Py.Cafe

# check out the Dash docs - https://dash.plotly.com/ 
# check out the Plotly docs - https://plotly.com/python/

from dash import Dash, Input, Output, callback, dcc, html
import plotly.express as px
import pandas as pd

# you can download the data here if you'd like: https://docs.google.com/spreadsheets/d/1O_zxndHKhKMIfJ9e7_M5L7b4F3S__d1nVnUS8iZn8yE/edit?gid=0#gid=0
df = pd.read_csv("https://raw.githubusercontent.com/Coding-with-Adam/Dash-by-Plotly/refs/heads/master/Other/NYC%20Marathon%20Results%2C%202024%20-%20Marathon%20Runner%20Results.csv")

fig = px.histogram(df, x='racesCount', range_y=[0,2000], range_x=[0,50])
# Convert `pace` column from string format (minutes:seconds) to numeric (float) in minutes
# def convert_pace_to_minutes(pace_str):
#     try:
#         minutes, seconds = map(int, pace_str.split(':'))
#         return minutes + seconds / 60
#     except ValueError:
#         return None

# # Apply conversion to the `pace` column
# df['pace_minutes'] = df['pace'].apply(convert_pace_to_minutes)

# # Drop rows where `pace_minutes` could not be calculated
# cleaned_data = df.dropna(subset=['pace_minutes'])

# # Define age groups
# bins = [10, 20, 30, 40, 50, 60, 70, 80, 90]
# labels = ['10-20', '20-30', '30-40', '40-50', '50-60', '60-70', '70-80', '80-90']

# # Create a new column for age groups
# cleaned_data['age_group'] = pd.cut(cleaned_data['age'], bins=bins, labels=labels, right=False)
# print(cleaned_data)

# fig = px.violin(
#     cleaned_data,
#     x='age_group',
#     y='pace_minutes',
#     title='Distribution of Minutes per Mile, by Age Group',
#     labels={'pace_minutes': 'Pace (minutes per mile)', 'age': 'Age'},
#     box=True
# )
# fig.update_xaxes(categoryorder='array', categoryarray=labels)


app = Dash(__name__)
app.layout = html.Div(
    children=[
        dcc.Graph(figure=fig)
    ]
)