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streamlit_test.py
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streamlit_test.py
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import streamlit as st
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
from utils import (
filter_since_year, filter_by_wind_types, pie_distribution,
get_color_dict, high_correlation
)
@st.cache
def get_df(path):
df = pd.read_csv(path)
_winds = [v.split(' mph') for v in df.wind.values]
df['wind_number'] = [int(w[0]) for w in _winds]
df['wind_types'] = [w[1][2:] for w in _winds]
df['year'] = [int(v[:4]) for v in df.date.values]
df['mounth'] = [int(v[5:7]) for v in df.date.values]
df_ab = df.groupby('ab_id').last()
return df, df_ab
def get_sub_df(key: str, value: str, from_df=None):
if from_df is None:
from_df = final_df
sub_df = from_df[from_df[key] == value]
return sub_df
def pitcher_page():
name = st.sidebar.selectbox(
'Which pitcher do you want to see?',
final_df['Pitchers Name'].value_counts().index[:30])
df = get_sub_df("Pitchers Name", name)
atbat_df = get_sub_df("Pitchers Name", name, from_df=final_atbat_df)
# Filtering
st.sidebar.markdown('## Filtering')
df, atbat_df = filter_since_year(df, atbat_df)
df, atbat_df = filter_by_wind_types(df, atbat_df)
pitch_type_count = df.pitch_type.value_counts()
st.markdown('## Pitch type distribution')
st.write(pie_distribution(counts=pitch_type_count))
# st.markdown('## Strike type distribution')
# st.write(pie_distribution(counts=pitch_type_count))
st.markdown('---\n## Pitch position scatter')
strike_zone_distribution(df, atbat_df, targets=['pitch_type', 'code', 'event'])
high_correlation(df, atbat_df)
def batter_page():
name = st.sidebar.selectbox(
'Which batter do you want to see?',
final_df['Batters Name'].value_counts().index[:30])
df = get_sub_df('Batters Name', name)
atbat_df = get_sub_df('Batters Name', name, from_df=final_atbat_df)
# Filtering
st.sidebar.markdown('## Filtering')
df, atbat_df = filter_since_year(df, atbat_df)
df, atbat_df = filter_by_wind_types(df, atbat_df)
st.markdown('---\n### Strike event distribution')
event_counts = atbat_df['event'].value_counts()
st.write(pie_distribution(counts=event_counts))
code_counts = atbat_df['code'].value_counts()
st.write(pie_distribution(counts=code_counts))
st.markdown('---\n## Strike zone')
strike_zone_distribution(df, atbat_df, targets=['code', 'event'])
high_correlation(df, atbat_df)
def strike_zone_distribution(df, atbat_df, targets):
def select_by_and_draw(df_, key, size=5):
availible_types = list(df_[key].value_counts().index)
st.markdown(f'### Select the {key} to show')
selected_types = st.multiselect('', availible_types, default=availible_types[:2])
type_to_color = get_color_dict(availible_types)
fig = scatter_zone_on_selected_types(df_, key, selected_types, type_to_color, size=size)
st.write(fig)
if 'pitch_type' in targets:
select_by_and_draw(df, 'pitch_type', size=3)
if 'code' in targets:
select_by_and_draw(df, 'code', size=3)
if 'event' in targets:
select_by_and_draw(atbat_df, 'event')
def scatter_zone_on_selected_types(df, key: str, selected_types: list, type_to_color: dict, size=5):
data = []
for _type in selected_types:
color = type_to_color[_type]
trace = go.Scatter(
x=df.px[df[key] == _type],
y=df.pz[df[key] == _type],
name=_type,
mode='markers',
marker=dict(size=size, color=color, line=dict(width=2, color=color)))
data.append(trace)
fig = go.Figure(data=data)
return fig
@st.cache
def get_final_basic_measure(columns):
return final_df[columns].mean(axis=0), final_df[columns].std(axis=0)
NUMERIC_COLUMNS = ['px', 'pz', 'start_speed', 'end_speed', 'spin_rate', 'spin_dir',
'break_angle', 'break_length', 'break_y']
final_df, final_atbat_df = get_df("../input/all_joined.csv.zip")
final_mean, final_std = get_final_basic_measure(NUMERIC_COLUMNS)
st.sidebar.markdown('# Analysis Target')
service_names = ('Pitcher', 'Batter')
service_type = st.sidebar.selectbox(
label="Type", options=service_names, index=0)
if service_type == service_names[0]:
pitcher_page()
elif service_type == service_names[1]:
batter_page()