Update app.py

app.py

@@ -1,172 +1,174 @@
-import seaborn as sns
-import streamlit as st
-from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
-import requests
-import polars as pl
-from datetime import date
-import pandas as pd
-import matplotlib
-
-
-
-# Display the app title and description
-st.markdown("""
-## tjStuff+ App
-
-##### By: Thomas Nestico ([@TJStats](https://x.com/TJStats))
-##### Code: [GitHub Repo](https://github.com/tnestico/streamlit_tjstuff)
-##### Data: [MLB](https://baseballsavant.mlb.com/) ([Gathered from my MLB Scraper](https://github.com/tnestico/mlb_scraper))
-
-#### About
-This Streamlit app tabulates and plots my pitching metric, tjStuff+, for all MLB players during the 2024 MLB Season
-
-About tjStuff+:
-* tjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type
-* tjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10
-* Pitch Grade is based off tjStuff+ and scales the data to the traditional 20-80 Scouting Scale for a given pitch type
-                            
-"""
-)
-
-
-# Dictionary to map pitch types to their corresponding colors and names
-pitch_colours = {
-    ## Fastballs ##
-    'FF': {'colour': '#FF007D', 'name': '4-Seam Fastball'},
-    'FA': {'colour': '#FF007D', 'name': 'Fastball'},
-    'SI': {'colour': '#98165D', 'name': 'Sinker'},
-    'FC': {'colour': '#BE5FA0', 'name': 'Cutter'},
-
-    ## Offspeed ##
-    'CH': {'colour': '#F79E70', 'name': 'Changeup'},
-    'FS': {'colour': '#FE6100', 'name': 'Splitter'},
-    'SC': {'colour': '#F08223', 'name': 'Screwball'},
-    'FO': {'colour': '#FFB000', 'name': 'Forkball'},
-
-    ## Sliders ##
-    'SL': {'colour': '#67E18D', 'name': 'Slider'},
-    'ST': {'colour': '#1BB999', 'name': 'Sweeper'},
-    'SV': {'colour': '#376748', 'name': 'Slurve'},
-
-    ## Curveballs ##
-    'KC': {'colour': '#311D8B', 'name': 'Knuckle Curve'},
-    'CU': {'colour': '#3025CE', 'name': 'Curveball'},
-    'CS': {'colour': '#274BFC', 'name': 'Slow Curve'},
-    'EP': {'colour': '#648FFF', 'name': 'Eephus'},
-
-    ## Others ##
-    'KN': {'colour': '#867A08', 'name': 'Knuckleball'},
-    'PO': {'colour': '#472C30', 'name': 'Pitch Out'},
-    'UN': {'colour': '#9C8975', 'name': 'Unknown'},
-}
-
-# Create dictionaries for pitch types and their attributes
-dict_colour = {key: value['colour'] for key, value in pitch_colours.items()}
-dict_pitch = {key: value['name'] for key, value in pitch_colours.items()}
-dict_pitch_desc_type = {value['name']: key for key, value in pitch_colours.items()}
-dict_pitch_name = {value['name']: value['colour'] for key, value in pitch_colours.items()}
-
-# Define a custom colormap for styling
-cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF', '#FFFFFF', '#FFB000'])
-
-# Initialize session state for cache status
-if 'cache_cleared' not in st.session_state:
-    st.session_state.cache_cleared = False
-
-# Function to fetch data and cache it
-@st.cache_data
-def fetch_data():
-    df = pl.read_csv("tjstuff_plus_pitch_data_2024.csv").fill_nan(None)
-    return df
-
-# Fetch and preprocess data
-df = fetch_data()
-df_plot = df.clone()
-df = df.filter(df['pitches'] >= 10).drop_nulls(subset=['pitch_grade', 'tj_stuff_plus'])
-df = df.sort(['pitcher_name', 'pitch_type'], descending=[False, False])
-
-# Cast columns to appropriate data types
-df = df.with_columns([
-    pl.col('tj_stuff_plus').cast(pl.Int64).alias('tj_stuff_plus'),
-    pl.col('pitches').cast(pl.Int64).alias('pitches'),
-    pl.col('pitcher_id').cast(pl.Int64).alias('pitcher_id'),
-    pl.col('pitch_grade').cast(pl.Int64).alias('pitch_grade')
-])
-
-# Define column configuration for Streamlit
-column_config_dict = {
-    'pitcher_id': 'Pitcher ID',
-    'pitcher_name': 'Pitcher Name',
-    'pitch_type': 'Pitch Type',
-    'pitches': 'Pitches',
-    'tj_stuff_plus': st.column_config.NumberColumn("tjStuff+", format="%.0f"),
-    'pitch_grade': st.column_config.NumberColumn("Pitch Grade", format="%.0f")
-}
-
-# Get unique pitch types for selection
-unique_pitch_types = [''] + sorted(df['pitch_type'].unique().to_list())
-unique_pitch_types = [dict_pitch.get(x, x) for x in unique_pitch_types]
-
-
-st.markdown("""
-#### tjStuff+ Table
-
-Filter and sort tjStuff+ Data for all MLB Pitchers
-"""
-           )
-# Create a selectbox widget for pitch types
-selected_pitch_types = st.selectbox('Select Pitch Types *(leave blank for all pitch types)*', unique_pitch_types)
-
-# Filter the DataFrame based on selected pitch types
-if selected_pitch_types == 'All':
-    df = df.filter(pl.col('pitch_type') == 'All').sort('tj_stuff_plus', descending=True)
-elif selected_pitch_types != '':
-    df = df.filter(pl.col('pitch_type') == dict_pitch_desc_type[selected_pitch_types]).sort('tj_stuff_plus', descending=True)
-
-# Convert Polars DataFrame to Pandas DataFrame and apply styling
-styled_df = df[['pitcher_id', 'pitcher_name', 'pitch_type', 'pitches', 'tj_stuff_plus', 'pitch_grade']].to_pandas().style
-
-# Apply background gradient styling to specific columns
-styled_df = styled_df.background_gradient(subset=['tj_stuff_plus'], cmap=cmap_sum, vmin=80, vmax=120)
-styled_df = styled_df.background_gradient(subset=['pitch_grade'], cmap=cmap_sum, vmin=20, vmax=80)
-
-# Display the styled DataFrame in Streamlit
-st.dataframe(styled_df, hide_index=True, column_config=column_config_dict, width=1500)
-
-# Create dictionaries for pitcher information
-pitcher_id_name = dict(zip(df_plot['pitcher_id'], df_plot['pitcher_name']))
-pitcher_id_name_id = dict(zip(df_plot['pitcher_id'], df_plot['pitcher_name'] + ' - ' + df_plot['pitcher_id']))
-pitcher_name_id_id = dict(zip(df_plot['pitcher_name'] + ' - ' + df_plot['pitcher_id'], df_plot['pitcher_id']))
-pitcher_id_position = dict(zip(df_plot['pitcher_id'], df_plot.drop_nulls(subset=['position'])['position']))
-
-
-st.markdown("""
-#### tjStuff+ Plot
-
-Visualize tjStuff+ and Pitching Grade by Pitcher
-"""
-           )
-
-# Create a selectbox widget for pitchers
-pitcher_id_name_select = st.selectbox('Select Pitcher', sorted(pitcher_name_id_id.keys()))
-
-# Get selected pitcher information
-pitcher_id = pitcher_name_id_id[pitcher_id_name_select]
-position = pitcher_id_position[pitcher_id]
-pitcher_name = pitcher_id_name[pitcher_id]
-
-import tjstuff_plot
-# Button to update plot
-
-# Get selected pitcher information
-pitcher_id = pitcher_name_id_id[pitcher_id_name_select]
-position = pitcher_id_position[pitcher_id]
-pitcher_name = pitcher_id_name[pitcher_id]
-
-import tjstuff_plot
-
-# Button to update plot
-if st.button('Update Plot'):
-    st.session_state.update_plot = True
-    tjstuff_plot.tjstuff_plot(df_plot, pitcher_id, position, pitcher_name)
-
+import seaborn as sns
+import streamlit as st
+from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
+import requests
+import polars as pl
+from datetime import date
+import pandas as pd
+import matplotlib
+
+
+
+# Display the app title and description
+st.markdown("""
+## tjStuff+ App
+
+##### By: Thomas Nestico ([@TJStats](https://x.com/TJStats))
+##### Code: [GitHub Repo](https://github.com/tnestico/streamlit_tjstuff)
+##### Data: [MLB](https://baseballsavant.mlb.com/) ([Gathered from my MLB Scraper](https://github.com/tnestico/mlb_scraper))
+
+#### About
+This Streamlit app tabulates and plots my pitching metric, tjStuff+, for all MLB players during the 2024 MLB Season
+
+About tjStuff+:
+* tjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type
+* tjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10
+* Pitch Grade is based off tjStuff+ and scales the data to the traditional 20-80 Scouting Scale for a given pitch type
+
+[Learn More about tjStuff+ here](https://github.com/tnestico/tjstuff_plus/tree/main)
+                            
+"""
+)
+
+
+# Dictionary to map pitch types to their corresponding colors and names
+pitch_colours = {
+    ## Fastballs ##
+    'FF': {'colour': '#FF007D', 'name': '4-Seam Fastball'},
+    'FA': {'colour': '#FF007D', 'name': 'Fastball'},
+    'SI': {'colour': '#98165D', 'name': 'Sinker'},
+    'FC': {'colour': '#BE5FA0', 'name': 'Cutter'},
+
+    ## Offspeed ##
+    'CH': {'colour': '#F79E70', 'name': 'Changeup'},
+    'FS': {'colour': '#FE6100', 'name': 'Splitter'},
+    'SC': {'colour': '#F08223', 'name': 'Screwball'},
+    'FO': {'colour': '#FFB000', 'name': 'Forkball'},
+
+    ## Sliders ##
+    'SL': {'colour': '#67E18D', 'name': 'Slider'},
+    'ST': {'colour': '#1BB999', 'name': 'Sweeper'},
+    'SV': {'colour': '#376748', 'name': 'Slurve'},
+
+    ## Curveballs ##
+    'KC': {'colour': '#311D8B', 'name': 'Knuckle Curve'},
+    'CU': {'colour': '#3025CE', 'name': 'Curveball'},
+    'CS': {'colour': '#274BFC', 'name': 'Slow Curve'},
+    'EP': {'colour': '#648FFF', 'name': 'Eephus'},
+
+    ## Others ##
+    'KN': {'colour': '#867A08', 'name': 'Knuckleball'},
+    'PO': {'colour': '#472C30', 'name': 'Pitch Out'},
+    'UN': {'colour': '#9C8975', 'name': 'Unknown'},
+}
+
+# Create dictionaries for pitch types and their attributes
+dict_colour = {key: value['colour'] for key, value in pitch_colours.items()}
+dict_pitch = {key: value['name'] for key, value in pitch_colours.items()}
+dict_pitch_desc_type = {value['name']: key for key, value in pitch_colours.items()}
+dict_pitch_name = {value['name']: value['colour'] for key, value in pitch_colours.items()}
+
+# Define a custom colormap for styling
+cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF', '#FFFFFF', '#FFB000'])
+
+# Initialize session state for cache status
+if 'cache_cleared' not in st.session_state:
+    st.session_state.cache_cleared = False
+
+# Function to fetch data and cache it
+@st.cache_data
+def fetch_data():
+    df = pl.read_csv("tjstuff_plus_pitch_data_2024.csv").fill_nan(None)
+    return df
+
+# Fetch and preprocess data
+df = fetch_data()
+df_plot = df.clone()
+df = df.filter(df['pitches'] >= 10).drop_nulls(subset=['pitch_grade', 'tj_stuff_plus'])
+df = df.sort(['pitcher_name', 'pitch_type'], descending=[False, False])
+
+# Cast columns to appropriate data types
+df = df.with_columns([
+    pl.col('tj_stuff_plus').cast(pl.Int64).alias('tj_stuff_plus'),
+    pl.col('pitches').cast(pl.Int64).alias('pitches'),
+    pl.col('pitcher_id').cast(pl.Int64).alias('pitcher_id'),
+    pl.col('pitch_grade').cast(pl.Int64).alias('pitch_grade')
+])
+
+# Define column configuration for Streamlit
+column_config_dict = {
+    'pitcher_id': 'Pitcher ID',
+    'pitcher_name': 'Pitcher Name',
+    'pitch_type': 'Pitch Type',
+    'pitches': 'Pitches',
+    'tj_stuff_plus': st.column_config.NumberColumn("tjStuff+", format="%.0f"),
+    'pitch_grade': st.column_config.NumberColumn("Pitch Grade", format="%.0f")
+}
+
+# Get unique pitch types for selection
+unique_pitch_types = [''] + sorted(df['pitch_type'].unique().to_list())
+unique_pitch_types = [dict_pitch.get(x, x) for x in unique_pitch_types]
+
+
+st.markdown("""
+#### tjStuff+ Table
+
+Filter and sort tjStuff+ Data for all MLB Pitchers
+"""
+           )
+# Create a selectbox widget for pitch types
+selected_pitch_types = st.selectbox('Select Pitch Types *(leave blank for all pitch types)*', unique_pitch_types)
+
+# Filter the DataFrame based on selected pitch types
+if selected_pitch_types == 'All':
+    df = df.filter(pl.col('pitch_type') == 'All').sort('tj_stuff_plus', descending=True)
+elif selected_pitch_types != '':
+    df = df.filter(pl.col('pitch_type') == dict_pitch_desc_type[selected_pitch_types]).sort('tj_stuff_plus', descending=True)
+
+# Convert Polars DataFrame to Pandas DataFrame and apply styling
+styled_df = df[['pitcher_id', 'pitcher_name', 'pitch_type', 'pitches', 'tj_stuff_plus', 'pitch_grade']].to_pandas().style
+
+# Apply background gradient styling to specific columns
+styled_df = styled_df.background_gradient(subset=['tj_stuff_plus'], cmap=cmap_sum, vmin=80, vmax=120)
+styled_df = styled_df.background_gradient(subset=['pitch_grade'], cmap=cmap_sum, vmin=20, vmax=80)
+
+# Display the styled DataFrame in Streamlit
+st.dataframe(styled_df, hide_index=True, column_config=column_config_dict, width=1500)
+
+# Create dictionaries for pitcher information
+pitcher_id_name = dict(zip(df_plot['pitcher_id'], df_plot['pitcher_name']))
+pitcher_id_name_id = dict(zip(df_plot['pitcher_id'], df_plot['pitcher_name'] + ' - ' + df_plot['pitcher_id']))
+pitcher_name_id_id = dict(zip(df_plot['pitcher_name'] + ' - ' + df_plot['pitcher_id'], df_plot['pitcher_id']))
+pitcher_id_position = dict(zip(df_plot['pitcher_id'], df_plot.drop_nulls(subset=['position'])['position']))
+
+
+st.markdown("""
+#### tjStuff+ Plot
+
+Visualize tjStuff+ and Pitching Grade by Pitcher
+"""
+           )
+
+# Create a selectbox widget for pitchers
+pitcher_id_name_select = st.selectbox('Select Pitcher', sorted(pitcher_name_id_id.keys()))
+
+# Get selected pitcher information
+pitcher_id = pitcher_name_id_id[pitcher_id_name_select]
+position = pitcher_id_position[pitcher_id]
+pitcher_name = pitcher_id_name[pitcher_id]
+
+import tjstuff_plot
+# Button to update plot
+
+# Get selected pitcher information
+pitcher_id = pitcher_name_id_id[pitcher_id_name_select]
+position = pitcher_id_position[pitcher_id]
+pitcher_name = pitcher_id_name[pitcher_id]
+
+import tjstuff_plot
+
+# Button to update plot
+if st.button('Update Plot'):
+    st.session_state.update_plot = True
+    tjstuff_plot.tjstuff_plot(df_plot, pitcher_id, position, pitcher_name)
+