Figure Friday 2024 - week 39

Dash Python
1

Week 39 of Figure-Friday highlights a data set on the annual measurements of plant abundance in New Mexico from 1915 to 2016. We hope this will be a good learning opportunity for all plant lovers out there :hugs:

For mapping enthusiasts out there, this data collection also references shapefiles for mapping the location of the plants by quadrats. If you would like to use the shapefiles please see the downloadable zipped directory in bullet point 9.

Sample figure:

image
image1886×901 60.1 KB

Code for sample figure: 
import plotly.express as px
import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/Figure-Friday/refs/heads/main/2024/week-39/Jornada_quadrat_annual_plant_counts.csv")
fig = px.bar(df, x='species_code', y='count')
fig.show()

Things to consider:

  • can you improve the sample figure built?
  • would a different figure tell the data story better?
  • can you use the shapefiles to plot your map?

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.

:point_right: If you prefer to collaborate with others on Discord, join the Plotly Discord channel .

Thank you to EDI Data Portal for the data.

2

Here is my submission for Figure Friday Week 39. The data is aggregated into decades, to make the plots less raggedy. Y-axis is the percentage of each genus count by decade, relative to the total count for the genus. The values for each genus over the decades adds to 100%. As noted, wonder what happened in the 1980s, and why so many genuses had their highest counts in the 1930s and 1960s. Here is a screen shot, with code pasted below it.

image
image761×548 130 KB

import polars as pl
import polars.selectors as cs
import plotly.express as px
import plotly.graph_objects as go
import numpy as np

#------------------------------------------------------------------------------#
#     Read 2 datasets as polars LazyFrames                                     #
#------------------------------------------------------------------------------#
df_species = (
    pl.scan_csv('./Data_Set/Jornada_quadrat_species_list.csv')
)
df_species
df_plant_counts = (
    pl.scan_csv('./Data_Set/Jornada_quadrat_annual_plant_counts.csv')
)

#------------------------------------------------------------------------------#
#     collect each lazyframes, join on the species code                        #
#     add columns for decade, decade based genus count and percentage          #
#------------------------------------------------------------------------------#
df_by_decade = (
    (
        df_plant_counts
        .collect()
    ).join(
        (
            df_species
            .collect()
        ),
        on = 'species_code'
    )
    .with_columns(
        decade = (pl.col('year')//10).cast(pl.String) + pl.lit('0s')
    )
    .group_by('decade', 'genus', 'species_code', 'species','common_name')
    .agg(pl.col('count').sum())
    .with_columns(GENUS_COUNT = pl.col('count').sum().over('genus'))
    .with_columns(GENUS_PCT = (pl.col('count') / pl.col('GENUS_COUNT')))
    .sort('decade', 'count')
    .pivot(
        on='genus',
        index='decade',
        values='GENUS_PCT',
        aggregate_function="sum"
    )
)
#------------------------------------------------------------------------------#
#     make px.line plot of of genus percentages by decade                      #
#------------------------------------------------------------------------------#
plot_cols = df_by_decade.columns[1:]
fig = px.line(
    df_by_decade,
    'decade',
    plot_cols,
)

#------------------------------------------------------------------------------#
#     update/format to plot                                                    #
#------------------------------------------------------------------------------#
my_title = 'New Mexico Relative Genus Percentage by Decade<br>'
my_title += '<sup>Each Genus totals 100%</sub>'
fig.update_layout(
    autosize=False,
    width=800,
    height=600,
    showlegend=True,
    legend_title="Genus",
    
    title=go.layout.Title(
        text=my_title.upper(),
        xref="paper",
        x=0
    ),
    xaxis=go.layout.XAxis(
        title=go.layout.xaxis.Title(
            text='decade'.upper()
        )
    ),
    yaxis=go.layout.YAxis(
        title=go.layout.yaxis.Title(
            text='Percentage - each genus total is 100%)'.upper()
            ),
    )
)
fig.layout.template = 'plotly_white' 
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False,tickformat = '.0%')

#------------------------------------------------------------------------------#
#     Add annotation to tell the story                                         #
#------------------------------------------------------------------------------#

text = 'A story of 1 hit wonders, what happened in the 1980s? Many genus<br>'
text += 'peaks in the 1930s and 1960s. Chamaesyce &  Descurainia<br>'
text += 'first appeared in 2000s with 89%, %96%'

fig.add_annotation(
    text = text,
    x = 0.05,
    y = 0.9,
    xanchor='left',
    yanchor='top',
    font=dict(size=12, color='blue'),
    align='left',
    xref='paper',
    yref='paper'
)

fig.show()
2 Likes
3

I did not succeed with mapping this data across the state of New Mexico but will keep trying

1 Like