FEC_Visualiser_Comparer_Ventes_Line_Chart.ipynb

Kernel: Python 3

FEC - Visualiser Comparer Ventes Line Chart

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Tags: #fec #plotly #naas #snippet #operations #linechart

Author: Florent Ravenel

Last update: 2023-04-12 (Created: 2023-02-08)

Description: Ce notebook vous permettra de visualiser et comparer les ventes de votre entreprise pour les périodes N et N-1 à l'aide de deux courbes de tendance. Vous pourrez facilement voir les tendances et les différences entre les deux périodes pour prendre des décisions éclairées pour améliorer vos ventes.

Input

Import libraries

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import naas
import random

Setup Variables

# Inputs
# Create sample financial data
data_n = {
    "ENTITY": ["Société X"] * 12,
    "SCENARIO": ["2022-12"] * 12,
    "LABEL": [
        "Janvier",
        "Février",
        "Mars",
        "Avril",
        "Mai",
        "Juin",
        "Juillet",
        "Aout",
        "Septembre",
        "Octobre",
        "Novembre",
        "Décembre",
    ],
    "GROUP": ["N"] * 12,
    "VAR": [random.randint(5000, 12000) for i in range(0, 12)],
}
data_n_1 = {
    "ENTITY": ["Société X"] * 12,
    "SCENARIO": ["2022-12"] * 12,
    "LABEL": [
        "Janvier",
        "Février",
        "Mars",
        "Avril",
        "Mai",
        "Juin",
        "Juillet",
        "Aout",
        "Septembre",
        "Octobre",
        "Novembre",
        "Décembre",
    ],
    "GROUP": ["N-1"] * 12,
    "VAR": [random.randint(5000, 10000) for i in range(0, 12)],
}

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

Model

Get data 'année N'

df_n = pd.DataFrame(data_n)
df_n.insert(loc=4, column="VALUE", value=df_n["VAR"].cumsum())
df_n

Get "Cashout" data

df_n_1 = pd.DataFrame(data_n_1)
df_n_1.insert(loc=4, column="VALUE", value=df_n_1["VAR"].cumsum())
df_n_1

Create barlinechart using Plotly

def create_linechart(
    df_n,
    df_n_1,
    xaxis_title=None,
    yaxis_title=None,
):
    # Create figure
    fig = go.Figure()

    # Add traces
    fig.add_trace(
        go.Scatter(
            name="N",
            x=df_n["LABEL"],
            y=df_n["VALUE"],
            marker=dict(color="blue"),
        )
    )
    fig.add_trace(
        go.Scatter(
            name="N-1",
            x=df_n_1["LABEL"],
            y=df_n_1["VALUE"],
            marker=dict(color="orange"),
        )
    )
    # Calc var
    last_n = df_n.loc[df_n.index[-1], "VALUE"]
    last_n_1 = df_n_1.loc[df_n_1.index[-1], "VALUE"]
    var = last_n - last_n_1
    if var > 0:
        var = f"+{var}"

    # Add figure title
    fig.update_layout(
        title=f"Ventes : {last_n} € ({var} € vs N-1)",
        title_font=dict(family="Arial", size=18, color="black"),
        legend=None,
        plot_bgcolor="#ffffff",
        width=1200,
        height=800,
        paper_bgcolor="white",
        xaxis_title=xaxis_title,
        xaxis_title_font=dict(family="Arial", size=12, color="black"),
        xaxis={"type": "category"},
    )

    # Set y-axes titles
    fig.update_yaxes(
        title_text=yaxis_title,
        title_font=dict(family="Arial", size=12, color="black"),
    )
    fig.show()
    return fig


fig = create_linechart(
    df_n,
    df_n_1,
    xaxis_title="Mois",
    yaxis_title="Valeur en €",
)

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)