{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f422a945-bba7-4485-9b7c-81adbb0e83e7",
   "metadata": {},
   "source": [
    "# Inflation and contributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "172ac561-6358-4a5e-bce6-47ad91cf32ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from pynsee.macrodata.get_series_list import get_series_list\n",
    "from pynsee.macrodata.get_series import get_series\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b024f55c-d5f9-4b36-8263-0fe0c88da78d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import sys\n",
    "logging.basicConfig(stream=sys.stdout,\n",
    "                    level=logging.INFO, \n",
    "                    format=\"%(message)s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d18c5a4-9c70-49bb-aef4-74c28d4f322d",
   "metadata": {},
   "source": [
    "*Formula to compute contributions of each sub-item to the inflation index*:\n",
    "\n",
    "$CTR_{m, k}^{a} = \\frac{1}{I_{m, a-1}} [w_a^{k} \\frac{I_{dec, a-1}}{I_{dec, a-1}^{k}} (I_{m, a-1}^{k} - I_{dec, a-1}^{k}) + w_{a-1}^{k} \\frac{I_{dec, a-2}}{I_{dec, a-2}^{k}} (I_{dec, a-1}^{k} - I_{m, a-1}^{k})]$\n",
    "\n",
    "\n",
    "* $k$ is the index of the sub-item, if it is not used the variable value corresponds to the agregated index\n",
    "* $I$ is the index in nominal values\n",
    "* $CTR_{m, a}^{k}$ contribution of sub-item k to the inflation index on year a and month m\n",
    "* $w_a^{k}$ is the weight of the sub-item k in the agregated index for year a\n",
    "* $m=dec$, december value is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff3e6436-d41a-461b-a162-0f6664e3d0b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pynsee import init_conn\n",
    "# init_conn(insee_key='key', insee_secret='secret')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1adc3bc8-7e00-4514-87e4-8cdb018230ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/48719b3a81bc306982ce59585ceb295f.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/d8d557aac16cb9a7c1a31036b2a0c591.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "INSEE API credentials have not been found: please try to reuse pynsee.init_conn to save them locally.\n",
      "Otherwise, you can still use environment variables as follow:\n",
      "\n",
      "import os\n",
      "os.environ['sirene_key'] = 'my_sirene_key'\n",
      "https://api.insee.fr/series/BDM/V1/datastructure/FR1/IPC-2015\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/b2515683c224ab87c8927b2bdc3e6f67.parquet\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/9bd33a4f18da8c55c6a8a738b9cf3cea.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/3b306dc859e0f04277c1f7690d3d0244.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "https://api.insee.fr/series/BDM/V1/codelist/FR1/CL_FORME-VENTE\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/9c4b3f64b46ac92b854f1598db4be7df.parquet\n",
      "https://api.insee.fr/series/BDM/V1/codelist/FR1/CL_COICOP2016\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/d4e7505bfa6045e450de2cec1bf82e19.parquet\n",
      "https://api.insee.fr/series/BDM/V1/codelist/FR1/CL_PRIX_CONSO\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/93063a7efe233ad55bdfaf99a868cdb9.parquet\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/2228a230b4a41ed9bd78040f81d78f53.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "https://api.insee.fr/series/BDM/V1/codelist/FR1/CL_MENAGES_IPC\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/cd30e083b3ae58677f34e31c10dfce99.parquet\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/a07e0a76a3f7ef139675af142cf5cd98.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/f54b5a78c2e29a3fae62bb8e3befcbe7.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/8b57390a7bb158325698a6674e17dfe1.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "https://api.insee.fr/series/BDM/V1/codelist/FR1/CL_BASIND\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/d7644e32958b326103c9c0cec4f3c9dc.parquet\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/665ccedc355adebe7c56a7d61d608cac.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/d76f88dba142a90bc89ed001f671cd09.parquet\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/be75364011bd8af0c1b63ccda41105a6.parquet\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/15fe4d6264916b9a1c0c1e0b9cbe6e35.parquet\n"
     ]
    }
   ],
   "source": [
    "# download a list of inflation dataset series identifiers\n",
    "series = get_series_list('IPC-2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59f7a894-6df5-4bff-8495-fe0655bd25f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# total inflation series identifier\n",
    "IdInflation = series.loc[(series.FREQ == \"M\") &\n",
    "            (series.COICOP2016 == \"00\") &\n",
    "            (series.NATURE == \"INDICE\") &\n",
    "            (series.REF_AREA == \"FE\") &\n",
    "            (series.MENAGES_IPC == \"ENSEMBLE\") &\n",
    "            (series.PRIX_CONSO == \"SO\")]\n",
    "\n",
    "# price indexes including in inflation total index\n",
    "IdIndices = series.loc[(series.FREQ == \"M\") &\n",
    "            (series.COICOP2016 == \"SO\") &\n",
    "            (series.NATURE == \"INDICE\") &\n",
    "            (series.REF_AREA == \"FE\") &\n",
    "            (series.MENAGES_IPC == \"ENSEMBLE\") &\n",
    "            (series.PRIX_CONSO.isin([\"4000\", \"4034\", \"4003\", \"4009\", \"4007\"]))]\n",
    "\n",
    "# weights of price indexes including in inflation total index\n",
    "IdPond = series.loc[(series.FREQ == \"A\") &\n",
    "            (series.COICOP2016 == \"SO\") &\n",
    "            (series.NATURE == \"POND\") &\n",
    "            (series.REF_AREA == \"FE\") &\n",
    "            (series.MENAGES_IPC == \"ENSEMBLE\") &\n",
    "            (series.PRIX_CONSO.isin([\"4000\", \"4034\", \"4003\", \"4009\", \"4007\"]))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "01d3cf0e-2e86-4d82-82be-7f38272667e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://api.insee.fr/series/BDM/V1/data/SERIES_BDM/001759970\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/d8d557aac16cb9a7c1a31036b2a0c591.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/48719b3a81bc306982ce59585ceb295f.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Metadata download:   0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/be75364011bd8af0c1b63ccda41105a6.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Metadata download: 100%|██████████| 1/1 [00:00<00:00, 32.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/07078a87381c71e51c92c602d6694841.parquet\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/f3805eaca1bdfd618a8ac8d53d410b60.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/1f5fd9f024c0440edd7f284422e178b1.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/4a0d6e080c92889d6e1412250ab53c51.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/4a452baed0d45d6ea1a029f6e96a856b.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/d15c39f20fa0bad72b8d48ff0f5ef778.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/75e55239e1aed0fea3ed0ba8ebc926f0.parquet\n",
      "https://api.insee.fr/series/BDM/V1/data/SERIES_BDM/001759963%2B001759966%2B001759967%2B001759968%2B001759969\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/d8d557aac16cb9a7c1a31036b2a0c591.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/07078a87381c71e51c92c602d6694841.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/f3805eaca1bdfd618a8ac8d53d410b60.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/1f5fd9f024c0440edd7f284422e178b1.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/4a0d6e080c92889d6e1412250ab53c51.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/4a452baed0d45d6ea1a029f6e96a856b.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/d15c39f20fa0bad72b8d48ff0f5ef778.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/8b5856c1a4882cc2d1bceca8c9ce93f2.parquet\n",
      "https://api.insee.fr/series/BDM/V1/data/SERIES_BDM/001761322%2B001761323%2B001761324%2B001761325%2B001761319\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/d8d557aac16cb9a7c1a31036b2a0c591.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/07078a87381c71e51c92c602d6694841.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/f3805eaca1bdfd618a8ac8d53d410b60.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/1f5fd9f024c0440edd7f284422e178b1.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/4a0d6e080c92889d6e1412250ab53c51.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/4a452baed0d45d6ea1a029f6e96a856b.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/d15c39f20fa0bad72b8d48ff0f5ef778.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Data saved:\n",
      "/home/onyxia/.cache/pynsee/pynsee/8bb0705ccf08b0aded5ba35599edb57e.parquet\n"
     ]
    }
   ],
   "source": [
    "# collect data\n",
    "inflation = get_series(IdInflation.IDBANK)\n",
    "indices = get_series(IdIndices.IDBANK)\n",
    "pond = get_series(IdPond.IDBANK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c779f51b-1135-48f1-b9a2-e9732137852f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute ponderation and annual growth rate\n",
    "pond['pond'] = pond.OBS_VALUE / pond.groupby(['DATE']).OBS_VALUE.transform('sum')\n",
    "indices['growth'] = (indices.OBS_VALUE/ indices.groupby(['IDBANK']).OBS_VALUE.shift(12) - 1) * 100\n",
    "inflation['growth'] = (inflation.OBS_VALUE/ inflation.groupby(['IDBANK']).OBS_VALUE.shift(12) - 1) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d2b4ce7-12fb-48e4-a41d-0d6eb686b52f",
   "metadata": {},
   "outputs": [],
   "source": [
    "inflation['month'] = inflation['DATE'].apply(lambda x: x.month)\n",
    "indices['month'] = indices['DATE'].apply(lambda x: x.month)\n",
    "\n",
    "inflation['year'] = inflation['DATE'].apply(lambda x: x.year)\n",
    "indices['year'] = indices['DATE'].apply(lambda x: x.year)\n",
    "pond['year'] = pond['DATE'].apply(lambda x: x.year)\n",
    "\n",
    "inflation = inflation.rename(columns = {\"OBS_VALUE\" : \"inflation\"})\n",
    "indices = indices.rename(columns = {\"OBS_VALUE\" : \"indices\"})\n",
    "\n",
    "inflationDec = inflation[inflation['month'] == 12]\n",
    "indicesDec = indices[indices['month'] == 12]\n",
    "\n",
    "indicesDec = indicesDec.rename(columns = {\"indices\" : \"indicesDec\"})\n",
    "indicesDec = indicesDec[[\"year\", \"COICOP2016\", \"indicesDec\", \"PRIX_CONSO\"]]\n",
    "\n",
    "inflationDec = inflationDec.rename(columns = {\"inflation\" : \"inflationDec\"})\n",
    "inflationDec = inflationDec[[\"year\", \"COICOP2016\", \"inflationDec\", \"PRIX_CONSO\"]]\n",
    "\n",
    "pond = pond[[\"year\", \"COICOP2016\", \"pond\", \"PRIX_CONSO\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d62cee5c-e379-47c4-832a-8a91e954474f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# merge all datasets\n",
    "data = indices.merge(indicesDec, on = [\"year\", \"COICOP2016\", \"PRIX_CONSO\"], how = 'left')\n",
    "data = data.merge(pond, on = [\"year\", \"COICOP2016\", \"PRIX_CONSO\"], how = 'left')\n",
    "data = data.merge(inflation[['DATE', 'inflation']], on = [\"DATE\"], how = 'left')\n",
    "data = data.merge(inflationDec[['year', 'inflationDec']], on = [\"year\"], how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82b71789-2e54-4b83-8fa2-d6a4efb0cd4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1983/648722610.py:4: DeprecationWarning: 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",
      "  data['contrib1'] = df.apply(lambda x: x['inflationDec'].shift(1) * x['pond'] *\n",
      "/tmp/ipykernel_1983/648722610.py:7: DeprecationWarning: 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",
      "  data['contrib2'] = df.apply(lambda x: x['pond'].shift(1) * x['inflationDec'].shift(2) / x['indicesDec'].shift(2) *\n",
      "/tmp/ipykernel_1983/648722610.py:10: DeprecationWarning: 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",
      "  data['contrib'] =  df.apply(lambda x: 100 * (x['contrib1'] + x['contrib2']) /\n"
     ]
    }
   ],
   "source": [
    "# compute contributions by coicop\n",
    "df = data.groupby(['month', 'COICOP2016', 'PRIX_CONSO'], as_index=False)\n",
    "\n",
    "data['contrib1'] = df.apply(lambda x: x['inflationDec'].shift(1) * x['pond'] * \n",
    "                      (x['indices'] / x['indicesDec'].shift(1) - 1)).reset_index(level=0, drop=True)\n",
    "\n",
    "data['contrib2'] = df.apply(lambda x: x['pond'].shift(1) * x['inflationDec'].shift(2) / x['indicesDec'].shift(2) *\n",
    "                      (x['indicesDec'].shift(1) - x['indices'].shift(1))).reset_index(level=0, drop=True)\n",
    "\n",
    "data['contrib'] =  df.apply(lambda x: 100 * (x['contrib1'] + x['contrib2']) /\n",
    "                            x['inflation'].shift(1)).reset_index(level=0, drop=True)        \n",
    "\n",
    "# reshape dataframe for plotting purposes\n",
    "data2 = data[data['year'] >= 2008].sort_values(by=\"DATE\").reset_index()\n",
    "data2 = data2[['DATE', 'PRIX_CONSO_label_en', 'contrib']]\n",
    "data2 = data2.pivot(index=\"DATE\", columns=\"PRIX_CONSO_label_en\", values=\"contrib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "024e697e-ff60-4f44-a886-3951b17618ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dfChecks = data.groupby(['DATE'], as_index=False)['contrib'].agg('sum')\n",
    "# dfChecks = dfChecks.merge(inflation[['DATE', 'growth']], on='DATE', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a241c980-bd12-4fdc-b567-136069add97c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1983/2763205618.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  inflation_merge['DATE'] = pd.to_datetime(inflation_merge['DATE'])\n"
     ]
    }
   ],
   "source": [
    "# check that the sum of contribution is equal to annual growth rate of inflation\n",
    "dfChecks = data.groupby(['DATE'], as_index=False)['contrib'].agg('sum')\n",
    "dfChecks['DATE'] = pd.to_datetime(dfChecks['DATE'])\n",
    "inflation_merge = inflation[['DATE', 'growth']]\n",
    "inflation_merge['DATE'] = pd.to_datetime(inflation_merge['DATE'])\n",
    "dfChecks = dfChecks.merge(inflation_merge, on='DATE', how='left')\n",
    "dfChecks = dfChecks[dfChecks['DATE'] >= \"2008-01-01\"].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "799b9c96-6f78-4063-84a5-0c13d8a823b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make color palette\n",
    "#colors = sns.color_palette(\"Set1\") + sns.color_palette(\"Set2\")\n",
    "\n",
    "colors = [sns.color_palette(\"Set1\")[1]] + \\\n",
    "        [c for i, c in enumerate(sns.color_palette(\"Set1\")) if i not in [1, 2, 5]]\n",
    "\n",
    "#\n",
    "# draw plot\n",
    "#\n",
    "\n",
    "ax = data2.plot(kind='bar', stacked=True, align='edge', width=0.8,\n",
    "                color=colors, figsize=(15,10))\n",
    "#dfChecks.plot(ax=ax, y = \"contrib\", marker=\"x\", color=\"b\", markersize=1)\n",
    "dfChecks.plot(ax=ax, y = \"growth\", marker=\"o\", color=\"black\", markersize=1)\n",
    "\n",
    "#add title\n",
    "plt.title(\"Inflation and contributions\")\n",
    "\n",
    "# customize x-axis tickers\n",
    "time_period = [str(x)[:7] for x in data2.index]\n",
    "ticklabels = ['']*len(data2.index)\n",
    "ticklabels[::6] = [item for item in time_period[::6]]\n",
    "ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))\n",
    "plt.gcf().autofmt_xdate()\n",
    "#remove legend\n",
    "ax.get_legend().remove()\n",
    "#remove x-axistitle\n",
    "ax.xaxis.label.set_visible(False)\n",
    "plt.legend(bbox_to_anchor=(0.5, -0.2), loc='center', ncol=len(data2.columns)+1)\n",
    "ax.secondary_yaxis('right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "598790ad-cd9e-4ced-89bf-f2641fe1a275",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Inflation, contributions with an energy focus\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a70ad90e-c911-49ea-8bfb-738ac0ff4574",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/75e55239e1aed0fea3ed0ba8ebc926f0.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/285dea1af843596db0f0a1d410c3f8c9.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n",
      "Previously saved data has been used:\n",
      "/home/onyxia/.cache/pynsee/pynsee/95c41f8095de2f15bb36273d634f7fd4.parquet\n",
      "Creation date: 2025-05-27, today\n",
      "Set update=True to get the most up-to-date data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1983/3402247406.py:76: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  inflation_merge['DATE'] = pd.to_datetime(inflation_merge['DATE'])\n",
      "/tmp/ipykernel_1983/3402247406.py:88: DeprecationWarning: 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",
      "  data['contrib1'] = df.apply(lambda x: x['inflationDec'].shift(1) * x['pond'] *\n",
      "/tmp/ipykernel_1983/3402247406.py:91: DeprecationWarning: 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",
      "  data['contrib2'] = df.apply(lambda x: x['pond'].shift(1) * x['inflationDec'].shift(2) / x['indicesDec'].shift(2) *\n",
      "/tmp/ipykernel_1983/3402247406.py:94: DeprecationWarning: 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",
      "  data['contrib'] =  df.apply(lambda x: 100 * (x['contrib1'] + x['contrib2']) /\n"
     ]
    }
   ],
   "source": [
    "# total inflation series identifier\n",
    "IdInflation = series.loc[(series.FREQ == \"M\") &\n",
    "            (series.COICOP2016 == \"00\") &\n",
    "            (series.NATURE == \"INDICE\") &\n",
    "            (series.REF_AREA == \"FE\") &\n",
    "            (series.MENAGES_IPC == \"ENSEMBLE\") &\n",
    "            (series.PRIX_CONSO == \"SO\")]\n",
    "\n",
    "# price indexes including in inflation total index\n",
    "IdIndices = series.loc[(series.FREQ == \"M\") &\n",
    "            (series.COICOP2016 == \"SO\") &\n",
    "            (series.NATURE == \"INDICE\") &\n",
    "            (series.REF_AREA == \"FE\") &\n",
    "            (series.MENAGES_IPC == \"ENSEMBLE\") &\n",
    "            (series.PRIX_CONSO.isin([\"4000\", \"4034\", \"4003\", \"4009\"]))]\n",
    "\n",
    "IdIndicesEnergy = series.loc[(series.FREQ == \"M\") &\n",
    "            (series.COICOP2016.isin([\"0452\", \"0451\", \"0453\", \"0454\", \"07221\", \"07222\", \"07223\"])) &\n",
    "            (series.NATURE == \"INDICE\") &\n",
    "            (series.REF_AREA == \"FE\") &\n",
    "            (series.MENAGES_IPC == \"ENSEMBLE\") &\n",
    "            (series.PRIX_CONSO.isin([\"SO\"]))]\n",
    "\n",
    "IdIndices = pd.concat([IdIndices, IdIndicesEnergy])\n",
    "\n",
    "# weights of price indexes including in inflation total index\n",
    "IdPond = series.loc[(series.FREQ == \"A\") &\n",
    "            (series.COICOP2016 == \"SO\") &\n",
    "            (series.NATURE == \"POND\") &\n",
    "            (series.REF_AREA == \"FE\") &\n",
    "            (series.MENAGES_IPC == \"ENSEMBLE\") &\n",
    "            (series.PRIX_CONSO.isin([\"4000\", \"4034\", \"4003\", \"4009\"]))]\n",
    "\n",
    "IdPondEnergy = series.loc[(series.FREQ == \"A\") &\n",
    "            (series.COICOP2016.isin([\"0452\", \"0451\", \"0453\", \"0454\", \"07221\", \"07222\", \"07223\"])) &\n",
    "            (series.NATURE == \"POND\") &\n",
    "            (series.REF_AREA == \"FE\") &\n",
    "            (series.MENAGES_IPC == \"ENSEMBLE\") &\n",
    "            (series.PRIX_CONSO.isin([\"SO\"]))]\n",
    "\n",
    "\n",
    "IdPond = pd.concat([IdPond, IdPondEnergy])\n",
    "\n",
    "# collect data\n",
    "inflation = get_series(IdInflation.IDBANK)\n",
    "indices = get_series(IdIndices.IDBANK)\n",
    "pond = get_series(IdPond.IDBANK)\n",
    "\n",
    "# compute ponderation and annual growth rate\n",
    "pond['pond'] = pond.OBS_VALUE / pond.groupby(['DATE']).OBS_VALUE.transform('sum')\n",
    "indices['growth'] = (indices.OBS_VALUE/ indices.groupby(['IDBANK']).OBS_VALUE.shift(12) - 1) * 100\n",
    "inflation['growth'] = (inflation.OBS_VALUE/ inflation.groupby(['IDBANK']).OBS_VALUE.shift(12) - 1) * 100\n",
    "\n",
    "inflation['month'] = inflation['DATE'].apply(lambda x: x.month)\n",
    "indices['month'] = indices['DATE'].apply(lambda x: x.month)\n",
    "\n",
    "inflation['year'] = inflation['DATE'].apply(lambda x: x.year)\n",
    "indices['year'] = indices['DATE'].apply(lambda x: x.year)\n",
    "pond['year'] = pond['DATE'].apply(lambda x: x.year)\n",
    "\n",
    "inflation = inflation.rename(columns = {\"OBS_VALUE\" : \"inflation\"})\n",
    "indices = indices.rename(columns = {\"OBS_VALUE\" : \"indices\"})\n",
    "\n",
    "inflationDec = inflation[inflation['month'] == 12]\n",
    "indicesDec = indices[indices['month'] == 12]\n",
    "\n",
    "indicesDec = indicesDec.rename(columns = {\"indices\" : \"indicesDec\"})\n",
    "indicesDec = indicesDec[[\"year\", \"COICOP2016\", \"indicesDec\", \"PRIX_CONSO\"]]\n",
    "\n",
    "inflationDec = inflationDec.rename(columns = {\"inflation\" : \"inflationDec\"})\n",
    "inflationDec = inflationDec[[\"year\", \"COICOP2016\", \"inflationDec\", \"PRIX_CONSO\"]]\n",
    "\n",
    "pond = pond[[\"year\", \"COICOP2016\", \"pond\", \"PRIX_CONSO\"]]\n",
    "\n",
    "inflation_merge = inflation[['DATE', 'inflation']]\n",
    "inflation_merge['DATE'] = pd.to_datetime(inflation_merge['DATE'])\n",
    "\n",
    "# merge all datasets\n",
    "data = indices.merge(indicesDec, on = [\"year\", \"COICOP2016\", \"PRIX_CONSO\"], how = 'left')\n",
    "data = data.merge(pond, on = [\"year\", \"COICOP2016\", \"PRIX_CONSO\"], how = 'left')\n",
    "data['DATE'] = pd.to_datetime(data['DATE'])\n",
    "data = data.merge(inflation_merge[['DATE', 'inflation']], on = [\"DATE\"], how = 'left')\n",
    "data = data.merge(inflationDec[['year', 'inflationDec']], on = [\"year\"], how = 'left')\n",
    "\n",
    "# compute contributions by coicop\n",
    "df = data.groupby(['month', 'COICOP2016', 'PRIX_CONSO'], as_index=False)\n",
    "\n",
    "data['contrib1'] = df.apply(lambda x: x['inflationDec'].shift(1) * x['pond'] * \n",
    "                      (x['indices'] / x['indicesDec'].shift(1) - 1)).reset_index(level=0, drop=True)\n",
    "\n",
    "data['contrib2'] = df.apply(lambda x: x['pond'].shift(1) * x['inflationDec'].shift(2) / x['indicesDec'].shift(2) *\n",
    "                      (x['indicesDec'].shift(1) - x['indices'].shift(1))).reset_index(level=0, drop=True)\n",
    "\n",
    "data['contrib'] =  df.apply(lambda x: 100 * (x['contrib1'] + x['contrib2']) /\n",
    "                            x['inflation'].shift(1)).reset_index(level=0, drop=True)        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "17caf337-bf41-4297-8812-1673b8c1cf85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:2: SyntaxWarning: invalid escape sequence '\\d'\n",
      "<>:2: SyntaxWarning: invalid escape sequence '\\d'\n",
      "/tmp/ipykernel_1983/3471955260.py:2: SyntaxWarning: invalid escape sequence '\\d'\n",
      "  return re.sub(\"\\d|\\.|\\s|-\", \"\", x)\n"
     ]
    }
   ],
   "source": [
    "def clean_string(x):\n",
    "    return re.sub(\"\\d|\\.|\\s|-\", \"\", x)\n",
    "    \n",
    "data['COICOP2016_label_en'] = data['COICOP2016_label_en'].apply(lambda x: clean_string(x))\n",
    "\n",
    "data['COICOP2016_label_en'] = np.where((data['COICOP2016'].isin([\"0453\", \"0454\", \"07223\"])),\n",
    "                                       \"other\",      \n",
    "                                       data['COICOP2016_label_en'])\n",
    "\n",
    "data['PRIX_CONSO_label_en'] = np.where((data['PRIX_CONSO'] == \"SO\"),\n",
    "                                       \"Energy - \" + data['COICOP2016_label_en'],      \n",
    "                                       data['PRIX_CONSO_label_en'])\n",
    "\n",
    "\n",
    "data2 = data.groupby(['DATE', \"year\",'PRIX_CONSO_label_en'], as_index=False).contrib.agg('sum')\n",
    "\n",
    "# reshape dataframe for plotting purposes\n",
    "data2 = data2[data2['year'] >= 2008].sort_values(by=\"DATE\").reset_index()\n",
    "data2 = data2[['DATE', 'PRIX_CONSO_label_en', 'contrib']]\n",
    "data2 = data2.pivot(index=\"DATE\", columns=\"PRIX_CONSO_label_en\", values=\"contrib\")\n",
    "data2 = data2.dropna()\n",
    "\n",
    "# check that the sum of contribution is equal to annual growth rate of inflation\n",
    "dfChecks = data.groupby(['DATE'], as_index=False)['contrib'].agg('sum')\n",
    "dfChecks = dfChecks.merge(inflation[['DATE', 'growth']], on='DATE', how='left')\n",
    "dfChecks = dfChecks[dfChecks['DATE'] >= \"2008-01-01\"].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "42f946f7-2eb9-4481-bce0-bbb45af6846c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make color palette\n",
    "colors = [c for i, c in enumerate(sns.color_palette(\"Blues\")) if i in range(5)][::-1] + \\\n",
    "        [c for i, c in enumerate(sns.color_palette(\"Set1\")) if i not in [1, 2, 5]]\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "45e67a6a-a5ec-4519-bca0-48e88174eef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#\n",
    "# draw plot\n",
    "#\n",
    "\n",
    "ax = data2.plot(kind='bar', stacked=True, align='edge', width=0.8,\n",
    "                color=colors, figsize=(15,10))\n",
    "#dfChecks.plot(ax=ax, y = \"contrib\", marker=\"x\", color=\"b\", markersize=1)\n",
    "dfChecks.plot(ax=ax, y = \"growth\", marker=\"o\", color=\"black\", markersize=1)\n",
    "\n",
    "#add title\n",
    "plt.title(\"Inflation, contributions with an energy focus\")\n",
    "\n",
    "# customize x-axis tickers\n",
    "time_period = [str(x)[:7] for x in data2.index]\n",
    "ticklabels = ['']*len(data2.index)\n",
    "ticklabels[::6] = [item for item in time_period[::6]]\n",
    "ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))\n",
    "plt.gcf().autofmt_xdate()\n",
    "#remove legend\n",
    "ax.get_legend().remove()\n",
    "#remove x-axistitle\n",
    "ax.xaxis.label.set_visible(False)\n",
    "plt.legend(bbox_to_anchor=(0.5, -0.2), loc='center', ncol=3)\n",
    "ax.secondary_yaxis('right')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}