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feat: add template
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@FlorentLvr
FlorentLvr authored Jun 26, 2024
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330 changes: 330 additions & 0 deletions LinkedIn/LinkedIn_Rank_Direct_Conversations_by_Messages_Count.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "corresponding-inspection",
"metadata": {
"execution": {
"iopub.execute_input": "2021-01-25T08:00:35.294800Z",
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"source": [
"<img width=\"8%\" alt=\"LinkedIn.png\" src=\"https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/.github/assets/logos/LinkedIn.png\" style=\"border-radius: 15%\">"
]
},
{
"cell_type": "markdown",
"id": "smooth-empire",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"# LinkedIn - Rank Direct Conversations by Messages Count"
]
},
{
"cell_type": "markdown",
"id": "b9ae9ea3-f575-4ffc-b3c9-285569effca5",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"**Tags:** #linkedin #messages #analytics #dataframe #growth #sales"
]
},
{
"cell_type": "markdown",
"id": "1a958a79-fd53-4f43-8bdf-8ba026979057",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"**Author:** [Florent Ravenel](https://www.linkedin.com/in/florent-ravenel/)"
]
},
{
"cell_type": "markdown",
"id": "a7549883-358e-415b-860c-e28e04e490ab",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"**Last update:** 2024-06-26 (Created: 2024-06-26)"
]
},
{
"cell_type": "markdown",
"id": "naas-description",
"metadata": {
"papermill": {},
"tags": [
"description"
]
},
"source": [
"**Description:** This notebook ranks direct conversations by message count. Don't forget to download your LinkedIn extract and use the messages.csv file as input."
]
},
{
"cell_type": "markdown",
"id": "70906835-ef47-4bf6-9e68-9841d72be87e",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"**References:**\n",
"- [LinkedIn Download data](https://www.linkedin.com/mypreferences/d/download-my-data)"
]
},
{
"cell_type": "markdown",
"id": "moderate-honey",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## Input"
]
},
{
"cell_type": "markdown",
"id": "import_cell",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"### Import libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "changed-romance",
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"import requests\n",
"import pandas as pd\n",
"from datetime import datetime"
]
},
{
"cell_type": "markdown",
"id": "continental-xerox",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"### Setup variables"
]
},
{
"cell_type": "markdown",
"id": "72e801c3-a434-446f-9575-3d3070edaf1f",
"metadata": {},
"source": [
"- `linkedin_url`: URL of LinkedIn profile\n",
"- `file_path`: Path to the messages.csv file\n",
"- `limit`: Limit of days to get the conversations"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "diverse-maximum",
"metadata": {
"papermill": {},
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"linkedin_url = \"https://www.linkedin.com/in/jeremyravenel\"\n",
"file_path = \"messages.csv\"\n",
"limit = 30"
]
},
{
"cell_type": "markdown",
"id": "inappropriate-theory",
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-15T10:12:55.887028Z",
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},
"papermill": {},
"tags": []
},
"source": [
"## Model"
]
},
{
"cell_type": "markdown",
"id": "a49e3013-462d-4b1f-8bd6-38fe14b1dae5",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"### Get messages"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ad6f4492-0c82-49a6-9c42-f9415a9ccf7f",
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"# Read csv\n",
"df_conversations = pd.read_csv(file_path)\n",
"\n",
"# Include only \"INBOX\"\n",
"df_conversations = df_conversations[df_conversations[\"FOLDER\"] == \"INBOX\"].sort_values(by=\"DATE\", ascending=False).reset_index(drop=True)\n",
"print(\"Messages:\", len(df_conversations))\n",
"print(\"Conversations:\", len(df_conversations[\"CONVERSATION ID\"].unique()))\n",
"df_conversations.head(1)"
]
},
{
"cell_type": "markdown",
"id": "approved-greek",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## Output"
]
},
{
"cell_type": "markdown",
"id": "0ed9fa57-ef5a-421d-be81-cf5848c80283",
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"### Rank Direct Conversations by Messages Count"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7553d14d-5471-4b99-8df7-1aa63730daf9",
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"def get_messages_counts(df_init, linkedin_url, limit):\n",
" # Init\n",
" public_id = linkedin_url.split(\"/in/\")[1].split(\"/\")[0]\n",
" df = df_init.copy()\n",
" \n",
" # Groupby conversation\n",
" df_output = df.groupby([\"CONVERSATION ID\"], as_index=False\n",
" ).agg({\"SENDER PROFILE URL\": \"nunique\", \"CONTENT\": \"count\"}\n",
" ).rename(columns={\"CONTENT\": 'COUNT'}\n",
" ).sort_values(by=\"COUNT\", ascending=False\n",
" ).reset_index(drop=True)\n",
" \n",
" # Keep only conversations (not group or one message without answer)\n",
" df_output = df_output[(df_output[\"SENDER PROFILE URL\"] == 2) & (df_output[\"COUNT\"] >= 2)].drop(\"SENDER PROFILE URL\", axis=1).reset_index(drop=True)\n",
" \n",
" # Get details\n",
" df_total = df[df[\"CONVERSATION ID\"].isin(df_output[\"CONVERSATION ID\"].unique())].groupby([\"CONVERSATION ID\", \"SENDER PROFILE URL\"], as_index=False).agg({\"CONTENT\": \"count\"})\n",
" df_me = df_total[df_total[\"SENDER PROFILE URL\"].str.contains(public_id)].reset_index(drop=True).rename(columns={\"CONTENT\": 'COUNT_ME'})\n",
" df_them = df_total[~df_total[\"SENDER PROFILE URL\"].str.contains(public_id)].reset_index(drop=True).rename(columns={\"CONTENT\": 'COUNT_THEM'})\n",
" df_output = pd.merge(df_output, df_me.drop(\"SENDER PROFILE URL\", axis=1), how=\"left\")\n",
" df_output = pd.merge(df_output, df_them, how=\"left\")\n",
" \n",
" # Get last messages\n",
" df_last = df[[\"CONVERSATION ID\", \"DATE\"]].drop_duplicates(\"CONVERSATION ID\")\n",
" df_last[\"DATE\"] = df_last[\"DATE\"].str[:-4]\n",
" df_last[\"DAYS_SINCE_LAST_MESSAGE\"] = (datetime.now() - pd.to_datetime(df_last[\"DATE\"])).dt.days\n",
" df_output = pd.merge(df_output, df_last, how=\"left\").rename(columns={\"DATE\": \"LAST_MESSAGE_DATE\"})\n",
" \n",
" # Filter on limit\n",
" df_output = df_output[df_output[\"DAYS_SINCE_LAST_MESSAGE\"] <= limit]\n",
" return df_output.reset_index(drop=True)\n",
"\n",
"df_messages_stat = get_messages_counts(df_conversations, linkedin_url, limit)\n",
"print(f\"Conversations over the last {limit} days:\", len(df_messages_stat))\n",
"df_messages_stat"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cbb54439-55b9-49dd-bb79-62949f24d7ba",
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}
],
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
},
"naas": {
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"notebook_path": "LinkedIn/LinkedIn_Send_conversation_to_HubSpot_communication.ipynb"
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"nbformat": 4,
"nbformat_minor": 5
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