join the Figure Friday session on September 12, at noon Eastern Time, to showcase your creation and receive feedback from the community.
“LockerNYC is a pilot program that allows New Yorkers to receive and send packages using secure lockers on public sidewalks.” What is the distribution of locker sizes in NYC?
Answer this question and a few others by using Plotly on the LockerNYC Reservations dataset.
Things to consider:
- what can you improve in the app or sample figure below (histograms)?
- would you like to tell a different data story using a different Dash app?
- how can you explore the data with Plotly Studio?
Sample figure:
Code for sample figure:
from dash import Dash, dcc
import dash_ag_grid as dag
import plotly.express as px
import pandas as pd
# download CSV sheet from Google Drive - https://drive.google.com/file/d/1EGnFTOAvYeuDd7CHajE1PdpWg-nA-IWz/view?usp=sharing
df = pd.read_csv('LockerNYC_Reservations_20250903.csv')
fig = px.histogram(df, x="Locker Size")
grid = dag.AgGrid(
rowData=df.to_dict("records"),
columnDefs=[{"field": i, 'filter': True, 'sortable': True} for i in df.columns],
dashGridOptions={"pagination": True},
# columnSize="sizeToFit"
)
app = Dash()
app.layout = [
grid,
dcc.Graph(figure=fig)
]
if __name__ == "__main__":
app.run(debug=False)
For community members that would like to build the data app with Plotly Studio, but don’t have the application yet, simply go to Plotly.com/studio. Please keep in mind that Plotly Studio is still in early access.
Below is a screenshot of a scatter map built by Plotly Studio on top of this dataset:
Participation Instructions:
- Create - use the weekly data set to build your own Plotly visualization or Dash app. Or, enhance the sample figure provided in this post, using Plotly or Dash.
- Submit - post your creation to LinkedIn or Twitter with the hashtags
#FigureFriday
and#plotly
by midnight Thursday, your time zone. Please also submit your visualization as a new post in this thread. - Celebrate - join the Figure Friday sessions to showcase your creation and receive feedback from the community.
If you prefer to collaborate with others on Discord, join the Plotly Discord channel.
Data Source:
Thank you to NYC Open Data for the data.