Links

Follow content published by weekdays by months

Tags: #linkedin #html #plotly #csv #image #content #analytics #dependency
Author: Florent Ravenel​
Last update: 2023-05-29 (Created: 2022-08-04)
Description: This notebook allows you to track and follow content published on LinkedIn by day of the week and month. To run this notebook, you must have already run LinkedIn_Get_profile_posts_stats.ipynb or LinkedIn_Get_company_posts_stats.ipynb to get your post stats in CSV.
Disclaimer: This code is in no way affiliated with, authorized, maintained, sponsored or endorsed by Linkedin or any of its affiliates or subsidiaries. It uses an independent and unofficial API. Use at your own risk.
This project violates Linkedin's User Agreement Section 8.2, and because of this, Linkedin may (and will) temporarily or permanently ban your account. We are not responsible for your account being banned.

Input

Import libraries

import naas
import pandas as pd
from datetime import datetime
import plotly.graph_objects as go
from pandas.tseries.offsets import MonthEnd
import calendar
from dateutil.relativedelta import relativedelta

Setup Variables

# Input
csv_input = f"LINKEDIN_PROFILE_POSTS.csv" # CSV path with your posts stats generated with 'LinkedIn_Get_profile_posts_stats.ipynb' or 'LinkedIn_Get_company_posts_stats.ipynb'
TITLE = "Content published frequency" # Chart title
​
# Outputs
name_output = "LINKEDIN_FOLLOW_CONTENT_VIEWS_REACH"
csv_output = f"{name_output}.csv"
html_output = f"{name_output}.html"
image_output = f"{name_output}.png"

Setup Naas dependency

naas.dependency.add()
​
# -> Uncomment the line below to remove your dependency
# naas.dependency.delete()

Model

Get your posts

Get posts feed from CSV stored in your local (Returns empty if CSV does not exist)
def read_csv(file_path):
try:
df = pd.read_csv(file_path)
except FileNotFoundError as e:
# Empty dataframe returned
return pd.DataFrame()
return df
​
​
df_posts = read_csv(csv_input)
print("✅ Posts fetched:", len(df_posts))
df_posts.head(1)

Get frequency

MONTH_ROLLING = 12
​
​
def get_frequency(df_init, col_date, x_axis, y_axis, col_value, type_value):
# Init variable
df = df_init.copy()
​
# Setup date column and create X and Y axis analysis
df[col_date] = pd.to_datetime(df[col_date].str[:18])
df["X_AXIS"] = df[col_date].dt.strftime(x_axis)
df["Y_AXIS"] = df[col_date].dt.strftime(y_axis)
df = df.rename(columns={col_value: "VALUE"})
​
# Groupby
to_group = [
"X_AXIS",
"Y_AXIS",
]
df = df.groupby(to_group, as_index=False).agg({"VALUE": type_value})
​
# Create empty value
d = datetime.now() + MonthEnd(1)
d2 = df["X_AXIS"].min()
idx = pd.date_range(d2, d, freq="m", normalize=True)
for x in idx:
x = x.strftime("%Y-%m")
data = [
{"X_AXIS": x, "Y_AXIS": "1", "VALUE": 0},
{"X_AXIS": x, "Y_AXIS": "2", "VALUE": 0},
{"X_AXIS": x, "Y_AXIS": "3", "VALUE": 0},
{"X_AXIS": x, "Y_AXIS": "4", "VALUE": 0},
{"X_AXIS": x, "Y_AXIS": "5", "VALUE": 0},
{"X_AXIS": x, "Y_AXIS": "6", "VALUE": 0},
{"X_AXIS": x, "Y_AXIS": "7", "VALUE": 0},
]
tmp_df = pd.DataFrame(data)
df = pd.concat([tmp_df, df])
​
# Group by with empty values
df = df.groupby(to_group, as_index=False).agg({"VALUE": "sum"})
​
# Sort values
month_min = datetime.now() + relativedelta(months=-MONTH_ROLLING)
df = df[
df["X_AXIS"].str.replace("-", "").astype(int) >= int(month_min.strftime("%Y%m"))
]
df = df.sort_values(by=["X_AXIS", "Y_AXIS"], ascending=[True, False])
return df.reset_index(drop=True)
​
​
df_plotly = get_frequency(
df_posts,
col_date="PUBLISHED_DATE",
x_axis="%Y-%m",
y_axis="%u",
col_value="ACTIVITY_ID",
type_value="count",
)
df_plotly # .tail(10)

Plot Heatmap

LOGO = "https://upload.wikimedia.org/wikipedia/commons/thumb/c/ca/LinkedIn_logo_initials.png/800px-LinkedIn_logo_initials.png" # Chart logo
COLOR = "#1293d2" # Chart primary color
​
​
def create_heatmap(
df,
x_value="X_AXIS",
y_value="Y_AXIS",
z_value="VALUE",
x_format="%H",
x_format_d="%H",
text="views",
):
​
# Add display values
df["X_AXIS_D"] = pd.to_datetime(df[x_value], format=x_format).dt.strftime(
x_format_d
)
df["Y_AXIS_D"] = df.apply(
lambda row: calendar.day_name[int(row[y_value]) - 1], axis=1
)
df["TEXT"] = (
df[z_value].astype(str)
+ f" {text} on "
+ df["Y_AXIS_D"]
+ "s, "
+ df["X_AXIS_D"]
)
​
# Create graph data
x = sorted(df[x_value].unique().tolist())
y = sorted(df[y_value].unique().tolist(), reverse=True)
​
def get_values(df, y, value):
values = []
for i in y:
tmp = df[df[y_value] == i].reset_index(drop=True)
data = tmp[value].tolist()
values.append(data)
return values
​
z = get_values(df, y, z_value)
hovertext = get_values(df, y, "TEXT")
​
# Colors
colors = [
[0.00, "#e7f4fa"],
[0.01, "#b7def1"],
[0.25, "#88c9e8"],
[0.50, "#59b3df"],
[1.00, "#1293d2"],
]
​
# Create fig
fig = go.Figure(
data=go.Heatmap(
x=df["X_AXIS_D"].unique().tolist(),
y=df["Y_AXIS_D"].unique().tolist(),
z=z,
text=hovertext,
hoverinfo="text",
type="heatmap",
colorscale=colors,
hoverongaps=False,
)
)
fig.add_layout_image(
dict(
source=LOGO,
xref="paper",
yref="paper",
x=-0.01,
y=1.045,
sizex=0.12,
sizey=0.12,
xanchor="right",
yanchor="bottom",
)
)
fig.update_traces(xgap=10, ygap=10, selector=dict(type="heatmap"), showscale=False)
total_value = "{:,.0f}".format(df[z_value].sum()).replace(",", " ")
fig.update_layout(
title=f"<b><span style='font-size: 20px;'>{TITLE}</span></b><br><span style='font-size: 18px;'>Total {text}: {total_value}</span>",
title_x=0.08,
title_font=dict(family="Arial", size=20, color="black"),
plot_bgcolor="#ffffff",
width=1200,
height=600,
yaxis_scaleanchor="x",
)
fig.show()
return fig
​
​
fig = create_heatmap(
df_plotly, x_format="%Y-%m", x_format_d="%b %Y", text="contents published"
)

Output

Save and share your csv file

# Save your dataframe in CSV
df_plotly.to_csv(csv_output, index=False)
​
# Share output with naas
naas.asset.add(csv_output)
​
# -> Uncomment the line below to remove your asset
# naas.asset.delete(csv_output)

Save and share your graph in HTML

# Save your graph in 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)

Save and share your graph in image

# Save your graph in PNG
fig.write_image(image_output)
​
# Share output with naas
naas.asset.add(image_output)
​
# -> Uncomment the line below to remove your asset
# naas.asset.delete(image_output)