notebooks/analysis.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consumer Price Index analysis\n",
    "\n",
    "By Ben Welsh\n",
    "\n",
    "A rudimentary analysis of the Consumer Price Index published by the U.S. Bureau of Labor Statistics. It was developed to verify the accuracy of the [cpi](https://github.com/datadesk/cpi) open-source Python wrapper that eases access to the official government data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Python tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from datetime import date, datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair_latimes as lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ThemeRegistry.enable('latimes')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.themes.register('latimes', lat.theme)\n",
    "alt.themes.enable('latimes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the development version of this library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "this_dir = os.path.dirname(os.getcwd())\n",
    "sys.path.insert(0, this_dir)\n",
    "import cpi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top-level numbers for the latest month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_last13(**kwargs):\n",
    "    df = cpi.series.get(**kwargs).to_dataframe()\n",
    "    # Filter down to monthly values\n",
    "    df = df[df.period_type == 'monthly']\n",
    "    # Cut down to the last 13 months\n",
    "    df = df.sort_values(\"date\").tail(14)\n",
    "    # Return it\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_last13(df):\n",
    "    # Calculate the monthly percentage change\n",
    "    df['pct_change'] = (df.value.pct_change()*100)\n",
    "    # Calculate the monthly percentage change\n",
    "    df['pct_change_rounded'] = df['pct_change'].round(1)\n",
    "    # Get latest months\n",
    "    latest_month, latest_change = df.sort_values(\"date\", ascending=False)[['date', 'pct_change_rounded']].to_records(index=False)[0]\n",
    "    previous_month, previous_change = df.sort_values(\"date\", ascending=False)[['date', 'pct_change_rounded']].to_records(index=False)[1]\n",
    "    # Pass it back\n",
    "    return dict(\n",
    "        latest_month=latest_month,\n",
    "        latest_change=latest_change,\n",
    "        previous_month=previous_month,\n",
    "        previous_change=previous_change,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query the seasonally-adjusted CPI-U, which is the variation used by the BLS in its release."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_cpiu_last13 = get_last13(seasonally_adjusted=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_cpi_analysis = analyze_last13(adjusted_cpiu_last13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'latest_month': '2019-11-01',\n",
       " 'latest_change': 0.3,\n",
       " 'previous_month': '2019-10-01',\n",
       " 'previous_change': 0.4}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjusted_cpi_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_food_analysis = analyze_last13(get_last13(seasonally_adjusted=True, items=\"Food\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'latest_month': '2019-11-01',\n",
       " 'latest_change': 0.1,\n",
       " 'previous_month': '2019-10-01',\n",
       " 'previous_change': 0.2}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjusted_food_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_energy_analysis = analyze_last13(get_last13(seasonally_adjusted=True, items=\"Energy\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'latest_month': '2019-11-01',\n",
       " 'latest_change': 0.8,\n",
       " 'previous_month': '2019-10-01',\n",
       " 'previous_change': 2.7}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjusted_energy_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_all_less_food_and_energy_analysis = analyze_last13(get_last13(seasonally_adjusted=True, items=\"All items less food and energy\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'latest_month': '2019-11-01',\n",
       " 'latest_change': 0.2,\n",
       " 'previous_month': '2019-10-01',\n",
       " 'previous_change': 0.2}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjusted_all_less_food_and_energy_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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urban consumers, seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": true, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M08\", \"period_code\": \"M08\", \"period_abbreviation\": \"AUG\", \"period_name\": \"August\", \"period_month\": 8, \"period_type\": \"monthly\", \"pct_change\": 0.05426274881812354, \"pct_change_rounded\": 0.1}, {\"year\": 2019, \"date\": \"2019-09-01\", \"value\": 256.358, \"series_id\": \"CUSR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": true, \"series_periodicity_id\": \"R\", 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average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M10\", \"period_code\": \"M10\", \"period_abbreviation\": \"OCT\", \"period_name\": \"October\", \"period_month\": 10, \"period_type\": \"monthly\", \"pct_change\": 0.3561425818581876, \"pct_change_rounded\": 0.4}, {\"year\": 2019, \"date\": \"2019-11-01\", \"value\": 257.936, \"series_id\": \"CUSR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": true, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M11\", \"period_code\": \"M11\", \"period_abbreviation\": \"NOV\", \"period_name\": \"November\", \"period_month\": 11, \"period_type\": \"monthly\", \"pct_change\": 0.2584823007645509, \"pct_change_rounded\": 0.3}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base = alt.Chart(\n",
    "    adjusted_cpiu_last13, \n",
    "    title=\"One-month percent change in CPI for All Urban Consumers (CPI-U), seasonally adjusted\"\n",
    ").properties(width=700)\n",
    "\n",
    "bars = base.mark_bar().encode(\n",
    "    x=alt.X(\n",
    "        \"date:O\",\n",
    "        timeUnit=\"utcyearmonth\",\n",
    "        axis=alt.Axis(title=None, labelAngle=0),\n",
    "    ),\n",
    "    y=alt.Y(\n",
    "        \"pct_change_rounded:Q\",\n",
    "        axis=alt.Axis(title=None),\n",
    "        scale=alt.Scale(domain=[\n",
    "            adjusted_cpiu_last13['pct_change'].min()-0.1,\n",
    "            adjusted_cpiu_last13['pct_change'].max()+0.05\n",
    "        ])\n",
    "    )\n",
    ")\n",
    "\n",
    "text = base.encode(\n",
    "    x=alt.X(\"date:O\", timeUnit=\"utcyearmonth\"),\n",
    "    y=\"pct_change_rounded:Q\",\n",
    "    text='pct_change_rounded'\n",
    ")\n",
    "\n",
    "textAbove = text.transform_filter(alt.datum.pct_change > 0).mark_text(\n",
    "    align='center',\n",
    "    baseline='middle',\n",
    "    fontSize=14,\n",
    "    dy=-10\n",
    ")\n",
    "\n",
    "textBelow = text.transform_filter(alt.datum.pct_change < 0).mark_text(\n",
    "    align='center',\n",
    "    baseline='middle',\n",
    "    fontSize=14,\n",
    "    dy=12\n",
    ")\n",
    "\n",
    "bars + textAbove + textBelow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the year over year change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "unadjusted_cpiu = cpi.series.get(seasonally_adjusted=False).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "unadjusted_cpiu_monthly = unadjusted_cpiu[unadjusted_cpiu.period_type == 'monthly'].sort_values(\"date\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>date</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>257.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>257.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>256.759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>256.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>256.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>256.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>256.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>255.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>254.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>252.776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>251.712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>251.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>252.038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date    value\n",
       "380  2019-11-01  257.208\n",
       "379  2019-10-01  257.346\n",
       "378  2019-09-01  256.759\n",
       "377  2019-08-01  256.558\n",
       "376  2019-07-01  256.571\n",
       "375  2019-06-01  256.143\n",
       "374  2019-05-01  256.092\n",
       "373  2019-04-01  255.548\n",
       "372  2019-03-01  254.202\n",
       "371  2019-02-01  252.776\n",
       "370  2019-01-01  251.712\n",
       "369  2018-12-01  251.233\n",
       "368  2018-11-01  252.038"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unadjusted_cpiu_monthly.head(13)[[\n",
    "    'date',\n",
    "    'value'\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lastest_unadjusted, one_year_ago_unadjusted = pd.concat([\n",
    "    unadjusted_cpiu_monthly.head(1),\n",
    "    unadjusted_cpiu_monthly.head(13).tail(1),\n",
    "]).value.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(257.208, 252.038)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lastest_unadjusted, one_year_ago_unadjusted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "yoy_change = round(((lastest_unadjusted-one_year_ago_unadjusted)/one_year_ago_unadjusted)*100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yoy_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./latest.json\", \"w\") as fp:\n",
    "    fp.write(json.dumps(dict(\n",
    "        all=adjusted_cpi_analysis,\n",
    "        food=adjusted_food_analysis,\n",
    "        energy=adjusted_energy_analysis,\n",
    "        less_food_and_energy=adjusted_all_less_food_and_energy_analysis,\n",
    "        yoy_change=yoy_change,\n",
    "    )))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_cpiu_last13[~pd.isnull(adjusted_cpiu_last13.pct_change_rounded)][[\n",
    "    'date',\n",
    "    'pct_change',\n",
    "    'pct_change_rounded'\n",
    "]].to_csv(\"./cpi-mom.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Match category analysis published by the BLS\n",
    "\n",
    "In an October 2018 [post](https://www.bls.gov/opub/ted/2018/consumer-prices-up-2-point-3-percent-over-year-ended-september-2018.htm) the BLS published the following chart showing the month to month percentage change in the Consumer Price Index for All Urban Consumers across a select group of categories. We will replicate it below.\n",
    "\n",
    "![](https://i.imgur.com/54RcrID.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query the data series charted by the BLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_items = cpi.series.get(seasonally_adjusted=False).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy = cpi.series.get(items=\"Energy\", seasonally_adjusted=False).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "food = cpi.series.get(items=\"Food\", seasonally_adjusted=False).to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a function to prepare each series for presentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prep_yoy(df):\n",
    "    # Trim down to monthly values\n",
    "    df = df[df.period_type == 'monthly']\n",
    "    # Calculate percentage change year to year\n",
    "    df['pct_change'] = df.value.pct_change(12)\n",
    "    # Trim down to the last 13 months\n",
    "    return df.sort_values(\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_items_prepped = prep_yoy(all_items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy_prepped = prep_yoy(energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_prepped = prep_yoy(food)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "three_cats = pd.concat([\n",
    "    all_items_prepped.tail(12*10),\n",
    "    energy_prepped.tail(12*10),\n",
    "    food_prepped.tail(12*10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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       "      displayChart(vegaEmbed);\n",
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       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
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\"x\": {\"type\": \"temporal\", \"axis\": {\"grid\": false, \"labelAngle\": 0, \"title\": null, \"values\": [1225497600000.0, 1288569600000.0, 1351728000000.0, 1414800000000.0, 1477958400000.0, 1541030400000.0]}, \"field\": \"date\", \"timeUnit\": \"yearmonth\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"format\": \"%\", \"title\": null}, \"field\": \"pct_change\", \"scale\": {\"domain\": [-0.4, 0.3]}}}, \"title\": \"12-month percentage change, Consumer Price Index, selected categories\", \"transform\": [{\"filter\": \"(datum.series_items_name === 'All items')\"}]}, {\"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"series_items_name\", \"legend\": {\"title\": \"Category\"}}, \"x\": {\"type\": \"temporal\", \"axis\": {\"grid\": false, \"labelAngle\": 0, \"title\": null, \"values\": [1225497600000.0, 1288569600000.0, 1351728000000.0, 1414800000000.0, 1477958400000.0, 1541030400000.0]}, \"field\": \"date\", \"timeUnit\": \"yearmonth\"}, \"y\": 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\"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M12\", \"period_code\": \"M12\", \"period_abbreviation\": \"DEC\", \"period_name\": \"December\", \"period_month\": 12, \"period_type\": \"monthly\", \"pct_change\": 0.015809710314522496}, {\"year\": 2019, \"date\": \"2019-01-01\", \"value\": 256.417, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M01\", \"period_code\": \"M01\", \"period_abbreviation\": \"JAN\", \"period_name\": \"January\", \"period_month\": 1, \"period_type\": \"monthly\", \"pct_change\": 0.016072214010881103}, {\"year\": 2019, \"date\": \"2019-02-01\", \"value\": 257.222, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M02\", \"period_code\": \"M02\", \"period_abbreviation\": \"FEB\", \"period_name\": \"February\", \"period_month\": 2, \"period_type\": \"monthly\", \"pct_change\": 0.01964592929685338}, {\"year\": 2019, \"date\": \"2019-03-01\", \"value\": 257.724, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M03\", \"period_code\": \"M03\", \"period_abbreviation\": \"MAR\", \"period_name\": \"March\", \"period_month\": 3, \"period_type\": \"monthly\", \"pct_change\": 0.021214882910012944}, {\"year\": 2019, \"date\": \"2019-04-01\", \"value\": 257.708, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M04\", \"period_code\": \"M04\", \"period_abbreviation\": \"APR\", \"period_name\": \"April\", \"period_month\": 4, \"period_type\": \"monthly\", \"pct_change\": 0.017767930839741153}, {\"year\": 2019, \"date\": \"2019-05-01\", \"value\": 258.11, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M05\", \"period_code\": \"M05\", \"period_abbreviation\": \"MAY\", \"period_name\": \"May\", \"period_month\": 5, \"period_type\": \"monthly\", \"pct_change\": 0.01980260610514506}, {\"year\": 2019, \"date\": \"2019-06-01\", \"value\": 258.064, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M06\", \"period_code\": \"M06\", \"period_abbreviation\": \"JUN\", \"period_name\": \"June\", \"period_month\": 6, \"period_type\": \"monthly\", \"pct_change\": 0.01908534105224091}, {\"year\": 2019, \"date\": \"2019-07-01\", \"value\": 258.274, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M07\", \"period_code\": \"M07\", \"period_abbreviation\": \"JUL\", \"period_name\": \"July\", \"period_month\": 7, \"period_type\": \"monthly\", \"pct_change\": 0.01784461626981315}, {\"year\": 2019, \"date\": \"2019-08-01\", \"value\": 258.41, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M08\", \"period_code\": \"M08\", \"period_abbreviation\": \"AUG\", \"period_name\": \"August\", \"period_month\": 8, \"period_type\": \"monthly\", \"pct_change\": 0.01705388523951412}, {\"year\": 2019, \"date\": \"2019-09-01\", \"value\": 258.943, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M09\", \"period_code\": \"M09\", \"period_abbreviation\": \"SEP\", \"period_name\": \"September\", \"period_month\": 9, \"period_type\": \"monthly\", \"pct_change\": 0.017885712264095188}, {\"year\": 2019, \"date\": \"2019-10-01\", \"value\": 259.632, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M10\", \"period_code\": \"M10\", \"period_abbreviation\": \"OCT\", \"period_name\": \"October\", \"period_month\": 10, \"period_type\": \"monthly\", \"pct_change\": 0.020734555233175245}, {\"year\": 2019, \"date\": \"2019-11-01\", \"value\": 259.467, \"series_id\": \"CUUR0000SAF1\", \"series_title\": \"Food in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SAF1\", \"series_items_code\": \"SAF1\", \"series_items_name\": \"Food\", \"period_id\": \"M11\", \"period_code\": \"M11\", \"period_abbreviation\": \"NOV\", \"period_name\": \"November\", \"period_month\": 11, \"period_type\": \"monthly\", \"pct_change\": 0.0200016510796881}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base = alt.Chart(\n",
    "    three_cats,\n",
    "    title=\"12-month percentage change, Consumer Price Index, selected categories\"\n",
    ").encode(\n",
    "    x=alt.X(\n",
    "        \"date:T\",\n",
    "        timeUnit=\"yearmonth\",\n",
    "        axis=alt.Axis(\n",
    "            title=None,\n",
    "            labelAngle=0,\n",
    "            grid=False,\n",
    "            # A truly gnarly hack from https://github.com/altair-viz/altair/issues/187\n",
    "            values=list(pd.to_datetime([\n",
    "                '2008-11-01',\n",
    "                '2010-11-01',\n",
    "                '2012-11-01',\n",
    "                '2014-11-01',\n",
    "                '2016-11-01',\n",
    "                '2018-11-01'\n",
    "            ]).astype(int) / 10 ** 6)\n",
    "        ),\n",
    "    ),\n",
    "    y=alt.Y(\n",
    "        \"pct_change:Q\",\n",
    "        axis=alt.Axis(title=None, format='%'),\n",
    "        scale=alt.Scale(domain=[-0.4, 0.3])\n",
    "    ),\n",
    "    color=alt.Color(\n",
    "        \"series_items_name:N\",\n",
    "        legend=alt.Legend(title=\"Category\"),\n",
    "        # scale=alt.Scale(range=[\"#423a51\", \"#449cb0\", \"#d09972\"])\n",
    "    )\n",
    ")\n",
    "\n",
    "all_items = base.transform_filter(\n",
    "    alt.datum.series_items_name == 'All items'\n",
    ").mark_line(strokeDash=[3, 2])\n",
    "\n",
    "other_items = base.transform_filter(\n",
    "    alt.datum.series_items_name != 'All items'\n",
    ").mark_line()\n",
    "\n",
    "(all_items + other_items).properties(width=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "three_cats.to_csv(\"./three-categories-yoy.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A similar chart with a shorter timeframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's another one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://i.imgur.com/h1nA41O.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_less_energy_and_food = cpi.series.get(items=\"All items less food and energy\", seasonally_adjusted=False).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_less_energy_and_food_prepped = prep_yoy(all_less_energy_and_food)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "two_cats = pd.concat([\n",
    "    all_items_prepped.tail(13),\n",
    "    all_less_energy_and_food_prepped.tail(13),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-3\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-3\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
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       "        .then(() => displayChart(vegaEmbed));\n",
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\"width\": 600, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-e693aea18a7281488a68c62107643574\": [{\"year\": 2018, \"date\": \"2018-11-01\", \"value\": 252.038, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M11\", \"period_code\": \"M11\", \"period_abbreviation\": \"NOV\", \"period_name\": \"November\", \"period_month\": 11, \"period_type\": \"monthly\", \"pct_change\": 0.021766010321524032}, {\"year\": 2018, \"date\": \"2018-12-01\", 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\"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M01\", \"period_code\": \"M01\", \"period_abbreviation\": \"JAN\", \"period_name\": \"January\", \"period_month\": 1, \"period_type\": \"monthly\", \"pct_change\": 0.015512351381991252}, {\"year\": 2019, \"date\": \"2019-02-01\", \"value\": 252.776, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M02\", \"period_code\": \"M02\", \"period_abbreviation\": \"FEB\", \"period_name\": \"February\", \"period_month\": 2, \"period_type\": \"monthly\", \"pct_change\": 0.01520135265933309}, {\"year\": 2019, \"date\": \"2019-03-01\", \"value\": 254.202, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M03\", \"period_code\": \"M03\", \"period_abbreviation\": \"MAR\", \"period_name\": \"March\", \"period_month\": 3, \"period_type\": \"monthly\", \"pct_change\": 0.018625227405691724}, {\"year\": 2019, \"date\": \"2019-04-01\", \"value\": 255.548, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M04\", \"period_code\": \"M04\", \"period_abbreviation\": \"APR\", \"period_name\": \"April\", \"period_month\": 4, \"period_type\": \"monthly\", \"pct_change\": 0.019964397755302565}, {\"year\": 2019, \"date\": \"2019-05-01\", \"value\": 256.092, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M05\", \"period_code\": \"M05\", \"period_abbreviation\": \"MAY\", \"period_name\": \"May\", \"period_month\": 5, \"period_type\": \"monthly\", \"pct_change\": 0.017902284687663972}, {\"year\": 2019, \"date\": \"2019-06-01\", \"value\": 256.143, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M06\", \"period_code\": \"M06\", \"period_abbreviation\": \"JUN\", \"period_name\": \"June\", \"period_month\": 6, \"period_type\": \"monthly\", \"pct_change\": 0.016484846560762545}, {\"year\": 2019, \"date\": \"2019-07-01\", \"value\": 256.571, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M07\", \"period_code\": \"M07\", \"period_abbreviation\": \"JUL\", \"period_name\": \"July\", \"period_month\": 7, \"period_type\": \"monthly\", \"pct_change\": 0.018114648063935146}, {\"year\": 2019, \"date\": \"2019-08-01\", \"value\": 256.558, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M08\", \"period_code\": \"M08\", \"period_abbreviation\": \"AUG\", \"period_name\": \"August\", \"period_month\": 8, \"period_type\": \"monthly\", \"pct_change\": 0.017497798894291483}, {\"year\": 2019, \"date\": \"2019-09-01\", \"value\": 256.759, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M09\", \"period_code\": \"M09\", \"period_abbreviation\": \"SEP\", \"period_name\": \"September\", \"period_month\": 9, \"period_type\": \"monthly\", \"pct_change\": 0.017113045131695204}, {\"year\": 2019, \"date\": \"2019-10-01\", \"value\": 257.346, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M10\", \"period_code\": \"M10\", \"period_abbreviation\": \"OCT\", \"period_name\": \"October\", \"period_month\": 10, \"period_type\": \"monthly\", \"pct_change\": 0.017640429444213845}, {\"year\": 2019, \"date\": \"2019-11-01\", \"value\": 257.208, \"series_id\": \"CUUR0000SA0\", \"series_title\": \"All items in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0\", \"series_items_code\": \"SA0\", \"series_items_name\": \"All items\", \"period_id\": \"M11\", \"period_code\": \"M11\", \"period_abbreviation\": \"NOV\", \"period_name\": \"November\", \"period_month\": 11, \"period_type\": \"monthly\", \"pct_change\": 0.020512779818916194}, {\"year\": 2018, \"date\": \"2018-11-01\", \"value\": 259.105, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M11\", \"period_code\": \"M11\", \"period_abbreviation\": \"NOV\", \"period_name\": \"November\", \"period_month\": 11, \"period_type\": \"monthly\", \"pct_change\": 0.022142710618086703}, {\"year\": 2018, \"date\": \"2018-12-01\", \"value\": 259.083, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M12\", \"period_code\": \"M12\", \"period_abbreviation\": \"DEC\", \"period_name\": \"December\", \"period_month\": 12, \"period_type\": \"monthly\", \"pct_change\": 0.021789886337642894}, {\"year\": 2019, \"date\": \"2019-01-01\", \"value\": 260.122, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M01\", \"period_code\": \"M01\", \"period_abbreviation\": \"JAN\", \"period_name\": \"January\", \"period_month\": 1, \"period_type\": \"monthly\", \"pct_change\": 0.021536455674329824}, {\"year\": 2019, \"date\": \"2019-02-01\", \"value\": 261.114, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M02\", \"period_code\": \"M02\", \"period_abbreviation\": \"FEB\", \"period_name\": \"February\", \"period_month\": 2, \"period_type\": \"monthly\", \"pct_change\": 0.020841885504509605}, {\"year\": 2019, \"date\": \"2019-03-01\", \"value\": 261.836, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M03\", \"period_code\": \"M03\", \"period_abbreviation\": \"MAR\", \"period_name\": \"March\", \"period_month\": 3, \"period_type\": \"monthly\", \"pct_change\": 0.020365535248041855}, {\"year\": 2019, \"date\": \"2019-04-01\", \"value\": 262.332, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M04\", \"period_code\": \"M04\", \"period_abbreviation\": \"APR\", \"period_name\": \"April\", \"period_month\": 4, \"period_type\": \"monthly\", \"pct_change\": 0.02064779690691565}, {\"year\": 2019, \"date\": \"2019-05-01\", \"value\": 262.59, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M05\", \"period_code\": \"M05\", \"period_abbreviation\": \"MAY\", \"period_name\": \"May\", \"period_month\": 5, \"period_type\": \"monthly\", \"pct_change\": 0.019889773137736988}, {\"year\": 2019, \"date\": \"2019-06-01\", \"value\": 263.177, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M06\", \"period_code\": \"M06\", \"period_abbreviation\": \"JUN\", \"period_name\": \"June\", \"period_month\": 6, \"period_type\": \"monthly\", \"pct_change\": 0.02126528442317932}, {\"year\": 2019, \"date\": \"2019-07-01\", \"value\": 263.566, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M07\", \"period_code\": \"M07\", \"period_abbreviation\": \"JUL\", \"period_name\": \"July\", \"period_month\": 7, \"period_type\": \"monthly\", \"pct_change\": 0.022100540200956198}, {\"year\": 2019, \"date\": \"2019-08-01\", \"value\": 264.169, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M08\", \"period_code\": \"M08\", \"period_abbreviation\": \"AUG\", \"period_name\": \"August\", \"period_month\": 8, \"period_type\": \"monthly\", \"pct_change\": 0.02386323116754263}, {\"year\": 2019, \"date\": \"2019-09-01\", \"value\": 264.522, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M09\", \"period_code\": \"M09\", \"period_abbreviation\": \"SEP\", \"period_name\": \"September\", \"period_month\": 9, \"period_type\": \"monthly\", \"pct_change\": 0.023577075328233388}, {\"year\": 2019, \"date\": \"2019-10-01\", \"value\": 265.059, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M10\", \"period_code\": \"M10\", \"period_abbreviation\": \"OCT\", \"period_name\": \"October\", \"period_month\": 10, \"period_type\": \"monthly\", \"pct_change\": 0.023144949298047335}, {\"year\": 2019, \"date\": \"2019-11-01\", \"value\": 265.108, \"series_id\": \"CUUR0000SA0L1E\", \"series_title\": \"All items less food and energy in U.S. city average, all urban consumers, not seasonally adjusted\", \"series_survey\": \"All urban consumers\", \"series_seasonally_adjusted\": false, \"series_periodicity_id\": \"R\", \"series_periodicity_code\": \"R\", \"series_periodicity_name\": \"Monthly\", \"series_area_id\": \"0000\", \"series_area_code\": \"0000\", \"series_area_name\": \"U.S. city average\", \"series_items_id\": \"SA0L1E\", \"series_items_code\": \"SA0L1E\", \"series_items_name\": \"All items less food and energy\", \"period_id\": \"M11\", \"period_code\": \"M11\", \"period_abbreviation\": \"NOV\", \"period_name\": \"November\", \"period_month\": 11, \"period_type\": \"monthly\", \"pct_change\": 0.02316821365855537}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base = alt.Chart(\n",
    "    two_cats,\n",
    "    title=\"12-month percent change in CPI for All Urban Consumers (CPI-U), not seasonally adjusted\"\n",
    ").encode(\n",
    "    x=alt.X(\n",
    "        \"date:T\",\n",
    "        timeUnit=\"utcyearmonth\",\n",
    "        axis=alt.Axis(\n",
    "            title=None,\n",
    "            labelAngle=0,\n",
    "            grid=False,\n",
    "            format=\"%b\"\n",
    "        )\n",
    "    ),\n",
    "    y=alt.Y(\n",
    "        \"pct_change:Q\",\n",
    "        axis=alt.Axis(title=\"Percent change\", format='%'),\n",
    "        scale=alt.Scale(domain=[0.012, 0.03])\n",
    "    ),\n",
    "    color=alt.Color(\n",
    "        \"series_items_name:N\",\n",
    "        legend=alt.Legend(title=\"Category\"),\n",
    "        # scale=alt.Scale(range=[\"#336EFF\", \"#B03A2E\",])\n",
    "    )\n",
    ")\n",
    "\n",
    "line = base.mark_line(strokeWidth=0.85)\n",
    "exes = base.transform_filter(alt.datum.series_items_name == 'All items').mark_point(shape=\"triangle-down\", size=25)\n",
    "points = base.transform_filter(alt.datum.series_items_name == 'All items less food and energy').mark_point(size=25, fill=\"#B03A2E\")\n",
    "\n",
    "(line + exes + points).properties(width=600, height=225)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "two_cats.to_csv(\"./two-categories-yoy.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
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 },
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