Follow content frequency
Tags: #linkedin #html #plotly #csv #image #content #analytics #automation
Last update: 2023-05-29 (Created: 2022-06-30)
Description: This notebook allows users to track how often they post content on LinkedIn and follow the frequency of their posts. 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.
from naas_drivers import linkedin
from os import path
import naas
import pandas as pd
from datetime import datetime
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Inputs
csv_input = "LINKEDIN_POSTS_XXXXXX.csv"
# Outputs
name_output = "Linkedin_Follow_number_of_company_content_views_monthly"
csv_output = f"{name_output}.csv"
html_output = f"{name_output}.html"
image_output = f"{name_output}.png"
# Period
PERIOD = "%Y-%m"
# Custom chart
primary_color = "#D3D3D3"
secundary_color = "#5C5C5C"
company_logo = "https://media-exp2.licdn.com/dms/image/C560BAQEOzG0TtTclXw/company-logo_400_400/0/1606695523917?e=1662595200&v=beta&t=2k73kaJGBZ4i1OtFmy-0Vms6uXu7bLd2AJbhrx_D4AA"
naas.dependency.add()
# -> Uncomment the line below to remove your dependency
# naas.dependency.delete()
All your posts will be stored in CSV.
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)
df_posts
def get_trend(df_init, col_date, col_value, label):
# Init variable
df = df_init.copy()
# By period
df[col_value] = df[col_value].astype(float)
df[col_date] = pd.to_datetime(df[col_date].str[:-6]).dt.strftime(PERIOD)
df = df.groupby(col_date, as_index=False).agg({col_value: "sum"})
# Calc sum cum
to_rename = {col_date: "DATE", col_value: "VARV"}
df = df.rename(columns=to_rename)
df["VALUE"] = df.agg({"VARV": "cumsum"})
# Add last month
if df.loc[df.index[-1], "DATE"] != datetime.now().strftime(PERIOD):
tmp_df = df[-1:].reset_index(drop=True)
tmp_df.loc[0, "DATE"] = datetime.now().strftime(PERIOD)
tmp_df.loc[0, "VARV"] = 0
df = pd.concat([df, tmp_df]).reset_index(drop=True)
# Calc order
df["ORDER"] = pd.to_datetime(df["DATE"]).dt.strftime("%Y%m%d")
# Filter data
df = df[df.ORDER.astype(int) >= 20210228]
df = df.drop("ORDER", axis=1)
# Calc var
df["VALUE_COMP"] = df["VALUE"] - df["VARV"]
df["VARP"] = df["VARV"] / abs(df["VALUE_COMP"])
df["LABEL"] = label
df = df[["DATE", "LABEL", "VALUE", "VALUE_COMP", "VARV", "VARP"]].reset_index(
drop=True
)
# Prep data
df["VALUE_D"] = df["VALUE"].map("{:,.0f}".format).str.replace(",", " ")
df["VARV_D"] = df["VARV"].map("{:,.0f}".format).str.replace(",", " ")
df.loc[df["VARV"] >= 0, "VARV_D"] = "+" + df["VARV_D"]
df["VARP_D"] = df["VARP"].map("{:,.0%}".format).str.replace(",", " ")
df.loc[df["VARP"] >= 0, "VARP_D"] = "+" + df["VARP_D"]
# Create hovertext
df["TEXT"] = (
"<b><span style='font-size: 14px;'>"
+ df["LABEL"].astype(str)
+ " "
+ df["DATE"].astype(str)
+ " : "
+ df["VALUE_D"]
+ "</span></b><br>"
"<span style='font-size: 12px;'>"
+ df["VARV_D"]
+ " this month ("
+ df["VARP_D"]
+ ")</span>"
)
df["TITLE"] = (
"<b><span style='font-size: 20px;'>"
+ df["LABEL"].astype(str)
+ " "
+ df["DATE"].astype(str)
+ " : "
+ df["VALUE_D"]
+ "</span></b><br>"
"<span style='font-size: 18px;'>"
+ df["VARV_D"]
+ " this month ("
+ df["VARP_D"]
+ ")</span>"
)
for index, row in df.iterrows():
if index > 0:
n = df.loc[df.index[index], "VARV"]
n_1 = df.loc[df.index[index - 1], "VARV"]
df.loc[df.index[index], "VARV_COMP"] = n_1
df.loc[df.index[index], "VARV_VARV"] = n - n_1
if n_1 > 0:
df.loc[df.index[index], "VARP_VARV"] = (n - n_1) / abs(n_1)
return df.reset_index(drop=True)
df_actual = get_trend(
df_posts, col_date="PUBLISHED_DATE", col_value="VIEWS", label="LinkedIn Posts views"
)
df_actual # .tail(5)
def create_barlinechart(
df,
label="DATE",
value="VALUE",
varv="VARV",
varp="VARP",
text="TEXT",
title="TITLE",
xaxis_title="Weeks",
yaxis_title_r=None,
yaxis_title_l=None,
primary_color=None,
secundary_color=None,
company_logo=None,
):
# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])
# Add traces
fig.add_trace(
go.Bar(
x=df[label],
y=df[varv],
hoverinfo="text",
text=df["VARV_D"],
hovertext=df[text],
marker=dict(color=primary_color),
),
secondary_y=False,
)
fig.add_trace(
go.Scatter(
x=df[label],
y=df[value],
mode="lines",
hoverinfo="text",
text=df["VALUE_D"],
hovertext=df[text],
line=dict(color=secundary_color, width=3),
),
secondary_y=True,
)
# Add figure title
title_text = text
for col in df.columns:
if col == title:
title_text = title
fig.update_layout(
title=df.loc[df.index[-1], title_text],
title_x=0.1,
title_font=dict(family="Arial", size=20, color="black"),
legend=None,
plot_bgcolor="#ffffff",
paper_bgcolor="#ffffff",
# width=1200,
# height=800,
autosize=False,
margin=dict(
l=100,
b=100,
),
margin_pad=10,
xaxis_title=xaxis_title,
xaxis_title_font=dict(family="Arial", size=14, color="black"),
)
# Add logo
fig.add_layout_image(
dict(
source=company_logo,
xref="paper",
yref="paper",
x=0.01,
y=1,
sizex=0.12,
sizey=0.12,
xanchor="right",
yanchor="bottom",
)
)
# Set y-axes titles
fig.update_yaxes(
title_text=yaxis_title_r,
title_font=dict(family="Arial", size=14, color="black"),
secondary_y=False,
)
fig.update_yaxes(
title_text=yaxis_title_l,
title_font=dict(family="Arial", size=14, color="black"),
secondary_y=True,
)
fig.update_traces(showlegend=False)
fig.show()
return fig
fig = create_barlinechart(
df_actual,
primary_color=primary_color,
secundary_color=secundary_color,
company_logo=company_logo,
xaxis_title="Months",
yaxis_title_r="New views",
yaxis_title_l="Total views",
)
# Save your dataframe in CSV
df_actual.to_csv(csv_output, index=False)
# Share output with naas
csv_link = naas.asset.add(csv_output)
# -> Uncomment the line below to remove your asset
# naas.asset.delete(csv_output)
# Save your graph in HTML
fig.write_html(html_output)
# Share output with naas
html_link = naas.asset.add(html_output, params={"inline": True})
# -> Uncomment the line below to remove your asset
# naas.asset.delete(html_output)
# Save your graph in PNG
fig.write_image(image_output)
# Share output with naas
image_link = naas.asset.add(image_output)
# -> Uncomment the line below to remove your asset
# naas.asset.delete(image_output)
Last modified 1mo ago