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Follow content engagements weekly
Tags: #linkedin #html #plotly #csv #image #content #analytics #dependency
Last update: 2023-05-29 (Created: 2022-06-30)
Description: This notebook helps you track and analyze your weekly content engagements on LinkedIn. 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.
import naas
import pandas as pd
from datetime import datetime
import plotly.graph_objects as go
# 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 = "Engagements" # Chart title
COL_VALUE = ["LIKES", "COMMENTS"] # Column to sum
# Outputs
name_output = "LINKEDIN_FOLLOW_CONTENT_ENGAGEMENTS_WEEKLY"
csv_output = f"{name_output}.csv"
html_output = f"{name_output}.html"
image_output = f"{name_output}.png"
naas.dependency.add()
# -> Uncomment the line below to remove your dependency
# naas.dependency.delete()
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)
DATE_FORMAT = "%Y-%m-%d"
PERIOD = "%Y%U"
PERIOD_D = "%Y-W%U"
PERIOD_TEXT = "This week"
def get_trend(df_init, col_date, col_value, agg_value, period_rolling=12):
# Init variable
df = df_init.copy()
# Groupby period
if isinstance(col_value, list):
df["VALUE"] = 0
for c in col_value:
df[c] = df[c].astype(float)
df["VALUE"] = df["VALUE"] + df[c]
col_value = "VALUE"
elif agg_value == "sum":
df[col_value] = df[col_value].astype(float)
df[col_date] = pd.to_datetime(df[col_date].str[:-6]).dt.strftime(DATE_FORMAT)
df = df.groupby(col_date, as_index=False).agg({col_value: agg_value})
# Rename column
to_rename = {col_date: "DATE_ISO", col_value: "VALUE"}
df = df.rename(columns=to_rename)
# Reindex value
d = datetime.now().date()
d2 = df.loc[df.index[0], "DATE_ISO"]
idx = pd.date_range(d2, d, freq="D")
df.set_index("DATE_ISO", drop=True, inplace=True)
df.index = pd.DatetimeIndex(df.index)
df = df.reindex(idx, fill_value=0)
df["DATE_ISO"] = pd.DatetimeIndex(df.index)
# Groupby month
df["DATE"] = pd.to_datetime(df["DATE_ISO"], format=DATE_FORMAT).dt.strftime(PERIOD)
# Plotly: Date display
df["DATE_D"] = pd.to_datetime(df["DATE_ISO"], format=DATE_FORMAT).dt.strftime(
PERIOD_D
)
df = df.groupby(["DATE", "DATE_D"], as_index=False).agg({"VALUE": agg_value})
# Calc variation
for index, row in df.iterrows():
if index > 0:
n = df.loc[df.index[index], "VALUE"]
n_1 = df.loc[df.index[index - 1], "VALUE"]
df.loc[df.index[index], "VALUE_COMP"] = n_1
df.loc[df.index[index], "VARV"] = n - n_1
if n_1 > 0:
df.loc[df.index[index], "VARP"] = (n - n_1) / abs(n_1)
df = df.fillna(0)
# Plotly: Value display
df["VALUE_D"] = (
"<b><span style='font-family: Arial;'>"
+ df["VALUE"].map("{:,.0f}".format).str.replace(",", " ")
+ "</span></b>"
)
# Plotly: Variation display
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"]
# Plotly: hovertext
df["TEXT"] = (
"<b><span style='font-size: 14px;'>"
+ df["DATE_D"].astype(str)
+ ": "
+ df["VALUE_D"]
+ "</span></b><br>"
"<span style='font-size: 12px;'>"
+ f"{PERIOD_TEXT}: "
+ df["VARV_D"]
+ " ("
+ df["VARP_D"]
+ ")</span>"
)
# Return month rolling
return df[-period_rolling:].reset_index(drop=True)
df_trend = get_trend(
df_posts, col_date="PUBLISHED_DATE", col_value=COL_VALUE, agg_value="sum"
)
df_trend
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_barchart(df, label="DATE_D", value="VALUE", value_d="VALUE_D", text="TEXT"):
# Init
fig = go.Figure()
# Create fig
fig.add_trace(
go.Bar(
x=df[label],
y=df[value],
text=df[value_d],
textposition="outside",
hoverinfo="text",
hovertext=df[text],
marker=dict(color=COLOR),
)
)
# Add logo
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(showlegend=False)
# Plotly: Create title
total_value = "{:,.0f}".format(df[value].sum()).replace(",", " ")
varv = df.loc[df.index[-1], "VARV"]
varp = df.loc[df.index[-1], "VARP"]
varv_d = "{:,.0f}".format(varv).replace(",", " ")
varp_d = "{:,.0%}".format(varp).replace(",", " ")
if varv >= 0:
varv_d = f"+{varv_d}"
varp_d = f"+{varp_d}"
title = f"<b><span style='font-size: 20px;'>{TITLE}</span></b><br><span style='font-size: 18px;'>Total: {total_value} | {PERIOD_TEXT}: {varv_d} ({varp_d})</span>"
fig.update_layout(
title=title,
title_x=0.09,
title_font=dict(family="Arial", color="black"),
paper_bgcolor="#ffffff",
plot_bgcolor="#ffffff",
width=1200,
height=600,
margin_pad=10,
)
fig.show()
return fig
fig = create_barchart(df_trend)
# Save your dataframe in CSV
df_trend.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 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 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)