FEC_Visualiser_Charges_Horizontal_Barchart.ipynb

Kernel: Python 3

FEC - Visualiser Charges Horizontal Barchart

Give Feedback | Bug report

Tags: #fec #plotly #horizontalbarchart #visualisation #charges #python

Author: Florent Ravenel

Last update: 2023-05-24 (Created: 2023-05-23)

Description: Ce notebook vous permettra de visualiser les charges de votre entreprise à l'aide d'un barchart horizontal.

References:

• Plotly Documentation

• Plotly Horizontal Barchart

Input

Import libraries

import plotly.graph_objects as go
import pandas as pd
import naas

Setup Variables

• data_charges: This variable is a dictionary that contains input data related to charges. It has four keys: "ENTITY", "SCENARIO", "LABEL", and "VALUE". The values associated with each key are lists that provide information about the entities, scenarios, labels, and values of the charges respectively. In this example, all the keys have fixed values repeated for each item in the list.

• html_output: This variable stores the file name or path for the HTML output.

• png_output: This variable stores the file name or path for the PNG output.

# Inputs
data_charges = {
    "ENTITY": ["Société X"] * 5,
    "SCENARIO": ["2022-12"] * 5,
    "LABEL": ["Charges 1", "Charges 2", "Charges 3", "Charges 4", "Charges 5"],
    "VALUE": [100, 200, 300, 400, 500],
}

# Outputs
html_output = "Charges_HorizontalBarchart.html"
png_output = "Charges_HorizontalBarchart.png"

Model

Récupération de la données "Charges"

df_charges = pd.DataFrame(data_charges)
df_charges

Visualise Charges

Long description of the function to visualise charges using a horizontal barchart from plotly.

fig = go.Figure(
    go.Bar(
        x=df_charges["VALUE"],
        y=df_charges["LABEL"],
        orientation="h",
        text=df_charges["VALUE"],
        textposition="auto",
        marker=dict(color="#cd3244")
    )
)
total = "{:,.0f} €".format(df_charges['VALUE'].sum()).replace(",", " ")
fig.update_layout(
    title=f"Toal Charges : {total}",
    title_font=dict(family="Arial", size=18, color="black"),
    plot_bgcolor="#ffffff",
    xaxis_title=None,
    xaxis_showticklabels=False,
    yaxis_title=None,
    margin_pad=10,
    margin_l=150,
    bargap=0.1,  # gap between bars of adjacent location coordinates.
    bargroupgap=0.2,  # gap between bars of the same location coordinate.
)

Output

Save and share your graph as PNG

fig.write_image(png_output)

# Share output with naas
naas.asset.add(png_output)

# -> Uncomment the line below to remove your asset
# naas.asset.delete(html_output)

Save and share your graph as HTML

fig.write_html(html_output)

# Share output with naas
naas.asset.add(html_output, params={"inline": True})

# -> Uncomment the line below to remove your asset
# naas.asset.delete(html_output)