4409 lines
395 KiB
Plaintext
4409 lines
395 KiB
Plaintext
{
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"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "2e8cf88b-cecf-409f-9c2d-c3762b233f05",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Requirement already satisfied: openpyxl in /opt/python/lib/python3.13/site-packages (3.1.5)\n",
|
||
"Requirement already satisfied: pycountry in /opt/python/lib/python3.13/site-packages (24.6.1)\n",
|
||
"Requirement already satisfied: et-xmlfile in /opt/python/lib/python3.13/site-packages (from openpyxl) (2.0.0)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!pip install openpyxl pycountry"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "0c550103-c268-4c43-8a76-4b7633418091",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
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||
"text": [
|
||
"Requirement already satisfied: kaleido in /opt/python/lib/python3.13/site-packages (1.2.0)\n",
|
||
"Requirement already satisfied: choreographer>=1.1.1 in /opt/python/lib/python3.13/site-packages (from kaleido) (1.2.1)\n",
|
||
"Requirement already satisfied: logistro>=1.0.8 in /opt/python/lib/python3.13/site-packages (from kaleido) (2.0.1)\n",
|
||
"Requirement already satisfied: orjson>=3.10.15 in /opt/python/lib/python3.13/site-packages (from kaleido) (3.11.5)\n",
|
||
"Requirement already satisfied: packaging in /opt/python/lib/python3.13/site-packages (from kaleido) (25.0)\n",
|
||
"Requirement already satisfied: pytest-timeout>=2.4.0 in /opt/python/lib/python3.13/site-packages (from kaleido) (2.4.0)\n",
|
||
"Requirement already satisfied: simplejson>=3.19.3 in /opt/python/lib/python3.13/site-packages (from choreographer>=1.1.1->kaleido) (3.20.2)\n",
|
||
"Requirement already satisfied: pytest>=7.0.0 in /opt/python/lib/python3.13/site-packages (from pytest-timeout>=2.4.0->kaleido) (9.0.2)\n",
|
||
"Requirement already satisfied: iniconfig>=1.0.1 in /opt/python/lib/python3.13/site-packages (from pytest>=7.0.0->pytest-timeout>=2.4.0->kaleido) (2.3.0)\n",
|
||
"Requirement already satisfied: pluggy<2,>=1.5 in /opt/python/lib/python3.13/site-packages (from pytest>=7.0.0->pytest-timeout>=2.4.0->kaleido) (1.6.0)\n",
|
||
"Requirement already satisfied: pygments>=2.7.2 in /opt/python/lib/python3.13/site-packages (from pytest>=7.0.0->pytest-timeout>=2.4.0->kaleido) (2.19.2)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!pip install --upgrade kaleido"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "126c8a80-d9ad-4816-84f0-0c3d580f62c8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt"
|
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]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "ff2261fb-9516-4410-b42d-3acc8dc1a460",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import os\n",
|
||
"import s3fs\n",
|
||
"os.environ[\"AWS_ACCESS_KEY_ID\"] = 'NJ0M3U6LSD2MKTFHA9E9'\n",
|
||
"os.environ[\"AWS_SECRET_ACCESS_KEY\"] = 'qS4fOELSpa4DhuhpbByIqF2A6WPX7bEXGoNro8qA'\n",
|
||
"os.environ[\"AWS_SESSION_TOKEN\"] = 'eyJhbGciOiJIUzUxMiIsInR5cCI6IkpXVCJ9.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.B6ak9wmZSk4l0_9O1CV03rdBgkz9y-tV6OrKHDCLbNGBJuZXDPX4jhvLUCwxsq99cd5bKwioh3Lzq_FWQvp_tA'\n",
|
||
"os.environ[\"AWS_DEFAULT_REGION\"] = 'us-east-1'\n",
|
||
"fs = s3fs.S3FileSystem(\n",
|
||
" client_kwargs={'endpoint_url': 'https://'+'minio-simple.lab.groupe-genes.fr'},\n",
|
||
" key = os.environ[\"AWS_ACCESS_KEY_ID\"], \n",
|
||
" secret = os.environ[\"AWS_SECRET_ACCESS_KEY\"], \n",
|
||
" token = os.environ[\"AWS_SESSION_TOKEN\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3d36f3f0-bd40-4a83-96d1-b46d75f5a4c5",
|
||
"metadata": {},
|
||
"source": [
|
||
"# data exploration"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eaf5c5a0-eb1c-4242-b893-7600e6def109",
|
||
"metadata": {},
|
||
"source": [
|
||
"Fonctions utiles"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "37e008b1-32d4-44be-9d23-1b90a5a26f89",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 2. BASIC INSPECTION\n",
|
||
"\n",
|
||
"def quick_info(df, name):\n",
|
||
" print(\"\\n\" + \"=\"*80)\n",
|
||
" print(f\"DATASET : {name}\")\n",
|
||
" print(\"=\"*80)\n",
|
||
" print(\"\\nShape :\", df.shape)\n",
|
||
" print(\"\\nColumns :\", df.columns.tolist())\n",
|
||
" print(\"\\nDtypes :\\n\", df.dtypes)\n",
|
||
" print(\"\\nMissing values (%) :\\n\", df.isna().mean().sort_values(ascending=False)*100)\n",
|
||
" print(\"\\nSample rows:\\n\", df.head(5))\n",
|
||
" print(\"\\nUnique values per column:\\n\", df.nunique().sort_values(ascending=False))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "e67a99ea-ddf4-4627-8f48-ec183c671acb",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_3144/2877144729.py:5: DtypeWarning: Columns (0,1,2,3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
|
||
" flows = pd.read_csv(f, sep=\";\")\n",
|
||
"/tmp/ipykernel_3144/2877144729.py:12: DtypeWarning: Columns (0,1,2,3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
|
||
" stocks = pd.read_csv(f, sep=\";\")\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"================================================================================\n",
|
||
"DATASET : STOCKS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Shape : (4880297, 18)\n",
|
||
"\n",
|
||
"Columns : ['Agreement - Code', 'Company - Id', 'Company - Ultimate Parent Id', 'Registrar Account - ID', 'Registrar Account - Region', 'RegistrarAccount - Country', 'Product - Asset Type', 'Product - Strategy', 'Product - Legal Status', 'Product - Is Dedie ?', 'Product - Fund', 'Product - Shareclass Type', 'Product - Shareclass Currency', 'Product - Isin', 'Centralisation Date', 'Quantity - AUM', 'Value - AUM CCY', 'Value - AUM €']\n",
|
||
"\n",
|
||
"Dtypes :\n",
|
||
" Agreement - Code object\n",
|
||
"Company - Id object\n",
|
||
"Company - Ultimate Parent Id object\n",
|
||
"Registrar Account - ID object\n",
|
||
"Registrar Account - Region object\n",
|
||
"RegistrarAccount - Country object\n",
|
||
"Product - Asset Type object\n",
|
||
"Product - Strategy object\n",
|
||
"Product - Legal Status object\n",
|
||
"Product - Is Dedie ? object\n",
|
||
"Product - Fund object\n",
|
||
"Product - Shareclass Type object\n",
|
||
"Product - Shareclass Currency object\n",
|
||
"Product - Isin object\n",
|
||
"Centralisation Date object\n",
|
||
"Quantity - AUM float64\n",
|
||
"Value - AUM CCY float64\n",
|
||
"Value - AUM € float64\n",
|
||
"dtype: object\n",
|
||
"\n",
|
||
"Missing values (%) :\n",
|
||
" Product - Asset Type 6.471553\n",
|
||
"Company - Id 2.330801\n",
|
||
"Company - Ultimate Parent Id 2.330801\n",
|
||
"Product - Strategy 0.001537\n",
|
||
"Product - Shareclass Type 0.000717\n",
|
||
"Agreement - Code 0.000000\n",
|
||
"RegistrarAccount - Country 0.000000\n",
|
||
"Registrar Account - Region 0.000000\n",
|
||
"Product - Legal Status 0.000000\n",
|
||
"Registrar Account - ID 0.000000\n",
|
||
"Product - Is Dedie ? 0.000000\n",
|
||
"Product - Fund 0.000000\n",
|
||
"Product - Shareclass Currency 0.000000\n",
|
||
"Product - Isin 0.000000\n",
|
||
"Centralisation Date 0.000000\n",
|
||
"Quantity - AUM 0.000000\n",
|
||
"Value - AUM CCY 0.000000\n",
|
||
"Value - AUM € 0.000000\n",
|
||
"dtype: float64\n",
|
||
"\n",
|
||
"Sample rows:\n",
|
||
" Agreement - Code Company - Id Company - Ultimate Parent Id \\\n",
|
||
"0 3 166.0 166.0 \n",
|
||
"1 3 166.0 166.0 \n",
|
||
"2 3 166.0 166.0 \n",
|
||
"3 3 166.0 166.0 \n",
|
||
"4 3 166.0 166.0 \n",
|
||
"\n",
|
||
" Registrar Account - ID Registrar Account - Region \\\n",
|
||
"0 200000647 France \n",
|
||
"1 200000647 France \n",
|
||
"2 200000647 France \n",
|
||
"3 200000647 France \n",
|
||
"4 200000647 France \n",
|
||
"\n",
|
||
" RegistrarAccount - Country Product - Asset Type Product - Strategy \\\n",
|
||
"0 France Diversified Patrimoine \n",
|
||
"1 France Diversified Patrimoine \n",
|
||
"2 France Diversified Patrimoine \n",
|
||
"3 France Diversified Patrimoine \n",
|
||
"4 France Diversified Patrimoine \n",
|
||
"\n",
|
||
" Product - Legal Status Product - Is Dedie ? Product - Fund \\\n",
|
||
"0 FCP NO Carmignac Patrimoine \n",
|
||
"1 FCP NO Carmignac Patrimoine \n",
|
||
"2 FCP NO Carmignac Patrimoine \n",
|
||
"3 FCP NO Carmignac Patrimoine \n",
|
||
"4 FCP NO Carmignac Patrimoine \n",
|
||
"\n",
|
||
" Product - Shareclass Type Product - Shareclass Currency Product - Isin \\\n",
|
||
"0 A EUR FR0010135103 \n",
|
||
"1 A EUR FR0010135103 \n",
|
||
"2 A EUR FR0010135103 \n",
|
||
"3 A EUR FR0010135103 \n",
|
||
"4 A EUR FR0010135103 \n",
|
||
"\n",
|
||
" Centralisation Date Quantity - AUM Value - AUM CCY Value - AUM € \n",
|
||
"0 2015-03-31 35.368 24648.6666 24648.6666 \n",
|
||
"1 2015-11-30 35.368 22413.0553 22413.0553 \n",
|
||
"2 2015-12-31 35.368 22051.2406 22051.2406 \n",
|
||
"3 2016-03-31 35.368 21626.1173 21626.1173 \n",
|
||
"4 2016-11-30 35.368 22489.4502 22489.4502 \n",
|
||
"\n",
|
||
"Unique values per column:\n",
|
||
" Value - AUM € 1697923\n",
|
||
"Value - AUM CCY 1689620\n",
|
||
"Quantity - AUM 554404\n",
|
||
"Registrar Account - ID 15532\n",
|
||
"Agreement - Code 2521\n",
|
||
"Company - Id 1970\n",
|
||
"Company - Ultimate Parent Id 1392\n",
|
||
"Product - Isin 491\n",
|
||
"Centralisation Date 130\n",
|
||
"Product - Fund 74\n",
|
||
"Product - Strategy 52\n",
|
||
"RegistrarAccount - Country 39\n",
|
||
"Registrar Account - Region 15\n",
|
||
"Product - Shareclass Type 11\n",
|
||
"Product - Legal Status 6\n",
|
||
"Product - Shareclass Currency 6\n",
|
||
"Product - Asset Type 5\n",
|
||
"Product - Is Dedie ? 2\n",
|
||
"dtype: int64\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"DATASET : FLOWS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Shape : (2574461, 24)\n",
|
||
"\n",
|
||
"Columns : ['Agreement - Code', 'Company - Id', 'Company - Ultimate Parent Id', 'Registrar Account - ID', 'Registrar Account - Region', 'RegistrarAccount - Country', 'Product - Asset Type', 'Product - Strategy', 'Product - Legal Status', 'Product - Is Dedie ?', 'Product - Fund', 'Product - Shareclass Type', 'Product - Shareclass Currency', 'Product - Isin', 'Centralisation Date', 'Quantity - Subscription', 'Quantity - Redemption', 'Quantity - NetFlows', 'Value Ccy - Subscription', 'Value Ccy - Redemption', 'Value Ccy - NetFlows', 'Value € - Subscription', 'Value € - Redemption', 'Value € - NetFlows']\n",
|
||
"\n",
|
||
"Dtypes :\n",
|
||
" Agreement - Code object\n",
|
||
"Company - Id object\n",
|
||
"Company - Ultimate Parent Id object\n",
|
||
"Registrar Account - ID object\n",
|
||
"Registrar Account - Region object\n",
|
||
"RegistrarAccount - Country object\n",
|
||
"Product - Asset Type object\n",
|
||
"Product - Strategy object\n",
|
||
"Product - Legal Status object\n",
|
||
"Product - Is Dedie ? object\n",
|
||
"Product - Fund object\n",
|
||
"Product - Shareclass Type object\n",
|
||
"Product - Shareclass Currency object\n",
|
||
"Product - Isin object\n",
|
||
"Centralisation Date object\n",
|
||
"Quantity - Subscription float64\n",
|
||
"Quantity - Redemption float64\n",
|
||
"Quantity - NetFlows float64\n",
|
||
"Value Ccy - Subscription float64\n",
|
||
"Value Ccy - Redemption float64\n",
|
||
"Value Ccy - NetFlows float64\n",
|
||
"Value € - Subscription float64\n",
|
||
"Value € - Redemption float64\n",
|
||
"Value € - NetFlows float64\n",
|
||
"dtype: object\n",
|
||
"\n",
|
||
"Missing values (%) :\n",
|
||
" Product - Asset Type 0.079589\n",
|
||
"Company - Id 0.059818\n",
|
||
"Company - Ultimate Parent Id 0.059818\n",
|
||
"Product - Strategy 0.000233\n",
|
||
"Product - Shareclass Type 0.000078\n",
|
||
"Registrar Account - ID 0.000000\n",
|
||
"RegistrarAccount - Country 0.000000\n",
|
||
"Agreement - Code 0.000000\n",
|
||
"Registrar Account - Region 0.000000\n",
|
||
"Product - Legal Status 0.000000\n",
|
||
"Product - Is Dedie ? 0.000000\n",
|
||
"Product - Fund 0.000000\n",
|
||
"Product - Shareclass Currency 0.000000\n",
|
||
"Product - Isin 0.000000\n",
|
||
"Centralisation Date 0.000000\n",
|
||
"Quantity - Subscription 0.000000\n",
|
||
"Quantity - Redemption 0.000000\n",
|
||
"Quantity - NetFlows 0.000000\n",
|
||
"Value Ccy - Subscription 0.000000\n",
|
||
"Value Ccy - Redemption 0.000000\n",
|
||
"Value Ccy - NetFlows 0.000000\n",
|
||
"Value € - Subscription 0.000000\n",
|
||
"Value € - Redemption 0.000000\n",
|
||
"Value € - NetFlows 0.000000\n",
|
||
"dtype: float64\n",
|
||
"\n",
|
||
"Sample rows:\n",
|
||
" Agreement - Code Company - Id Company - Ultimate Parent Id \\\n",
|
||
"0 003 166 166 \n",
|
||
"1 003 166 166 \n",
|
||
"2 003 166 166 \n",
|
||
"3 003 166 166 \n",
|
||
"4 003 166 166 \n",
|
||
"\n",
|
||
" Registrar Account - ID Registrar Account - Region \\\n",
|
||
"0 200127202 France \n",
|
||
"1 406533 France \n",
|
||
"2 406533 France \n",
|
||
"3 406533 France \n",
|
||
"4 406533 France \n",
|
||
"\n",
|
||
" RegistrarAccount - Country Product - Asset Type Product - Strategy \\\n",
|
||
"0 France Equity Investissement \n",
|
||
"1 France Diversified Patrimoine \n",
|
||
"2 France Equity Investissement \n",
|
||
"3 France Equity Investissement \n",
|
||
"4 France Equity Investissement \n",
|
||
"\n",
|
||
" Product - Legal Status Product - Is Dedie ? ... Centralisation Date \\\n",
|
||
"0 SICAV NO ... 2020-11-05 \n",
|
||
"1 FCP NO ... 2015-03-09 \n",
|
||
"2 FCP NO ... 2016-10-26 \n",
|
||
"3 FCP NO ... 2018-10-18 \n",
|
||
"4 FCP NO ... 2019-04-08 \n",
|
||
"\n",
|
||
" Quantity - Subscription Quantity - Redemption Quantity - NetFlows \\\n",
|
||
"0 1636.00 0.000 1636.000 \n",
|
||
"1 144.69 0.000 144.690 \n",
|
||
"2 0.00 -8.321 -8.321 \n",
|
||
"3 0.00 -22.083 -22.083 \n",
|
||
"4 0.00 -465.992 -465.992 \n",
|
||
"\n",
|
||
" Value Ccy - Subscription Value Ccy - Redemption Value Ccy - NetFlows \\\n",
|
||
"0 280983.00 0.00 280983.00 \n",
|
||
"1 99985.13 0.00 99985.13 \n",
|
||
"2 0.00 -9384.76 -9384.76 \n",
|
||
"3 0.00 -25227.40 -25227.40 \n",
|
||
"4 0.00 -563775.76 -563775.76 \n",
|
||
"\n",
|
||
" Value € - Subscription Value € - Redemption Value € - NetFlows \n",
|
||
"0 280983.00 0.00 280983.00 \n",
|
||
"1 99985.13 0.00 99985.13 \n",
|
||
"2 0.00 -9384.76 -9384.76 \n",
|
||
"3 0.00 -25227.40 -25227.40 \n",
|
||
"4 0.00 -563775.76 -563775.76 \n",
|
||
"\n",
|
||
"[5 rows x 24 columns]\n",
|
||
"\n",
|
||
"Unique values per column:\n",
|
||
" Value € - NetFlows 2018916\n",
|
||
"Value Ccy - NetFlows 1972319\n",
|
||
"Value € - Redemption 1323531\n",
|
||
"Value Ccy - Redemption 1296468\n",
|
||
"Value € - Subscription 955890\n",
|
||
"Value Ccy - Subscription 926633\n",
|
||
"Quantity - NetFlows 667586\n",
|
||
"Quantity - Redemption 374378\n",
|
||
"Quantity - Subscription 359661\n",
|
||
"Registrar Account - ID 9805\n",
|
||
"Centralisation Date 2780\n",
|
||
"Company - Id 1929\n",
|
||
"Agreement - Code 1626\n",
|
||
"Company - Ultimate Parent Id 1283\n",
|
||
"Product - Isin 474\n",
|
||
"Product - Fund 70\n",
|
||
"Product - Strategy 49\n",
|
||
"RegistrarAccount - Country 34\n",
|
||
"Registrar Account - Region 15\n",
|
||
"Product - Shareclass Type 10\n",
|
||
"Product - Shareclass Currency 6\n",
|
||
"Product - Legal Status 6\n",
|
||
"Product - Asset Type 5\n",
|
||
"Product - Is Dedie ? 2\n",
|
||
"dtype: int64\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"DATASET : NAV/PRICES\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Shape : (30333, 13)\n",
|
||
"\n",
|
||
"Columns : ['NavDate', 'LegalForm', 'Cod', 'PortfolioName', 'PTFCurrency', 'PortfolioAum_Eur', 'ShareClassIsin', 'ShareClassName', 'ShareClassCurrency', 'ShareClassPrice', 'NumberOfShares', 'ShareClassAumLocalCur', 'ShareClassAum_EUR']\n",
|
||
"\n",
|
||
"Dtypes :\n",
|
||
" 0\n",
|
||
"NavDate object\n",
|
||
"LegalForm object\n",
|
||
"Cod object\n",
|
||
"PortfolioName object\n",
|
||
"PTFCurrency object\n",
|
||
"PortfolioAum_Eur object\n",
|
||
"ShareClassIsin object\n",
|
||
"ShareClassName object\n",
|
||
"ShareClassCurrency object\n",
|
||
"ShareClassPrice object\n",
|
||
"NumberOfShares object\n",
|
||
"ShareClassAumLocalCur object\n",
|
||
"ShareClassAum_EUR object\n",
|
||
"dtype: object\n",
|
||
"\n",
|
||
"Missing values (%) :\n",
|
||
" 0\n",
|
||
"NavDate 0.0\n",
|
||
"LegalForm 0.0\n",
|
||
"Cod 0.0\n",
|
||
"PortfolioName 0.0\n",
|
||
"PTFCurrency 0.0\n",
|
||
"PortfolioAum_Eur 0.0\n",
|
||
"ShareClassIsin 0.0\n",
|
||
"ShareClassName 0.0\n",
|
||
"ShareClassCurrency 0.0\n",
|
||
"ShareClassPrice 0.0\n",
|
||
"NumberOfShares 0.0\n",
|
||
"ShareClassAumLocalCur 0.0\n",
|
||
"ShareClassAum_EUR 0.0\n",
|
||
"dtype: float64\n",
|
||
"\n",
|
||
"Sample rows:\n",
|
||
" 0 NavDate LegalForm Cod PortfolioName \\\n",
|
||
"0 31/12/2009 SICAV CC Carmignac Portfolio Climate Transition \n",
|
||
"1 31/12/2009 SICAV CFB Carmignac Portfolio Flexible Bond \n",
|
||
"2 31/12/2009 FCP CCT Carmignac Court Terme \n",
|
||
"3 31/12/2009 FCP CE Carmignac Emergents \n",
|
||
"4 31/12/2009 SICAV CAD Carmignac Portfolio Asia Discovery \n",
|
||
"\n",
|
||
"0 PTFCurrency PortfolioAum_Eur ShareClassIsin ShareClassName \\\n",
|
||
"0 EUR 941059600 LU0164455502 A EUR ACC \n",
|
||
"1 EUR 57063272.31 LU0336084032 A EUR ACC \n",
|
||
"2 EUR 788828666.5 FR0010149161 A EUR ACC \n",
|
||
"3 EUR 1508087050 FR0010149302 A EUR ACC \n",
|
||
"4 EUR 149490224.2 LU0336083810 A EUR ACC \n",
|
||
"\n",
|
||
"0 ShareClassCurrency ShareClassPrice NumberOfShares ShareClassAumLocalCur \\\n",
|
||
"0 EUR 287.21 3276555.83 941059600 \n",
|
||
"1 EUR 1016.833 56118.62745 57063272.31 \n",
|
||
"2 EUR 3687.84 213899.9161 788828666.5 \n",
|
||
"3 EUR 559.82 2693878.478 1508087050 \n",
|
||
"4 EUR 884.9 168934.5962 149490224.2 \n",
|
||
"\n",
|
||
"0 ShareClassAum_EUR \n",
|
||
"0 941059600 \n",
|
||
"1 57063272.31 \n",
|
||
"2 788828666.5 \n",
|
||
"3 1508087050 \n",
|
||
"4 149490224.2 \n",
|
||
"\n",
|
||
"Unique values per column:\n",
|
||
" 0\n",
|
||
"ShareClassAum_EUR 30211\n",
|
||
"ShareClassAumLocalCur 30032\n",
|
||
"NumberOfShares 28910\n",
|
||
"ShareClassPrice 14747\n",
|
||
"PortfolioAum_Eur 5505\n",
|
||
"ShareClassIsin 416\n",
|
||
"NavDate 210\n",
|
||
"ShareClassName 90\n",
|
||
"Cod 55\n",
|
||
"PortfolioName 55\n",
|
||
"LegalForm 6\n",
|
||
"ShareClassCurrency 6\n",
|
||
"PTFCurrency 2\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"with fs.open(\n",
|
||
" \"projet-bdc-data/carmignac/Flows ENSAE V2 -20251105.csv\",\n",
|
||
" \"rb\"\n",
|
||
") as f:\n",
|
||
" flows = pd.read_csv(f, sep=\";\")\n",
|
||
"\n",
|
||
"with fs.open('projet-bdc-data/carmignac/Monthly AUM and NAV since 2010.xlsx', 'rb') as f:\n",
|
||
" nav_raw = pd.read_excel(f, header=None, engine=\"openpyxl\")\n",
|
||
"nav = nav_raw[0].str.split(\",\", expand=True)\n",
|
||
"nav.columns = nav.iloc[0]\n",
|
||
"with fs.open('projet-bdc-data/carmignac/AUM ENSAE V2 -20251105.csv', 'rb') as f:\n",
|
||
" stocks = pd.read_csv(f, sep=\";\")\n",
|
||
"\n",
|
||
"nav = nav[1:].reset_index(drop=True)\n",
|
||
"\n",
|
||
"quick_info(stocks, \"STOCKS\")\n",
|
||
"quick_info(flows, \"FLOWS\")\n",
|
||
"quick_info(nav, \"NAV/PRICES\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "9bc92c9f-216c-475e-bfb8-edc1a4e839f6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Date conversion done.\n",
|
||
"NAV numeric conversion done.\n",
|
||
"String normalization done.\n",
|
||
"\n",
|
||
"ISIN missing in FLOWS but present in STOCKS : 17\n",
|
||
"\n",
|
||
"ISIN missing in STOCKS but present in FLOWS : 0\n",
|
||
"\n",
|
||
"ISIN missing in NAV but present in FLOWS : 67\n",
|
||
"\n",
|
||
"ISIN missing in NAV but present in STOCKS : 76\n",
|
||
"\n",
|
||
"Accounts in STOCKS but NEVER in FLOWS : 5777\n",
|
||
"\n",
|
||
"Accounts in FLOWS but NEVER in STOCKS : 118\n",
|
||
"\n",
|
||
"CLIENT BEHAVIOR (first 5 rows):\n",
|
||
" Registrar Account - ID n_days n_transactions total_netflows mean_flow \\\n",
|
||
"0 100000028 3 3 -109.238 -36.412667 \n",
|
||
"1 100000042 1 1 -660.115 -660.115000 \n",
|
||
"2 100000065 1 1 -174.646 -174.646000 \n",
|
||
"3 100000069 65 73 -7479.755 -102.462397 \n",
|
||
"4 100000073 1 1 -133.402 -133.402000 \n",
|
||
"\n",
|
||
" std_flow total_subscription total_redemption churn_ratio \n",
|
||
"0 49.280511 0.000 -109.238 -1.092380e+11 \n",
|
||
"1 NaN 0.000 -660.115 -6.601150e+11 \n",
|
||
"2 NaN 0.000 -174.646 -1.746460e+11 \n",
|
||
"3 2168.971331 33320.402 -40800.157 -1.224480e+00 \n",
|
||
"4 NaN 0.000 -133.402 -1.334020e+11 \n",
|
||
"\n",
|
||
"FUND BEHAVIOR (first 5 rows):\n",
|
||
" Product - Isin n_accounts n_days total_netflows vol_flows\n",
|
||
"0 FR0010135103 2690 2723 -2.571327e+07 2622.609244\n",
|
||
"1 FR0010147603 733 2719 -2.562187e+06 1206.248205\n",
|
||
"2 FR0010148981 1841 2722 -3.609440e+06 1051.069183\n",
|
||
"3 FR0010148999 454 2306 -7.130297e+05 1265.364138\n",
|
||
"4 FR0010149112 934 2000 -9.438901e+05 1834.961721\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 1. CLEAN DATES (formats différents)\n",
|
||
"\n",
|
||
"stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"], errors=\"coerce\")\n",
|
||
"flows[\"Centralisation Date\"] = pd.to_datetime(flows[\"Centralisation Date\"], errors=\"coerce\")\n",
|
||
"nav[\"NavDate\"] = pd.to_datetime(nav[\"NavDate\"], format=\"%d/%m/%Y\", errors=\"coerce\")\n",
|
||
"\n",
|
||
"print(\"Date conversion done.\")\n",
|
||
"\n",
|
||
"# 2. CLEAN NUMERIC COLUMNS FOR NAV FILE\n",
|
||
"\n",
|
||
"num_cols = [\"PortfolioAum_Eur\",\"ShareClassPrice\",\"NumberOfShares\",\n",
|
||
" \"ShareClassAumLocalCur\",\"ShareClassAum_EUR\"]\n",
|
||
"\n",
|
||
"for col in num_cols:\n",
|
||
" nav[col] = (\n",
|
||
" nav[col]\n",
|
||
" .astype(str)\n",
|
||
" .str.replace(\",\", \".\", regex=False)\n",
|
||
" .str.replace(\" \", \"\")\n",
|
||
" .astype(float)\n",
|
||
" )\n",
|
||
"\n",
|
||
"print(\"NAV numeric conversion done.\")\n",
|
||
"\n",
|
||
"# 3. STANDARDIZE STRINGS FOR JOIN KEYS\n",
|
||
"\n",
|
||
"def norm(df):\n",
|
||
" for col in df.columns:\n",
|
||
" if df[col].dtype == \"object\":\n",
|
||
" df[col] = df[col].astype(str).str.strip().str.upper()\n",
|
||
" return df\n",
|
||
"\n",
|
||
"stocks = norm(stocks)\n",
|
||
"flows = norm(flows)\n",
|
||
"nav = norm(nav)\n",
|
||
"\n",
|
||
"print(\"String normalization done.\")\n",
|
||
"\n",
|
||
"\n",
|
||
"# 4. ANALYSE RELATIONS ACROSS FILES\n",
|
||
"\n",
|
||
"# Unique sets\n",
|
||
"isin_stocks = set(stocks[\"Product - Isin\"].unique())\n",
|
||
"isin_flows = set(flows[\"Product - Isin\"].unique())\n",
|
||
"isin_nav = set(nav[\"ShareClassIsin\"].unique())\n",
|
||
"\n",
|
||
"print(\"\\nISIN missing in FLOWS but present in STOCKS :\", len(isin_stocks - isin_flows))\n",
|
||
"print(\"\\nISIN missing in STOCKS but present in FLOWS :\", len(isin_flows - isin_stocks))\n",
|
||
"print(\"\\nISIN missing in NAV but present in FLOWS :\", len(isin_flows - isin_nav))\n",
|
||
"print(\"\\nISIN missing in NAV but present in STOCKS :\", len(isin_stocks - isin_nav))\n",
|
||
"\n",
|
||
"\n",
|
||
"# 5. CLIENTS: STOCKS VS FLOWS\n",
|
||
"\n",
|
||
"acc_stocks = set(stocks[\"Registrar Account - ID\"].unique())\n",
|
||
"acc_flows = set(flows[\"Registrar Account - ID\"].unique())\n",
|
||
"\n",
|
||
"print(\"\\nAccounts in STOCKS but NEVER in FLOWS :\", len(acc_stocks - acc_flows))\n",
|
||
"print(\"\\nAccounts in FLOWS but NEVER in STOCKS :\", len(acc_flows - acc_stocks))\n",
|
||
"\n",
|
||
"\n",
|
||
"# 6. CLIENT ACTIVITY METRICS (DETAILED)\n",
|
||
"\n",
|
||
"client_behavior = flows.groupby(\"Registrar Account - ID\").agg(\n",
|
||
" n_days=(\"Centralisation Date\", lambda x: x.nunique()),\n",
|
||
" n_transactions=(\"Quantity - NetFlows\", \"count\"),\n",
|
||
" total_netflows=(\"Quantity - NetFlows\", \"sum\"),\n",
|
||
" mean_flow=(\"Quantity - NetFlows\", \"mean\"),\n",
|
||
" std_flow=(\"Quantity - NetFlows\", \"std\"),\n",
|
||
" total_subscription=(\"Quantity - Subscription\", \"sum\"),\n",
|
||
" total_redemption=(\"Quantity - Redemption\", \"sum\")\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"# Add churn metric\n",
|
||
"client_behavior[\"churn_ratio\"] = (\n",
|
||
" client_behavior[\"total_redemption\"] /\n",
|
||
" (client_behavior[\"total_subscription\"] + 1e-9)\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"\\nCLIENT BEHAVIOR (first 5 rows):\\n\", client_behavior.head())\n",
|
||
"\n",
|
||
"\n",
|
||
"# 7. FUNDS ACTIVITY METRICS\n",
|
||
"\n",
|
||
"fund_behavior = flows.groupby(\"Product - Isin\").agg(\n",
|
||
" n_accounts=(\"Registrar Account - ID\", \"nunique\"),\n",
|
||
" n_days=(\"Centralisation Date\", lambda x: x.nunique()),\n",
|
||
" total_netflows=(\"Quantity - NetFlows\", \"sum\"),\n",
|
||
" vol_flows=(\"Quantity - NetFlows\", \"std\")\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"print(\"\\nFUND BEHAVIOR (first 5 rows):\\n\", fund_behavior.head())\n",
|
||
"\n",
|
||
"\n",
|
||
"# 8. SAVE INTERMEDIATE\n",
|
||
"\n",
|
||
"client_behavior.to_csv(\"client_behavior.csv\", index=False)\n",
|
||
"fund_behavior.to_csv(\"fund_behavior.csv\", index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "afb51598-3a7b-41f2-8d25-5b4b8bfb1c8a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"FULL usable ISIN : 407\n",
|
||
"Stocks only ISIN : 17\n",
|
||
"Flows only ISIN : 0\n",
|
||
"Missing NAV : 76\n",
|
||
"All ISIN groups saved into 4 separate files.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"valid_full = isin_stocks & isin_flows & isin_nav\n",
|
||
"stocks_only = isin_stocks - isin_flows\n",
|
||
"flows_only = isin_flows - isin_stocks\n",
|
||
"missing_nav = (isin_stocks | isin_flows) - isin_nav\n",
|
||
"\n",
|
||
"print(\"FULL usable ISIN :\", len(valid_full))\n",
|
||
"print(\"Stocks only ISIN :\", len(stocks_only))\n",
|
||
"print(\"Flows only ISIN :\", len(flows_only))\n",
|
||
"print(\"Missing NAV :\", len(missing_nav))\n",
|
||
"\n",
|
||
"pd.DataFrame({\"isin\": list(valid_full)}).to_csv(\"isin_full.csv\", index=False)\n",
|
||
"pd.DataFrame({\"isin\": list(stocks_only)}).to_csv(\"isin_stocks_only.csv\", index=False)\n",
|
||
"pd.DataFrame({\"isin\": list(flows_only)}).to_csv(\"isin_flows_only.csv\", index=False)\n",
|
||
"pd.DataFrame({\"isin\": list(missing_nav)}).to_csv(\"isin_missing_nav.csv\", index=False)\n",
|
||
"\n",
|
||
"print(\"All ISIN groups saved into 4 separate files.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "61e0c71a-a1c6-4ed8-ba15-b7a9badc4d4a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" Registrar Account - ID n_days n_transactions total_netflows mean_flow \\\n",
|
||
"0 100000028 3 3 -109.238 -36.412667 \n",
|
||
"1 100000042 1 1 -660.115 -660.115000 \n",
|
||
"2 100000065 1 1 -174.646 -174.646000 \n",
|
||
"3 100000069 65 73 -7479.755 -102.462397 \n",
|
||
"4 100000073 1 1 -133.402 -133.402000 \n",
|
||
"\n",
|
||
" std_flow total_subscription total_redemption churn_ratio \\\n",
|
||
"0 49.280511 0.000 -109.238 -1.092380e+08 \n",
|
||
"1 NaN 0.000 -660.115 -6.601150e+08 \n",
|
||
"2 NaN 0.000 -174.646 -1.746460e+08 \n",
|
||
"3 2168.971331 33320.402 -40800.157 -1.224480e+00 \n",
|
||
"4 NaN 0.000 -133.402 -1.334020e+08 \n",
|
||
"\n",
|
||
" churn_flag activity_score flow_volatility inertia_ratio \n",
|
||
"0 0 1.386294 49.280511 0.998921 \n",
|
||
"1 0 0.693147 0.000000 0.999640 \n",
|
||
"2 0 0.693147 0.000000 0.999640 \n",
|
||
"3 0 4.304065 2168.971331 0.976619 \n",
|
||
"4 0 0.693147 0.000000 0.999640 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"eps = 1e-6\n",
|
||
"\n",
|
||
"client_behavior[\"churn_ratio\"] = (\n",
|
||
" client_behavior[\"total_redemption\"] /\n",
|
||
" (client_behavior[\"total_subscription\"] + eps)\n",
|
||
")\n",
|
||
"\n",
|
||
"client_behavior[\"churn_flag\"] = (\n",
|
||
" client_behavior[\"total_redemption\"] > client_behavior[\"total_subscription\"]\n",
|
||
").astype(int)\n",
|
||
"\n",
|
||
"client_behavior[\"activity_score\"] = np.log1p(client_behavior[\"n_transactions\"])\n",
|
||
"\n",
|
||
"client_behavior[\"flow_volatility\"] = client_behavior[\"std_flow\"].fillna(0)\n",
|
||
"\n",
|
||
"client_behavior[\"inertia_ratio\"] = (\n",
|
||
" 1 - client_behavior[\"n_days\"] / flows[\"Centralisation Date\"].nunique()\n",
|
||
")\n",
|
||
"\n",
|
||
"print(client_behavior.head())\n",
|
||
"\n",
|
||
"client_behavior.to_csv(\"client_behavior_clean.csv\", index=False)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "8ee7e911-eb73-4846-b545-661140411c1b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_3144/1645623303.py:17: RuntimeWarning: invalid value encountered in scalar divide\n",
|
||
" .apply(lambda x: x[\"Value - AUM €\"].max() / x[\"Value - AUM €\"].sum()) \\\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" Registrar Account - ID n_isin_held n_funds_held n_asset_types \\\n",
|
||
"0 100000014 1 1 1 \n",
|
||
"1 100000016 2 2 2 \n",
|
||
"2 100000028 1 1 1 \n",
|
||
"3 100000038 3 3 2 \n",
|
||
"4 100000042 1 1 1 \n",
|
||
"\n",
|
||
" n_strategies total_aum median_aum concentration_ratio \n",
|
||
"0 1 0.0000 0.0 NaN \n",
|
||
"1 2 0.0000 0.0 NaN \n",
|
||
"2 1 126236.2184 0.0 1.0 \n",
|
||
"3 3 0.0000 0.0 NaN \n",
|
||
"4 1 446362.9015 0.0 1.0 \n",
|
||
" n_isin_held n_funds_held n_asset_types n_strategies total_aum \\\n",
|
||
"count 12501.000000 12501.000000 12501.000000 12501.000000 1.250100e+04 \n",
|
||
"mean 5.514759 4.408367 2.082473 4.109271 4.218474e+08 \n",
|
||
"std 10.434698 5.472756 1.254048 4.714800 5.618341e+09 \n",
|
||
"min 1.000000 1.000000 1.000000 1.000000 -2.586805e+08 \n",
|
||
"25% 1.000000 1.000000 1.000000 1.000000 0.000000e+00 \n",
|
||
"50% 2.000000 2.000000 2.000000 2.000000 2.587605e+05 \n",
|
||
"75% 6.000000 5.000000 3.000000 5.000000 8.817014e+06 \n",
|
||
"max 469.000000 67.000000 6.000000 48.000000 4.780234e+11 \n",
|
||
"\n",
|
||
" median_aum concentration_ratio \n",
|
||
"count 1.250100e+04 7708.000000 \n",
|
||
"mean 2.573991e+05 0.790503 \n",
|
||
"std 3.487976e+06 0.261535 \n",
|
||
"min -2.317333e+06 -2.591840 \n",
|
||
"25% 0.000000e+00 0.576503 \n",
|
||
"50% 0.000000e+00 0.972159 \n",
|
||
"75% 1.474502e+02 1.000000 \n",
|
||
"max 2.215373e+08 2.983529 \n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_3144/1645623303.py:17: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
|
||
" .apply(lambda x: x[\"Value - AUM €\"].max() / x[\"Value - AUM €\"].sum()) \\\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Diversification per account\n",
|
||
"account_div = stocks.groupby(\"Registrar Account - ID\").agg(\n",
|
||
" n_isin_held=(\"Product - Isin\", \"nunique\"),\n",
|
||
" n_funds_held=(\"Product - Fund\", \"nunique\"),\n",
|
||
" n_asset_types=(\"Product - Asset Type\", \"nunique\"),\n",
|
||
" n_strategies=(\"Product - Strategy\", \"nunique\"),\n",
|
||
" total_aum=(\"Value - AUM €\", \"sum\"),\n",
|
||
" median_aum=(\"Value - AUM €\", \"median\")\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"# Concentration ratio per account\n",
|
||
"aum_by_account_fund = stocks.groupby(\n",
|
||
" [\"Registrar Account - ID\", \"Product - Fund\"]\n",
|
||
")[\"Value - AUM €\"].sum().reset_index()\n",
|
||
"\n",
|
||
"concentration = aum_by_account_fund.groupby(\"Registrar Account - ID\") \\\n",
|
||
" .apply(lambda x: x[\"Value - AUM €\"].max() / x[\"Value - AUM €\"].sum()) \\\n",
|
||
" .reset_index(name=\"concentration_ratio\")\n",
|
||
"\n",
|
||
"# Merge diversification + concentration\n",
|
||
"account_static = account_div.merge(concentration, on=\"Registrar Account - ID\", how=\"left\")\n",
|
||
"\n",
|
||
"print(account_static.head())\n",
|
||
"print(account_static.describe())\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "76f6fa0d-9d7a-4145-af1c-986d83947f91",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" Registrar Account - ID country region\n",
|
||
"0 100000014 SWITZERLAND SWITZERLAND\n",
|
||
"1 100000016 UNITED KINGDOM UNITED KINGDOM\n",
|
||
"2 100000028 UNITED KINGDOM UNITED KINGDOM\n",
|
||
"3 100000038 SWITZERLAND SWITZERLAND\n",
|
||
"4 100000042 UNITED KINGDOM UNITED KINGDOM\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Geographic info per account\n",
|
||
"geo = stocks.groupby(\"Registrar Account - ID\").agg(\n",
|
||
" country=(\"RegistrarAccount - Country\", lambda x: x.mode()[0]),\n",
|
||
" region=(\"Registrar Account - Region\", lambda x: x.mode()[0])\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"print(geo.head())\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "b31584e4-b3c4-4fdd-9fd9-87372a7440ec",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
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"data": {
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"application/vnd.plotly.v1+json": {
|
||
"config": {
|
||
"plotlyServerURL": "https://plot.ly"
|
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},
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"data": [
|
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{
|
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"colorscale": [
|
||
[
|
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0,
|
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"rgb(255,247,236)"
|
||
],
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[
|
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1,
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"rgb(255,247,236)"
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]
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],
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"customdata": {
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"bdata": "AAAAAAAAJEAAAAAAAAAIQAAAAAAAABBAAAAAAAAAGEAAAAAAAAAYQAAAAAAAACJAAAAAAAAAFEAAAAAAAAAkQAAAAAAAAABAAAAAAAAAFEAAAAAAAAAAQAAAAAAAABRAAAAAAAAACEAAAAAAAADwPwAAAAAAAPA/AAAAAAAAAEAAAAAAAAAUQAAAAAAAABhA",
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"dtype": "f8",
|
||
"shape": "18, 1"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>account_bin=Very low (1–9)<br>iso3=%{location}<br>n_accounts=%{customdata[0]}<extra></extra>",
|
||
"hovertext": [
|
||
"CANADA",
|
||
"CZECH REPUBLIC",
|
||
"DENMARK",
|
||
"FINLAND",
|
||
"GREECE",
|
||
"HONG KONG",
|
||
"IRELAND",
|
||
"ISRAEL",
|
||
"JAPAN",
|
||
"LIECHTENSTEIN",
|
||
"LITHUANIA",
|
||
"MALTA",
|
||
"MAURITIUS",
|
||
"NORWAY",
|
||
"POLAND",
|
||
"SOUTH AFRICA",
|
||
"TAIWAN",
|
||
"UNITED ARAB EMIRATES"
|
||
],
|
||
"locations": [
|
||
"CAN",
|
||
"CZE",
|
||
"DNK",
|
||
"FIN",
|
||
"GRC",
|
||
"HKG",
|
||
"IRL",
|
||
"ISR",
|
||
"JPN",
|
||
"LIE",
|
||
"LTU",
|
||
"MLT",
|
||
"MUS",
|
||
"NOR",
|
||
"POL",
|
||
"ZAF",
|
||
"TWN",
|
||
"ARE"
|
||
],
|
||
"name": "Very low (1–9)",
|
||
"showlegend": true,
|
||
"showscale": false,
|
||
"type": "choropleth",
|
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"z": [
|
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|
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|
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|
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|
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1,
|
||
1,
|
||
1
|
||
]
|
||
},
|
||
{
|
||
"colorscale": [
|
||
[
|
||
0,
|
||
"rgb(254,232,200)"
|
||
],
|
||
[
|
||
1,
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"rgb(254,232,200)"
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]
|
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|
||
"dtype": "f8",
|
||
"shape": "17, 1"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>account_bin=Low (10–49)<br>iso3=%{location}<br>n_accounts=%{customdata[0]}<extra></extra>",
|
||
"hovertext": [
|
||
"AUSTRIA",
|
||
"MONACO",
|
||
"NETHERLANDS",
|
||
"PORTUGAL",
|
||
"SINGAPORE",
|
||
"SWEDEN",
|
||
"ARGENTINA",
|
||
"BRAZIL",
|
||
"CHILE",
|
||
"COLOMBIA",
|
||
"MEXICO",
|
||
"PERU",
|
||
"URUGUAY",
|
||
"PARAGUAY",
|
||
"BOLIVIA",
|
||
"ECUADOR",
|
||
"VENEZUELA"
|
||
],
|
||
"locations": [
|
||
"AUT",
|
||
"MCO",
|
||
"NLD",
|
||
"PRT",
|
||
"SGP",
|
||
"SWE",
|
||
"ARG",
|
||
"BRA",
|
||
"CHL",
|
||
"COL",
|
||
"MEX",
|
||
"PER",
|
||
"URY",
|
||
"PRY",
|
||
"BOL",
|
||
"ECU",
|
||
"VEN"
|
||
],
|
||
"name": "Low (10–49)",
|
||
"showlegend": true,
|
||
"showscale": false,
|
||
"type": "choropleth",
|
||
"z": [
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
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1,
|
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|
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1,
|
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1,
|
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|
||
1,
|
||
1,
|
||
1
|
||
]
|
||
},
|
||
{
|
||
"colorscale": [
|
||
[
|
||
0,
|
||
"rgb(253,212,158)"
|
||
],
|
||
[
|
||
1,
|
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"rgb(253,212,158)"
|
||
]
|
||
],
|
||
"customdata": {
|
||
"bdata": "AAAAAABAZEAAAAAAAIBWQA==",
|
||
"dtype": "f8",
|
||
"shape": "2, 1"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>account_bin=Medium (50–199)<br>iso3=%{location}<br>n_accounts=%{customdata[0]}<extra></extra>",
|
||
"hovertext": [
|
||
"BELGIUM",
|
||
"GERMANY"
|
||
],
|
||
"locations": [
|
||
"BEL",
|
||
"DEU"
|
||
],
|
||
"name": "Medium (50–199)",
|
||
"showlegend": true,
|
||
"showscale": false,
|
||
"type": "choropleth",
|
||
"z": [
|
||
1,
|
||
1
|
||
]
|
||
},
|
||
{
|
||
"colorscale": [
|
||
[
|
||
0,
|
||
"rgb(253,187,132)"
|
||
],
|
||
[
|
||
1,
|
||
"rgb(253,187,132)"
|
||
]
|
||
],
|
||
"customdata": {
|
||
"bdata": "AAAAAABwfkAAAAAAALB0QAAAAAAAQHVAAAAAAABwdEAAAAAAAHCDQA==",
|
||
"dtype": "f8",
|
||
"shape": "5, 1"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>account_bin=High (200–999)<br>iso3=%{location}<br>n_accounts=%{customdata[0]}<extra></extra>",
|
||
"hovertext": [
|
||
"ITALY",
|
||
"LUXEMBOURG",
|
||
"SPAIN",
|
||
"SWITZERLAND",
|
||
"UNITED KINGDOM"
|
||
],
|
||
"locations": [
|
||
"ITA",
|
||
"LUX",
|
||
"ESP",
|
||
"CHE",
|
||
"GBR"
|
||
],
|
||
"name": "High (200–999)",
|
||
"showlegend": true,
|
||
"showscale": false,
|
||
"type": "choropleth",
|
||
"z": [
|
||
1,
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||
1,
|
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1,
|
||
1,
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||
1
|
||
]
|
||
},
|
||
{
|
||
"colorscale": [
|
||
[
|
||
0,
|
||
"rgb(252,141,89)"
|
||
],
|
||
[
|
||
1,
|
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"rgb(252,141,89)"
|
||
]
|
||
],
|
||
"customdata": {
|
||
"bdata": "AAAAAADgpEAAAAAAADiSQA==",
|
||
"dtype": "f8",
|
||
"shape": "2, 1"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>account_bin=Very high (≥ 1,000)<br>iso3=%{location}<br>n_accounts=%{customdata[0]}<extra></extra>",
|
||
"hovertext": [
|
||
"FRANCE",
|
||
"UNITED STATES"
|
||
],
|
||
"locations": [
|
||
"FRA",
|
||
"USA"
|
||
],
|
||
"name": "Very high (≥ 1,000)",
|
||
"showlegend": true,
|
||
"showscale": false,
|
||
"type": "choropleth",
|
||
"z": [
|
||
1,
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||
1
|
||
]
|
||
}
|
||
],
|
||
"layout": {
|
||
"geo": {
|
||
"center": {},
|
||
"coastlinecolor": "rgba(0,0,0,0.3)",
|
||
"domain": {
|
||
"x": [
|
||
0,
|
||
1
|
||
],
|
||
"y": [
|
||
0,
|
||
1
|
||
]
|
||
},
|
||
"projection": {
|
||
"type": "natural earth"
|
||
},
|
||
"showcoastlines": true,
|
||
"showframe": false
|
||
},
|
||
"legend": {
|
||
"title": {
|
||
"text": "Number of registrar accounts<br>with flow activity"
|
||
},
|
||
"tracegroupgap": 0
|
||
},
|
||
"margin": {
|
||
"b": 0,
|
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"l": 0,
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"r": 0,
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"t": 40
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},
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{
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"error_x": {
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"color": "#2a3f5f"
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},
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"error_y": {
|
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"color": "#2a3f5f"
|
||
},
|
||
"marker": {
|
||
"line": {
|
||
"color": "#E5ECF6",
|
||
"width": 0.5
|
||
},
|
||
"pattern": {
|
||
"fillmode": "overlay",
|
||
"size": 10,
|
||
"solidity": 0.2
|
||
}
|
||
},
|
||
"type": "bar"
|
||
}
|
||
],
|
||
"barpolar": [
|
||
{
|
||
"marker": {
|
||
"line": {
|
||
"color": "#E5ECF6",
|
||
"width": 0.5
|
||
},
|
||
"pattern": {
|
||
"fillmode": "overlay",
|
||
"size": 10,
|
||
"solidity": 0.2
|
||
}
|
||
},
|
||
"type": "barpolar"
|
||
}
|
||
],
|
||
"carpet": [
|
||
{
|
||
"aaxis": {
|
||
"endlinecolor": "#2a3f5f",
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"minorgridcolor": "white",
|
||
"startlinecolor": "#2a3f5f"
|
||
},
|
||
"baxis": {
|
||
"endlinecolor": "#2a3f5f",
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"minorgridcolor": "white",
|
||
"startlinecolor": "#2a3f5f"
|
||
},
|
||
"type": "carpet"
|
||
}
|
||
],
|
||
"choropleth": [
|
||
{
|
||
"colorbar": {
|
||
"outlinewidth": 0,
|
||
"ticks": ""
|
||
},
|
||
"type": "choropleth"
|
||
}
|
||
],
|
||
"contour": [
|
||
{
|
||
"colorbar": {
|
||
"outlinewidth": 0,
|
||
"ticks": ""
|
||
},
|
||
"colorscale": [
|
||
[
|
||
0,
|
||
"#0d0887"
|
||
],
|
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[
|
||
0.1111111111111111,
|
||
"#46039f"
|
||
],
|
||
[
|
||
0.2222222222222222,
|
||
"#7201a8"
|
||
],
|
||
[
|
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0.3333333333333333,
|
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"#9c179e"
|
||
],
|
||
[
|
||
0.4444444444444444,
|
||
"#bd3786"
|
||
],
|
||
[
|
||
0.5555555555555556,
|
||
"#d8576b"
|
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||
}
|
||
}
|
||
},
|
||
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# GEOGRAPHIC COVERAGE — AUM MAP (ACCOUNTS WITH ≥1 FLOW ONLY)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import plotly.express as px\n",
|
||
"import pycountry\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# Helper: country name → ISO-3 code\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"def country_to_iso3(name):\n",
|
||
" try:\n",
|
||
" return pycountry.countries.lookup(name).alpha_3\n",
|
||
" except:\n",
|
||
" return None\n",
|
||
"\n",
|
||
"# Harmonize country labels\n",
|
||
"stocks[\"RegistrarAccount - Country\"] = stocks[\"RegistrarAccount - Country\"].replace({\n",
|
||
" \"US OFFSHORE\": \"UNITED STATES\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"flows[\"RegistrarAccount - Country\"] = flows[\"RegistrarAccount - Country\"].replace({\n",
|
||
" \"US OFFSHORE\": \"UNITED STATES\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 0. Identify registrar accounts with at least one non-zero flow\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"active_accounts = (\n",
|
||
" flows.loc[flows[\"Value € - NetFlows\"] != 0, \"Registrar Account - ID\"]\n",
|
||
" .unique()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Restrict STOCKS to accounts with flows\n",
|
||
"stocks_active = stocks[\n",
|
||
" stocks[\"Registrar Account - ID\"].isin(active_accounts)\n",
|
||
"].copy()\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 1. Aggregate number of ACTIVE accounts per country\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"country_accounts = (\n",
|
||
" stocks_active\n",
|
||
" .groupby(\"RegistrarAccount - Country\")[\"Registrar Account - ID\"]\n",
|
||
" .nunique()\n",
|
||
" .reset_index(name=\"n_accounts\")\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 2. Handle LATAM explicitly (visual redistribution)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"latam_countries = [\n",
|
||
" \"ARGENTINA\", \"BRAZIL\", \"CHILE\", \"COLOMBIA\", \"MEXICO\",\n",
|
||
" \"PERU\", \"URUGUAY\", \"PARAGUAY\", \"BOLIVIA\",\n",
|
||
" \"ECUADOR\", \"VENEZUELA\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"latam_value = country_accounts.loc[\n",
|
||
" country_accounts[\"RegistrarAccount - Country\"] == \"LATAM\",\n",
|
||
" \"n_accounts\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"if not latam_value.empty:\n",
|
||
" latam_share = latam_value.iloc[0] / len(latam_countries)\n",
|
||
"\n",
|
||
" latam_df = pd.DataFrame({\n",
|
||
" \"RegistrarAccount - Country\": latam_countries,\n",
|
||
" \"n_accounts\": latam_share\n",
|
||
" })\n",
|
||
"\n",
|
||
" country_accounts = pd.concat(\n",
|
||
" [\n",
|
||
" country_accounts[\n",
|
||
" ~country_accounts[\"RegistrarAccount - Country\"].isin(\n",
|
||
" [\"LATAM\", \"INTERNATIONAL\", \"UNKNOWN\", \"US OFFSHORE\"]\n",
|
||
" )\n",
|
||
" ],\n",
|
||
" latam_df\n",
|
||
" ],\n",
|
||
" ignore_index=True\n",
|
||
" )\n",
|
||
"\n",
|
||
"else:\n",
|
||
" country_accounts = country_accounts[\n",
|
||
" ~country_accounts[\"RegistrarAccount - Country\"].isin(\n",
|
||
" [\"INTERNATIONAL\", \"UNKNOWN\", \"US OFFSHORE\"]\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 3. Convert country names to ISO-3\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"country_accounts[\"iso3\"] = country_accounts[\"RegistrarAccount - Country\"].apply(country_to_iso3)\n",
|
||
"\n",
|
||
"# Diagnostic: unmapped values\n",
|
||
"unmapped = country_accounts[country_accounts[\"iso3\"].isna()]\n",
|
||
"if not unmapped.empty:\n",
|
||
" print(\"Unmapped country labels:\")\n",
|
||
" print(unmapped[\"RegistrarAccount - Country\"].unique())\n",
|
||
"\n",
|
||
"country_accounts = country_accounts.dropna(subset=[\"iso3\"])\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 4. Discretize by order of magnitude (explicit labels)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"bins = [0, 10, 50, 200, 1000, np.inf]\n",
|
||
"labels = [\n",
|
||
" \"Very low (1–9)\",\n",
|
||
" \"Low (10–49)\",\n",
|
||
" \"Medium (50–199)\",\n",
|
||
" \"High (200–999)\",\n",
|
||
" \"Very high (≥ 1,000)\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"country_accounts[\"account_bin\"] = pd.cut(\n",
|
||
" country_accounts[\"n_accounts\"],\n",
|
||
" bins=bins,\n",
|
||
" labels=labels,\n",
|
||
" include_lowest=True\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 5. World map — AUM coverage (active accounts only)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"fig = px.choropleth(\n",
|
||
" country_accounts,\n",
|
||
" locations=\"iso3\",\n",
|
||
" color=\"account_bin\",\n",
|
||
" hover_name=\"RegistrarAccount - Country\",\n",
|
||
" hover_data={\"n_accounts\": True},\n",
|
||
" category_orders={\"account_bin\": labels},\n",
|
||
" color_discrete_sequence=px.colors.sequential.OrRd,\n",
|
||
" #title=\"Geographic distribution of registrar accounts with observed flows\"\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" margin=dict(l=0, r=0, t=40, b=0),\n",
|
||
" legend_title_text=\"Number of registrar accounts<br>with flow activity\",\n",
|
||
" geo=dict(\n",
|
||
" showframe=False,\n",
|
||
" showcoastlines=True,\n",
|
||
" coastlinecolor=\"rgba(0,0,0,0.3)\",\n",
|
||
" projection_type=\"natural earth\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"fig.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
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|
||
"metadata": {},
|
||
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|
||
{
|
||
"name": "stdout",
|
||
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|
||
"text": [
|
||
"Global raw totals (flows):\n",
|
||
" - sum netflows €: -18256951755.077896\n",
|
||
" - sum abs netflows €: 283031932185.3349\n",
|
||
" - n rows: 2574461\n",
|
||
" - n accounts: 6842\n",
|
||
"\n",
|
||
"Accounts with >1 country label in STOCKS (should be low): 136\n",
|
||
"\n",
|
||
"Top accounts with multi-country labels (examples):\n",
|
||
" Registrar Account - ID country n_countries_in_stocks\n",
|
||
"8724 406197 SWITZERLAND 16\n",
|
||
"6933 366024 SWEDEN 14\n",
|
||
"12030 420207 LUXEMBOURG 13\n",
|
||
"12499 OFF DISTRIBUTION LUXEMBOURG 13\n",
|
||
"3539 200127410 SWITZERLAND 12\n",
|
||
"5534 348454 SWITZERLAND 11\n",
|
||
"6352 365376 SWITZERLAND 11\n",
|
||
"5470 307388 SWITZERLAND 9\n",
|
||
"36 13463 SWITZERLAND 8\n",
|
||
"12400 422778 LUXEMBOURG 7\n",
|
||
"\n",
|
||
"Top 15 countries by total_abs_flows (robust):\n",
|
||
" country total_abs_flows total_signed_flows n_accounts_active \\\n",
|
||
"13 ITALY 4.263941e+10 -3.201953e+09 478 \n",
|
||
"26 SPAIN 4.099818e+10 1.575358e+09 330 \n",
|
||
"6 FRANCE 3.488683e+10 -4.092174e+09 2627 \n",
|
||
"17 LUXEMBOURG 3.100404e+10 -1.909475e+09 310 \n",
|
||
"7 GERMANY 1.502239e+10 -4.128081e+09 80 \n",
|
||
"1 BELGIUM 1.437804e+10 -3.546591e+09 145 \n",
|
||
"28 SWITZERLAND 1.141769e+10 -2.356821e+09 275 \n",
|
||
"31 UNITED KINGDOM 3.397122e+09 -1.094136e+08 605 \n",
|
||
"27 SWEDEN 1.038465e+09 8.745456e+07 37 \n",
|
||
"14 JAPAN 6.973177e+08 -2.703144e+08 2 \n",
|
||
"20 MONACO 6.387020e+08 -9.208744e+07 12 \n",
|
||
"21 NETHERLANDS 5.477981e+08 -2.581311e+08 32 \n",
|
||
"23 PORTUGAL 5.187647e+08 3.011212e+07 14 \n",
|
||
"0 AUSTRIA 3.782388e+08 2.282761e+07 9 \n",
|
||
"32 UNITED STATES 3.641362e+08 3.272825e+07 1161 \n",
|
||
"\n",
|
||
" n_flow_days share_multicountry_accounts avg_abs_flows_per_active_account \n",
|
||
"13 2758 0.022587 8.920378e+07 \n",
|
||
"26 2718 0.054746 1.242369e+08 \n",
|
||
"6 2724 0.034566 1.328010e+07 \n",
|
||
"17 2719 0.574681 1.000130e+08 \n",
|
||
"7 2715 0.156396 1.877799e+08 \n",
|
||
"1 2721 0.049440 9.915890e+07 \n",
|
||
"28 2720 0.605867 4.151887e+07 \n",
|
||
"31 2748 0.001728 5.615078e+06 \n",
|
||
"27 2658 0.585541 2.806663e+07 \n",
|
||
"14 1629 0.000000 3.486588e+08 \n",
|
||
"20 1863 0.596097 5.322517e+07 \n",
|
||
"21 2611 0.173396 1.711869e+07 \n",
|
||
"23 1433 0.000000 3.705462e+07 \n",
|
||
"0 2118 0.647902 4.202653e+07 \n",
|
||
"32 1116 0.000000 3.136401e+05 \n",
|
||
"\n",
|
||
"France vs UK diagnostics:\n",
|
||
" country total_abs_flows total_signed_flows n_accounts_active \\\n",
|
||
"6 FRANCE 3.488683e+10 -4.092174e+09 2627 \n",
|
||
"31 UNITED KINGDOM 3.397122e+09 -1.094136e+08 605 \n",
|
||
"\n",
|
||
" n_flow_days share_multicountry_accounts avg_abs_flows_per_active_account \n",
|
||
"6 2724 0.034566 1.328010e+07 \n",
|
||
"31 2748 0.001728 5.615078e+06 \n",
|
||
"\n",
|
||
"France: top 10 accounts driving flows:\n",
|
||
" country Registrar Account - ID acc_abs_flows\n",
|
||
"2797 FRANCE PRIVATE CLIENT 2.379152e+09\n",
|
||
"2695 FRANCE 418652 1.162739e+09\n",
|
||
"787 FRANCE 200127454 8.858410e+08\n",
|
||
"1265 FRANCE 365596 7.407851e+08\n",
|
||
"875 FRANCE 200127809 7.233502e+08\n",
|
||
"1540 FRANCE 405760 7.060290e+08\n",
|
||
"877 FRANCE 200127811 5.845121e+08\n",
|
||
"2648 FRANCE 417622 5.822746e+08\n",
|
||
"271 FRANCE 200002327 5.160009e+08\n",
|
||
"1593 FRANCE 406163 5.046222e+08\n",
|
||
"\n",
|
||
"UK: top 10 accounts driving flows:\n",
|
||
" country Registrar Account - ID acc_abs_flows\n",
|
||
"4971 UNITED KINGDOM 200000225 3.112362e+08\n",
|
||
"5127 UNITED KINGDOM 200009976 2.835532e+08\n",
|
||
"5150 UNITED KINGDOM 200048865 2.719247e+08\n",
|
||
"5454 UNITED KINGDOM 365486 1.840476e+08\n",
|
||
"5161 UNITED KINGDOM 200072503 1.233866e+08\n",
|
||
"5200 UNITED KINGDOM 200102162 7.662158e+07\n",
|
||
"5039 UNITED KINGDOM 200001522 6.594899e+07\n",
|
||
"5165 UNITED KINGDOM 200079169 5.913266e+07\n",
|
||
"5230 UNITED KINGDOM 200127603 5.872504e+07\n",
|
||
"5260 UNITED KINGDOM 200128363 5.599629e+07\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 0) QUICK SANITY: global totals\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"print(\"Global raw totals (flows):\")\n",
|
||
"print(\" - sum netflows €:\", flows[\"Value € - NetFlows\"].sum())\n",
|
||
"print(\" - sum abs netflows €:\", flows[\"Value € - NetFlows\"].abs().sum())\n",
|
||
"print(\" - n rows:\", len(flows))\n",
|
||
"print(\" - n accounts:\", flows[\"Registrar Account - ID\"].nunique())\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 1) DEFINE ACTIVE ACCOUNTS (≥ 1 non-zero flow)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"active_accounts = (\n",
|
||
" flows.loc[flows[\"Value € - NetFlows\"] != 0, \"Registrar Account - ID\"]\n",
|
||
" .unique()\n",
|
||
")\n",
|
||
"\n",
|
||
"flows_active = flows[flows[\"Registrar Account - ID\"].isin(active_accounts)].copy()\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 2) COUNTRY MAPPING FROM STOCKS + DIAGNOSTIC\n",
|
||
"# Check if accounts are multi-country in STOCKS (bad sign)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"acc_country_counts = (\n",
|
||
" stocks.groupby(\"Registrar Account - ID\")[\"RegistrarAccount - Country\"]\n",
|
||
" .nunique()\n",
|
||
" .rename(\"n_countries_in_stocks\")\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"\\nAccounts with >1 country label in STOCKS (should be low):\",\n",
|
||
" (acc_country_counts[\"n_countries_in_stocks\"] > 1).sum())\n",
|
||
"\n",
|
||
"# Use mode for mapping (as you did), but now we will measure if it's messy\n",
|
||
"account_country = (\n",
|
||
" stocks.groupby(\"Registrar Account - ID\")[\"RegistrarAccount - Country\"]\n",
|
||
" .agg(lambda x: x.mode().iloc[0])\n",
|
||
" .reset_index()\n",
|
||
" .rename(columns={\"RegistrarAccount - Country\": \"country\"})\n",
|
||
")\n",
|
||
"\n",
|
||
"# Merge diagnostics\n",
|
||
"account_country = account_country.merge(acc_country_counts, on=\"Registrar Account - ID\", how=\"left\")\n",
|
||
"\n",
|
||
"# Look at the worst offenders\n",
|
||
"print(\"\\nTop accounts with multi-country labels (examples):\")\n",
|
||
"print(account_country.sort_values(\"n_countries_in_stocks\", ascending=False).head(10))\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 3) CLEAN FLOW AGGREGATION TO AVOID DOUBLE COUNTING\n",
|
||
"# Key step: aggregate flows first, then take abs, then sum.\n",
|
||
"# Two options:\n",
|
||
"# - Option A: Account x Date (recommended for \"activity intensity\")\n",
|
||
"# - Option B: Account x ISIN x Date (keeps instrument dimension)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Option A (recommended): aggregate per account-date\n",
|
||
"flows_acc_day = (\n",
|
||
" flows_active\n",
|
||
" .groupby([\"Registrar Account - ID\", \"Centralisation Date\"], as_index=False)[\"Value € - NetFlows\"]\n",
|
||
" .sum()\n",
|
||
")\n",
|
||
"\n",
|
||
"flows_acc_day[\"abs_flow\"] = flows_acc_day[\"Value € - NetFlows\"].abs()\n",
|
||
"\n",
|
||
"# Attach country\n",
|
||
"flows_acc_day = flows_acc_day.merge(\n",
|
||
" account_country[[\"Registrar Account - ID\", \"country\", \"n_countries_in_stocks\"]],\n",
|
||
" on=\"Registrar Account - ID\",\n",
|
||
" how=\"left\"\n",
|
||
")\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 4) COUNTRY TOTALS (robust)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"country_flows = (\n",
|
||
" flows_acc_day\n",
|
||
" .groupby(\"country\", as_index=False)\n",
|
||
" .agg(\n",
|
||
" total_abs_flows=(\"abs_flow\", \"sum\"),\n",
|
||
" total_signed_flows=(\"Value € - NetFlows\", \"sum\"),\n",
|
||
" n_accounts_active=(\"Registrar Account - ID\", \"nunique\"),\n",
|
||
" n_flow_days=(\"Centralisation Date\", \"nunique\"),\n",
|
||
" share_multicountry_accounts=(\"n_countries_in_stocks\", lambda x: (x > 1).mean())\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"# Helpful ratios\n",
|
||
"country_flows[\"avg_abs_flows_per_active_account\"] = country_flows[\"total_abs_flows\"] / country_flows[\"n_accounts_active\"].replace(0, np.nan)\n",
|
||
"\n",
|
||
"# Sort\n",
|
||
"country_flows = country_flows.sort_values(\"total_abs_flows\", ascending=False)\n",
|
||
"\n",
|
||
"print(\"\\nTop 15 countries by total_abs_flows (robust):\")\n",
|
||
"print(country_flows.head(15))\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 5) FOCUS CHECK: France vs UK\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"focus = country_flows[country_flows[\"country\"].isin([\"FRANCE\", \"UNITED KINGDOM\", \"UK\", \"UNITED-KINGDOM\"])].copy()\n",
|
||
"print(\"\\nFrance vs UK diagnostics:\")\n",
|
||
"print(focus)\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 6) EXTRA CHECK: is France driven by a few accounts?\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"flows_acc = (\n",
|
||
" flows_acc_day\n",
|
||
" .groupby([\"country\", \"Registrar Account - ID\"], as_index=False)\n",
|
||
" .agg(acc_abs_flows=(\"abs_flow\", \"sum\"))\n",
|
||
")\n",
|
||
"\n",
|
||
"fr = flows_acc[flows_acc[\"country\"] == \"FRANCE\"].sort_values(\"acc_abs_flows\", ascending=False)\n",
|
||
"uk = flows_acc[flows_acc[\"country\"] == \"UNITED KINGDOM\"].sort_values(\"acc_abs_flows\", ascending=False)\n",
|
||
"\n",
|
||
"print(\"\\nFrance: top 10 accounts driving flows:\")\n",
|
||
"print(fr.head(10))\n",
|
||
"\n",
|
||
"print(\"\\nUK: top 10 accounts driving flows:\")\n",
|
||
"print(uk.head(10))\n"
|
||
]
|
||
},
|
||
{
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"ename": "RuntimeError",
|
||
"evalue": "\n\nKaleido requires Google Chrome to be installed.\n\nEither download and install Chrome yourself following Google's instructions for your operating system,\nor install it from your terminal by running:\n\n $ plotly_get_chrome\n\n",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||
"\u001b[31mChromeNotFoundError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[31mChromeNotFoundError\u001b[39m: ",
|
||
"\nThe above exception was the direct cause of the following exception:\n",
|
||
"\u001b[31mChromeNotFoundError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/plotly/io/_kaleido.py:398\u001b[39m, in \u001b[36mto_image\u001b[39m\u001b[34m(fig, format, width, height, scale, validate, engine)\u001b[39m\n\u001b[32m 388\u001b[39m height = (\n\u001b[32m 389\u001b[39m height\n\u001b[32m 390\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m fig_dict.get(\u001b[33m\"\u001b[39m\u001b[33mlayout\u001b[39m\u001b[33m\"\u001b[39m, {}).get(\u001b[33m\"\u001b[39m\u001b[33mheight\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m (...)\u001b[39m\u001b[32m 395\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m defaults.default_height\n\u001b[32m 396\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m398\u001b[39m img_bytes = \u001b[43mkaleido\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcalc_fig_sync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 399\u001b[39m \u001b[43m \u001b[49m\u001b[43mfig_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 400\u001b[39m \u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 401\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdefaults\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdefault_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 402\u001b[39m \u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 404\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdefaults\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdefault_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 405\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 406\u001b[39m \u001b[43m \u001b[49m\u001b[43mtopojson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdefaults\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtopojson\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 407\u001b[39m \u001b[43m \u001b[49m\u001b[43mkopts\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkopts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 408\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 409\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ChromeNotFoundError:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/kaleido/__init__.py:171\u001b[39m, in \u001b[36mcalc_fig_sync\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 170\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m171\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_sync_server\u001b[49m\u001b[43m.\u001b[49m\u001b[43moneshot_async_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcalc_fig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m=\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/kaleido/_sync_server.py:131\u001b[39m, in \u001b[36moneshot_async_run\u001b[39m\u001b[34m(func, args, kwargs)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(res, \u001b[38;5;167;01mBaseException\u001b[39;00m):\n\u001b[32m--> \u001b[39m\u001b[32m131\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m res\n\u001b[32m 132\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/kaleido/_sync_server.py:122\u001b[39m, in \u001b[36moneshot_async_run.<locals>.run\u001b[39m\u001b[34m(func, q, *args, **kwargs)\u001b[39m\n\u001b[32m 121\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m122\u001b[39m q.put(\u001b[43masyncio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;66;03m# noqa: BLE001\u001b[39;00m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/asyncio/runners.py:195\u001b[39m, in \u001b[36mrun\u001b[39m\u001b[34m(main, debug, loop_factory)\u001b[39m\n\u001b[32m 194\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Runner(debug=debug, loop_factory=loop_factory) \u001b[38;5;28;01mas\u001b[39;00m runner:\n\u001b[32m--> \u001b[39m\u001b[32m195\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrunner\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmain\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/asyncio/runners.py:118\u001b[39m, in \u001b[36mRunner.run\u001b[39m\u001b[34m(self, coro, context)\u001b[39m\n\u001b[32m 117\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m118\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_loop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 119\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m exceptions.CancelledError:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/asyncio/base_events.py:725\u001b[39m, in \u001b[36mBaseEventLoop.run_until_complete\u001b[39m\u001b[34m(self, future)\u001b[39m\n\u001b[32m 723\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m'\u001b[39m\u001b[33mEvent loop stopped before Future completed.\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m725\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfuture\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/kaleido/__init__.py:101\u001b[39m, in \u001b[36mcalc_fig\u001b[39m\u001b[34m(fig, path, opts, topojson, kopts)\u001b[39m\n\u001b[32m 100\u001b[39m kopts[\u001b[33m\"\u001b[39m\u001b[33mn\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[32m1\u001b[39m \u001b[38;5;66;03m# should we force this?\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m101\u001b[39m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mKaleido\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkopts\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m k:\n\u001b[32m 102\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m k.calc_fig(\n\u001b[32m 103\u001b[39m fig,\n\u001b[32m 104\u001b[39m path=path,\n\u001b[32m 105\u001b[39m opts=opts,\n\u001b[32m 106\u001b[39m topojson=topojson,\n\u001b[32m 107\u001b[39m )\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/kaleido/kaleido.py:164\u001b[39m, in \u001b[36mKaleido.__init__\u001b[39m\u001b[34m(self, page_generator, n, timeout, width, height, stepper, plotlyjs, mathjax, *args, **kwargs)\u001b[39m\n\u001b[32m 163\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ChromeNotFoundError:\n\u001b[32m--> \u001b[39m\u001b[32m164\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ChromeNotFoundError(\n\u001b[32m 165\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mKaleido v1 and later requires Chrome to be installed. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 166\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mTo install Chrome, use the CLI command `kaleido_get_chrome`, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 167\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mor from Python, use either `kaleido.get_chrome()` \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 168\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mor `kaleido.get_chrome_sync()`.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 169\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mChromeNotFoundError\u001b[39;00m\n\u001b[32m 171\u001b[39m \u001b[38;5;66;03m# do this during open because it requires close\u001b[39;00m\n",
|
||
"\u001b[31mChromeNotFoundError\u001b[39m: Kaleido v1 and later requires Chrome to be installed. To install Chrome, use the CLI command `kaleido_get_chrome`, or from Python, use either `kaleido.get_chrome()` or `kaleido.get_chrome_sync()`.",
|
||
"\nDuring handling of the above exception, another exception occurred:\n",
|
||
"\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 178\u001b[39m\n\u001b[32m 166\u001b[39m fig.update_layout(\n\u001b[32m 167\u001b[39m margin=\u001b[38;5;28mdict\u001b[39m(l=\u001b[32m0\u001b[39m, r=\u001b[32m0\u001b[39m, t=\u001b[32m40\u001b[39m, b=\u001b[32m0\u001b[39m),\n\u001b[32m 168\u001b[39m legend_title_text=\u001b[33m\"\u001b[39m\u001b[33mTotal absolute net flows (€)\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 174\u001b[39m )\n\u001b[32m 175\u001b[39m )\n\u001b[32m 177\u001b[39m fig.show()\n\u001b[32m--> \u001b[39m\u001b[32m178\u001b[39m \u001b[43mfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrite_image\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtotal_flow_intensity_world.png\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/plotly/basedatatypes.py:3895\u001b[39m, in \u001b[36mBaseFigure.write_image\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 3891\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mengine\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 3892\u001b[39m warnings.warn(\n\u001b[32m 3893\u001b[39m ENGINE_PARAM_DEPRECATION_MSG, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel=\u001b[32m2\u001b[39m\n\u001b[32m 3894\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m3895\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrite_image\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/plotly/io/_kaleido.py:528\u001b[39m, in \u001b[36mwrite_image\u001b[39m\u001b[34m(fig, file, format, scale, width, height, validate, engine)\u001b[39m\n\u001b[32m 524\u001b[39m \u001b[38;5;28mformat\u001b[39m = infer_format(path, \u001b[38;5;28mformat\u001b[39m)\n\u001b[32m 526\u001b[39m \u001b[38;5;66;03m# Request image\u001b[39;00m\n\u001b[32m 527\u001b[39m \u001b[38;5;66;03m# Do this first so we don't create a file if image conversion fails\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m528\u001b[39m img_data = \u001b[43mto_image\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 529\u001b[39m \u001b[43m \u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 530\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 531\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 532\u001b[39m \u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 533\u001b[39m \u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 534\u001b[39m \u001b[43m \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[43m=\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 536\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 538\u001b[39m \u001b[38;5;66;03m# Open file\u001b[39;00m\n\u001b[32m 539\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 540\u001b[39m \u001b[38;5;66;03m# We previously failed to make sense of `file` as a pathlib object.\u001b[39;00m\n\u001b[32m 541\u001b[39m \u001b[38;5;66;03m# Attempt to write to `file` as an open file descriptor.\u001b[39;00m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/plotly/io/_kaleido.py:410\u001b[39m, in \u001b[36mto_image\u001b[39m\u001b[34m(fig, format, width, height, scale, validate, engine)\u001b[39m\n\u001b[32m 398\u001b[39m img_bytes = kaleido.calc_fig_sync(\n\u001b[32m 399\u001b[39m fig_dict,\n\u001b[32m 400\u001b[39m opts=\u001b[38;5;28mdict\u001b[39m(\n\u001b[32m (...)\u001b[39m\u001b[32m 407\u001b[39m kopts=kopts,\n\u001b[32m 408\u001b[39m )\n\u001b[32m 409\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ChromeNotFoundError:\n\u001b[32m--> \u001b[39m\u001b[32m410\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(PLOTLY_GET_CHROME_ERROR_MSG)\n\u001b[32m 412\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 413\u001b[39m \u001b[38;5;66;03m# Kaleido v0\u001b[39;00m\n\u001b[32m 414\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ENABLE_KALEIDO_V0_DEPRECATION_WARNINGS:\n",
|
||
"\u001b[31mRuntimeError\u001b[39m: \n\nKaleido requires Google Chrome to be installed.\n\nEither download and install Chrome yourself following Google's instructions for your operating system,\nor install it from your terminal by running:\n\n $ plotly_get_chrome\n\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# GEOGRAPHIC COVERAGE — TOTAL FLOW INTENSITY (LATAM + USA FIXED)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import plotly.express as px\n",
|
||
"import pycountry\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# Helper: country name → ISO-3\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"def country_to_iso3(name):\n",
|
||
" try:\n",
|
||
" return pycountry.countries.lookup(name).alpha_3\n",
|
||
" except:\n",
|
||
" return None\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 0. Harmonize country labels (USA + special buckets)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"stocks[\"RegistrarAccount - Country\"] = stocks[\"RegistrarAccount - Country\"].replace({\n",
|
||
" \"US OFFSHORE\": \"UNITED STATES\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"flows[\"RegistrarAccount - Country\"] = flows[\"RegistrarAccount - Country\"].replace({\n",
|
||
" \"US OFFSHORE\": \"UNITED STATES\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 1. Aggregate flows at Account × Date level (robust)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"flows_acc_day = (\n",
|
||
" flows\n",
|
||
" .assign(abs_flow=lambda x: x[\"Value € - NetFlows\"].abs())\n",
|
||
" .groupby([\"Registrar Account - ID\", \"Centralisation Date\"], as_index=False)\n",
|
||
" .agg(abs_flow=(\"abs_flow\", \"sum\"))\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 2. Map each Registrar Account to a country (from STOCKS)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"account_country = (\n",
|
||
" stocks\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"RegistrarAccount - Country\"]\n",
|
||
" .agg(lambda x: x.mode().iloc[0])\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"flows_acc_day = flows_acc_day.merge(\n",
|
||
" account_country,\n",
|
||
" on=\"Registrar Account - ID\",\n",
|
||
" how=\"left\"\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 3. Aggregate TOTAL absolute flows per country\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"country_flows = (\n",
|
||
" flows_acc_day\n",
|
||
" .groupby(\"RegistrarAccount - Country\", as_index=False)\n",
|
||
" .agg(\n",
|
||
" total_abs_flows=(\"abs_flow\", \"sum\"),\n",
|
||
" n_accounts_active=(\"Registrar Account - ID\", \"nunique\")\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 4. Handle LATAM explicitly (visual redistribution)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"latam_countries = [\n",
|
||
" \"ARGENTINA\", \"BRAZIL\", \"CHILE\", \"COLOMBIA\", \"MEXICO\",\n",
|
||
" \"PERU\", \"URUGUAY\", \"PARAGUAY\", \"BOLIVIA\",\n",
|
||
" \"ECUADOR\", \"VENEZUELA\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"latam_row = country_flows[\n",
|
||
" country_flows[\"RegistrarAccount - Country\"] == \"LATAM\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"if not latam_row.empty:\n",
|
||
" latam_flow = latam_row[\"total_abs_flows\"].iloc[0]\n",
|
||
" latam_accounts = latam_row[\"n_accounts_active\"].iloc[0]\n",
|
||
"\n",
|
||
" latam_df = pd.DataFrame({\n",
|
||
" \"RegistrarAccount - Country\": latam_countries,\n",
|
||
" \"total_abs_flows\": latam_flow / len(latam_countries),\n",
|
||
" \"n_accounts_active\": latam_accounts / len(latam_countries)\n",
|
||
" })\n",
|
||
"\n",
|
||
" country_flows = pd.concat(\n",
|
||
" [\n",
|
||
" country_flows[\n",
|
||
" ~country_flows[\"RegistrarAccount - Country\"].isin(\n",
|
||
" [\"LATAM\", \"INTERNATIONAL\", \"UNKNOWN\"]\n",
|
||
" )\n",
|
||
" ],\n",
|
||
" latam_df\n",
|
||
" ],\n",
|
||
" ignore_index=True\n",
|
||
" )\n",
|
||
"\n",
|
||
"else:\n",
|
||
" country_flows = country_flows[\n",
|
||
" ~country_flows[\"RegistrarAccount - Country\"].isin(\n",
|
||
" [\"INTERNATIONAL\", \"UNKNOWN\"]\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 5. Convert country names to ISO-3 (with diagnostics)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"country_flows[\"iso3\"] = country_flows[\"RegistrarAccount - Country\"].apply(country_to_iso3)\n",
|
||
"\n",
|
||
"unmapped = country_flows[country_flows[\"iso3\"].isna()]\n",
|
||
"if not unmapped.empty:\n",
|
||
" print(\"Unmapped country labels:\")\n",
|
||
" print(unmapped[\"RegistrarAccount - Country\"].unique())\n",
|
||
"\n",
|
||
"country_flows = country_flows.dropna(subset=[\"iso3\"])\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 6. Discretize flows (order-of-magnitude legend)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"bins = [0, 5e8, 2e9, 1e10, 3e10, np.inf]\n",
|
||
"labels = [\n",
|
||
" \"< €0.5bn\",\n",
|
||
" \"€0.5–2bn\",\n",
|
||
" \"€2–10bn\",\n",
|
||
" \"€10–30bn\",\n",
|
||
" \"> €30bn\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"country_flows[\"flow_bin\"] = pd.cut(\n",
|
||
" country_flows[\"total_abs_flows\"],\n",
|
||
" bins=bins,\n",
|
||
" labels=labels,\n",
|
||
" include_lowest=True\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 7. World map — total flow intensity\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"fig = px.choropleth(\n",
|
||
" country_flows,\n",
|
||
" locations=\"iso3\",\n",
|
||
" color=\"flow_bin\",\n",
|
||
" hover_name=\"RegistrarAccount - Country\",\n",
|
||
" hover_data={\n",
|
||
" \"total_abs_flows\": \":,.0f\",\n",
|
||
" \"n_accounts_active\": True\n",
|
||
" },\n",
|
||
" category_orders={\"flow_bin\": labels},\n",
|
||
" color_discrete_sequence=px.colors.sequential.OrRd\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" margin=dict(l=0, r=0, t=40, b=0),\n",
|
||
" legend_title_text=\"Total absolute net flows (€)\",\n",
|
||
" geo=dict(\n",
|
||
" showframe=False,\n",
|
||
" showcoastlines=True,\n",
|
||
" coastlinecolor=\"rgba(0,0,0,0.3)\",\n",
|
||
" projection_type=\"natural earth\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "36b93cfb-d7f5-4ba4-a86b-024247bddc5a",
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>RegistrarAccount - Country</th>\n",
|
||
" <th>n_accounts</th>\n",
|
||
" <th>iso3</th>\n",
|
||
" <th>account_bin</th>\n",
|
||
" <th>total_abs_flows</th>\n",
|
||
" <th>n_accounts_active</th>\n",
|
||
" <th>share_accounts</th>\n",
|
||
" <th>share_flows</th>\n",
|
||
" <th>flow_per_account</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>LUXEMBOURG</td>\n",
|
||
" <td>331.0</td>\n",
|
||
" <td>LUX</td>\n",
|
||
" <td>High (200–999)</td>\n",
|
||
" <td>6.445076e+10</td>\n",
|
||
" <td>310.0</td>\n",
|
||
" <td>0.047619</td>\n",
|
||
" <td>0.228139</td>\n",
|
||
" <td>1.947153e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>ITALY</td>\n",
|
||
" <td>487.0</td>\n",
|
||
" <td>ITA</td>\n",
|
||
" <td>High (200–999)</td>\n",
|
||
" <td>6.008376e+10</td>\n",
|
||
" <td>478.0</td>\n",
|
||
" <td>0.070062</td>\n",
|
||
" <td>0.212681</td>\n",
|
||
" <td>1.233753e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24</th>\n",
|
||
" <td>SPAIN</td>\n",
|
||
" <td>340.0</td>\n",
|
||
" <td>ESP</td>\n",
|
||
" <td>High (200–999)</td>\n",
|
||
" <td>4.995133e+10</td>\n",
|
||
" <td>330.0</td>\n",
|
||
" <td>0.048914</td>\n",
|
||
" <td>0.176814</td>\n",
|
||
" <td>1.469157e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>FRANCE</td>\n",
|
||
" <td>2672.0</td>\n",
|
||
" <td>FRA</td>\n",
|
||
" <td>Very high (≥ 1,000)</td>\n",
|
||
" <td>4.753435e+10</td>\n",
|
||
" <td>2631.0</td>\n",
|
||
" <td>0.384405</td>\n",
|
||
" <td>0.168259</td>\n",
|
||
" <td>1.778980e+07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>GERMANY</td>\n",
|
||
" <td>90.0</td>\n",
|
||
" <td>DEU</td>\n",
|
||
" <td>Medium (50–199)</td>\n",
|
||
" <td>1.960362e+10</td>\n",
|
||
" <td>80.0</td>\n",
|
||
" <td>0.012948</td>\n",
|
||
" <td>0.069392</td>\n",
|
||
" <td>2.178180e+08</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" RegistrarAccount - Country n_accounts iso3 account_bin \\\n",
|
||
"15 LUXEMBOURG 331.0 LUX High (200–999) \n",
|
||
"12 ITALY 487.0 ITA High (200–999) \n",
|
||
"24 SPAIN 340.0 ESP High (200–999) \n",
|
||
"6 FRANCE 2672.0 FRA Very high (≥ 1,000) \n",
|
||
"7 GERMANY 90.0 DEU Medium (50–199) \n",
|
||
"\n",
|
||
" total_abs_flows n_accounts_active share_accounts share_flows \\\n",
|
||
"15 6.445076e+10 310.0 0.047619 0.228139 \n",
|
||
"12 6.008376e+10 478.0 0.070062 0.212681 \n",
|
||
"24 4.995133e+10 330.0 0.048914 0.176814 \n",
|
||
"6 4.753435e+10 2631.0 0.384405 0.168259 \n",
|
||
"7 1.960362e+10 80.0 0.012948 0.069392 \n",
|
||
"\n",
|
||
" flow_per_account \n",
|
||
"15 1.947153e+08 \n",
|
||
"12 1.233753e+08 \n",
|
||
"24 1.469157e+08 \n",
|
||
"6 1.778980e+07 \n",
|
||
"7 2.178180e+08 "
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# ------------------------------------------------------------\n",
|
||
"# 8. Merge accounts & flows at country level\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"country_summary = (\n",
|
||
" country_accounts\n",
|
||
" .merge(\n",
|
||
" country_flows[[\n",
|
||
" \"RegistrarAccount - Country\",\n",
|
||
" \"total_abs_flows\",\n",
|
||
" \"n_accounts_active\"\n",
|
||
" ]],\n",
|
||
" on=\"RegistrarAccount - Country\",\n",
|
||
" how=\"inner\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"# Totals (for shares)\n",
|
||
"total_accounts = country_summary[\"n_accounts\"].sum()\n",
|
||
"total_flows = country_summary[\"total_abs_flows\"].sum()\n",
|
||
"\n",
|
||
"country_summary[\"share_accounts\"] = country_summary[\"n_accounts\"] / total_accounts\n",
|
||
"country_summary[\"share_flows\"] = country_summary[\"total_abs_flows\"] / total_flows\n",
|
||
"country_summary[\"flow_per_account\"] = (\n",
|
||
" country_summary[\"total_abs_flows\"] / country_summary[\"n_accounts\"]\n",
|
||
")\n",
|
||
"\n",
|
||
"country_summary.sort_values(\n",
|
||
" \"total_abs_flows\", ascending=False\n",
|
||
").head()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "fa687277-05fe-405e-967d-9ba91dce2a95",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>RegistrarAccount - Country</th>\n",
|
||
" <th>n_accounts</th>\n",
|
||
" <th>total_abs_flows</th>\n",
|
||
" <th>share_accounts</th>\n",
|
||
" <th>share_flows</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>30</th>\n",
|
||
" <td>UNITED STATES</td>\n",
|
||
" <td>1166.0</td>\n",
|
||
" <td>3.891997e+08</td>\n",
|
||
" <td>0.167746</td>\n",
|
||
" <td>0.001378</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>29</th>\n",
|
||
" <td>UNITED KINGDOM</td>\n",
|
||
" <td>622.0</td>\n",
|
||
" <td>3.576470e+09</td>\n",
|
||
" <td>0.089484</td>\n",
|
||
" <td>0.012660</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" RegistrarAccount - Country n_accounts total_abs_flows share_accounts \\\n",
|
||
"30 UNITED STATES 1166.0 3.891997e+08 0.167746 \n",
|
||
"29 UNITED KINGDOM 622.0 3.576470e+09 0.089484 \n",
|
||
"\n",
|
||
" share_flows \n",
|
||
"30 0.001378 \n",
|
||
"29 0.012660 "
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Countries with many accounts but low flow intensity\n",
|
||
"low_flow_high_accounts = (\n",
|
||
" country_summary\n",
|
||
" .query(\"share_accounts > 0.05 and share_flows < 0.05\")\n",
|
||
" .sort_values(\"n_accounts\", ascending=False)\n",
|
||
")\n",
|
||
"\n",
|
||
"low_flow_high_accounts[\n",
|
||
" [\n",
|
||
" \"RegistrarAccount - Country\",\n",
|
||
" \"n_accounts\",\n",
|
||
" \"total_abs_flows\",\n",
|
||
" \"share_accounts\",\n",
|
||
" \"share_flows\"\n",
|
||
" ]\n",
|
||
"]\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "bbebadf0-19a3-426a-bfb1-96b48a94832f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# STYLE — PUBLICATION\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"sns.set_theme(\n",
|
||
" style=\"whitegrid\",\n",
|
||
" context=\"paper\",\n",
|
||
" font_scale=1.2\n",
|
||
")\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# DATA PREP\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"])\n",
|
||
"flows[\"Centralisation Date\"] = pd.to_datetime(flows[\"Centralisation Date\"])\n",
|
||
"\n",
|
||
"snapshot_date = stocks[\"Centralisation Date\"].max()\n",
|
||
"\n",
|
||
"aum = (\n",
|
||
" stocks.loc[stocks[\"Centralisation Date\"] == snapshot_date]\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value - AUM €\"]\n",
|
||
" .sum()\n",
|
||
" .rename(\"aum_eur\")\n",
|
||
")\n",
|
||
"\n",
|
||
"flow_activity = (\n",
|
||
" flows\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value € - NetFlows\"]\n",
|
||
" .apply(lambda x: x.abs().mean())\n",
|
||
" .rename(\"avg_abs_flow\")\n",
|
||
")\n",
|
||
"\n",
|
||
"df = pd.concat([aum, flow_activity], axis=1).dropna()\n",
|
||
"df = df[(df[\"aum_eur\"] > 0) & (df[\"avg_abs_flow\"] > 0)]\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# ECDF FUNCTION\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"def ecdf(x):\n",
|
||
" x = np.sort(x)\n",
|
||
" y = np.arange(1, len(x) + 1) / len(x)\n",
|
||
" return x, y\n",
|
||
"\n",
|
||
"x_aum, y_aum = ecdf(df[\"aum_eur\"])\n",
|
||
"x_flow, y_flow = ecdf(df[\"avg_abs_flow\"])\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# PLOT\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots(figsize=(7.5, 5))\n",
|
||
"\n",
|
||
"ax.plot(x_aum, y_aum, label=\"Assets under management (AUM)\", linewidth=2)\n",
|
||
"ax.plot(x_flow, y_flow, label=\"Average absolute net flows\", linewidth=2)\n",
|
||
"\n",
|
||
"# Percentiles to annotate\n",
|
||
"percentiles = [0.5, 0.75, 0.9]\n",
|
||
"\n",
|
||
"for p in percentiles:\n",
|
||
" q_aum = np.quantile(df[\"aum_eur\"], p)\n",
|
||
" q_flow = np.quantile(df[\"avg_abs_flow\"], p)\n",
|
||
"\n",
|
||
" ax.scatter(q_aum, p, s=25)\n",
|
||
" ax.scatter(q_flow, p, s=25)\n",
|
||
"\n",
|
||
" ax.text(\n",
|
||
" q_aum, p,\n",
|
||
" f\"{int(p*100)}%\",\n",
|
||
" ha=\"left\", va=\"bottom\", fontsize=9\n",
|
||
" )\n",
|
||
"\n",
|
||
"ax.set_xscale(\"log\")\n",
|
||
"ax.set_xlabel(\"€ (log scale)\")\n",
|
||
"ax.set_ylabel(\"Cumulative share of registrar accounts\")\n",
|
||
"ax.set_title(\"Empirical Distributions of Account Size and Flow Activity\")\n",
|
||
"ax.legend(frameon=False)\n",
|
||
"\n",
|
||
"sns.despine()\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "9d647926-4bea-415d-9eb4-9745d76565e7",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# STYLE — PUBLICATION\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"sns.set_theme(\n",
|
||
" style=\"whitegrid\",\n",
|
||
" context=\"paper\",\n",
|
||
" font_scale=1.2\n",
|
||
")\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# DATA PREP\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"])\n",
|
||
"flows[\"Centralisation Date\"] = pd.to_datetime(flows[\"Centralisation Date\"])\n",
|
||
"\n",
|
||
"snapshot_date = stocks[\"Centralisation Date\"].max()\n",
|
||
"\n",
|
||
"# AUM snapshot\n",
|
||
"aum = (\n",
|
||
" stocks.loc[stocks[\"Centralisation Date\"] == snapshot_date]\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value - AUM €\"]\n",
|
||
" .sum()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Flow activity\n",
|
||
"flow_activity = (\n",
|
||
" flows\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value € - NetFlows\"]\n",
|
||
" .apply(lambda x: x.abs().mean())\n",
|
||
")\n",
|
||
"\n",
|
||
"df = pd.concat([aum, flow_activity], axis=1)\n",
|
||
"df.columns = [\"aum_eur\", \"avg_abs_flow\"]\n",
|
||
"df = df.dropna()\n",
|
||
"df = df[(df[\"aum_eur\"] > 0) & (df[\"avg_abs_flow\"] > 0)]\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# QUANTILES\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"quantiles = [0.5, 0.75, 0.9, 0.95, 0.99]\n",
|
||
"\n",
|
||
"rows = []\n",
|
||
"for q in quantiles:\n",
|
||
" rows.append({\n",
|
||
" \"Percentile\": f\"{int(q*100)}%\",\n",
|
||
" \"Variable\": \"Assets under management (AUM)\",\n",
|
||
" \"Value\": df[\"aum_eur\"].quantile(q)\n",
|
||
" })\n",
|
||
" rows.append({\n",
|
||
" \"Percentile\": f\"{int(q*100)}%\",\n",
|
||
" \"Variable\": \"Average absolute net flows\",\n",
|
||
" \"Value\": df[\"avg_abs_flow\"].quantile(q)\n",
|
||
" })\n",
|
||
"\n",
|
||
"q_df = pd.DataFrame(rows)\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# PLOT\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"plt.figure(figsize=(8, 5))\n",
|
||
"\n",
|
||
"sns.barplot(\n",
|
||
" data=q_df,\n",
|
||
" x=\"Percentile\",\n",
|
||
" y=\"Value\",\n",
|
||
" hue=\"Variable\"\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.yscale(\"log\")\n",
|
||
"plt.ylabel(\"€ (log scale)\")\n",
|
||
"plt.xlabel(\"Registrar account percentile\")\n",
|
||
"#plt.title(\"Account Size vs Flow Activity Across the Distribution\")\n",
|
||
"plt.legend(frameon=False)\n",
|
||
"\n",
|
||
"sns.despine()\n",
|
||
"plt.tight_layout()\n",
|
||
"\n",
|
||
"plt.savefig(\n",
|
||
" \"aum_vs_flow_activity_concentration_top10.png\",\n",
|
||
" dpi=300,\n",
|
||
" bbox_inches=\"tight\"\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "dc2aed5b-67a1-453d-9d74-d47a25135658",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# ------------------------------------------------------------\n",
|
||
"# 5. Convert country names to ISO-3\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"def country_to_iso3(name):\n",
|
||
" try:\n",
|
||
" return pycountry.countries.lookup(name).alpha_3\n",
|
||
" except:\n",
|
||
" return None\n",
|
||
"\n",
|
||
"country_flows[\"iso3\"] = country_flows[\"country\"].apply(country_to_iso3)\n",
|
||
"\n",
|
||
"# Diagnostic: unmapped countries\n",
|
||
"unmapped = country_flows[country_flows[\"iso3\"].isna()]\n",
|
||
"if not unmapped.empty:\n",
|
||
" print(\"Unmapped country labels:\")\n",
|
||
" print(unmapped[\"country\"].unique())\n",
|
||
"\n",
|
||
"country_flows = country_flows.dropna(subset=[\"iso3\"])\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 6. Discretize total absolute flows (order of magnitude)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"bins = [0, 5e8, 2e9, 1e10, 3e10, np.inf]\n",
|
||
"labels = [\n",
|
||
" \"< €0.5bn\",\n",
|
||
" \"€0.5–2bn\",\n",
|
||
" \"€2–10bn\",\n",
|
||
" \"€10–30bn\",\n",
|
||
" \"> €30bn\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"\n",
|
||
"country_flows[\"flow_bin\"] = pd.cut(\n",
|
||
" country_flows[\"total_abs_flows\"],\n",
|
||
" bins=bins,\n",
|
||
" labels=labels,\n",
|
||
" include_lowest=True\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 7. World map — total absolute net flows\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"fig = px.choropleth(\n",
|
||
" country_flows,\n",
|
||
" locations=\"iso3\",\n",
|
||
" color=\"flow_bin\",\n",
|
||
" hover_name=\"country\",\n",
|
||
" hover_data={\n",
|
||
" \"total_abs_flows\": \":,.0f\",\n",
|
||
" \"n_accounts_active\": True\n",
|
||
" },\n",
|
||
" category_orders={\"flow_bin\": labels},\n",
|
||
" color_discrete_sequence=px.colors.sequential.OrRd,\n",
|
||
" title=\"Geographic distribution of total absolute net flows (€)\"\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" margin=dict(l=0, r=0, t=60, b=0),\n",
|
||
" legend_title_text=\"Total absolute net flows (€)\",\n",
|
||
" geo=dict(\n",
|
||
" showframe=False,\n",
|
||
" showcoastlines=True,\n",
|
||
" coastlinecolor=\"rgba(0,0,0,0.3)\",\n",
|
||
" projection_type=\"natural earth\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "a37023a0-388c-4883-a193-36d4c3eeead2",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# STYLE\n",
|
||
"# =========================\n",
|
||
"sns.set_theme(style=\"whitegrid\", context=\"paper\", font_scale=1.2)\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# DATA PREP\n",
|
||
"# =========================\n",
|
||
"stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"])\n",
|
||
"flows[\"Centralisation Date\"] = pd.to_datetime(flows[\"Centralisation Date\"])\n",
|
||
"\n",
|
||
"snapshot_date = stocks[\"Centralisation Date\"].max()\n",
|
||
"\n",
|
||
"# AUM snapshot par account\n",
|
||
"aum = (\n",
|
||
" stocks.loc[stocks[\"Centralisation Date\"] == snapshot_date]\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value - AUM €\"]\n",
|
||
" .sum()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Flow activity totale par account\n",
|
||
"flows_activity = (\n",
|
||
" flows\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value € - NetFlows\"]\n",
|
||
" .apply(lambda x: x.abs().sum())\n",
|
||
")\n",
|
||
"\n",
|
||
"df = pd.concat([aum, flows_activity], axis=1)\n",
|
||
"df.columns = [\"aum_eur\", \"abs_flows\"]\n",
|
||
"df = df.dropna()\n",
|
||
"df = df[(df[\"aum_eur\"] > 0) & (df[\"abs_flows\"] > 0)]\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# TOP 10 % VS BOTTOM 90 %\n",
|
||
"# =========================\n",
|
||
"top_pct = 0.10\n",
|
||
"df_sorted = df.sort_values(\"aum_eur\", ascending=False)\n",
|
||
"cutoff = int(len(df_sorted) * top_pct)\n",
|
||
"\n",
|
||
"top = df_sorted.iloc[:cutoff]\n",
|
||
"bottom = df_sorted.iloc[cutoff:]\n",
|
||
"\n",
|
||
"aum_top = top[\"aum_eur\"].sum()\n",
|
||
"aum_bottom = bottom[\"aum_eur\"].sum()\n",
|
||
"\n",
|
||
"flows_top = top[\"abs_flows\"].sum()\n",
|
||
"flows_bottom = bottom[\"abs_flows\"].sum()\n",
|
||
"\n",
|
||
"# Normalisation\n",
|
||
"aum_total = aum_top + aum_bottom\n",
|
||
"flows_total = flows_top + flows_bottom\n",
|
||
"\n",
|
||
"plot_df = pd.DataFrame({\n",
|
||
" \"Metric\": [\"AUM\", \"Flow activity\"],\n",
|
||
" \"Top 10%\": [aum_top / aum_total, flows_top / flows_total],\n",
|
||
" \"Bottom 90%\": [aum_bottom / aum_total, flows_bottom / flows_total],\n",
|
||
"})\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# PLOT — STACKED BAR\n",
|
||
"# =========================\n",
|
||
"fig, ax = plt.subplots(figsize=(6, 4))\n",
|
||
"\n",
|
||
"ax.bar(\n",
|
||
" plot_df[\"Metric\"],\n",
|
||
" plot_df[\"Top 10%\"],\n",
|
||
" label=\"Top 10% of registrar accounts\",\n",
|
||
" color=\"steelblue\"\n",
|
||
")\n",
|
||
"\n",
|
||
"ax.bar(\n",
|
||
" plot_df[\"Metric\"],\n",
|
||
" plot_df[\"Bottom 90%\"],\n",
|
||
" bottom=plot_df[\"Top 10%\"],\n",
|
||
" label=\"Bottom 90% of registrar accounts\",\n",
|
||
" color=\"lightgrey\"\n",
|
||
")\n",
|
||
"\n",
|
||
"ax.set_ylim(0, 1)\n",
|
||
"ax.set_ylabel(\"Share of total\")\n",
|
||
"ax.set_title(\"Concentration of AUM and Flow Activity\\nTop 10% vs Bottom 90% of Accounts\")\n",
|
||
"\n",
|
||
"ax.legend(frameon=False)\n",
|
||
"sns.despine()\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7f64f406-6f06-4d56-9684-834aaccefe5c",
|
||
"metadata": {},
|
||
"source": [
|
||
"# MERGE"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "e9bb67ab-9029-4ace-b960-b3d6e0b8683c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 1. Merge behavior (flows) with static diversification (stocks)\n",
|
||
"client_master = client_behavior.merge(\n",
|
||
" account_static,\n",
|
||
" on=\"Registrar Account - ID\",\n",
|
||
" how=\"left\"\n",
|
||
")\n",
|
||
"\n",
|
||
"# 2. Add geographic info\n",
|
||
"client_master = client_master.merge(\n",
|
||
" geo,\n",
|
||
" on=\"Registrar Account - ID\",\n",
|
||
" how=\"left\"\n",
|
||
")\n",
|
||
"\n",
|
||
"# 3. Create additional engineered features\n",
|
||
"client_master[\"log_total_aum\"] = np.log1p(client_master[\"total_aum\"].clip(lower=0))\n",
|
||
"client_master[\"log_median_aum\"] = np.log1p(client_master[\"median_aum\"].clip(lower=0))\n",
|
||
"\n",
|
||
"\n",
|
||
"# 4. Replace NaN flow volatility with 0 (inactive accounts)\n",
|
||
"client_master[\"flow_volatility\"] = client_master[\"flow_volatility\"].fillna(0)\n",
|
||
"\n",
|
||
"# 5. Fill missing diversification metrics with 0 (for accounts without stocks)\n",
|
||
"client_master[[\"n_isin_held\",\"n_funds_held\",\"n_asset_types\",\"n_strategies\"]] = \\\n",
|
||
" client_master[[\"n_isin_held\",\"n_funds_held\",\"n_asset_types\",\"n_strategies\"]].fillna(0)\n",
|
||
"\n",
|
||
"# 6. Fill missing geography as “UNKNOWN”\n",
|
||
"client_master[\"country\"] = client_master[\"country\"].fillna(\"UNKNOWN\")\n",
|
||
"client_master[\"region\"] = client_master[\"region\"].fillna(\"UNKNOWN\")\n",
|
||
"\n",
|
||
"# 7. Export\n",
|
||
"client_master.to_csv(\"client_master.csv\", index=False)\n",
|
||
"\n",
|
||
"print(client_master.head())\n",
|
||
"print(client_master.describe(include='all'))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "ea64d610-7816-4ead-8af5-33999fee87b4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"stocks.to_csv('stocks.csv')\n",
|
||
"flows.to_csv('flows.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "4808a08d-57cd-4a6b-af8e-e1cc461ea4ca",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.11"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|