Leverage Great Tables (great_tables) python package to showcase month on month change percentage of Euro economic sentiment indicators.

esi.py

from great_tables import GT, md, html, system_fonts
import pandas as pd

power_cie_prepared_tbl = pd.read_csv("./data/2023_cie_power_cons.csv")

# Create a Great Tables object
ciep_gt_tbl = GT(data=power_cie_prepared_tbl)

# Apply wider color ranges & formatting
gt_tbl = ciep_gt_tbl \
    .fmt_percent(columns=['Hydro', 'Nuclear', 'Wind', 'Solar', 'Geothermal', 'Biomass', 'Gas',
                          'Coal', 'Oil', 'Unknown', 'Hydro Discharge', 'Battery Discharge'],
                 decimals=1) \
    .fmt_number(columns=['CO2 Intensity'],
                 decimals=0) \
    .data_color(
        columns=['CO2 Intensity'],
        palette=[
            "#00A600", "#E6E600", "#E8C32E", "#D69C4E", "#Dc863B", "sienna", "sienna4", "tomato4", "brown"],
        domain=[0, 900]
        ) \
    .data_color(
        columns=['Hydro', 'Nuclear', 'Wind', 'Solar','Geothermal'],
        palette=["#00A600", "chartreuse3", "chartreuse4", "snow"][::-1],
        domain=[0, 1]
        ) \
    .data_color(
        columns=['Hydro', 'Geothermal'],
        palette=["#00A600", "chartreuse3", "chartreuse4", "snow"][::-1],
        domain=[0, 1]
        ) \
    .data_color(
        columns=['Biomass'],
        palette=["snow", "#EEC900", "#E8C32E", "#D69C4E"],
        domain=[0, 0.3]
        ) \
    .data_color(
        columns=['Gas', 'Coal', 'Oil'],
        palette=["tomato4", "sienna4", "#D69C4E", "#Dc863B", "snow"][::-1],
        domain=[0, 1]
        ) \
    .data_color(
        columns=['Zone','Unknown', 'Hydro Discharge', 'Battery Discharge'],
        palette=["snow", "snow", "snow", 'snow']
        ) \
    .cols_width(
        {'CO2 Intensity': '58px','Hydro': '58px', 'Nuclear': '58px', 'Wind': '58px', 'Solar': '58px',
         'Geothermal': '58px', 'Biomass': '58px', 'Gas': '58px', 'Coal': '58px',
         'Oil': '58px', 'Unknown': '58px', 'Hydro Discharge': '58px',
         'Battery Discharge': '58px'}
        ) \
    .tab_header(
        title=md("2023 Mean **Carbon Intensity** (gCO2eq/kWh) and **Power Consumption** Breakdown (%)")
        ) \
    .tab_source_note(
        md(
            '<br><div style="text-align: left;">'
            "**Source**: api.electricitymap.org" 
            " | **Methodology**: https://www.electricitymaps.com/methodology." 
            " Some emissions factors are based on IPCC 2014 defaults, while some are based on more accurate regional factors."
            " <br>All zones are publicly available on the *Carbon intensity and emission factors* tab via Google docs link."
            " Rooftop solar not included in California ISO figures for California"
            "</div>"
            "<br>"
            )
        ) \
    .tab_options(
        source_notes_font_size='x-small',
        source_notes_padding=3,
        table_font_names=system_fonts("humanist"),
        data_row_padding='1px',
        heading_background_color='antiquewhite',
        source_notes_background_color='antiquewhite',
        column_labels_background_color='antiquewhite',
        table_background_color='snow',
        data_row_padding_horizontal=3,
        column_labels_padding_horizontal=58
        ) \
    .cols_align(    
        align='center'
        ) \
    .cols_align(
        align='left', 
        columns=['Zone']
        ) \
    .opt_table_outline()

gt_tbl