269 lines
7.8 KiB
Plaintext
269 lines
7.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# AUM Analysis\n",
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"\n",
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"This notebook sums the **Value - AUM €** by **Product - Asset Type** and by **Product - Fund** from the AUM sample data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting openpyxl\n",
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" Downloading openpyxl-3.1.5-py2.py3-none-any.whl.metadata (2.5 kB)\n",
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"Collecting et-xmlfile (from openpyxl)\n",
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" Downloading et_xmlfile-2.0.0-py3-none-any.whl.metadata (2.7 kB)\n",
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"Downloading openpyxl-3.1.5-py2.py3-none-any.whl (250 kB)\n",
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"Downloading et_xmlfile-2.0.0-py3-none-any.whl (18 kB)\n",
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"Installing collected packages: et-xmlfile, openpyxl\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [openpyxl]1/2\u001b[0m [openpyxl]\n",
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"\u001b[1A\u001b[2KSuccessfully installed et-xmlfile-2.0.0 openpyxl-3.1.5\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"!pip install openpyxl\n",
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"import os\n",
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"import s3fs\n",
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"import seaborn as sns\n",
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"import plotly.express as px"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"fs = s3fs.S3FileSystem(\n",
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" client_kwargs={'endpoint_url': 'https://'+'minio-simple.lab.groupe-genes.fr'},\n",
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" key = os.environ[\"AWS_ACCESS_KEY_ID\"], \n",
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" secret = os.environ[\"AWS_SECRET_ACCESS_KEY\"], \n",
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" token = os.environ[\"AWS_SESSION_TOKEN\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_8794/3768862044.py:5: DtypeWarning: Columns (0,1,2,3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
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" stocks = pd.read_csv(f, sep=\";\")\n"
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]
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}
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],
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"source": [
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"#with fs.open('projet-bdc-data//carmignac/Flows ENSAE V2 -20251105.csv', 'rb') as f:\n",
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" #flows = pd.read_csv(f, sep=\";\")\n",
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"\n",
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"with fs.open('projet-bdc-data//carmignac/AUM ENSAE V2 -20251105.csv', 'rb') as f:\n",
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" stocks = pd.read_csv(f, sep=\";\")\n",
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"\n",
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"#with fs.open('projet-bdc-data/carmignac/Monthly AUM and NAV since 2010.xlsx', 'rb') as f:#\n",
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" #nav_raw = pd.read_excel(f, header=None, engine=\"openpyxl\")\n",
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"\n",
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"#nav = nav_raw[0].str.split(\",\", expand=True)\n",
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"#nav.columns = nav.iloc[0]\n",
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"#nav = nav[1:].reset_index(drop=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Date conversion done.\n"
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]
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}
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],
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"source": [
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"stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"], errors=\"coerce\")\n",
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"#flows[\"Centralisation Date\"] = pd.to_datetime(flows[\"Centralisation Date\"], errors=\"coerce\")\n",
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"#nav[\"NavDate\"] = pd.to_datetime(nav[\"NavDate\"], format=\"%d/%m/%Y\", errors=\"coerce\")\n",
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"\n",
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"print(\"Date conversion done.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Sum of AUM by Product - Asset Type"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"AUM (€) by Product - Asset Type:\n",
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"Product - Asset Type\n",
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"Diversified 2.249487e+12\n",
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"Fixed Income 1.901982e+12\n",
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"Equity 9.811712e+11\n",
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"Alternative 1.208047e+11\n",
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"NaN 1.786480e+10\n",
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"Private Assets 2.205183e+09\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Product - Asset Type</th>\n",
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" <th>Total AUM (€)</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Diversified</td>\n",
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" <td>2.249487e+12</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Fixed Income</td>\n",
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" <td>1.901982e+12</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Equity</td>\n",
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" <td>9.811712e+11</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Alternative</td>\n",
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" <td>1.208047e+11</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>NaN</td>\n",
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" <td>1.786480e+10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>Private Assets</td>\n",
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" <td>2.205183e+09</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Product - Asset Type Total AUM (€)\n",
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"0 Diversified 2.249487e+12\n",
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"1 Fixed Income 1.901982e+12\n",
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"2 Equity 9.811712e+11\n",
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"3 Alternative 1.208047e+11\n",
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"4 NaN 1.786480e+10\n",
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"5 Private Assets 2.205183e+09"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Sum Value - AUM € per Product - Asset Type\n",
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"aum_by_asset_type = stocks.groupby('Product - Asset Type', dropna=False)['Value - AUM €'].sum().sort_values(ascending=False)\n",
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"\n",
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"print(\"AUM (€) by Product - Asset Type:\")\n",
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"print(aum_by_asset_type.to_string())\n",
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"\n",
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"# Display as DataFrame for nicer formatting\n",
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"aum_by_asset_type_df = aum_by_asset_type.reset_index()\n",
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"aum_by_asset_type_df.columns = ['Product - Asset Type', 'Total AUM (€)']\n",
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"aum_by_asset_type_df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Sum of AUM by Product - Fund"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Sum Value - AUM € per Product - Fund\n",
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"aum_by_fund = stocks.groupby('Product - Fund', dropna=False)['Value - AUM €'].sum().sort_values(ascending=False)\n",
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"\n",
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"print(\"AUM (€) by Product - Fund:\")\n",
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"print(aum_by_fund.to_string())\n",
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"\n",
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"# Display as DataFrame for nicer formatting\n",
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"aum_by_fund_df = aum_by_fund.reset_index()\n",
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"aum_by_fund_df.columns = ['Product - Fund', 'Total AUM (€)']\n",
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"aum_by_fund_df"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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