PyCafe - Vizro - LinkedIn Connections Analysis

app.py
linkedin_input_data.csv
requirements.txt
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
# LinkedIn example
import random
import pandas as pd

import vizro.models as vm
import vizro.plotly.express as px

from vizro import Vizro
from vizro.models.types import capture


# Load the data
df = pd.read_csv("linkedin_input_data.csv")

# Clean the data
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

icons = ["👨", "👩", "👧", "👦", "🧒"]


@capture("graph")
def role_chart(data_frame, top_n=5):
    # Sample data preparation
    data_frame = data_frame['Position'].value_counts().nlargest(top_n).reset_index(name="Frequency")
    data_frame = data_frame.sort_values(by='Frequency', ascending=True)

    # Create a new DataFrame to hold the dots
    dot_list = []
    for i, row in data_frame.iterrows():
        dot_list.append(pd.DataFrame({
            'Position': [row['Position']] * row['Frequency'],
            'Frequency': list(range(1, row['Frequency'] + 1)),
            'Icon': [random.choice(icons) for _ in range(row['Frequency'])]
        }))
    dots_df = pd.concat(dot_list, ignore_index=True)

    fig = px.scatter(dots_df, x='Frequency', y='Position', title=f'Top {top_n} most frequent positions', text="Icon")
    fig.update_layout(yaxis=dict(showgrid=False))

    return fig


@capture("graph")
def company_chart(data_frame, top_n=10):
    # 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=0.6)
    fig.update_layout(legend=dict(x=1, y=1, orientation='v'))

    return fig


@capture("graph")
def growth_cumulative_chart(data_frame):
    # Plot the growth of my connections over time
    data_frame = data_frame.groupby('Year').size().reset_index(name="Count per Year")
    data_frame['Cumulative Yearly Connections'] = data_frame['Count per Year'].cumsum()

    line_fig = px.line(data_frame, x="Year", y='Cumulative Yearly Connections', color=px.Constant("Cumulative Connections"))
    line_fig.update_traces(yaxis='y2', line_color='#F08D41')

    bar_fig = px.bar(data_frame, x='Year', y='Count per Year', color=px.Constant("Count per Year"), title='Yearly growth of my connections')
    bar_fig.add_traces(line_fig.data)

    bar_fig.update_layout(
        yaxis=dict(title='Per Year Connections [Bar]'),
        yaxis2=dict(title='Cumulative Connections [Line]', overlaying='y', side='right'),
        legend=dict(title="", yanchor="bottom", y=1, xanchor="right", x=1),
    )

    return bar_fig


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="Position",
            selector=vm.Dropdown(
                options=df['Position'].value_counts().loc[lambda x: x >= 5].index.tolist()
            )
        ),
        vm.Filter(column="Company"),
        vm.Parameter(
            targets=["role_chart_id.top_n"],
            selector=vm.Slider(min=1, max=30, step=1, marks={1: "1", 10: "10", 20: "20", 30: "30"}, value=30,
                               title="Top N roles")
        ),
        vm.Parameter(
            targets=["company_chart_id.top_n"],
            selector=vm.Slider(min=1, max=20, step=1, marks={1: "1", 5: "5", 10: "10", 15: "15", 20: "20"}, value=5,
                               title="Top N companies")
        ),
        vm.Filter(column="Year", selector=vm.RangeSlider(step=1, marks=None)),
    ],
)

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

Vizro().build(dashboard).run(debug=False)
Py.Cafe

linkedin-connections-analysis

LinkedIn Connections Analysis
