rename_df(stats_prct, col_dict=stats_cols)
    stats_other = rename_df(stats_other, col_dict=stats_cols)

    return stats_prct, stats_other


def show_player_stats_comparison(stats_prct, stats_other):
    fig, ax = plt.subplots(figsize=(18, 9))

    ax = plt.subplot(121, polar=True)
    ax.set_title('Percentage statistics')
    radar_plot(ax=ax, df=stats_prct, max_val=1)

    ax = plt.subplot(122, polar=True)
    ax.set_title('Others statistics')
    radar_plot(ax=ax, df=stats_other, max_val=10)

    # plt.show()


player_one = '<NAME>'
player_two = '<NAME>'
# Function code just hide above because it's a repeat from previous part
stats_prct, stats_other = get_players_stats(
    player_one=player_one, player_two=player_two)

st.markdown(f'#### Stats comparison between {player_one} and {player_two}')
st.pyplot(show_player_stats_comparison(stats_prct, stats_other))
st.markdown('---')

# st.selectbox('Select player_one', ['<NAME>','<NAME>','<NAME>'])
# st.multiselect('Multiselect', ['<NAME>','<NAME>','<NAME>'])
# # player_one = '<NAME>'
# # player_two = '<NAME>'
# # # Function code just hide above because it's a repeat from previous part
# # stats_prct, stats_other = get_players_stats(
# #     player_one=player_one, player_two=player_two)
# #
# # st.markdown(f'#### Stats comparison between {player_one} and {player_two}')
# # st.pyplot(show_player_stats_comparison(stats_prct, stats_other))
# # st.markdown('---')

st.header('Which team has the most winning since 2004 season ?')
winning_teams = np.where(
    games['HOME_TEAM_WINS'] == 1,
    games['HOME_TEAM_ID'],
    games['VISITOR_TEAM_ID'])
winning_teams = pd.DataFrame(winning_teams, columns=['TEAM_ID'])
winning_teams = winning_teams.merge(teams[['TEAM_ID', 'NICKNAME']], on='TEAM_ID')[
    'NICKNAME'].value_counts().to_frame().reset_index()
winning_teams.columns = ['TEAM NAME', 'Number of wins']

st.pyplot(
    plot_top(
        winning_teams,
        column='Number of wins',
        label_col='TEAM NAME',
        max_plot=10))
st.markdown('---')

st.header('<NAME> NBA story')
st.markdown('### How many games he played ?')

bryant_games = games_details[games_details['PLAYER_NAME'] == '<NAME>']
st.markdown(f'He played **{len(bryant_games)}** games !')

st.markdown(
    '### What'
    's his overall statistics and is it better than LeBron James ?')

player_one = '<NAME>'
player_two = '<NAME>'
# Function code just hide above because it's a repeat from previous part
stats_prct, stats_other = get_players_stats(
    player_one=player_one, player_two=player_two)

st.markdown(f'#### Stats comparison between {player_one} and {player_two}')
st.pyplot(show_player_stats_comparison(stats_prct, stats_other))

st.markdown('Where did he played ?')

teams_id = bryant_games['TEAM_ID'].unique()
bryant_teams = teams[teams['TEAM_ID'].isin(
    teams_id)]['NICKNAME'].values.tolist()
st.markdown(
    f"He played on the following teams : **{' '.join(bryant_teams)}**.")
st.markdown('---')

st.title('NBA 2K20 Data Visualization')
"""
### 400 + NBA 2k20 players with 14 attributes
"""

data['weight'] = [float(data['weight'][i].split()[3])
                  for i in range(len(data))]
data['height'] = [float(data['height'][i].split()[-1])
                  for i in range(len(data))]
data['salary'] = [int(data['salary'][i].split('$')[1])
                  for i in range(len(data))]
data['jersey'] = [int(data['jersey'][i].split('#')[1])
                  for i in range(len(data))]

data['b_day'] = data['b_day'].apply(
    lambda x: datetime.strptime(
        x, '%m/%d/%y').date())
data['age'] = (datetime.today().date() - data['b_day']
               ).astype('<m8[Y]').astype('int64')

data['draft_round'] = data['draft_round'].apply(
    lambda x: 0 if x == 'Undrafted' else int(x))
data['draft_peak'] = data['draft_peak'].apply(
    lambda x: 0 if x == 'Undrafted' else int(x))

data['college'] = data['college'].fillna('No college')
data['team'] = data['team'].fillna('No team')

# st.dataframe(data.head())

# plt.figure(figsize=(30,15))
# sns.set(font_scale=1.8)
# st.pyplot(sns.heatmap(data.corr(),cmap='Blues',annot=True))
# Download a single file and make its content available as a string.

st.header('Height and weight distribution')

fig = make_subplots(rows=1, cols=2, specs=[
                    [{"type": "histogram"}, {"type": "histogram"}]])
fig.add_trace(go.Histogram(x=data['height'] * 100,
                           xbins=dict(
                               start=150,
                               end=280,
                               size=3
),
    name='height, cm', hovertemplate='Count: %{y}<br>Height: %{x}cm'
), col=1, row=1)
fig.add_trace(go.Scatter(x=[data['height'].mean() * 100,
                            data['height'].mean() * 100],
                         y=[0,
                            91],
                         mode='lines',
                         name='Mean height',
                         hovertemplate='Mean: %{x:.2f}'))
fig.update_layout(hovermode='x')
fig.add_trace(go.Histogram(x=data['weight'],
                           xbins=dict(
                               start=min(data['weight']),
                               end=max(data['weight']),
                               size=3
),
    name='weight, kg', hovertemplate='Count: %{y}<br>Weight: %{x}kg'
), col=2, row=1)
fig.add_trace(go.Scatter(x=[data['weight'].mean(), data['weight'].mean()], y=[0, 91],
                         mode='lines',
                         name='Mean weight', hovertemplate='Mean: %{x:.2f}'), col=2, row=1)

fig.update_layout(title={
    'text': "Height and weight distribution",
    'y': 0.9,
    'x': 0.5,
    'xanchor': 'center',
    'yanchor': 'top'})
# fig.show()
st.plotly_chart(fig)

fig = px.scatter(data, x="weight", y="height",
                 marginal_x="box", marginal_y="violin",
                 color_discrete_sequence=['orange']
                 )
st.plotly_chart(fig)
st.markdown('---')

st.header('Country and college distribution')

country_count = data['country'].value_counts()
fig = go.Figure(
    go.Pie(
        labels=country_count.index,
        values=country_count.values,
        hole=0.4,
        textinfo="none"))
fig.update_layout(title={
    'text': "Percentage of players by country",
    'y': 0.9,
    'x': 0.5,
    'xanchor': 'center',
    'yanchor': 'top'})
st.plotly_chart(fig)
st.markdown(
    'There are about 75% of players from USA and much less from every other country.')


college_count = data['college'].value_counts()
fig = go.Figure(
    go.Pie(
        labels=college_count.index,
        values=college_count.values,
        hole=0.4,
        textinfo="none"))
fig.update_layout(title={
    'text': "Percentage of players by college",
    'y': 0.9,
    'x': 0.5,
    'xanchor': 'center',
    'yanchor': 'top'})
st.plotly_chart(fig)
st.markdown('About 15% of players are not from college. The highest percents of players have Kentucky and Duke colleges.')
st.markdown('---')


st.header('Team rating')

data_team = data[['team', 'rating']].groupby('team').mean().reset_index()
data_team = data_team.sort_values(by='rating', ascending=False)

fig = px.bar(data_team.query("team != 'No team'"),
             x='team', y='rating', color='team',
             labels={'rating': 'mean rating of players'},
             title='Mean rating of players for each team',
             color_discrete_sequence=px.colors.qualitative.Safe)
# fig.show()
st.plotly_chart(fig)
st.markdown(
    'The strongest team is '
    'Los Angeles Clippers'
    ' with mean rating of players 79.3 and the weakest is '
    'Detroit Pistons'
    ' with rating 73.9')
st.markdown('---')

st.header('Height by country')

data_height = data[['height', 'country']].groupby(
    'country').mean().reset_index()
data_height = data_height.sort_values(by='height', ascending=False)

fig = px.bar(data_height,
             x='country', y='height', color='country',
             labels={'height': 'mean height of players'},
             color_discrete_sequence=px.colors.qualitative.Vivid,
             title='Mean height of each country'
             )
st.plotly_chart(fig)

st.markdown('Represents mean height of players for each country. The tallest players are from Austria and the lowest are from Puerto Rico')
st.markdown('---')

st.header('Salary by draft_round')

fig = px.box(data, x="draft_round", y="salary",
             color="draft_round",
             title='Salary exploring by draft_round',
             points='all'
             )
fig.update_traces(quartilemethod="exclusive")
st.plotly_chart(fig)

st.markdown(
    """
draft round 0 (undrafted players): 50 % of salary data is between 1.4M and 4.76, median is 2.56M
draft round 1: 50 % of salary data is between 2.9M and 15.6, median is 6.5M
draft round 2: 50 % of salary data is between 1.4M and 8.5, median is 1.7M
So, the most paid players drom draft round 1, where the max salary is 40.2M. The draft round 2 have bigger range of salary data then data of undrafted players, but median of salary from draft round 2 less by 0.8M.
""")
st.markdown('---')

# -*- coding: utf-8 -*-
# Copyright 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

# More info: https://github.com/thliang01/streamlit-nba

from datetime import datetime
from math import pi

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
from plotly.subplots import make_subplots

plt.style.use('fivethirtyeight')
st.set_option('deprecation.showPyplotGlobalUse', False)

st.title('NBA Data Visualization')

# ----- Load data -----

st.markdown('## Load data')

games_details = pd.read_csv('data/NBA-games-data/games_details.csv')
if st.checkbox('Show games_details'):
    st.write(games_details.head(10))
# st.dataframe(games_details.head(5))

players = pd.read_csv('data/NBA-games-data/players.csv')
if st.checkbox('Show players'):
    st.write(players.head(10))
# st.dataframe(players.head(5))

teams = pd.read_csv('data/NBA-games-data/teams.csv')
if st.checkbox('Show teams'):
    st.write(teams.head(10))
# st.dataframe(teams.head(5))

ranking = pd.read_csv('data/NBA-games-data/ranking.csv')
if st.checkbox('Show ranking'):
    st.write(ranking.head(10))
# st.dataframe(ranking.head(5))

games = pd.read_csv('data/NBA-games-data/games.csv')
if st.checkbox('Show games'):
    st.write(games.head(10))
# st.dataframe(games.head(5))

data = pd.read_csv('data/NBA2K/nba2k20-full.csv')
if st.checkbox('Show nba2k20'):
    st.write(data.head(10))
# st.write(df.head(5))

# ----- ----- -----

st.title('NBA games Data Visualization')
"""
### Dataset with all NBA games from 2004 season to dec 2020
---
"""

st.header('Who are the players with most games played ?')


def plot_top(df, column, label_col=None, max_plot=5):
    top_df = df.sort_values(column, ascending=False).head(max_plot)

    height = top_df[column]
    x = top_df.index if label_col is None else top_df[label_col]

    gold, silver, bronze, other = ('#FFA400', '#bdc3c7', '#cd7f32', '#3498db')
    colors = [gold if i == 0 else silver if i == 1 else bronze if i ==
              2 else other for i in range(0, len(top_df))]

    fig, ax = plt.subplots(figsize=(18, 7))
    ax.bar(x, height, color=colors)
    plt.xticks(x, x, rotation=60)
    plt.xlabel(label_col)
    plt.ylabel(column)
    plt.title(f'Top {max_plot} of {column}')
    # plt.show()


players_name = games_details['PLAYER_NAME']
val_cnt = players_name.value_counts().to_frame().reset_index()
val_cnt.columns = ['PLAYER_NAME', 'Number of games']

st.pyplot(
    plot_top(
        val_cnt,
        column='Number of games',
        label_col='PLAYER_NAME',
        max_plot=10))

st.markdown('---')

st.header('Is most game played means most time played ?')


def convert_min(x):
    if pd.isna(x):
        return 0
    x = str(x).split(':')
    if len(x) < 2:
        return int(x[0])
    else:
        return int(x[0]) * 60 + int(x[1])


df_tmp = games_details[['PLAYER_NAME', 'MIN']]
df_tmp.loc[:, 'MIN'] = df_tmp['MIN'].apply(convert_min)
agg = df_tmp.groupby('PLAYER_NAME').agg('sum').reset_index()
agg.columns = ['PLAYER_NAME', 'Number of seconds played']

st.pyplot(
    plot_top(
        agg,
        column='Number of seconds played',
        label_col='PLAYER_NAME',
        max_plot=10))

st.markdown('And the answer is yes ! <NAME> is truly a living legend !')
st.markdown('---')

st.header('What are the Lebron James average stats compare to overall stats ?')

stats_cols = {
    'FGM': 'Field Goals Made',
    'FGA': 'Field Goals Attempted',
    'FG_PCT': 'Field Goal Percentage',
    'FG3M': 'Three Pointers Made',
    'FG3A': 'Three Pointers Attempted',
    'FG3_PCT': 'Three Point Percentage',
    'FTM': 'Free Throws Made',
    'FTA': 'Free Throws Attempted',
    'FT_PCT': 'Free Throw Percentage',
    'OREB': 'Offensive Rebounds',
    'DREB': 'Defensive Rebounds',
    'REB': 'Rebounds',
    'AST': 'Assists',
    'TO': 'Turnovers',
    'STL': 'Steals',
    'BLK': 'Blocked Shots',
    'PF': 'Personal Foul',
    'PTS': 'Points',
    'PLUS_MINUS': 'Plus-Minus'
}


def agg_on_columns(df, agg_var, operation=['mean']):
    return df[agg_var].agg(operation)


# Remove players that didn't played at a game
df_tmp = games_details[~games_details['MIN'].isna()]
del df_tmp['MIN']

# Define key statistics columns, one for percentage variable and one for
# other important statistics
prct_var = ['FG_PCT', 'FG3_PCT', 'FT_PCT']
other_var = ['REB', 'AST', 'STL', 'PF', 'BLK']

# Create a specific dataset for <NAME>
lebron_james_df = df_tmp[df_tmp['PLAYER_NAME'] == '<NAME>']

overall_agg_prct = agg_on_columns(
    df=df_tmp,
    agg_var=prct_var,
    operation=['mean'])
overall_agg_other = agg_on_columns(
    df=df_tmp,
    agg_var=other_var,
    operation=['mean'])

lebron_james_stats_prct = agg_on_columns(
    df=lebron_james_df,
    agg_var=prct_var,
    operation=['mean'])
lebron_james_stats_other = agg_on_columns(
    df=lebron_james_df,
    agg_var=other_var,
    operation=['mean'])

stats_prct = pd.concat([lebron_james_stats_prct, overall_agg_prct])
stats_other = pd.concat([lebron_james_stats_other, overall_agg_other])

stats_prct.index = ['<NAME>', 'overall stats']
stats_other.index = ['<NAME>', 'overall stats']


def rename_df(df, col_dict):
    cols = df.columns
    new_cols = [(col_dict[c] if c in col_dict else c) for c in cols]
    df.columns = new_cols
    return df


stats_prct = rename_df(stats_prct, col_dict=stats_cols)
stats_other = rename_df(stats_other, col_dict=stats_cols)


def radar_plot(ax, df, max_val=1):
    # number of variable
    categories = list(df)
    N = len(categories)

    # What will be the angle of each axis in the plot? (we divide the plot /
    # number of variable)
    angles = [n / float(N) * 2 * pi for n in range(N)]
    angles += angles[:1]

    # Draw one axe per variable + add labels labels yet
    plt.xticks(angles[:-1], categories, color='black', size=12)

    # Draw ylabels
    ax.set_rlabel_position(0)
    yticks = [max_val * i / 4 for i in range(1, 4)]
    plt.yticks(yticks, [str(e) for e in yticks], color="grey", size=10)
    plt.ylim(0, max_val)

    # We are going to plot the first line of the data frame.
    # But we need to repeat the first value to close the circular graph:
    colors = ['b', 'r', 'g']
    for i in range(len(df)):
        values = df.values[i].flatten().tolist()
        values += values[:1]
        color = colors[i]

        # Plot data
        ax.plot(
            angles,
            values,
            linewidth=1,
            linestyle='solid',
            color=color,
            label=df.index[i])

        # Fill area
        ax.fill(angles, values, color, alpha=0.1)

    # Add legend
    plt.legend(loc=0, bbox_to_anchor=(0.1, 0.1), prop={'size': 13})


st.markdown('#### Stats comparison between Lebron James and overall statistics')
fig, ax = plt.subplots(figsize=(18, 9))

ax = plt.subplot(121, polar=True)
ax.set_title('Percentage statistics')
radar_plot(ax=ax, df=stats_prct, max_val=1)

ax = plt.subplot(122, polar=True)
ax.set_title('Others statistics')
radar_plot(ax=ax, df=stats_other, max_val=10)

st.pyplot(fig)
st.markdown('As we can see <NAME> is above average ! ')
st.markdown('---')

st.header('Compare players statistics (example: Curry vs Harden)')


def get_players_stats(player_one, player_two):
    # Remove players that didn't played at a game
    df_tmp = games_details[~games_details['MIN'].isna()]
    del df_tmp['MIN']

    # Define key statistics columns, one for percentage variable and one for
    # other important statistics
    prct_var = ['FG_PCT', 'FG3_PCT', 'FT_PCT']
    other_var = ['REB', 'AST', 'STL', 'PF', 'BLK']

    # Create a specific dataset for LeBron James
    player_one_df = df_tmp[df_tmp['PLAYER_NAME'] == player_one]
    player_two_df = df_tmp[df_tmp['PLAYER_NAME'] == player_two]

    player_one_agg_prct = agg_on_columns(
        df=player_one_df, agg_var=prct_var, operation=['mean'])
    player_one_agg_other = agg_on_columns(
        df=player_one_df, agg_var=other_var, operation=['mean'])

    player_two_agg_prct = agg_on_columns(
        df=player_two_df, agg_var=prct_var, operation=['mean'])
    player_two_agg_other = agg_on_columns(
        df=player_two_df, agg_var=other_var, operation=['mean'])

    stats_prct = pd.concat([player_one_agg_prct, player_two_agg_prct])
    stats_other = pd.concat([player_one_agg_other, player_two_agg_other])

    stats_prct.index = [player_one, player_two]
    stats_other.index = [player_one, player_two]

    stats_prct = rename_df(stats_prct, col_dict=stats_cols)
    stats_other = rename_df(stats_other, col_dict=stats_cols)

    return stats_prct, stats_other


def show_player_stats_comparison(stats_prct, stats_other):
    fig, ax = plt.subplots(figsize=(18, 9))

    ax = plt.subplot(121, polar=True)
    ax.set_title('Percentage statistics')
    radar_plot(ax=ax, df=stats_prct, max_val=1)

    ax = plt.subplot(122, polar=True)
    ax.set_title('Others statistics')
    radar_plot(ax=ax, df=stats_other, max_val=10)

    # plt.show()


player_one = '<NAME>'
player_two = '<NAME>'
# Function code just hide above because it's a repeat from previous part
stats_prct, stats_other = get_players_stats(
    player_one=player_one, player_two=player_two)

st.markdown(f'#### Stats comparison between {player_one} and {player_two}')
st.pyplot(show_player_stats_comparison(stats_prct, stats_other))
st.markdown('---')

# st.selectbox('Select player_one', ['<NAME>','<NAME>','<NAME>'])
# st.multiselect('Multiselect', ['<NAME>','<NAME>','<NAME>'])
# # player_one = '<NAME>'
# # player_two = '<NAME>'
# # # Function code just hide above because it's a repeat from previous part
# # stats_prct, stats_other = get_players_stats(
# #     player_one=player_one, player_two=player_two)
# #
# # st.markdown(f'#### Stats comparison between {player_one} and {player_two}')
# # st.pyplot(show_player_stats_comparison(stats_prct, stats_other))
# # st.markdown('---')

st.header('Which team has the most winning since 2004 season ?')
winning_teams = np.where(
    games['HOME_TEAM_WINS'] == 1,
    games['HOME_TEAM_ID'],
    games['VISITOR_TEAM_ID'])
winning_teams = pd.DataFrame(winning_teams, columns=['TEAM_ID'])
winning_teams = winning_teams.merge(teams[['TEAM_ID', 'NICKNAME']], on='TEAM_ID')[
    'NICKNAME'].value_counts().to_frame().reset_index()
winning_teams.columns = ['TEAM NAME', 'Number of wins']

st.pyplot(
    plot_top(
        winning_teams,
        column='Number of wins',
        label_col='TEAM NAME',
        max_plot=10))
st.markdown('---')

st.header('<NAME> NBA story')
st.markdown('### How many games he played ?')

bryant_games = games_details[games_details['PLAYER_NAME'] == '<NAME>']
st.markdown(f'He played **{len(bryant_games)}** games !')

st.markdown(
    '### What'
    's his overall statistics and is it better than LeBron James ?')

player_one = '<NAME>'
player_two = '<NAME>'
# Function code just hide above because it's a repeat from previous part
stats_prct, stats_other = get_players_stats(
    player_one=player_one, player_two=player_two)

st.markdown(f'#### Stats comparison between {player_one} and {player_two}')
st.pyplot(show_player_stats_comparison(stats_prct, stats_other))

st.markdown('Where did he played ?')

teams_id = bryant_games['TEAM_ID'].unique()
bryant_teams = teams[teams['TEAM_ID'].isin(
    teams_id)]['NICKNAME'].values.tolist()
st.markdown(
    f"He played on the following teams : **{' '.join(bryant_teams)}**.")
st.markdown('---')

st.title('NBA 2K20 Data Visualization')
"""
### 400 + NBA 2k20 players with 14 attributes
"""

data['weight'] = [float(data['weight'][i].split()[3])
                  for i in range(len(data))]
data['height'] = [float(data['height'][i].split()[-1])
                  for i in range(len(data))]
data['salary'] = [int(data['salary'][i].split('$')[1])
                  for i in range(len(data))]
data['jersey'] = [int(data['jersey'][i].split('#')[1])
                  for i in range(len(data))]

data['b_day'] = data['b_day'].apply(
    lambda x: datetime.strptime(
        x, '%m/%d/%y').date())
data['age'] = (datetime.today().date() - data['b_day']
               ).astype('<m8[Y]').astype('int64')

data['draft_round'] = data['draft_round'].apply(
    lambda x: 0 if x == 'Undrafted' else int(x))
data['draft_peak'] = data['draft_peak'].apply(
    lambda x: 0 if x == 'Undrafted' else int(x))

data['college'] = data['college'].fillna('No college')
data['team'] = data['team'].fillna('No team')

# st.dataframe(data.head())

# plt.figure(figsize=(30,15))
# sns.set(font_scale=1.8)
# st.pyplot(sns.heatmap(data.corr(),cmap='Blues',annot=True))
# Download a single file and make its content available as a string.

st.header('Height and weight distribution')

fig = make_subplots(rows=1, cols=2, specs=[
                    [{"type": "histogram"}, {"type": "histogram"}]])
fig.add_trace(go.Histogram(x=data['height'] * 100,
                           xbins=dict(
                               start=150,
                               end=280,
                               size=3
),
    name='height, cm', hovertemplate='Count: %{y}<br>Height: %{x}cm'
), col=1, row=1)
fig.add_trace(go.Scatter(x=[data['height'].mean() * 100,
                            data['height'].mean() * 100],
                         y=[0,
                            91],
                         mode='lines',
                         name='Mean height',
                         hovertemplate='Mean: %{x:.2f}'))
fig.update_layout(hovermode='x')
fig.add_trace(go.Histogram(x=data['weight'],
                           xbins=dict(
                               start=min(data['weight']),
                               end=max(data['weight']),
                               size=3
),
    name='weight, kg', hovertemplate='Count: %{y}<br>Weight: %{x}kg'
), col=2, row=1)
fig.add_trace(go.Scatter(x=[data['weight'].mean(), data['weight'].mean()], y=[0, 91],
                         mode='lines',
                         name='Mean weight', hovertemplate='Mean: %{x:.2f}'), col=2, row=1)

fig.update_layout(title={
    'text': "Height and weight distribution",
    'y': 0.9,
    'x': 0.5,
    'xanchor': 'center',
    'yanchor': 'top'})
# fig.show()
st.plotly_chart(fig)

fig = px.scatter(data, x="weight", y="height",
                 marginal_x="box", marginal_y="violin",
                 color_discrete_sequence=['orange']
                 )
st.plotly_chart(fig)
st.markdown('---')

st.header('Country and college distribution')

country_count = data['country'].value_counts()
fig = go.Figure(
    go.Pie(
        labels=country_count.index,
        values=country_count.values,
        hole=0.4,
        textinfo="none"))
fig.update_layout(title={
    'text': "Percentage of players by country",
    'y': 0.9,
    'x': 0.5,
    'xanchor': 'center',
    'yanchor': 'top'})
st.plotly_chart(fig)
st.markdown(
    'There are about 75% of players from USA and much less from every other country.')


college_count = data['college'].value_counts()
fig = go.Figure(
    go.Pie(
        labels=college_count.index,
        values=college_count.values,
        hole=0.4,
        textinfo="none"))
fig.update_layout(title={
    'text': "Percentage of players by college",
    'y': 0.9,
    'x': 0.5,
    'xanchor': 'center',
    'yanchor': 'top'})
st.plotly_chart(fig)
st.markdown('About 15% of players are not from college. The highest percents of players have Kentucky and Duke colleges.')
st.markdown('---')


st.header('Team rating')

data_team = data[['team', 'rating']].groupby('team').mean().reset_index()
data_team = data_team.sort_values(by='rating', ascending=False)

fig = px.bar(data_team.query("team != 'No team'"),
             x='team', y='rating', color='team',
             labels={'rating': 'mean rating of players'},
             title='Mean rating of players for each team',
             color_discrete_sequence=px.colors.qualitative.Safe)
# fig.show()
st.plotly_chart(fig)
st.markdown(
    'The strongest team is '
    'Los Angeles Clippers'
    ' with mean rating of players 79.3 and the weakest is '
    'Detroit Pistons'
    ' with rating 73.9')
st.markdown('---')

st.header('Height by country')

data_height = data[['height', 'country']].groupby(
    'country').mean().reset_index()
data_height = data_height.sort_values(by='height', ascending=False)

fig = px.bar(data_height,
             x='country', y='height', color='country',
             labels={'height': 'mean height of players'},
             color_discrete_sequence=px.colors.qualitative.Vivid,
             title='Mean height of each country'
             )
st.plotly_chart(fig)

st.markdown('Represents mean height of players for each country. The tallest players are from Austria and the lowest are from Puerto Rico')
st.markdown('---')

st.header('Salary by draft_round')

fig = px.box(data, x="draft_round", y="salary",
             color="draft_round",
             title='Salary exploring by draft_round',
             points='all'
             )
fig.update_traces(quartilemethod="exclusive")
st.plotly_chart(fig)

st.markdown(
    """
draft round 0 (undrafted players): 50 % of salary data is between 1.4M and 4.76, median is 2.56M
draft round 1: 50 % of salary data is between 2.9M and 15.6, median is 6.5M
draft round 2: 50 % of salary data is between 1.4M and 8.5, median is 1.7M
So, the most paid players drom draft round 1, where the max salary is 40.2M. The draft round 2 have bigger range of salary data then data of undrafted players, but median of salary from draft round 2 less by 0.8M.
""")
st.markdown('---')
# -*- coding: utf-8 -*- # Copyright 2020 <NAME> # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # More info: https://github.com/thliang01/streamlit-nba # ----- Load data ----- # Load data') # st.dataframe(games_details.head(5)) # st.dataframe(players.head(5)) # st.dataframe(teams.head(5)) # st.dataframe(ranking.head(5)) # st.dataframe(games.head(5)) # st.write(df.head(5)) # ----- ----- ----- ### Dataset with all NBA games from 2004 season to dec 2020 --- # plt.show() # Remove players that didn't played at a game # Define key statistics columns, one for percentage variable and one for # other important statistics # Create a specific dataset for <NAME> # number of variable # What will be the angle of each axis in the plot? (we divide the plot / # number of variable) # Draw one axe per variable + add labels labels yet # Draw ylabels # We are going to plot the first line of the data frame. # But we need to repeat the first value to close the circular graph: # Plot data # Fill area # Add legend ### Stats comparison between Lebron James and overall statistics') # Remove players that didn't played at a game # Define key statistics columns, one for percentage variable and one for # other important statistics # Create a specific dataset for LeBron James # plt.show() # Function code just hide above because it's a repeat from previous part ### Stats comparison between {player_one} and {player_two}') # st.selectbox('Select player_one', ['<NAME>','<NAME>','<NAME>']) # st.multiselect('Multiselect', ['<NAME>','<NAME>','<NAME>']) # # player_one = '<NAME>' # # player_two = '<NAME>' # # # Function code just hide above because it's a repeat from previous part # # stats_prct, stats_other = get_players_stats( # #     player_one=player_one, player_two=player_two) # # # # st.markdown(f'#### Stats comparison between {player_one} and {player_two}') # # st.pyplot(show_player_stats_comparison(stats_prct, stats_other)) # # st.markdown('---') ## How many games he played ?') ## What' # Function code just hide above because it's a repeat from previous part ### Stats comparison between {player_one} and {player_two}') ### 400 + NBA 2k20 players with 14 attributes # st.dataframe(data.head()) # plt.figure(figsize=(30,15)) # sns.set(font_scale=1.8) # st.pyplot(sns.heatmap(data.corr(),cmap='Blues',annot=True)) # Download a single file and make its content available as a string. # fig.show() # fig.show() draft round 0 (undrafted players): 50 % of salary data is between 1.4M and 4.76, median is 2.56M draft round 1: 50 % of salary data is between 2.9M and 15.6, median is 6.5M draft round 2: 50 % of salary data is between 1.4M and 8.5, median is 1.7M So, the most paid players drom draft round 1, where the max salary is 40.2M. The draft round 2 have bigger range of salary data then data of undrafted players, but median of salary from draft round 2 less by 0.8M.