docs/examples/example_inflation_contribution.ipynb

{
 "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": [],
   "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": [
      "Existing environment variables used, instead of locally saved credentials\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting datasets list: 100%|██████████| 201/201 [00:00<00:00, 1630.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPC-2015 : metadata update triggered because it is not found locally\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data saved: /home/onyxia/.cache/pynsee/pynsee/1877832c17b585d477598932d4295b9c\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": "stderr",
     "output_type": "stream",
     "text": [
      "1/1 - Getting series: 100%|██████████| 1/1 [00:00<00:00,  3.70it/s]\n",
      "1/1 - Getting series: 100%|██████████| 5/5 [00:01<00:00,  3.18it/s]\n",
      "1/1 - Getting series: 100%|██████████| 5/5 [00:00<00:00, 43.81it/s]\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": [],
   "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": 11,
   "id": "a241c980-bd12-4fdc-b567-136069add97c",
   "metadata": {},
   "outputs": [],
   "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 = dfChecks.merge(inflation[['DATE', 'growth']], on='DATE', how='left')\n",
    "dfChecks = dfChecks[dfChecks['DATE'] >= \"2008-01-01\"].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "799b9c96-6f78-4063-84a5-0c13d8a823b0",
   "metadata": {},
   "outputs": [
    {
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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": 13,
   "id": "598790ad-cd9e-4ced-89bf-f2641fe1a275",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Inflation, contributions with an energy focus\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a70ad90e-c911-49ea-8bfb-738ac0ff4574",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1/1 - Getting series: 100%|██████████| 11/11 [00:03<00:00,  3.44it/s]\n",
      "1/1 - Getting series: 100%|██████████| 11/11 [00:00<00:00, 49.74it/s]\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",
    "# 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')\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": 15,
   "id": "17caf337-bf41-4297-8812-1673b8c1cf85",
   "metadata": {},
   "outputs": [],
   "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": 16,
   "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": 17,
   "id": "45e67a6a-a5ec-4519-bca0-48e88174eef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}