cleaning the repo's arch
This commit is contained in:
parent
f4353149a6
commit
220057e4bc
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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27
Data_Exploration/explore.ipynb
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27
Data_Exploration/explore.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bd938e6e",
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"metadata": {},
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"source": [
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"**Short notebook to test connectivity with S3 services and explore the data**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8c4e04e6",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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201
dataloader.ipynb
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dataloader.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "126c8a80-d9ad-4816-84f0-0c3d580f62c8",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "ff2261fb-9516-4410-b42d-3acc8dc1a460",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import s3fs\n",
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"os.environ[\"AWS_ACCESS_KEY_ID\"] = 'N1DBJCHI7YTK9AVMG6XT'\n",
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"os.environ[\"AWS_SECRET_ACCESS_KEY\"] = 'SRCPMh8a1eQxX6Z09GeDxZoD55MBpnkJzyBctLII'\n",
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"os.environ[\"AWS_SESSION_TOKEN\"] = 'eyJhbGciOiJIUzUxMiIsInR5cCI6IkpXVCJ9.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.sLXOE8w930_dXU0yNWroaDvaTvcUUCONMcbgbKeMEduQebXQjOS7gEQxo-I7Q2oqLFb_dhg1zBlwx5VpNjyTMA'\n",
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"os.environ[\"AWS_DEFAULT_REGION\"] = 'us-east-1'\n",
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"fs = s3fs.S3FileSystem(\n",
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" client_kwargs={'endpoint_url': 'https://'+'minio-simple.lab.groupe-genes.fr'},\n",
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" key = os.environ[\"AWS_ACCESS_KEY_ID\"], \n",
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" secret = os.environ[\"AWS_SECRET_ACCESS_KEY\"], \n",
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" token = os.environ[\"AWS_SESSION_TOKEN\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "dc546698-76dc-4eaf-b9e2-7602953bf8f5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Morningstar Global Asset Class</th>\n",
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" <th>Morningstar Global Category</th>\n",
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" <th>Morningstar Category</th>\n",
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" <th>Combined Country</th>\n",
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" <th>Combined Channel Type</th>\n",
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" <th>Combined Type</th>\n",
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" <th>Month/Year (Record Date)</th>\n",
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" <th>Combined Net Assets</th>\n",
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" <th>Combined Net Sales</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>Luxembourg</td>\n",
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" <td>Proprietary</td>\n",
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" <td>Domestic</td>\n",
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" <td>Jan 2015</td>\n",
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" <td>11170000.0</td>\n",
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" <td>799301.47</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>Luxembourg</td>\n",
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" <td>Proprietary</td>\n",
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" <td>Domestic</td>\n",
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" <td>Feb 2015</td>\n",
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" <td>21210000.0</td>\n",
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" <td>8922456.46</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>Luxembourg</td>\n",
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" <td>Proprietary</td>\n",
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" <td>Domestic</td>\n",
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" <td>Mar 2015</td>\n",
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" <td>23670000.0</td>\n",
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" <td>1718627.81</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>Luxembourg</td>\n",
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" <td>Proprietary</td>\n",
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" <td>Domestic</td>\n",
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" <td>Apr 2015</td>\n",
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" <td>22720000.0</td>\n",
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" <td>-670097.35</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>Luxembourg</td>\n",
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" <td>Proprietary</td>\n",
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" <td>Domestic</td>\n",
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" <td>May 2015</td>\n",
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" <td>23550000.0</td>\n",
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" <td>204625.93</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Morningstar Global Asset Class Morningstar Global Category \\\n",
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"0 NaN NaN \n",
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"1 NaN NaN \n",
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"2 NaN NaN \n",
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"3 NaN NaN \n",
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"4 NaN NaN \n",
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"\n",
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" Morningstar Category Combined Country Combined Channel Type Combined Type \\\n",
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"0 NaN Luxembourg Proprietary Domestic \n",
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"1 NaN Luxembourg Proprietary Domestic \n",
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"2 NaN Luxembourg Proprietary Domestic \n",
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"3 NaN Luxembourg Proprietary Domestic \n",
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"4 NaN Luxembourg Proprietary Domestic \n",
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"\n",
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" Month/Year (Record Date) Combined Net Assets Combined Net Sales \n",
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"0 Jan 2015 11170000.0 799301.47 \n",
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"1 Feb 2015 21210000.0 8922456.46 \n",
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"2 Mar 2015 23670000.0 1718627.81 \n",
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"3 Apr 2015 22720000.0 -670097.35 \n",
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"4 May 2015 23550000.0 204625.93 "
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"with fs.open('projet-bdc-data/carmignac/Data Modélisation/market data/broadridge_Global Market data MS.csv', 'rb') as f:\n",
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" df = pd.read_csv(f)\n",
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"\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7494bffd-83b5-42e2-b17e-d04c90f3b59e",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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