PyCafe - Vizro - LinkedIn Connection Insights

Connections 2.csv
app.py
requirements.txt
app.py
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# LinkedIn example

from typing import List, Optional

import pandas as pd
import plotly.graph_objects as go
import vizro.models as vm
import vizro.plotly.express as px
from plotly.subplots import make_subplots
from vizro import Vizro
from vizro._themes._color_values import COLORS
from vizro.actions import filter_interaction
from vizro.models.types import capture

df = pd.read_csv("Connections 2.csv")
df.dropna(subset=["Position", "Company"], inplace=True)
df["Connected On"] = pd.to_datetime(df["Connected On"], format="%d-%b-%y")
df.set_index("Connected On", drop=False, inplace=True)
df["Year"] = df["Connected On"].dt.year

@capture("graph")
def role_chart(data_frame, top_n=20):
    # Plot the most common roles for my connections
    data_frame = data_frame['Position'].value_counts().nlargest(top_n).reset_index(name="Frequency")
    data_frame = data_frame.sort_values(by='Frequency', ascending=True)
    return px.bar(data_frame, x='Frequency', y='Position', title=f'Top {top_n} most frequent Positions')


@capture("graph")
def company_chart(data_frame, top_n=20):
    # Plot the most common companies among my connections
    data_frame = data_frame['Company'].value_counts().nlargest(top_n).reset_index(name="Count")
    fig = px.pie(data_frame, names='Company', values='Count', title=f'Top {top_n} most connected Companies', hole=0.3)
    fig.update_layout(legend=dict(x=-0.5, y=1))  # Move the legend to the top-left corner
    return fig


@capture("graph")
def growth_cumulative_chart(data_frame):
    data_frame = data_frame.groupby('Year').size().reset_index(name="Count per Year")
    data_frame['Cumulative Yearly Connections'] = data_frame['Count per Year'].cumsum()

    fig_line = px.line(data_frame, x="Year", y='Cumulative Yearly Connections')
    fig_bar = px.bar(data_frame, x='Year', y='Count per Year', title='Yearly growth of my connections')

    fig_bar.add_traces(fig_line.data)

    return fig_bar


page = vm.Page(
    title="LinkedIn data",
    layout=vm.Layout(grid=[
        [0, 1],
        [0, 2]
    ]),
    components=[
        vm.Graph(id="role_chart_id", figure=role_chart(data_frame=df)),
        vm.Graph(id="company_chart_id",figure=company_chart(data_frame=df)),
        vm.Graph(figure=growth_cumulative_chart(data_frame=df)),
    ],
    controls=[
        vm.Filter(column="Company"),
        vm.Filter(column="Position"),
        vm.Filter(column="Year", selector=vm.RangeSlider(step=1, marks=None, value=[2020, 2024])),
        vm.Parameter(
            targets=["role_chart_id.top_n", "company_chart_id.top_n"],
            selector=vm.Slider(min=1, max=50, step=1, marks={1: "1", 10: "10", 20: "20", 30: "30", 40: "40", 50: "50"}, value=20) 
        )
    ],
)

dashboard = vm.Dashboard(pages=[page])

Vizro().build(dashboard).run()
Py.Cafe

linkedin-connection-insights

LinkedIn Connection Insights
