notebooks/pandas/exploratory-analysis-with-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "29c94824-75b9-4a19-867f-e201aedd3e91",
   "metadata": {},
   "source": [
    "<center><img src=\"https://github.com/pandas-dev/pandas/raw/main/web/pandas/static/img/pandas.svg\" alt=\"pandas Logo\" style=\"width: 800px;\"/></center>\n",
    "\n",
    "# Exploratory Analysis with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b158bed-a83e-4dea-9b45-057abed60386",
   "metadata": {},
   "source": [
    "## Overview\n",
    "1. Data Access and Reading\n",
    "1. Understanding the Pandas Data Structure\n",
    "1. Exploratory Data Analysis\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "| Concepts | Importance | Notes |\n",
    "| --- | --- | --- |\n",
    "| [Python Quickstart](../../foundations/quickstart) | Necessary | Intro to `dict` |\n",
    "| [Numpy Basics](../numpy/numpy-basics) | Necessary | |\n",
    "\n",
    "* **Time to learn**: 30 minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366eef66-be17-45cf-a31c-32737f9cd09a",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee6c1e64-c987-4aeb-b4ad-70fb95687705",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a626f0ed-c89d-4d04-953d-1556a54a5e0d",
   "metadata": {},
   "source": [
    "## Data Access and Reading\n",
    "The data for this example/notebook is available from the CSIRO Kennaook/Cape Grim page\n",
    "https://www.csiro.au/en/research/natural-environment/atmosphere/latest-greenhouse-gas-data\n",
    "\n",
    "More details on the science motivation, data overview, and data citation practices are available on that website. We staged data for you locally in preparation for this workshop - which would be the same as going to the page, downloading, and uploading to this Jupyterhub Space."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977b4fd0-f8c2-4036-a2f6-2ccfed9b6516",
   "metadata": {},
   "source": [
    "### Try Reading the Data with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62747147-33f9-4166-a1d2-a0cae7e50524",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'utf-8' codec can't decode byte 0x91 in position 38419: invalid start byte",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../data/CapeGrim_CO2_data_download.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1898\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1895\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[1;32m   1897\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1898\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1899\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1900\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/io/parsers/c_parser_wrapper.py:93\u001b[0m, in \u001b[0;36mCParserWrapper.__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype_backend\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyarrow\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m     91\u001b[0m     \u001b[38;5;66;03m# Fail here loudly instead of in cython after reading\u001b[39;00m\n\u001b[1;32m     92\u001b[0m     import_optional_dependency(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyarrow\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reader \u001b[38;5;241m=\u001b[39m \u001b[43mparsers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTextReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munnamed_cols \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reader\u001b[38;5;241m.\u001b[39munnamed_cols\n\u001b[1;32m     97\u001b[0m \u001b[38;5;66;03m# error: Cannot determine type of 'names'\u001b[39;00m\n",
      "File \u001b[0;32mparsers.pyx:574\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mparsers.pyx:663\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._get_header\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mparsers.pyx:874\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mparsers.pyx:891\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._check_tokenize_status\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mparsers.pyx:2053\u001b[0m, in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m<frozen codecs>:322\u001b[0m, in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0x91 in position 38419: invalid start byte"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/CapeGrim_CO2_data_download.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40349624-862f-42ad-8c38-5a38ed52d9a2",
   "metadata": {},
   "source": [
    "### Troubleshooting the Data Reading\n",
    "By default, we cannot read the data! We need to pass more information to pandas to be able to read the data. For example, if we open the file with a text reader, we notice there is a good amount of **metadata** at the top of the file\n",
    "\n",
    "**Metadata: Data about our data. This provides helpful links, when data was processed, and units. All of this data is critical to understanding our data before we perform analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3fb7e42a-b82c-4185-86a5-b33145295018",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/ipykernel_85673/2633201338.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.\n",
      "  df = pd.read_csv(\"../data/CapeGrim_CO2_data_download.csv\",\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YYYY</th>\n",
       "      <th>MM</th>\n",
       "      <th>DD</th>\n",
       "      <th>DATE</th>\n",
       "      <th>CO2(ppm)</th>\n",
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       "<p>584 rows × 8 columns</p>\n",
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      "text/plain": [
       "     YYYY  MM  DD        DATE  CO2(ppm)  SD(ppm)  GR(ppm/yr)   Source\n",
       "0    1976   5  15  1976.36890   328.861    0.165         NaN  in situ\n",
       "1    1976   6  15  1976.45360   328.988    0.285         NaN  in situ\n",
       "2    1976   7  15  1976.53550   329.653    0.282         NaN  in situ\n",
       "3    1976   8  15  1976.62020   330.550    0.201         NaN  in situ\n",
       "4    1976   9  15  1976.70490   330.872    0.234         NaN  in situ\n",
       "..    ...  ..  ..         ...       ...      ...         ...      ...\n",
       "579  2024   8  15  2024.62022   420.795    0.157         NaN  in situ\n",
       "580  2024   9  15  2024.70492   421.180    0.139         NaN  in situ\n",
       "581  2024  10  15  2024.78689   421.397    0.097         NaN  in situ\n",
       "582  2024  11  15  2024.87158   421.414    0.156         NaN  in situ\n",
       "583  2024  12  15  2024.95355   421.377    0.171         NaN  in situ\n",
       "\n",
       "[584 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/CapeGrim_CO2_data_download.csv\",\n",
    "                 skiprows=24, # the number of rows at the top of the file with metadata\n",
    "                 skipfooter=70, # the number of rows at the bottom of the file with metadata\n",
    "                 encoding = \"ISO-8859-1\") # there are some special characters - we need a specific encoding\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d38cd3c-7817-4252-b37b-2e0e09d8e954",
   "metadata": {},
   "source": [
    "## Understanding the Pandas Data Structure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2929f775-0170-499d-bb1a-0dc9f10eda0a",
   "metadata": {},
   "source": [
    "### The pandas [`DataFrame`](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe)...\n",
    "...is a **labeled**, two-dimensional columnar structure, similar to a table, spreadsheet, or the R `data.frame`.\n",
    "\n",
    "![dataframe schematic](https://github.com/pandas-dev/pandas/raw/main/doc/source/_static/schemas/01_table_dataframe.svg \"Schematic of a pandas DataFrame\")\n",
    "\n",
    "The `columns` that make up our `DataFrame` can be lists, dictionaries, NumPy arrays, pandas `Series`, or many other data types not mentioned here. Within these `columns`, you can have data values of many different data types used in Python and NumPy, including text, numbers, and dates/times. The first column of a `DataFrame`, shown in the image above in dark gray, is uniquely referred to as an `index`; this column contains information characterizing each row of our `DataFrame`. Similar to any other `column`, the `index` can label rows by text, numbers, datetime objects, and many other data types. Datetime objects are a quite popular way to label rows.\n",
    "\n",
    "For our first example using Pandas DataFrames, we start by reading in some data in comma-separated value (`.csv`) format. We retrieve this dataset from our local data directory; however, the dataset was originally contained within the CSIRO data page. This dataset contains many types of greenhouse gas data, including gas measurements at Kennaook/Cape Grim. For more information on this dataset, review the description [here](https://www.csiro.au/en/research/natural-environment/atmosphere/latest-greenhouse-gas-data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57cf0ff7-5286-4aa2-a372-8ac2577a211e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=584, step=1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d66f8f7-27d4-4674-b92a-9907cc818f83",
   "metadata": {},
   "source": [
    "The `DataFrame` index, as described above, contains information characterizing rows; each row has a unique ID value, which is displayed in the index column.  By default, the IDs for rows in a `DataFrame` are represented as sequential integers, which start at 0.\n",
    "\n",
    "At the moment, the index column of our DataFrame is not very helpful for humans. However, Pandas has clever ways to make index columns more human-readable. The next example demonstrates how to use optional keyword arguments to convert DataFrame index IDs to a human-friendly datetime format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3db1bcf9-1247-4ffc-ac35-515643648dae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/ipykernel_85673/3271322259.py:1: FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
      "  df = pd.read_csv(\"../data/CapeGrim_CO2_data_download.csv\",\n"
     ]
    },
    {
     "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>CO2(ppm)</th>\n",
       "      <th>SD(ppm)</th>\n",
       "      <th>GR(ppm/yr)</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-05-15</th>\n",
       "      <td>1976.36890</td>\n",
       "      <td>328.861</td>\n",
       "      <td>0.165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-06-15</th>\n",
       "      <td>1976.45360</td>\n",
       "      <td>328.988</td>\n",
       "      <td>0.285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-07-15</th>\n",
       "      <td>1976.53550</td>\n",
       "      <td>329.653</td>\n",
       "      <td>0.282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-15</th>\n",
       "      <td>1976.62020</td>\n",
       "      <td>330.550</td>\n",
       "      <td>0.201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-09-15</th>\n",
       "      <td>1976.70490</td>\n",
       "      <td>330.872</td>\n",
       "      <td>0.234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-15</th>\n",
       "      <td>2024.62022</td>\n",
       "      <td>420.795</td>\n",
       "      <td>0.157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-15</th>\n",
       "      <td>2024.70492</td>\n",
       "      <td>421.180</td>\n",
       "      <td>0.139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-15</th>\n",
       "      <td>2024.78689</td>\n",
       "      <td>421.397</td>\n",
       "      <td>0.097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-15</th>\n",
       "      <td>2024.87158</td>\n",
       "      <td>421.414</td>\n",
       "      <td>0.156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-15</th>\n",
       "      <td>2024.95355</td>\n",
       "      <td>421.377</td>\n",
       "      <td>0.171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>584 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE  CO2(ppm)  SD(ppm)  GR(ppm/yr)   Source\n",
       "date                                                          \n",
       "1976-05-15  1976.36890   328.861    0.165         NaN  in situ\n",
       "1976-06-15  1976.45360   328.988    0.285         NaN  in situ\n",
       "1976-07-15  1976.53550   329.653    0.282         NaN  in situ\n",
       "1976-08-15  1976.62020   330.550    0.201         NaN  in situ\n",
       "1976-09-15  1976.70490   330.872    0.234         NaN  in situ\n",
       "...                ...       ...      ...         ...      ...\n",
       "2024-08-15  2024.62022   420.795    0.157         NaN  in situ\n",
       "2024-09-15  2024.70492   421.180    0.139         NaN  in situ\n",
       "2024-10-15  2024.78689   421.397    0.097         NaN  in situ\n",
       "2024-11-15  2024.87158   421.414    0.156         NaN  in situ\n",
       "2024-12-15  2024.95355   421.377    0.171         NaN  in situ\n",
       "\n",
       "[584 rows x 5 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/CapeGrim_CO2_data_download.csv\",\n",
    "                 skiprows=24, # the number of rows at the top of the file with metadata\n",
    "                 skipfooter=70, # the number of rows at the bottom of the file with metadata\n",
    "                 encoding = \"ISO-8859-1\", # there are some special characters - we need a specific encoding\n",
    "                 parse_dates={\"date\":[\"MM\", \"DD\", \"YYYY\"]}, # parse the date columns\n",
    "                 engine=\"python\", # make sure we use the python engine\n",
    "                 index_col=0) \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267ba581-30f0-4c60-bf31-d7367cbe73fa",
   "metadata": {},
   "source": [
    "Each of our data rows is now helpfully labeled by a datetime-object-like index value; this means that we can now easily identify data values not only by named columns, but also by date labels on rows. This is a sneak preview of the `DatetimeIndex` functionality of Pandas; this functionality enables a large portion of Pandas' timeseries-related usage. Don't worry; `DatetimeIndex` will be discussed in full detail later on this page. In the meantime, let's look at the columns of data read in from the `.csv` file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e5fbabe-c3d4-4f6b-98cf-4c7a766a1e3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DATE', 'CO2(ppm)', 'SD(ppm)', 'GR(ppm/yr)', 'Source'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959b12e7-1fa7-4a08-97f6-6de7613b9405",
   "metadata": {},
   "source": [
    "### The pandas [`Series`](https://pandas.pydata.org/docs/user_guide/dsintro.html#series)...\n",
    "\n",
    "...is essentially any one of the columns of our `DataFrame`. A `Series` also includes the index column from the source `DataFrame`, in order to provide a label for each value in the `Series`.\n",
    "\n",
    "![pandas Series](https://github.com/pandas-dev/pandas/raw/main/doc/source/_static/schemas/01_table_series.svg \"Schematic of a pandas Series\")\n",
    "\n",
    "The pandas `Series` is a fast and capable 1-dimensional array of nearly any data type we could want, and it can behave very similarly to a NumPy `ndarray` or a Python `dict`. You can take a look at any of the `Series` that make up your `DataFrame`, either by using its column name and the Python `dict` notation, or by using dot-shorthand with the column name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "736f5c1f-811f-491d-a88a-a38523f32c32",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1976-05-15    328.861\n",
       "1976-06-15    328.988\n",
       "1976-07-15    329.653\n",
       "1976-08-15    330.550\n",
       "1976-09-15    330.872\n",
       "               ...   \n",
       "2024-08-15    420.795\n",
       "2024-09-15    421.180\n",
       "2024-10-15    421.397\n",
       "2024-11-15    421.414\n",
       "2024-12-15    421.377\n",
       "Name: CO2(ppm), Length: 584, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"CO2(ppm)\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eadd2213-3c61-4e77-888d-036e99ab59e9",
   "metadata": {},
   "source": [
    "### Slicing and Dicing the `DataFrame` and `Series`\n",
    "\n",
    "In this section, we will expand on topics covered in the previous sections on this page. One of the most important concepts to learn about Pandas is that it allows you to _**access anything by its associated label**_, regardless of data organization structure.\n",
    "\n",
    "#### Indexing a `Series`\n",
    "\n",
    "As a review of previous examples, we'll start our next example by pulling a `Series` out of our `DataFrame` using its column label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f164f4e0-4fc9-47c7-9a82-f6430d47f314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1976-05-15    328.861\n",
       "1976-06-15    328.988\n",
       "1976-07-15    329.653\n",
       "1976-08-15    330.550\n",
       "1976-09-15    330.872\n",
       "               ...   \n",
       "2024-08-15    420.795\n",
       "2024-09-15    421.180\n",
       "2024-10-15    421.397\n",
       "2024-11-15    421.414\n",
       "2024-12-15    421.377\n",
       "Name: CO2(ppm), Length: 584, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_series = df[\"CO2(ppm)\"]\n",
    "\n",
    "co2_series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42671ce8-f9f9-4908-a0e8-79da3e49a9ba",
   "metadata": {},
   "source": [
    "You can use syntax similar to that of NumPy `ndarrays` to index, select, and subset with Pandas `Series`, as shown in this example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b9d7079-9a42-44a0-9fb8-767056325b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1982-01-15    337.306\n",
       "1982-02-15    337.127\n",
       "1982-03-15    337.274\n",
       "1982-04-15    337.698\n",
       "1982-05-15    338.032\n",
       "1982-06-15    338.186\n",
       "1982-07-15    338.551\n",
       "1982-08-15    338.898\n",
       "1982-09-15    338.822\n",
       "1982-10-15    338.634\n",
       "1982-11-15    338.557\n",
       "Name: CO2(ppm), dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_series[\"1982-01-01\":\"1982-12-01\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158cb905-8627-47aa-a24c-a0bff1511ec5",
   "metadata": {},
   "source": [
    "This is an example of label-based slicing. With label-based slicing, Pandas will automatically find a range of values based on the labels you specify."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd742fa1-942c-482a-a5cb-904de9378144",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Info</p>\n",
    "    As opposed to index-based slices, label-based slices are <b>inclusive</b> of the final value.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ddd090-cb28-4b48-95b1-c0cf7ac6ff64",
   "metadata": {},
   "source": [
    "If you already have some knowledge of xarray (used with ACT), you will quite likely know how to create slice objects by hand. This can also be used in pandas, as shown below. If you are completely unfamiliar with xarray, it will be covered on a later Pythia tutorial page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3878a0e-c8ff-4cee-a35d-b78c49930376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1982-01-15    337.306\n",
       "1982-02-15    337.127\n",
       "1982-03-15    337.274\n",
       "1982-04-15    337.698\n",
       "1982-05-15    338.032\n",
       "1982-06-15    338.186\n",
       "1982-07-15    338.551\n",
       "1982-08-15    338.898\n",
       "1982-09-15    338.822\n",
       "1982-10-15    338.634\n",
       "1982-11-15    338.557\n",
       "Name: CO2(ppm), dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_series[slice(\"1982-01-01\", \"1982-12-01\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3083bf69-6866-4e9d-b348-c8a3cc353e8a",
   "metadata": {},
   "source": [
    "#### Using `.iloc` and `.loc` to index\n",
    "\n",
    "In this section, we introduce ways to access data that are preferred by Pandas over the methods listed above. When accessing by label, it is preferred to use the `.loc` method, and when accessing by index, the `.iloc` method is preferred. These methods behave similarly to the notation introduced above, but provide more speed, security, and rigor in your value selection. Using these methods can also help you avoid [chained assignment warnings](https://pandas.pydata.org/docs/user_guide/indexing.html#returning-a-view-versus-a-copy) generated by pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4714f036-0724-4c26-8b5c-2863b222aa69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(330.55)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_series.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c3ab144c-eec4-4996-92a6-d646fac5b710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1976-05-15    328.861\n",
       "1976-06-15    328.988\n",
       "1976-07-15    329.653\n",
       "1976-08-15    330.550\n",
       "1976-09-15    330.872\n",
       "1976-10-15    330.899\n",
       "1976-11-15    330.883\n",
       "1976-12-15    330.677\n",
       "1977-01-15    330.529\n",
       "1977-02-15    330.543\n",
       "1977-03-15    330.724\n",
       "1977-04-15    330.805\n",
       "Name: CO2(ppm), dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_series.iloc[0:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1a44e88-fd88-4a54-8f62-6cf0ceb83c09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(328.861)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_series.loc[\"1976-05-15\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d343152-92d1-406b-a739-74c6f24ef52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1977-01-15    330.529\n",
       "1977-02-15    330.543\n",
       "1977-03-15    330.724\n",
       "1977-04-15    330.805\n",
       "1977-05-15    331.007\n",
       "1977-06-15    331.500\n",
       "1977-07-15    331.800\n",
       "1977-08-15    332.327\n",
       "1977-09-15    332.940\n",
       "1977-10-15    333.034\n",
       "1977-11-15    332.778\n",
       "1977-12-15    332.377\n",
       "Name: CO2(ppm), dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_series.loc[\"1977-01-01\":\"1977-12-31\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "391bcb25-2d6c-4290-b64a-61ede4eedd47",
   "metadata": {},
   "source": [
    "#### Extending to the `DataFrame`\n",
    "\n",
    "These subsetting capabilities can also be used in a full `DataFrame`; however, if you use the same syntax, there are issues, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "71c5081a-7a5f-4023-bebd-2b069de7137d",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'1977-01-01'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '1977-01-01'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1977-01-01\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: '1977-01-01'"
     ]
    }
   ],
   "source": [
    "df[\"1977-01-01\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cadcc3d2-33ac-4eb9-8d8a-8c8eda28296c",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-danger\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Danger</p>\n",
    "    Attempting to use <code>Series</code> subsetting with a <code>DataFrame</code> can crash your program. A proper way to subset a <code>DataFrame</code> is shown below.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4150a602-b570-4bb1-a57d-86fa8bd0e57c",
   "metadata": {},
   "source": [
    "When indexing a `DataFrame`, pandas will not assume as readily the intention of your code. In this case, using a row label by itself will not work; **with `DataFrames`, labels are used for identifying columns**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5541bd2d-2a60-43b7-b29b-0fbd97f00105",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"CO2(ppm)\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74876a5e-45ba-498d-82b2-427fec27d878",
   "metadata": {},
   "source": [
    "As shown below, you also cannot subset columns in a `DataFrame` using integer indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f882ea6a-80ab-4202-a1ac-671eb0d12550",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/mambaforge/envs/cape-k-student-workshop-2025-dev/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "df[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9af1a1-135a-4e4a-9a12-d1e2f3216b73",
   "metadata": {},
   "source": [
    "From earlier examples, we know that we can use an index or label with a DataFrame to pull out a column as a Series, and we know that we can use an index or label with a Series to pull out a single value. Therefore, by chaining brackets, we can pull any individual data value out of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba0be7c5-24cb-4d55-996f-fb0c7a41313b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(337.698)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"CO2(ppm)\"][\"1982-04-15\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f2a3afcb-4e51-4408-bf8e-c627a9f289c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bw/c9j8z20x45s2y20vv6528qjc0000gq/T/ipykernel_85673/1011012163.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  df[\"CO2(ppm)\"][71]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(337.698)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"CO2(ppm)\"][71]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9f50ba-d672-481a-8e4d-d4ec01f2090f",
   "metadata": {},
   "source": [
    "However, subsetting data using this chained-bracket technique is not preferred by Pandas. As described above, Pandas prefers us to use the .loc and .iloc methods for subsetting. In addition, these methods provide a clearer, more efficient way to extract specific data from a DataFrame, as illustrated below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fe9dd517-f1cb-4939-93f4-fce09a4761ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(337.698)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"1982-04-15\", \"CO2(ppm)\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9414d625-8b1e-40e6-b9c0-915c9bbbafef",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Info</p>\n",
    "    When using this syntax to pull individual data values from a DataFrame, make sure to list the row first, and then the column.\n",
    "</div>\n",
    "\n",
    "The `.loc` and `.iloc` methods also allow us to pull entire rows out of a `DataFrame`, as shown in these examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "22ac8003-e2ae-404b-ad0a-abf4e6283405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE          1982.2849\n",
       "CO2(ppm)        337.698\n",
       "SD(ppm)           0.241\n",
       "GR(ppm/yr)     0.562012\n",
       "Source          in situ\n",
       "Name: 1982-04-15 00:00:00, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"1982-04-15\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "19bc9afd-4e63-4b55-b37a-fd2a42e02f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>CO2(ppm)</th>\n",
       "      <th>SD(ppm)</th>\n",
       "      <th>GR(ppm/yr)</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982-01-15</th>\n",
       "      <td>1982.0384</td>\n",
       "      <td>337.306</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.718994</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-02-15</th>\n",
       "      <td>1982.1233</td>\n",
       "      <td>337.127</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.805023</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-03-15</th>\n",
       "      <td>1982.2000</td>\n",
       "      <td>337.274</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.656982</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-15</th>\n",
       "      <td>1982.2849</td>\n",
       "      <td>337.698</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.562012</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-05-15</th>\n",
       "      <td>1982.3671</td>\n",
       "      <td>338.032</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.557007</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-06-15</th>\n",
       "      <td>1982.4521</td>\n",
       "      <td>338.186</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.559998</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-07-15</th>\n",
       "      <td>1982.5342</td>\n",
       "      <td>338.551</td>\n",
       "      <td>0.186</td>\n",
       "      <td>1.001010</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-08-15</th>\n",
       "      <td>1982.6192</td>\n",
       "      <td>338.898</td>\n",
       "      <td>0.153</td>\n",
       "      <td>1.299990</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-09-15</th>\n",
       "      <td>1982.7041</td>\n",
       "      <td>338.822</td>\n",
       "      <td>0.213</td>\n",
       "      <td>1.245030</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-10-15</th>\n",
       "      <td>1982.7863</td>\n",
       "      <td>338.634</td>\n",
       "      <td>0.195</td>\n",
       "      <td>1.416990</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-11-15</th>\n",
       "      <td>1982.8712</td>\n",
       "      <td>338.557</td>\n",
       "      <td>0.163</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DATE  CO2(ppm)  SD(ppm)  GR(ppm/yr)   Source\n",
       "date                                                         \n",
       "1982-01-15  1982.0384   337.306    0.193    0.718994  in situ\n",
       "1982-02-15  1982.1233   337.127    0.179    0.805023  in situ\n",
       "1982-03-15  1982.2000   337.274    0.290    0.656982  in situ\n",
       "1982-04-15  1982.2849   337.698    0.241    0.562012  in situ\n",
       "1982-05-15  1982.3671   338.032    0.187    0.557007  in situ\n",
       "1982-06-15  1982.4521   338.186    0.153    0.559998  in situ\n",
       "1982-07-15  1982.5342   338.551    0.186    1.001010  in situ\n",
       "1982-08-15  1982.6192   338.898    0.153    1.299990  in situ\n",
       "1982-09-15  1982.7041   338.822    0.213    1.245030  in situ\n",
       "1982-10-15  1982.7863   338.634    0.195    1.416990  in situ\n",
       "1982-11-15  1982.8712   338.557    0.163    1.500000  in situ"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"1982-01-01\":\"1982-12-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9915482c-3f37-4339-9b37-654afd55da4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE          1976.6202\n",
       "CO2(ppm)         330.55\n",
       "SD(ppm)           0.201\n",
       "GR(ppm/yr)          NaN\n",
       "Source          in situ\n",
       "Name: 1976-08-15 00:00:00, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f605e810-965c-4b1a-8663-cfd2959715f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DATE</th>\n",
       "      <th>CO2(ppm)</th>\n",
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       "      <th>GR(ppm/yr)</th>\n",
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       "  <tbody>\n",
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       "      <th>1976-05-15</th>\n",
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       "      <td>328.861</td>\n",
       "      <td>0.165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
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       "    <tr>\n",
       "      <th>1976-06-15</th>\n",
       "      <td>1976.4536</td>\n",
       "      <td>328.988</td>\n",
       "      <td>0.285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-07-15</th>\n",
       "      <td>1976.5355</td>\n",
       "      <td>329.653</td>\n",
       "      <td>0.282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-15</th>\n",
       "      <td>1976.6202</td>\n",
       "      <td>330.550</td>\n",
       "      <td>0.201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-09-15</th>\n",
       "      <td>1976.7049</td>\n",
       "      <td>330.872</td>\n",
       "      <td>0.234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-10-15</th>\n",
       "      <td>1976.7869</td>\n",
       "      <td>330.899</td>\n",
       "      <td>0.015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-11-15</th>\n",
       "      <td>1976.8716</td>\n",
       "      <td>330.883</td>\n",
       "      <td>0.243</td>\n",
       "      <td>2.14600</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-12-15</th>\n",
       "      <td>1976.9536</td>\n",
       "      <td>330.677</td>\n",
       "      <td>0.187</td>\n",
       "      <td>2.51199</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-15</th>\n",
       "      <td>1977.0384</td>\n",
       "      <td>330.529</td>\n",
       "      <td>0.235</td>\n",
       "      <td>2.14697</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-02-15</th>\n",
       "      <td>1977.1233</td>\n",
       "      <td>330.543</td>\n",
       "      <td>0.198</td>\n",
       "      <td>1.77701</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-03-15</th>\n",
       "      <td>1977.2000</td>\n",
       "      <td>330.724</td>\n",
       "      <td>0.119</td>\n",
       "      <td>2.06799</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-04-15</th>\n",
       "      <td>1977.2849</td>\n",
       "      <td>330.805</td>\n",
       "      <td>0.178</td>\n",
       "      <td>2.13501</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DATE  CO2(ppm)  SD(ppm)  GR(ppm/yr)   Source\n",
       "date                                                         \n",
       "1976-05-15  1976.3689   328.861    0.165         NaN  in situ\n",
       "1976-06-15  1976.4536   328.988    0.285         NaN  in situ\n",
       "1976-07-15  1976.5355   329.653    0.282         NaN  in situ\n",
       "1976-08-15  1976.6202   330.550    0.201         NaN  in situ\n",
       "1976-09-15  1976.7049   330.872    0.234         NaN  in situ\n",
       "1976-10-15  1976.7869   330.899    0.015         NaN  in situ\n",
       "1976-11-15  1976.8716   330.883    0.243     2.14600  in situ\n",
       "1976-12-15  1976.9536   330.677    0.187     2.51199  in situ\n",
       "1977-01-15  1977.0384   330.529    0.235     2.14697  in situ\n",
       "1977-02-15  1977.1233   330.543    0.198     1.77701  in situ\n",
       "1977-03-15  1977.2000   330.724    0.119     2.06799  in situ\n",
       "1977-04-15  1977.2849   330.805    0.178     2.13501  in situ"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:12]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f830aae-1c22-433a-9f8c-c5b54f2cb205",
   "metadata": {},
   "source": [
    "In the next example, we illustrate how you can use slices of rows and lists of columns to create a smaller `DataFrame` out of an existing `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9479a818-56ba-42e0-bbaf-1b86f1538c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2(ppm)</th>\n",
       "      <th>SD(ppm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1982-01-15</th>\n",
       "      <td>337.306</td>\n",
       "      <td>0.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-02-15</th>\n",
       "      <td>337.127</td>\n",
       "      <td>0.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-03-15</th>\n",
       "      <td>337.274</td>\n",
       "      <td>0.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-04-15</th>\n",
       "      <td>337.698</td>\n",
       "      <td>0.241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-05-15</th>\n",
       "      <td>338.032</td>\n",
       "      <td>0.187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-06-15</th>\n",
       "      <td>338.186</td>\n",
       "      <td>0.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-07-15</th>\n",
       "      <td>338.551</td>\n",
       "      <td>0.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-08-15</th>\n",
       "      <td>338.898</td>\n",
       "      <td>0.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-09-15</th>\n",
       "      <td>338.822</td>\n",
       "      <td>0.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-10-15</th>\n",
       "      <td>338.634</td>\n",
       "      <td>0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-11-15</th>\n",
       "      <td>338.557</td>\n",
       "      <td>0.163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CO2(ppm)  SD(ppm)\n",
       "date                         \n",
       "1982-01-15   337.306    0.193\n",
       "1982-02-15   337.127    0.179\n",
       "1982-03-15   337.274    0.290\n",
       "1982-04-15   337.698    0.241\n",
       "1982-05-15   338.032    0.187\n",
       "1982-06-15   338.186    0.153\n",
       "1982-07-15   338.551    0.186\n",
       "1982-08-15   338.898    0.153\n",
       "1982-09-15   338.822    0.213\n",
       "1982-10-15   338.634    0.195\n",
       "1982-11-15   338.557    0.163"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\n",
    "    \"1982-01-01\":\"1982-12-01\",  # slice of rows\n",
    "    [\"CO2(ppm)\", \"SD(ppm)\"],  # list of columns\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c456cf-b821-4e4d-9dc2-afaf60807ae8",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Info</p>\n",
    "    There are certain limitations to these subsetting techniques. For more information on these limitations, as well as a comparison of <code>DataFrame</code> and <code>Series</code> indexing methods, see the <a href=\"https://pandas.pydata.org/docs/user_guide/indexing.html\">Pandas indexing documentation.</a>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "970220cc-6e6e-49d2-9166-379c53a9f2ef",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "### Get a Quick Look at the Beginning/End of your `DataFrame`\n",
    "Pandas also gives you a few shortcuts to quickly investigate entire `DataFrames`. The `head` method shows the first five rows of a `DataFrame`, and the `tail` method shows the last five rows of a `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "296badb6-fd6c-4285-9fad-019f5d876fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>CO2(ppm)</th>\n",
       "      <th>SD(ppm)</th>\n",
       "      <th>GR(ppm/yr)</th>\n",
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       "      <th>1976-05-15</th>\n",
       "      <td>1976.3689</td>\n",
       "      <td>328.861</td>\n",
       "      <td>0.165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
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       "    <tr>\n",
       "      <th>1976-06-15</th>\n",
       "      <td>1976.4536</td>\n",
       "      <td>328.988</td>\n",
       "      <td>0.285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
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       "    <tr>\n",
       "      <th>1976-07-15</th>\n",
       "      <td>1976.5355</td>\n",
       "      <td>329.653</td>\n",
       "      <td>0.282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-15</th>\n",
       "      <td>1976.6202</td>\n",
       "      <td>330.550</td>\n",
       "      <td>0.201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-09-15</th>\n",
       "      <td>1976.7049</td>\n",
       "      <td>330.872</td>\n",
       "      <td>0.234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DATE  CO2(ppm)  SD(ppm)  GR(ppm/yr)   Source\n",
       "date                                                         \n",
       "1976-05-15  1976.3689   328.861    0.165         NaN  in situ\n",
       "1976-06-15  1976.4536   328.988    0.285         NaN  in situ\n",
       "1976-07-15  1976.5355   329.653    0.282         NaN  in situ\n",
       "1976-08-15  1976.6202   330.550    0.201         NaN  in situ\n",
       "1976-09-15  1976.7049   330.872    0.234         NaN  in situ"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "33f488e6-7b08-4742-a9ad-e63d6014e85e",
   "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>CO2(ppm)</th>\n",
       "      <th>SD(ppm)</th>\n",
       "      <th>GR(ppm/yr)</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-08-15</th>\n",
       "      <td>2024.62022</td>\n",
       "      <td>420.795</td>\n",
       "      <td>0.157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-15</th>\n",
       "      <td>2024.70492</td>\n",
       "      <td>421.180</td>\n",
       "      <td>0.139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-15</th>\n",
       "      <td>2024.78689</td>\n",
       "      <td>421.397</td>\n",
       "      <td>0.097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-15</th>\n",
       "      <td>2024.87158</td>\n",
       "      <td>421.414</td>\n",
       "      <td>0.156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-15</th>\n",
       "      <td>2024.95355</td>\n",
       "      <td>421.377</td>\n",
       "      <td>0.171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE  CO2(ppm)  SD(ppm)  GR(ppm/yr)   Source\n",
       "date                                                          \n",
       "2024-08-15  2024.62022   420.795    0.157         NaN  in situ\n",
       "2024-09-15  2024.70492   421.180    0.139         NaN  in situ\n",
       "2024-10-15  2024.78689   421.397    0.097         NaN  in situ\n",
       "2024-11-15  2024.87158   421.414    0.156         NaN  in situ\n",
       "2024-12-15  2024.95355   421.377    0.171         NaN  in situ"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b6d80a9-3ed8-4210-a9b8-b754ae30f257",
   "metadata": {},
   "source": [
    "### Quick Plots of Your Data\n",
    "A good way to explore your data is by making a simple plot. Pandas contains its own `plot` method; this allows us to plot Pandas series without needing `matplotlib`.  In this example, we plot the `Nino34` series of our `df` `DataFrame` in this way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0afba302-2ed0-4962-a577-a3c03e7ced05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"CO2(ppm)\"].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc22d7e6-0aa5-40f7-83d6-abf3de74d597",
   "metadata": {},
   "source": [
    "Before, we called `.plot()`, which generated a single line plot. Line plots can be helpful for understanding some types of data, but there are other types of data that can be better understood with different plot types. For example, if your data values form a distribution, you can better understand them using a histogram plot.\n",
    "\n",
    "The code for plotting histogram data differs in two ways from the code above for the line plot. First, two series are being used from the `DataFrame` instead of one.  Second, after calling the `plot` method, we call an additional method called `hist`, which converts the plot into a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e1f5cbdd-87f6-4c7f-9e03-31c341ca23e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['CO2(ppm)']].plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48f1bec-4918-4e36-a8a0-d3fed98ab7e1",
   "metadata": {},
   "source": [
    "The histogram plot helped us better understand our data; there are clear differences in the distributions. To even better understand this type of data, it may also be helpful to create a box plot. This can be done using the same line of code, with one change: we call the `box` method instead of `hist`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "00c768bb-384d-4f6b-9719-28c133595fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['CO2(ppm)']].plot.box();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd43ebeb-9b10-4a55-9f49-27b841181b8a",
   "metadata": {},
   "source": [
    "Just like the histogram plot, this box plot indicates a clear difference in the distributions. Using multiple types of plot in this way can be useful for verifying large datasets. The pandas plotting methods are capable of creating many different types of plots. To see how to use the plotting methods to generate each type of plot, please review the [pandas plot documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f4707df-96e1-4314-8ea6-39369db0c1e1",
   "metadata": {},
   "source": [
    "#### Customize your Plot\n",
    "The pandas plotting methods are, in fact, wrappers for similar methods in matplotlib. This means that you can customize pandas plots by including keyword arguments to the plotting methods.  These keyword arguments, for the most part, are equivalent to their matplotlib counterparts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e52d0db1-07eb-44d2-860a-073357996471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['CO2(ppm)'].plot(\n",
    "    color='black', # Change the color\n",
    "    linewidth=2, # Make the line wider\n",
    "    xlabel='Year', # Add a label to the xaxis\n",
    "    ylabel=f'$CO^{2}$ [ppm]', # Add a label to the axis including units and latex syntax\n",
    "    figsize=(8, 6), # Change the dimensions of the figure\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "836e140e-0830-41b0-b13f-815c357948a6",
   "metadata": {},
   "source": [
    "Although plotting data can provide a clear visual picture of data values, sometimes a more quantitative look at data is warranted. As elaborated on in the next section, this can be achieved using the `describe` method.  The `describe` method is called on the entire `DataFrame`, and returns various summarized statistics for each column in the `DataFrame`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab03a16-6739-450d-b522-51cd0566dce6",
   "metadata": {},
   "source": [
    "### Basic Statistics\n",
    "\n",
    "We can garner statistics for a `DataFrame` by using the `describe` method. When this method is called on a `DataFrame`, a set of statistics is returned in tabular format.  The columns match those of the `DataFrame`, and the rows indicate different statistics, such as minimum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "edc72336-d731-4472-8b41-65689c6d395e",
   "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>CO2(ppm)</th>\n",
       "      <th>SD(ppm)</th>\n",
       "      <th>GR(ppm/yr)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>584.000000</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>572.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2000.661426</td>\n",
       "      <td>370.293872</td>\n",
       "      <td>0.170344</td>\n",
       "      <td>1.881241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.060892</td>\n",
       "      <td>25.916594</td>\n",
       "      <td>0.057848</td>\n",
       "      <td>0.644109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1976.368900</td>\n",
       "      <td>328.861000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>-0.102997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1988.515025</td>\n",
       "      <td>348.600750</td>\n",
       "      <td>0.136900</td>\n",
       "      <td>1.449760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2000.662568</td>\n",
       "      <td>367.511500</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>1.874510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.808060</td>\n",
       "      <td>391.303250</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2.276505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2024.953550</td>\n",
       "      <td>421.414000</td>\n",
       "      <td>0.610600</td>\n",
       "      <td>3.920990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              DATE    CO2(ppm)     SD(ppm)  GR(ppm/yr)\n",
       "count   584.000000  584.000000  584.000000  572.000000\n",
       "mean   2000.661426  370.293872    0.170344    1.881241\n",
       "std      14.060892   25.916594    0.057848    0.644109\n",
       "min    1976.368900  328.861000    0.015000   -0.102997\n",
       "25%    1988.515025  348.600750    0.136900    1.449760\n",
       "50%    2000.662568  367.511500    0.167000    1.874510\n",
       "75%    2012.808060  391.303250    0.200000    2.276505\n",
       "max    2024.953550  421.414000    0.610600    3.920990"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69330d78-4bd9-4bb1-be11-172f9ac20554",
   "metadata": {},
   "source": [
    "You can also view specific statistics using corresponding methods. In this example, we look at the mean values in the entire `DataFrame`, using the `mean` method.  When such methods are called on the entire `DataFrame`, a `Series` is returned. The indices of this `Series` are the column names in the `DataFrame`, and the values are the calculated values (in this case, mean values) for the `DataFrame` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "46058d4f-0101-4771-bf01-24129fb054e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(370.29387157534245)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"CO2(ppm)\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ebe0e2f-251d-4f39-a58a-ad4bd6313eb8",
   "metadata": {},
   "source": [
    "### Subsetting Using the Datetime Column\n",
    "\n",
    "Slicing is a useful technique for subsetting a `DataFrame`, but there are also other options that can be equally useful. In this section, some of these additional techniques are covered.\n",
    "\n",
    "If your `DataFrame` uses datetime values for indices, you can select data from only one month using `df.index.month`. In this example, we specify the number 1, which only selects data from January."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a7e4977-7e77-4408-82bd-fa4127b70986",
   "metadata": {},
   "source": [
    "This example shows how to create a new column containing the month portion of the datetime index for each data row. The value returned by `df.index.month` is used to obtain the data for this new column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "08b1e2e4-2e4c-4dd3-b3ab-3c2dc05de9e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['month'] = df.index.month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48648973-78c1-40f5-a5cf-c1646ffd3766",
   "metadata": {},
   "source": [
    "This next example illustrates how to use the new month column to calculate average monthly values over the other data columns. First, we use the `groupby` method to group the other columns by the month.  Second, we take the average (mean) to obtain the monthly averages. Finally, we plot the resulting data as a line plot by simply calling `plot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b35e993e-afc6-421a-98be-650c02864c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F6BIvo1AjIiIO7/x8mDUHz+Lj6cbsBzvh7+NpdElisAAfT6bf3YqfnrqdQe0aYLPDF5tPcsfMn/n/fj5MfmH1WbxPoUZERABYdyidN346CMDfh7ejZWiAwRVJdRJZrzb/39hO/GdiLO3DzeQWWJn54wH6zFrNNztOV4vxNgo1IiJCcuZFnozfjt0O93eJ4N6YcKNLkmqqc+NAFj/egzdHd6CB2YfTFy4yJX4HI2evZ+uJDENrU6gREanhCq02Jn2+nfO5BbRuEMBfh7YxuiSp5tzcTAzv2JCVT9/BM/1bUNvLne0nL3DP7PVsOHLOsLo8DLuziIhUC/9Y+itbT2Tg7+PB7Ac74ePpbnRJ4iRqebkzqXdzRnWOYNbyg+xPyaJrk0DD6lGoERGpwZbtSebf644B8M/72tOonq/BFYkzqh/gwz/ujcZSZMXNwFWn1f0kIlJDHUvP5dmFuwD4v15NGdAm1OCKxNl5exjbyqdQIyJSA+UXWnns061kW4ro0rguzw6IMrokkZumUCMiUgP9+Zs9/JqSTZCfF+880AlPd30diPPTn2IRkRrmyy1JfJl4CjcTvHV/R0ICfIwuSaRCKNSIiNQg+85k8dI3ewB4ql8LejQLMrgikYpTrlAze/ZsoqOjCQgIICAggNjYWJYuXep43mQyXfExc+ZMxzkWi4XJkycTFBSEr68vQ4cO5dSpU9e8b1FREX/6059o0qQJtWrVomnTprz88svYbM6xFbqISHWQlV/I459txVJk446oYB6/o5nRJYlUqHKFmvDwcGbMmEFiYiKJiYn07t2bYcOGsXfvXgCSk5NLPebOnYvJZOKee+5xXGPq1KksXryY+Ph41q1bR05ODoMHD8ZqvfreEf/4xz947733eOedd9i/fz+vvfYaM2fO5O23377Bty0iUrPY7Xb+uHAXx8/l0bBOLd4Y1cHQqbcilcFkv8nNGgIDA5k5cyYTJky47Lnhw4eTnZ3NihUrAMjMzCQ4OJj58+czevRoAM6cOUNERAQ//PADAwYMuOI9Bg8eTEhICB9++KHj2D333EPt2rWZP3/+VWuzWCxYLP/dRTQrK4uIiAgyMzMJCNCeJiJSc/x77VH+vmQ/nu4mFk7sToeIOkaXJFJmWVlZmM3m635/3/CYGqvVSnx8PLm5ucTGxl72fGpqKkuWLCkVdrZu3UphYSH9+/d3HAsLC6Nt27asX7/+qvfq2bMnK1as4ODB4o3Wdu7cybp167j77ruvWWNcXBxms9nxiIiIKO/bFBFxeonHzzNj6a8AvDS4tQKNuKxyryi8e/duYmNjyc/Px8/Pj8WLF9O6devLzps3bx7+/v6MHDnScSwlJQUvLy/q1q1b6tyQkBBSUlKues/nnnuOzMxMWrZsibu7O1arlVdeeYUxY8Zcs9bp06fz1FNPOX4uaakREakp0nMsTPp8O0U2O0PahzGuWyOjSxKpNOUONVFRUezYsYMLFy6waNEixo8fz+rVqy8LNnPnzmXs2LH4+Fx/qqDdbsdkunrf7oIFC/j000/5/PPPadOmDTt27GDq1KmEhYUxfvz4q77O29sbb2/vsr85EREXYrXZmRq/g5SsfG4J9iVuZLtr/q4VcXblDjVeXl40a1Y8Yr5z585s2bKFt956i/fff99xztq1azlw4AALFiwo9drQ0FAKCgrIyMgo1VqTlpZG9+7dr3rPZ599lueff577778fgHbt2nHixAni4uKuGWpERGqyt1YcYt3hdGp5ujP7wRj8vLXdn7i2m16nxm63lxqMC/Dhhx8SExND+/btSx2PiYnB09OThIQEx7Hk5GT27NlzzVCTl5eHm1vpUt3d3TWlW0TkKlYdSOPtlYcAiBvZjhYh/gZXJFL5yhXbX3jhBQYOHEhERATZ2dnEx8ezatUqli1b5jgnKyuLhQsXMmvWrMtebzabmTBhAk8//TT16tUjMDCQZ555hnbt2tG3b1/HeX369GHEiBFMmjQJgCFDhvDKK68QGRlJmzZt2L59O6+//jqPPPLIjb5vERGXdfrCRaYt2IHdDmO7RjK8Y0OjSxKpEuUKNampqYwbN47k5GTMZjPR0dEsW7aMfv36Oc6Jj4/HbrdfdRDvG2+8gYeHB6NGjeLixYv06dOHjz/+GHf3/+7seeTIEdLT0x0/v/3227z00ks8/vjjpKWlERYWxqOPPsqf//zn8r5fERGXVlBk44nPtpGRV0i7hmZeGnz5RA4RV3XT69Q4k7LOcxcRcVZ//XYvH68/ToCPB0uevI2IwNpGlyRy0yp9nRoREalevt91ho/XHwfg9VEdFGikxlGoERFxAUfO5vDcf3YB8Ngdt9C3dYjBFYlUPYUaEREnl1dQxGOfbiW3wErXJoE83a+F0SWJGEKhRkTEidntdv60eA8HU3MI9vfm7Qc64uGuX+1SM+lPvoiIE4vfksRX20/jZoK3x3Skvv/1V3EXcVUKNSIiTmrP6Uz+8u1eAJ4d0JJuTesZXJGIsRRqRESckNVm58kvtlNQZKNvq/o82qup0SWJGE6hRkTECf20P5Wj6bnUqe3JrPs64OamjSpFFGpERJzQx78cB2DMrZGYa3saW4xINaFQIyLiZH5NyWLD0XO4u5l4sFsjo8sRqTYUakREnMy8S6sGD2gTQsM6tYwtRqQaUagREXEiGbkFfLXtNAAP92hicDUi1YtCjYiIE4nfkoSlyEabsAA6N6prdDki1YpCjYiIkyiy2pi/4TgAv+veGJNJM55EfkuhRkTESSTsS+VMZj6Bvl4MaR9mdDki1Y5CjYiIk/jo0gDhB26NxMfT3dhiRKohhRoRESew90wmm4+dx0PTuEWuSqFGRMQJlCy2N7BdA0LN2rRS5EoUakREqrlzORa+2XkGKB4gLCJXplAjIlLNxW9JoqDIRnS4mU6RdYwuR6TaUqgREanGCq025m84AcDDPTSNW+RaFGpERKqxZXtSSMnKJ8jPm7vbNTC6HJFqTaFGRKQa+/jSNO6xXSPx9tA0bpFrUagREammdp26wNYTGXi6mxjbLdLockSqPYUaEZFqqmQa9+DoMOr7axq3yPUo1IiIVENp2fl8t0vTuEXKQ6FGRKQa+mJTEoVWOx0j69A+oo7R5Yg4BYUaEZFqpqDIxqebiqdxq5VGpOwUakREqpkfdidzNttCSICmcYuUh0KNiEg1U7Ib94NdG+Hprl/TImWlvy0iItXI9pMZ7Ey6gJe7G2O6ahq3SHmUK9TMnj2b6OhoAgICCAgIIDY2lqVLlzqeN5lMV3zMnDnTcY7FYmHy5MkEBQXh6+vL0KFDOXXq1HXvffr0aR588EHq1atH7dq16dChA1u3bi1P+SIi1V7JYntD2ocR5OdtbDEiTqZcoSY8PJwZM2aQmJhIYmIivXv3ZtiwYezduxeA5OTkUo+5c+diMpm45557HNeYOnUqixcvJj4+nnXr1pGTk8PgwYOxWq1XvW9GRgY9evTA09OTpUuXsm/fPmbNmkWdOnVu7F2LiFRDqVn5LNmVDBTv8yQi5WOy2+32m7lAYGAgM2fOZMKECZc9N3z4cLKzs1mxYgUAmZmZBAcHM3/+fEaPHg3AmTNniIiI4IcffmDAgAFXvMfzzz/PL7/8wtq1a2+mVLKysjCbzWRmZhIQEHBT1xIRqWivLz/Av1Yepkvjuiyc2N3ockSqjbJ+f9/wmBqr1Up8fDy5ubnExsZe9nxqaipLliwpFXa2bt1KYWEh/fv3dxwLCwujbdu2rF+//qr3+vbbb+ncuTP33Xcf9evXp2PHjnzwwQfXrdFisZCVlVXqISJSHVmKrHy26SQAv+vexOBqRJxTuUPN7t278fPzw9vbm4kTJ7J48WJat2592Xnz5s3D39+fkSNHOo6lpKTg5eVF3bp1S50bEhJCSkrKVe959OhRZs+eTfPmzfnxxx+ZOHEiTz75JJ988sk1a42Li8NsNjseERER5Xy3IiJV4/udyZzLLaCB2Yf+bUKMLkfEKZU71ERFRbFjxw42btzIY489xvjx49m3b99l582dO5exY8fi43P9/Ursdjsmk+mqz9tsNjp16sSrr75Kx44defTRR/nDH/7A7Nmzr3nd6dOnk5mZ6XgkJSVd/w2KiFQxu93uGCD8YDdN4xa5UeX+m+Pl5UWzZs3o3LkzcXFxtG/fnrfeeqvUOWvXruXAgQP8/ve/L3U8NDSUgoICMjIySh1PS0sjJOTq/zJp0KDBZa1BrVq14uTJk9es1dvb2zFTq+QhIlLdbD2Rwe7TmXh7uDHmVk3jFrlRN/3PAbvdjsViKXXsww8/JCYmhvbt25c6HhMTg6enJwkJCY5jycnJ7Nmzh+7drz4orkePHhw4cKDUsYMHD9KoUaObLV9ExHAli+0N79CQQF8vY4sRcWIe5Tn5hRdeYODAgURERJCdnU18fDyrVq1i2bJljnOysrJYuHAhs2bNuuz1ZrOZCRMm8PTTT1OvXj0CAwN55plnaNeuHX379nWc16dPH0aMGMGkSZMAmDZtGt27d+fVV19l1KhRbN68mTlz5jBnzpwbfd8iItVCcuZFlu0pHlM4Xvs8idyUcoWa1NRUxo0bR3JyMmazmejoaJYtW0a/fv0c58THx2O32xkzZswVr/HGG2/g4eHBqFGjuHjxIn369OHjjz/G3d3dcc6RI0dIT093/NylSxcWL17M9OnTefnll2nSpAlvvvkmY8eOLe/7FRGpVj7deAKrzU7XJoG0DlMXucjNuOl1apyJ1qkRkeokv9BKbNwKMvIKee/BGO5qG2p0SSLVUqWvUyMiIjfn2x1nyMgrpGGdWvRtVd/ockScnkKNiIgB7Ha7Y4DwQ7GN8NA0bpGbpr9FIiIG2HzsPPuTs/DxdGN0Fy0MKlIRFGpERAzw0S/HARjZKZw6tTWNW6QiKNSIiFSxUxl5LN9XPI37d5rGLVJhFGpERKrY/I0nsNmhR7N6tAjxN7ocEZehUCMiUoUuFliJ31y8D5124xapWAo1IiJVaPH202ReLCQisBa9W2oat0hFUqgREakixbtxHwNgfGxj3N1MBlck4loUakREqsiGI+c4mJpDbS937uusadwiFU2hRkSkipQstndPp3DMtTyNLUbEBSnUiIhUgZPn8vhpfyqg3bhFKotCjYhIFfhkw3HsdujVIphm9f2MLkfEJSnUiIhUslxLEQsSi6dxP6xWGpFKo1AjIlLJvtp+muz8IhrXq83tLYKNLkfEZSnUiIhUIrvdzse/XJrG3b0xbprGLVJpFGpERCrR2kPpHDmbi5+3B/fGhBtdjohLU6gREalEH1+axn1vTDj+PprGLVKZFGpERCrJsfRcVv6aBmgat0hVUKgREakkn2w4DsCdUcE0CfI1thiRGkChRkSkEmTnF7Iw8RQAD/fQbtwiVUGhRkSkEizaeoocSxG3BPtyW/Mgo8sRqREUakREKpjNZmfehhMA/K57Y0wmTeMWqQoKNSIiFWz1obMcS8/F39uDkZ00jVukqijUiIhUsI9+OQ7AqC4R+Hp7GFuMSA2iUCMiUoEOp+Ww5uBZTCYYH9vY6HJEahSFGhGRClQyjbtPyxAi69U2thiRGkahRkSkgmTlF/KfrSXTuBsbW4xIDaRQIyJSQb7ckkRegZUWIX50v6We0eWI1DgKNSIiFcBqs/OJYxp3E03jFjGAQo2ISAX4+dc0Tp7Pw1zLk+Edw4wuR6RGKleomT17NtHR0QQEBBAQEEBsbCxLly51PG8yma74mDlzpuMci8XC5MmTCQoKwtfXl6FDh3Lq1Kky1xAXF4fJZGLq1KnlKV1EpFKV7MZ9f5cIantpGreIEcoVasLDw5kxYwaJiYkkJibSu3dvhg0bxt69ewFITk4u9Zg7dy4mk4l77rnHcY2pU6eyePFi4uPjWbduHTk5OQwePBir1Xrd+2/ZsoU5c+YQHR1dzrcpIlJ5DqZms+5wOm4mGBfbyOhyRGqscoWaIUOGcPfdd9OiRQtatGjBK6+8gp+fHxs3bgQgNDS01OObb77hzjvvpGnTpgBkZmby4YcfMmvWLPr27UvHjh359NNP2b17Nz/99NM1752Tk8PYsWP54IMPqFu37g2+XRGRilfSStO/dSjhdTWNW8QoNzymxmq1Eh8fT25uLrGxsZc9n5qaypIlS5gwYYLj2NatWyksLKR///6OY2FhYbRt25b169df835PPPEEgwYNom/fvmWu0WKxkJWVVeohIlKRMvMK+WpbcRf67zSNW8RQ5e743b17N7GxseTn5+Pn58fixYtp3br1ZefNmzcPf39/Ro4c6TiWkpKCl5fXZS0tISEhpKSkXPWe8fHxbNu2jS1btpSr1ri4OP72t7+V6zUiIuWxIPEk+YU2Wob607VJoNHliNRo5W6piYqKYseOHWzcuJHHHnuM8ePHs2/fvsvOmzt3LmPHjsXHx+e617Tb7Ved/piUlMSUKVP49NNPy3St35o+fTqZmZmOR1JSUrleLyJyLVabnXnri6dxP9xDu3GLGK3cLTVeXl40a9YMgM6dO7Nlyxbeeust3n//fcc5a9eu5cCBAyxYsKDUa0NDQykoKCAjI6NUa01aWhrdu3e/4v22bt1KWloaMTExjmNWq5U1a9bwzjvvYLFYcHd3v+Jrvb298fb2Lu9bFBEpk4R9qZy+cJG6tT0Z1qGh0eWI1Hg3vU6N3W7HYrGUOvbhhx8SExND+/btSx2PiYnB09OThIQEx7Hk5GT27Nlz1VDTp08fdu/ezY4dOxyPzp07M3bsWHbs2HHVQCMiUtk+Xn8MgDG3RuLjqd9FIkYrV0vNCy+8wMCBA4mIiCA7O5v4+HhWrVrFsmXLHOdkZWWxcOFCZs2addnrzWYzEyZM4Omnn6ZevXoEBgbyzDPP0K5du1IDgPv06cOIESOYNGkS/v7+tG3bttR1fH19qVev3mXHRUSqyv7kLDYePY+7m4kHu2kat0h1UK5Qk5qayrhx40hOTsZsNhMdHc2yZcvo16+f45z4+Hjsdjtjxoy54jXeeOMNPDw8GDVqFBcvXqRPnz58/PHHpVpcjhw5Qnp6+g2+JRGRyjfv0jTuu9qEElanlrHFiAgAJrvdbje6iKqSlZWF2WwmMzOTgIAAo8sRESd1PreA2LgVWIpsLJwYS5fGmvUkUpnK+v2tvZ9ERMopfstJLEU22jYMoHMjLQYqUl0o1IiIlEOR1cZ87cYtUi0p1IiIlMPyfakkZ+ZTz9eLwdENjC5HRH5DoUZEpBw++qV4GvcDXTWNW6S6UagRESmjPacz2XI8Aw9N4xaplhRqRETKqGQ37rvbNSAkoHzbtohI5VOoEREpg/QcC9/uOANoN26R6kqhRkSkDL7YdJICq4324WY6RtQxuhwRuQKFGhGR6yi02pi/sWQ3bk3jFqmuFGpERK5j6Z4U0rItBPt7c3c7TeMWqa4UakREruPjS9O4x3aNxMtDvzZFqiv97RQRuYadSRfYdvICnu4mHugaaXQ5InINCjUiItdQMo17SHQY9f01jVukOlOoERG5irTsfL7fVTyNe3z3xsYWIyLXpVAjInIVn286SaHVTqfIOrTXNG6Rak+hRkTkCgqKbHy68SQAv+vRxOBqRKQsFGpERK5gye4zpOdYCAnwZmDbUKPLEZEyUKgREbmCj385DsC4bo3wdNevShFnoL+pIiL/Y0fSBXaeysTL3Y37b9U0bhFnoVAjIvI/PtlwHIDB0Q0I8vM2thgRKTOFGhGR3ziXY+H7nckAPKRp3CJORaFGROQ3FiQmUWC1ER1upoOmcYs4FYUaEZFLrDY7n12axj2uWyODqxGR8lKoERG5ZMX+VE5fuEjd2p4MaR9mdDkiUk4KNSIil3yy4QQAo7pE4OPpbnA1IlJeCjUiIsCRszmsO5yOyQQPdlXXk4gzUqgREQHmX2ql6dOyPhGBtQ2uRkRuhEKNiNR4uZYiFm09BcBDsY2NLUZEbphCjYjUeIu3nybbUkSTIF96NgsyuhwRuUEKNSJSo9ntdscKwg92a4Sbm8nYgkTkhinUiEiNtunYeQ6m5lDL0517Y8KNLkdEbkK5Qs3s2bOJjo4mICCAgIAAYmNjWbp0qeN5k8l0xcfMmTMd51gsFiZPnkxQUBC+vr4MHTqUU6dOXfO+cXFxdOnSBX9/f+rXr8/w4cM5cOBAOd+qiMjlSgYID+/YEHMtT4OrEZGbUa5QEx4ezowZM0hMTCQxMZHevXszbNgw9u7dC0BycnKpx9y5czGZTNxzzz2Oa0ydOpXFixcTHx/PunXryMnJYfDgwVit1qved/Xq1TzxxBNs3LiRhIQEioqK6N+/P7m5uTf4tkVEICUzn2V7UwB4KFbTuEWcnclut9tv5gKBgYHMnDmTCRMmXPbc8OHDyc7OZsWKFQBkZmYSHBzM/PnzGT16NABnzpwhIiKCH374gQEDBpTpnmfPnqV+/fqsXr2aXr16lbnWrKwszGYzmZmZBAQElPl1IuKaXk84yL9WHOLWxoF8OTHW6HJE5CrK+v19w2NqrFYr8fHx5ObmEht7+S+D1NRUlixZUirsbN26lcLCQvr37+84FhYWRtu2bVm/fn2Z752ZmQkUB6prsVgsZGVllXqIiAAUFNn4fNOlfZ7USiPiEsodanbv3o2fnx/e3t5MnDiRxYsX07p168vOmzdvHv7+/owcOdJxLCUlBS8vL+rWrVvq3JCQEFJSUsp0f7vdzlNPPUXPnj1p27btNc+Ni4vDbDY7HhEREWW6h4i4vmV7U0jPsVDf35sBbUKNLkdEKkC5Q01UVBQ7duxg48aNPPbYY4wfP559+/Zddt7cuXMZO3YsPj4+172m3W7HZCrbNMpJkyaxa9cuvvjii+ueO336dDIzMx2PpKSkMt1DRFzfJ+uPAzDm1ki8PDQRVMQVeJT3BV5eXjRr1gyAzp07s2XLFt566y3ef/99xzlr167lwIEDLFiwoNRrQ0NDKSgoICMjo1RrTVpaGt27d7/uvSdPnsy3337LmjVrCA+//tRLb29vvL29y/rWRKSG2Hcmi8QTGXi4mXiga6TR5YhIBbnpf57Y7XYsFkupYx9++CExMTG0b9++1PGYmBg8PT1JSEhwHEtOTmbPnj3XDDV2u51Jkybx1VdfsXLlSpo0aXKzZYtIDTZ/43EABrQNJSTg+q3JIuIcytVS88ILLzBw4EAiIiLIzs4mPj6eVatWsWzZMsc5WVlZLFy4kFmzZl32erPZzIQJE3j66aepV68egYGBPPPMM7Rr146+ffs6zuvTpw8jRoxg0qRJADzxxBN8/vnnfPPNN/j7+zvG35jNZmrVqnVDb1xEaqbMvEIWbz8NwEPdNEBYxJWUK9SkpqYybtw4kpOTMZvNREdHs2zZMvr16+c4Jz4+HrvdzpgxY654jTfeeAMPDw9GjRrFxYsX6dOnDx9//DHu7u6Oc44cOUJ6errj59mzZwNwxx13lLrWRx99xO9+97vyvAURqeEWbk0iv9BGy1B/bm1y7RmUIuJcbnqdGmeidWpEajabzc6ds1Zx4lwer4xoy9iuaqkRcQaVvk6NiIizWXPoLCfO5eHv48HwDg2NLkdEKphCjYjUGCX7PN0bE46vd7knf4pINadQIyI1QtL5PFYeSANgnAYIi7gkhRoRqRE+3XgCux1uax5E02A/o8sRkUqgUCMiLi+/0MqCxOIVxR+KbWxsMSJSaRRqRMTlfbvzDBfyCmlYpxa9W9Y3uhwRqSQKNSLi0ux2O59sOA7Ag90a4e5Wtn3mRMT5KNSIiEvbnnSBPaez8PJwY3SXCKPLEZFKpFAjIi6tZBr3kOgwAn29DK5GRCqTQo2IuKz0HAtLdiUD8FCspnGLuDqFGhFxWQu2JFFgtdE+og7tI+oYXY6IVDKFGhFxSUVWG59uLO560m7cIjWDQo2IuKSf9qeRnJlPoK8Xg6IbGF2OiFQBhRoRcUnzNx4HYHSXCHw83Y0tRkSqhEKNiLicw2nZ/HL4HG4mGNs10uhyRKSKKNSIiMspmcbdp1UI4XVrG1yNiFQVhRoRcSk5liIWbTsNaBq3SE2jUCMiLmXxtlPkWIpoGuxLj1uCjC5HRKqQQo2IuIzifZ6Ku57GdWuEm/Z5EqlRFGpExGVsOHqOQ2k51PZy556YcKPLEZEqplAjIi6jZIDwiI4NCfDxNLgaEalqCjUi4hKSMy+yfF8qAA/FNja2GBExhEKNiLiEzzedxGqz07VJIFGh/kaXIyIGUKgREadnKbLyxeaTgFppRGoyhRoRcXrL9qSQnlNASIA3/duEGF2OiBhEoUZEnF7JNO4Hbm2Ep7t+rYnUVPrbLyJObc/pTLaeyMDDzcSYWyOMLkdEDKRQIyJOrWQa98B2Dagf4GNwNSJiJIUakXI4eS6PnUkXjC5DLrmQV8DXO7TPk4gUU6gRKaOEfakMeHMNI2ev5+jZHKPLEWBh4iksRTZaNQigc6O6RpcjIgZTqBEpg3nrj/Po/EQuFlqx2ux8vyvZ6JJqPJvNzvyNxV1PD8U2wmTSPk8iNZ1Cjcg12Gx2/v79Pv7y7V5sdmjdIACAJQo1hlt98Cwnz+cR4OPBsA5hRpcjItVAuULN7NmziY6OJiAggICAAGJjY1m6dKnjeZPJdMXHzJkzHedYLBYmT55MUFAQvr6+DB06lFOnTl333u+++y5NmjTBx8eHmJgY1q5dW57SRcotv9DKE59v49/rjgHwx7ui+OIP3fB0N3EgNZtDqdkGV1izfbLhOAD3dY6gtpeHscWISLVQrlATHh7OjBkzSExMJDExkd69ezNs2DD27t0LQHJycqnH3LlzMZlM3HPPPY5rTJ06lcWLFxMfH8+6devIyclh8ODBWK3Wq953wYIFTJ06lRdffJHt27dz2223MXDgQE6ePHmDb1vk2s7lWHjgg40s3ZOCl7sbb93fgcfvaIa5tie3NQ8GYMlutdYY5cS5XFYdPAvAg900QFhEipnsdrv9Zi4QGBjIzJkzmTBhwmXPDR8+nOzsbFasWAFAZmYmwcHBzJ8/n9GjRwNw5swZIiIi+OGHHxgwYMAV79G1a1c6derE7NmzHcdatWrF8OHDiYuLu2ptFosFi8Xi+DkrK4uIiAgyMzMJCAi4ofcrru9Yei4Pf7SZ4+fyMNfyZM64GLo2red4ftHWUzy9cCfN6/uR8NTtBlZac72yZB8frD3G7S2CmffIrUaXIyKVLCsrC7PZfN3v7xseU2O1WomPjyc3N5fY2NjLnk9NTWXJkiWlws7WrVspLCykf//+jmNhYWG0bduW9evXX/E+BQUFbN26tdRrAPr373/V15SIi4vDbDY7HhERWphLrm3rifOMfPcXjp/LI7xuLRY91r1UoAHo2zoET3cTh9JyOKguqCp3scDKl4nFXdaaxi0iv1XuULN79278/Pzw9vZm4sSJLF68mNatW1923rx58/D392fkyJGOYykpKXh5eVG3bumplyEhIaSkpFzxfunp6VitVkJCQsr8mhLTp08nMzPT8UhKSirr25QaaMmuZMZ8sImMvELah5tZ/HgPmtX3u+w8cy1PepV0QWnAcJX7dudpMi8WEhFYizui6htdjohUI+UONVFRUezYsYONGzfy2GOPMX78ePbt23fZeXPnzmXs2LH4+Fx/hU+73X7d6Zj/+3xZXuPt7e0Y1FzyEPlfdrudD9Yc5YnPt1FQZKNvqxC++L9uBPt7X/U1g6IbAMXjam6yB1fKwW63M2998TTuB7s2wt1N07hF5L/KHWq8vLxo1qwZnTt3Ji4ujvbt2/PWW2+VOmft2rUcOHCA3//+96WOh4aGUlBQQEZGRqnjaWlpl7XElAgKCsLd3f2yVplrvUakrKw2O3/5di+v/LAfgN91b8z742KuO5umb+sQvNzdOJyWw8FULcRXVbadzGBfchbeHm6M6qzuZBEp7abXqbHb7aUG4wJ8+OGHxMTE0L59+1LHY2Ji8PT0JCEhwXEsOTmZPXv20L179yte38vLi5iYmFKvAUhISLjqa0TKIq+giEfnJ/LJhhOYTPCnQa34y5DWZfrXf4CPJ71alHRBnansUuWSkt24h7YPo66vl8HViEh1U65Q88ILL7B27VqOHz/O7t27efHFF1m1ahVjx451nJOVlcXChQsva6UBMJvNTJgwgaeffpoVK1awfft2HnzwQdq1a0ffvn0d5/Xp04d33nnH8fNTTz3Fv//9b+bOncv+/fuZNm0aJ0+eZOLEiTfynkVIy87n/jkb+Wl/Gt4ebrz7QCd+f1vTcq1KO/hSF9T36oKqEmezLfxwaRr9Q7GNjS1GRKqlcq1YlZqayrhx40hOTsZsNhMdHc2yZcvo16+f45z4+Hjsdjtjxoy54jXeeOMNPDw8GDVqFBcvXqRPnz58/PHHuLu7O845cuQI6enpjp9Hjx7NuXPnePnll0lOTqZt27b88MMPNGqkmQ9SfofTshk/dwunL1wk0NeLDx7qTMwN7BvUp1V9vDzcOHo2lwOp2bQM1ZityhS/+SSFVjsdI+vQLtxsdDkiUg3d9Do1zqSs89zFdW08eo7/+ySRrPwiGterzccP30rjIN8bvt4fPkkkYV8qk3s34+n+URVYqfxWkdVGz3/8TEpWPm+Mbs+IjuFGlyQiVajS16kRcTZfbz/NuA83kZVfREyjunz1eI+bCjTw3y6oJbvUBVWZEvalkpKVTz1fL+5u18DockSkmlKoEZdnt9t5Z+Uhpi7YQaHVzt3tQvns910JrICBpn1ahRR3QaXnsj9ZC/FVlpIBwvffGoG3h/t1zhaRmkqhRlxaodXG9K9288/lBwH4v15NeWdMJ3w8K+aL0c/bgzujSvaC0iyoynAwNZsNR8/hZoIHumocnYhcnUKNuKwcSxET5iUSvyUJNxP8v2FteOHuVrhV8IJtg6LDAHVBVZb5l1pp+rUOoWGdWgZXIyLVWblmP4k4i5TMfB7+eAv7k7Oo5enO22M60rd15SzW2Kdlfbw93Dh+Lo+9Z7Jo21AzcypKdn4hX20r2eepsbHFiEi1p5YacTn7k7MY8e4v7E/OIsjPmwWPdqu0QAPg6+3BnZf2ICpZR0UqxlfbTpNbYOWWYF+631Lv+i8QkRpNoUZcypqDZ7nvvQ0kZ+bTrL4fix/vTnR4nUq/r/aCqnh2u51PNhwHiltpyrMwoojUTAo14jK+TEzikY+3kGMpomuTQBZN7E5EYO0quXfvlvXx8XTjxKUuKLl564+c48jZXHy93BnZqaHR5YiIE1CoEadnt9t5ffkB/vifXRTZ7AzvEMYnE27FXNuzymrw9fagd8viLqjvd6kLqiKUtNKM7BSOv0/V/b8UEeelUCNOraDIxtNf7uRfKw8DMOnOZrwxuoMha5kMandpFtTuM+qCukmnL1wkYV8qAONiNY1bRMpGs5/EaWVeLGTi/K1sOHoOdzcTrwxvy/23RhpWz50tg6nl6U7S+YvsPp1ZJWN5XNXnm05gs0Ns03q0CPE3uhwRcRJqqRGndCojj3tnr2fD0XP4erkz93ddDA00ALW9POjdqrgLaolmQd0wS5GV+M1JADykVhoRKQeFGnE6e05nMuLd9RxKyyE0wIeFE7tze4tgo8sCYFA77QV1s37Yncy53AJCA3zoV4lT8UXE9SjUiFNZ+Wsqo97fwNlsCy1D/Vn8RHdah1WfHdfvjKpPLU93TmVcZNepTKPLcUol+zyN7RqJh7t+RYlI2ek3hjiN+RtP8Pt5ieQVWLmteRBfToylgbl6LZtfy8udPuqCumG7T2Wy/eQFPN1NhncniojzUaiRas9msxP3w35e+noPNjvcFxPO3N91IaCaTvMdHK0uqBtVMo377nYNCPb3NrYYEXE6mv0k1Vp+oZWnF+5kyaW1X57u14JJvZtV69Vl74iqT20vd05fuMiOpAt0jKxrdElOISO3gG93Fu90rgHCInIj1FIj1VZGbgHjPtzEkl3JeLqbeGN0eyb3aV6tAw2Aj6c7fVsVD3BdooX4yuzLxCQsRTbahAXQSUFQRG6AQo1USyfO5XLP7PVsOZ6Bv48H8x65lREdw40uq8zuvjQL6gftBVUmVpudTzcVDxB+KLZRtQ+uIlI9KdRItbPtZAYj313P0fRcGtapxaLHutP9liCjyyqXO6KC8fVy50xmPtuTLhhdTrW36kAaSecvYq7lydD22udJRG6MQo1UK8v2pDBmzkbO5RbQtmEAix/v7pQryvp4utO3tbqgyqpkGveozuHU8qr6LS5ExDUo1Ei1kFdQxL9WHOKxz7ZiKbLRu2V9FvxfLPUDfIwu7YYN+k0XlM2mLqirOZ6ey+qDZzGZ4MFuGiAsIjdOs5/EUHkFRXy68QTvrz7KudwCAB7sFslfh7Rx+oXXerUIxs/bg+TMfLYnZRDTKNDokqql+RuLW2nuaBFMo3q+BlcjIs5MoUYMkVdQxPwNJ5iz5r9hJjKwNlP7NmdEx4YuMVDUx9Odfq1DWLz9NN/vSlaouYK8giIWJpbs89TY2GJExOkp1EiVyrUUMX9jcZg5fynMNKpXm0l3NmN4x4Z4OnnrzP8a1K4Bi7efZunuFF4a1Bo3N+cPaxXpmx1nyMovIjKwdrXZv0tEnJdCjVSJXEsRn2w4wQdrS4eZyb2bM7xDmNN3NV3NbS2C8Pf2ICUrn20nM+jcWK01Jex2u2OA8LhujRT4ROSmKdRIpcqxFPHJhuN8sOYoGXmFADS+FGaGuXCYKeHtUdwF9dWlLiiFmv9KPJHB/uQsvD3cuK+z86xBJCLVl0KNVIocSxHz1h/n32v/G2aaBPkyuXczhrZ3/TDzW4OiG/DV9tP8sDuZPw9WF1SJklaa4R0aUqe2l8HViIgrUKiRCpWdX+joZrpQw8NMiZ7Ng/D38SAt20LiiQxubaLWmrSsfJZe2sV8nPZ5EpEKolAjFSI7v7C4ZWbdMUeYaRrky+Q+zRgSXTPDTAlvD3f6tw5l0bZTLNl1RqEG+GJzEkU2O50i69C2odnockTERSjUyE0pCTMfrD1G5sX/hpkn+zRnSPsw3NXVAsDg6AYs2naKpXtS+POQNjX6c8kvtPLZpX2exndvbGwxIuJSyvXP59mzZxMdHU1AQAABAQHExsaydOnSUufs37+foUOHYjab8ff3p1u3bpw8edLx/JEjRxgxYgTBwcEEBAQwatQoUlNTr3nfoqIi/vSnP9GkSRNq1apF06ZNefnll7HZbOUpXypQVn4hb684RM9//Mw/lx8k82IhTYN9eev+DiQ8dTvDOzas0V/c/6tHsyACSrqgjp83uhxDzd9wgrRsCw3r1OKutqFGlyMiLqRcLTXh4eHMmDGDZs2aATBv3jyGDRvG9u3badOmDUeOHKFnz55MmDCBv/3tb5jNZvbv34+PT/FS97m5ufTv35/27duzcuVKAF566SWGDBnCxo0bcXO7csb6xz/+wXvvvce8efNo06YNiYmJPPzww5jNZqZMmXIz71/KKSu/kI9/KR4AnJVfBMAtwcUtM4Oj1TJzNV4ebvRvE8p/tp5iye5kujatZ3RJhsjOL+TdVYcBmNK3Od4e2udJRCqOyW6339SmNIGBgcycOZMJEyZw//334+npyfz586947vLlyxk4cCAZGRkEBAQAkJGRQWBgIAkJCfTt2/eKrxs8eDAhISF8+OGHjmP33HMPtWvXvuq9riQrKwuz2UxmZqbj/lI2mReLw8yH6/4bZprV9+PJPs0Z1K6BwkwZ/HwgjYc/2kKQnzebXuhTIz+zNxIO8taKQzQN9mX51F41eqyViJRdWb+/b/g3itVqJT4+ntzcXGJjY7HZbCxZsoQWLVowYMAA6tevT9euXfn6668dr7FYLJhMJry9vR3HfHx8cHNzY926dVe9V8+ePVmxYgUHDx4EYOfOnaxbt4677777mjVaLBaysrJKPaR8Mi8W8uZPB+n5j5W88dNBsvKLaFbfj3+N6ciPU3sxVONmyqzHLUGYa3mSnmNh87Ga1wV1PreAf689CsDT/aIUaESkwpX7t8ru3bvx8/PD29ubiRMnsnjxYlq3bk1aWho5OTnMmDGDu+66i+XLlzNixAhGjhzJ6tWrAejWrRu+vr4899xz5OXlkZuby7PPPovNZiM5Ofmq93zuuecYM2YMLVu2xNPTk44dOzJ16lTGjBlzzVrj4uIwm82OR0RERHnfbo2VebGQNxKKw8ybPx0iO7+I5vX9eFth5oZ5ebgxoE0IAEt2nzG4mqr37s+HyS2w0rZhAAM1lkZEKkG5Q01UVBQ7duxg48aNPPbYY4wfP559+/Y5Bu0OGzaMadOm0aFDB55//nkGDx7Me++9B0BwcDALFy7ku+++w8/Pz9GU1KlTJ9zdr963vmDBAj799FM+//xztm3bxrx58/jnP//JvHnzrlnr9OnTyczMdDySkpLK+3ZrnMy8Ql6/FGbeWvHfMPPOA8VhRjOabs6g6DAAlu1JwWq7qZ5fp5KceZFPLu3G/Uz/KC1AKCKVotxTur28vBwDhTt37syWLVt46623ePvtt/Hw8KB169alzm/VqlWprqX+/ftz5MgR0tPT8fDwoE6dOoSGhtKkSZOr3vPZZ5/l+eef5/777wegXbt2nDhxgri4OMaPH3/V13l7e5fq6pKry8wr5MN1R/nol+NkW4rHzLQI8WNKnxYMbBuqL6EK0v2WetSp7Ul6TgGbjp2j+y1BRpdUJf614jAFRTZubRyojStFpNLc9Do1drsdi8WCl5cXXbp04cCBA6WeP3jwII0aXb5iaFBQ8S/zlStXkpaWxtChQ696j7y8vMtmRrm7u2tKdwW4kFfA3HXHSoWZqBB/pvRtzl1tFGYqmqe7GwNah7IgMYklu5JrRKg5lp7Ll4nFraTP3hWFyaQ/UyJSOcoVal544QUGDhxIREQE2dnZxMfHs2rVKpYtWwYUt6iMHj2aXr16ceedd7Js2TK+++47Vq1a5bjGRx99RKtWrQgODmbDhg1MmTKFadOmERUV5TinT58+jBgxgkmTJgEwZMgQXnnlFSIjI2nTpg3bt2/n9ddf55FHHqmAj6BmupBXwIeXwkzOpTDTMtSfKX2aM0BhplINim7AgsQklu1J4W9D27j8gNk3Eg5itdm5MyqYLtrQU0QqUblCTWpqKuPGjSM5ORmz2Ux0dDTLli2jX79+AIwYMYL33nuPuLg4nnzySaKioli0aBE9e/Z0XOPAgQNMnz6d8+fP07hxY1588UWmTZtW6j4l3VMl3n77bV566SUef/xx0tLSCAsL49FHH+XPf/7zzbz3GikjtzjMfLxeYcYosbfUo25tT87lFrDp2Hl6NHPd1pp9Z7L4dmfxoOin+0dd52wRkZtz0+vUOJOauk5NodXGqYyL/GdrEh//cpzcAitQHGam9m1O/9YKM1Vt+le7+GJzEmNujSRuZDujy6k0Ez7ewopf0xgc3YB3HuhkdDki4qTK+v2tvZ9cRGZeISfP53HifC4nz+eRdD6PE+fyOHk+jzMXLvLbiTatGgQwpU9z+rcOUZgxyKB2YXyxOYkf96bw/4a5ZhfU1hPnWfFrGu5uJp7q18LockSkBlCocRJFVhvJmfmcPF8cVE6cKw4uxf+d61jl92q8PdxoExbAo7ffQr9WCjNG69Y0kEBfL87nFrDx6Hl6NnetLii73c5ry4onDdzbKZymwX4GVyQiNYFCTTWSnV/c2nLyUgvLbx+nMy5SdJ11TYL8vGlUrzaRgbWJCKxNo8DaRF76OdjPW0GmGvFwd+OutqF8vukkS3afcblQs+5wOpuOncfL3Y0pfZsbXY6I1BAKNVXIarOTkpXPyUutLMVdRRcvBZlcMvIKr/l6L3c3wgNrEXkpsEQEFgeWRvV8iQisRW0v/e90JoPaNeDzTSdZtieFl4e1xdNFuqDsdjszfyxupXmwWyPC6tQyuCIRqSn0LVjBci1FJGX8b/dQ8X+fyrhIgfXaa+sE+noReSmsRP6mpSUysDahAT5qbXEhXZsEUs/Xi3O5BWw4co5eLrIo3Y97U9h1KpPaXu48fuctRpcjIjWIQs1NstvtPP3lTo6fKx6gm55TcM3zPdxMhNetVdw99JvAEhlY3Nri7+NZRZWL0Uq6oD7bdJIlu5JdItRYbXb+ubx449nf92xCkJ9W9BaRqqNQc5NMJhMbjp4jOTPfccxcy5NG9X7TPRT433EuDcw+LjnTRW7MoOgGfLbpJMv2pvD3Ec7fBbV4+2kOp+VgruXJ73s1NbocEalhFGoqwHN3tcTT3a04yNStjbm2WlukbLo2qUeQnxfpOQX8cjidO6LqG13SDbMUWXkjobiV5rE7biFArY4iUsWc+5+F1cTwjg0ZFN2Atg3NCjRSLu5uJga2bQDAD7uTDa7m5izYksTpCxep7+/N+NjGRpcjIjWQQo2Iwe5uVxxqftybSkGRc27SmldQxL9WHAZgcp/m1PJyN7giEamJFGpEDHZrk0CC/LzJvFjIL0fSr/+Caujj9cdJz7EQEViL0Z0jjC5HRGoohRoRg7m7mbi7XSgAS3Y5XxdU5sVC3lt1BIBpfVvg5aFfKyJiDP32EakGBjm6oFKcrgtqzpojZOUX0SLEj2EdGhpdjojUYAo1ItVA58aB1Pf3Jju/iHWHzxpdTpmdzbYwd91xAJ7uH4W7FocUEQMp1IhUA8VdUMWtNUt2pRhcTdn9fz8f5mKhlfYRdejfOsTockSkhlOoEakmBkUXh5rl+1KwFFkNrub6TmXk8fmmkwD8cUAUJpNaaUTEWAo1ItVETGRdQgIudUEdqv6zoN766RAFVhvdb6lHj2autcu4iDgnhRqRasLtNwvxVfdZUIfTcli07RQAzwyIMrgaEZFiCjUi1cjgS11QCftSyS+svl1QbyQcxGaHfq1D6BRZ1+hyREQAhRqRaqVTZF1CA3zIthSxtpp2Qe05ncmS3cmYTPB0/xZGlyMi4qBQI1KNuJWaBXXG4GqubOaPBwAY1j6MlqEBBlcjIvJfCjUi1UzJLKif9qdVuy6oTUfPsfrgWTzcTEzrp1YaEaleFGpEqpmOEXVoYPYhx1LEmoPVZyE+u93uaKUZ3SWCRvV8Da5IRKQ0hRqRaqZUF9Tu6jMLatWBsySeyMDbw43JvZsbXY6IyGUUakSqIUcXVDWZBWWz/beVZnz3xoSafQyuSETkcgo1ItVQx4g6NKxTi9wCK6sOGN8FtWR3MvuSs/Dz9uCx228xuhwRkStSqBGphkwmE3e3CwWM74Iqstp4PeEgAH+4rSl1fb0MrUdE5GoUakSqqUHRYQCs2G9sF9Sibac4lp5LoK8XE25rYlgdIiLXo1AjUk21DzfTsE4t8gqsrDqQZkgN+YVW3vrpEACP33ELft4ehtQhIlIWCjUi1ZTJZHIMGP7eoL2gPtt0kjOZ+TQw+/Bgt0aG1CAiUlYKNSLV2KBLU7tX7E/jYkHVdkHlWIp49+fDADzZpzk+nu5Ven8RkfIqV6iZPXs20dHRBAQEEBAQQGxsLEuXLi11zv79+xk6dChmsxl/f3+6devGyZMnHc8fOXKEESNGEBwcTEBAAKNGjSI1NfW69z59+jQPPvgg9erVo3bt2nTo0IGtW7eWp3wRpxMdbia8bi0uFlr5uYq7oD5ad4xzuQU0CfLl3pjwKr23iMiNKFeoCQ8PZ8aMGSQmJpKYmEjv3r0ZNmwYe/fuBYoDS8+ePWnZsiWrVq1i586dvPTSS/j4FK9pkZubS//+/TGZTKxcuZJffvmFgoIChgwZgs1mu+p9MzIy6NGjB56enixdupR9+/Yxa9Ys6tSpc+PvXMQJ/LYLakkVdkFl5BYwZ81RAKb1a4Gnuxp1RaT6M9ntdvvNXCAwMJCZM2cyYcIE7r//fjw9PZk/f/4Vz12+fDkDBw4kIyODgIDijfAyMjIIDAwkISGBvn37XvF1zz//PL/88gtr1669mVLJysrCbDaTmZnpuL9Idbf7VCZD3lmHj6cb217qR22vyh+sG7d0P++vPkqrBgEsmdwTNzdTpd9TRORqyvr9fcP//LJarcTHx5Obm0tsbCw2m40lS5bQokULBgwYQP369enatStff/214zUWiwWTyYS3t7fjmI+PD25ubqxbt+6q9/r222/p3Lkz9913H/Xr16djx4588MEH163RYrGQlZVV6iHibNo2DCAysDb5hTZ+/rXyF+JLzcpn3vrjADw7oIUCjYg4jXKHmt27d+Pn54e3tzcTJ05k8eLFtG7dmrS0NHJycpgxYwZ33XUXy5cvZ8SIEYwcOZLVq1cD0K1bN3x9fXnuuefIy8sjNzeXZ599FpvNRnLy1ZvWjx49yuzZs2nevDk//vgjEydO5Mknn+STTz65Zq1xcXGYzWbHIyIiorxvV8RwxQvxlewFdabS7/f2ykPkF9qIaVSXO6PqV/r9REQqSrm7nwoKCjh58iQXLlxg0aJF/Pvf/2b16tXUqVOHhg0bMmbMGD7//HPH+UOHDsXX15cvvvgCKO6Ceuyxxzh27Bhubm6MGTOGffv20bVrV959990r3tPLy4vOnTuzfv16x7Enn3ySLVu2sGHDhqvWarFYsFgsjp+zsrKIiIhQ95M4nT2nMxn8dnEX1NY/9cO3ktaLOXkuj96zVlFksxP/f93o1rRepdxHRKQ8ytr9VO7fjF5eXjRr1gyAzp07s2XLFt566y3efvttPDw8aN26danzW7VqVaprqX///hw5coT09HQ8PDyoU6cOoaGhNGly9ZVKGzRocMXrLlq06Jq1ent7l+rqEnFWbcICaFSvNifO5bHy1zSGtA+rlPu8+dNBimx2erUIVqAREadz01Ma7HY7FosFLy8vunTpwoEDB0o9f/DgQRo1unzRrqCgIOrUqcPKlStJS0tj6NChV71Hjx49ynxdEVdkMpkca9ZU1iyoAynZLN5xGoBn+0dVyj1ERCpTuVpqXnjhBQYOHEhERATZ2dnEx8ezatUqli1bBsCzzz7L6NGj6dWrF3feeSfLli3ju+++Y9WqVY5rfPTRR7Rq1Yrg4GA2bNjAlClTmDZtGlFR//0l2qdPH0aMGMGkSZMAmDZtGt27d+fVV19l1KhRbN68mTlz5jBnzpwK+AhEnMOg6Aa8u+oIPx9II9dSVOFdULOWH8Buh4FtQ2kXbq7Qa4uIVIVy/VZMTU1l3LhxJCcnYzabiY6OZtmyZfTr1w+AESNG8N577xEXF8eTTz5JVFQUixYtomfPno5rHDhwgOnTp3P+/HkaN27Miy++yLRp00rdp6R7qkSXLl1YvHgx06dP5+WXX6ZJkya8+eabjB079mbeu4hTad0ggCZBvhxLz2XFr2kMrcAuqB1JF1i+LxU3Ezzdv0WFXVdEpCrd9Do1zkTr1Iiz++ePB3jn58MMaBPC++M6V9h1H/z3JtYdTufemHD+eV/7CruuiEhFqPR1akSk6pVM7f75wFlyLEUVcs31h9NZdzgdT3cTU/o0r5BriogYQaFGxIm0auBP0yBfCopsrNh//T3Trsdut/Paj8WD8B+4NZKIwNo3fU0REaMo1Ig4kd/uBfV9BcyC+ml/GjuSLlDL050neje76euJiBhJoUbEyZSEmtUHz5KdX3jD17Ha7PzzUivNwz0aU9/fp0LqExExikKNiJOJCvHnluCSLqi0G77OdzvPcCA1mwAfDx7tdUsFVigiYgyFGhEnU9wFVTyd+0a7oAqtNl5POAjAo7ffgrm2Z4XVJyJiFIUaESdUsrrwmoNnybqBLqgFW5I4eT6PID8vHu7RuIKrExExhkKNiBNqEeJHs/p+FFht/LSvfLOg8gut/GvFIQAm3dmM2l6VszmmiEhVU6gRcUI3sxfUJxuOk5ZtoWGdWozpGlkZ5YmIGEKhRsRJlcyCWnPoLJkXy9YFlZVfyLurjgAwtW9zvD3cK60+EZGqplAj4qRahPjTIsSPQqudhDJ2Qf177TEu5BVyS7AvIzo2rOQKRUSqlkKNiBMb1K54FtQPu6/fBXUux8KHa48C8Ez/KDzc9ddfRFyLfquJOLFB0aEArD10lsy8a3dBzV51hNwCK+0amrmrbWhVlCciUqUUakScWLP6/kSF+FNotbN8X8pVzztz4SKfbDwBwDMDojCZTFVVoohIlVGoEXFyJQOGl1yjC+rtlYcoKLJxa5NAejUPqqrSRESqlEKNiJO7+9LU7nWH0rmQV3DZ88fSc/ky8RQAf1QrjYi4MIUaESfXrL4fLUP9KbLZWb738llQryccxGqz07tlfTo3DjSgQhGRqqFQI+ICBl+lC2rfmSy+23kGgKf7t6jyukREqpJCjYgLKOmC+uVwOhm5/+2CmrX8AABD2ofRJsxsSG0iIlVFoUbEBTQN9qNVg4DiLqhLs6ASj59nxa9puLuZmNa3ucEViohUPoUaERdR0gX1/a5k7HY7r/1Y3EpzX0w4TYP9jCxNRKRKKNSIuIiSLqj1R87x7c4zbD52Hi8PN57so1YaEakZFGpEXESTIF/ahAVgtdl59j+7ABjXrRFhdWoZXJmISNVQqBFxISUL8RUU2fD1cufxO24xuCIRkaqjUCPiQgZd6oICmHBbU+r5eRtYjYhI1fIwugARqTiN6vkyunMEh8/m8PvbmhhdjohIlVKoEXEx/7g32ugSREQMoe4nERERcQkKNSIiIuISFGpERETEJSjUiIiIiEsoV6iZPXs20dHRBAQEEBAQQGxsLEuXLi11zv79+xk6dChmsxl/f3+6devGyZMnHc8fOXKEESNGEBwcTEBAAKNGjSI1NbXMNcTFxWEymZg6dWp5ShcREREXV65QEx4ezowZM0hMTCQxMZHevXszbNgw9u7dCxQHlp49e9KyZUtWrVrFzp07eemll/Dx8QEgNzeX/v37YzKZWLlyJb/88gsFBQUMGTIEm8123ftv2bKFOXPmEB2t2R0iIiJSmslut9tv5gKBgYHMnDmTCRMmcP/99+Pp6cn8+fOveO7y5csZOHAgGRkZBAQEAJCRkUFgYCAJCQn07dv3qvfJycmhU6dOvPvuu/z973+nQ4cOvPnmm+WqNSsrC7PZTGZmpuP+IiIiUr2V9fv7hsfUWK1W4uPjyc3NJTY2FpvNxpIlS2jRogUDBgygfv36dO3ala+//trxGovFgslkwtv7v6uc+vj44Obmxrp16655vyeeeIJBgwZdM/j8L4vFQlZWVqmHiIiIuKZyh5rdu3fj5+eHt7c3EydOZPHixbRu3Zq0tDRycnKYMWMGd911F8uXL2fEiBGMHDmS1atXA9CtWzd8fX157rnnyMvLIzc3l2effRabzUZycvJV7xkfH8+2bduIi4srV61xcXGYzWbHIyIiorxvV0RERJxEuUNNVFQUO3bsYOPGjTz22GOMHz+effv2OcbEDBs2jGnTptGhQweef/55Bg8ezHvvvQdAcHAwCxcu5LvvvsPPz8/RlNSpUyfc3d2veL+kpCSmTJnCp59+6hibU1bTp08nMzPT8UhKSirv2xUREREnUe5tEry8vGjWrBkAnTt3ZsuWLbz11lu8/fbbeHh40Lp161Lnt2rVqlTXUv/+/Tly5Ajp6el4eHhQp04dQkNDadLkyvvUbN26lbS0NGJiYhzHrFYra9as4Z133sFisVw1EHl7e5fq6hIRERHXddN7P9ntdiwWC15eXnTp0oUDBw6Uev7gwYM0atTostcFBQUBsHLlStLS0hg6dOgVr9+nTx92795d6tjDDz9My5Ytee65564aaERERKRmKVeoeeGFFxg4cCARERFkZ2cTHx/PqlWrWLZsGQDPPvsso0ePplevXtx5550sW7aM7777jlWrVjmu8dFHH9GqVSuCg4PZsGEDU6ZMYdq0aURFRTnO6dOnDyNGjGDSpEn4+/vTtm3bUnX4+vpSr169y46LiIhIzVWuUJOamsq4ceNITk7GbDYTHR3NsmXL6NevHwAjRozgvffeIy4ujieffJKoqCgWLVpEz549Hdc4cOAA06dP5/z58zRu3JgXX3yRadOmlbpPSfeUiIiISFnd9Do1ziQzM5M6deqQlJSkdWpEREScRFZWFhEREVy4cAGz2XzV8256TI0zyc7OBtDUbhERESeUnZ19zVBTo1pqbDYbZ86cwd/fH5PJZHQ5Vaok5aqV6uboc6wY+hwrhj7HiqHPsWJU5udot9vJzs4mLCwMN7err0ZTo1pq3NzcCA8PN7oMQ5VsRio3R59jxdDnWDH0OVYMfY4Vo7I+x2u10JS44W0SRERERKoThRoRERFxCQo1NYS3tzd/+ctftMLyTdLnWDH0OVYMfY4VQ59jxagOn2ONGigsIiIirkstNSIiIuISFGpERETEJSjUiIiIiEtQqBERERGXoFDj4uLi4ujSpQv+/v7Ur1+f4cOHc+DAAaPLcmpxcXGYTCamTp1qdClO6fTp0zz44IPUq1eP2rVr06FDB7Zu3Wp0WU6lqKiIP/3pTzRp0oRatWrRtGlTXn75ZWw2m9GlVWtr1qxhyJAhhIWFYTKZ+Prrr0s9b7fb+etf/0pYWBi1atXijjvuYO/evcYUW41d63MsLCzkueeeo127dvj6+hIWFsZDDz3EmTNnqqQ2hRoXt3r1ap544gk2btxIQkICRUVF9O/fn9zcXKNLc0pbtmxhzpw5REdHG12KU8rIyKBHjx54enqydOlS9u3bx6xZs6hTp47RpTmVf/zjH7z33nu888477N+/n9dee42ZM2fy9ttvG11atZabm0v79u155513rvj8a6+9xuuvv84777zDli1bCA0NpV+/fo59A6XYtT7HvLw8tm3bxksvvcS2bdv46quvOHjwIEOHDq2a4uxSo6SlpdkB++rVq40uxelkZ2fbmzdvbk9ISLDffvvt9ilTphhdktN57rnn7D179jS6DKc3aNAg+yOPPFLq2MiRI+0PPvigQRU5H8C+ePFix882m80eGhpqnzFjhuNYfn6+3Ww229977z0DKnQO//s5XsnmzZvtgP3EiROVXo9aamqYzMxMAAIDAw2uxPk88cQTDBo0iL59+xpditP69ttv6dy5M/fddx/169enY8eOfPDBB0aX5XR69uzJihUrOHjwIAA7d+5k3bp13H333QZX5ryOHTtGSkoK/fv3dxzz9vbm9ttvZ/369QZW5vwyMzMxmUxV0iJboza0rOnsdjtPPfUUPXv2pG3btkaX41Ti4+PZtm0bW7ZsMboUp3b06FFmz57NU089xQsvvMDmzZt58skn8fb25qGHHjK6PKfx3HPPkZmZScuWLXF3d8dqtfLKK68wZswYo0tzWikpKQCEhISUOh4SEsKJEyeMKMkl5Ofn8/zzz/PAAw9UyWahCjU1yKRJk9i1axfr1q0zuhSnkpSUxJQpU1i+fDk+Pj5Gl+PUbDYbnTt35tVXXwWgY8eO7N27l9mzZyvUlMOCBQv49NNP+fzzz2nTpg07duxg6tSphIWFMX78eKPLc2omk6nUz3a7/bJjUjaFhYXcf//92Gw23n333Sq5p0JNDTF58mS+/fZb1qxZQ3h4uNHlOJWtW7eSlpZGTEyM45jVamXNmjW88847WCwW3N3dDazQeTRo0IDWrVuXOtaqVSsWLVpkUEXO6dlnn+X555/n/vvvB6Bdu3acOHGCuLg4hZobFBoaChS32DRo0MBxPC0t7bLWG7m+wsJCRo0axbFjx1i5cmWVtNKAZj+5PLvdzqRJk/jqq69YuXIlTZo0Mbokp9OnTx92797Njh07HI/OnTszduxYduzYoUBTDj169LhsSYGDBw/SqFEjgypyTnl5ebi5lf717e7urindN6FJkyaEhoaSkJDgOFZQUMDq1avp3r27gZU5n5JAc+jQIX766Sfq1atXZfdWS42Le+KJJ/j888/55ptv8Pf3d/Qbm81matWqZXB1zsHf3/+yMUi+vr7Uq1dPY5PKadq0aXTv3p1XX32VUaNGsXnzZubMmcOcOXOMLs2pDBkyhFdeeYXIyEjatGnD9u3bef3113nkkUeMLq1ay8nJ4fDhw46fjx07xo4dOwgMDCQyMpKpU6fy6quv0rx5c5o3b86rr75K7dq1eeCBBwysuvq51ucYFhbGvffey7Zt2/j++++xWq2O753AwEC8vLwqt7hKn18lhgKu+Pjoo4+MLs2paUr3jfvuu+/sbdu2tXt7e9tbtmxpnzNnjtElOZ2srCz7lClT7JGRkXYfHx9706ZN7S+++KLdYrEYXVq19vPPP1/x9+H48ePtdnvxtO6//OUv9tDQULu3t7e9V69e9t27dxtbdDV0rc/x2LFjV/3e+fnnnyu9NpPdbrdXbmwSERERqXwaUyMiIiIuQaFGREREXIJCjYiIiLgEhRoRERFxCQo1IiIi4hIUakRERMQlKNSIiIiIS1CoEREREZegUCMiNdpf//pXOnToYHQZIlIBFGpEpMYwmUx8/fXXRpchIpVEoUZERERcgkKNiFS5O+64g8mTJzN16lTq1q1LSEgIc+bMITc3l4cffhh/f39uueUWli5d6njN6tWrufXWW/H29qZBgwY8//zzFBUVlbrmk08+yR//+EcCAwMJDQ3lr3/9q+P5xo0bAzBixAhMJpPj5xLz58+ncePGmM1m7r//frKzsyvzIxCRSqBQIyKGmDdvHkFBQWzevJnJkyfz2GOPcd9999G9e3e2bdvGgAEDGDduHHl5eZw+fZq7776bLl26sHPnTmbPns2HH37I3//+98uu6evry6ZNm3jttdd4+eWXSUhIAGDLli0AfPTRRyQnJzt+Bjhy5Ahff/0133//Pd9//z2rV69mxowZVfdhiEiF0C7dIlLl7rjjDqxWK2vXrgXAarViNpsZOXIkn3zyCQApKSk0aNCADRs28N1337Fo0SL279+PyWQC4N133+W5554jMzMTNze3y64JcOutt9K7d29HQDGZTCxevJjhw4c7zvnrX//KzJkzSUlJwd/fH4A//vGPrFmzho0bN1bFxyEiFUQtNSJiiOjoaMd/u7u7U69ePdq1a+c4FhISAkBaWhr79+8nNjbWEWgAevToQU5ODqdOnbriNQEaNGhAWlradWtp3LixI9CU53UiUr0o1IiIITw9PUv9bDKZSh0rCTA2mw273V4q0ACUNDL/9viVrmmz2W6olrK8TkSqF4UaEan2Wrduzfr16/ltb/n69evx9/enYcOGZb6Op6cnVqu1MkoUkWpAoUZEqr3HH3+cpKQkJk+ezK+//so333zDX/7yF5566inc3Mr+a6xx48asWLGClJQUMjIyKrFiETGCQo2IVHsNGzbkhx9+YPPmzbRv356JEycyYcIE/vSnP5XrOrNmzSIhIYGIiAg6duxYSdWKiFE0+0lERERcglpqRERExCUo1IiIiIhLUKgRERERl6BQIyIiIi5BoUZERERcgkKNiIiIuASFGhEREXEJCjUiIiLiEhRqRERExCUo1IiIiIhLUKgRERERl/D/A6f+TjVdPxSuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('month')[\"CO2(ppm)\"].mean().plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "748569e0-aaf0-41f7-b9db-25f0bba5df8b",
   "metadata": {},
   "source": [
    "### Investigating Extreme Values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c4afe6-e72b-4ad1-93a0-b72df7d93d4b",
   "metadata": {},
   "source": [
    "If you need to search for rows that meet a specific criterion, you can use **conditional indexing**.  In this example, we search for rows where the $CO^{2}$ values exceed the 95th percentile across all the data from the beginning of the record:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "84bed818-3ef0-44e4-98a1-1dd29d51a89d",
   "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>CO2(ppm)</th>\n",
       "      <th>SD(ppm)</th>\n",
       "      <th>GR(ppm/yr)</th>\n",
       "      <th>Source</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-07-15</th>\n",
       "      <td>2022.53425</td>\n",
       "      <td>414.547</td>\n",
       "      <td>0.184</td>\n",
       "      <td>1.87201</td>\n",
       "      <td>in situ</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-15</th>\n",
       "      <td>2022.61918</td>\n",
       "      <td>415.053</td>\n",
       "      <td>0.178</td>\n",
       "      <td>1.84100</td>\n",
       "      <td>in situ</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15</th>\n",
       "      <td>2022.70411</td>\n",
       "      <td>415.279</td>\n",
       "      <td>0.122</td>\n",
       "      <td>1.84702</td>\n",
       "      <td>in situ</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-15</th>\n",
       "      <td>2022.78630</td>\n",
       "      <td>415.304</td>\n",
       "      <td>0.126</td>\n",
       "      <td>1.89099</td>\n",
       "      <td>in situ</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-15</th>\n",
       "      <td>2022.87123</td>\n",
       "      <td>415.329</td>\n",
       "      <td>0.117</td>\n",
       "      <td>1.99701</td>\n",
       "      <td>in situ</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-15</th>\n",
       "      <td>2022.95342</td>\n",
       "      <td>415.190</td>\n",
       "      <td>0.166</td>\n",
       "      <td>2.04501</td>\n",
       "      <td>in situ</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-15</th>\n",
       "      <td>2023.03836</td>\n",
       "      <td>414.958</td>\n",
       "      <td>0.189</td>\n",
       "      <td>2.00900</td>\n",
       "      <td>in situ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-15</th>\n",
       "      <td>2023.12329</td>\n",
       "      <td>414.989</td>\n",
       "      <td>0.169</td>\n",
       "      <td>2.03000</td>\n",
       "      <td>in situ</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-15</th>\n",
       "      <td>2023.20000</td>\n",
       "      <td>415.174</td>\n",
       "      <td>0.173</td>\n",
       "      <td>2.11401</td>\n",
       "      <td>in situ</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-15</th>\n",
       "      <td>2023.28493</td>\n",
       "      <td>415.379</td>\n",
       "      <td>0.146</td>\n",
       "      <td>2.27103</td>\n",
       "      <td>in situ</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-15</th>\n",
       "      <td>2023.36712</td>\n",
       "      <td>415.713</td>\n",
       "      <td>0.164</td>\n",
       "      <td>2.44498</td>\n",
       "      <td>in situ</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-15</th>\n",
       "      <td>2023.45205</td>\n",
       "      <td>416.113</td>\n",
       "      <td>0.149</td>\n",
       "      <td>2.64999</td>\n",
       "      <td>in situ</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-15</th>\n",
       "      <td>2023.53425</td>\n",
       "      <td>416.556</td>\n",
       "      <td>0.195</td>\n",
       "      <td>3.01300</td>\n",
       "      <td>in situ</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-15</th>\n",
       "      <td>2023.61918</td>\n",
       "      <td>417.083</td>\n",
       "      <td>0.204</td>\n",
       "      <td>3.23898</td>\n",
       "      <td>in situ</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-15</th>\n",
       "      <td>2023.70411</td>\n",
       "      <td>417.393</td>\n",
       "      <td>0.137</td>\n",
       "      <td>3.43399</td>\n",
       "      <td>in situ</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-15</th>\n",
       "      <td>2023.78630</td>\n",
       "      <td>417.575</td>\n",
       "      <td>0.126</td>\n",
       "      <td>3.69400</td>\n",
       "      <td>in situ</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-15</th>\n",
       "      <td>2023.87123</td>\n",
       "      <td>417.774</td>\n",
       "      <td>0.185</td>\n",
       "      <td>3.80099</td>\n",
       "      <td>in situ</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-15</th>\n",
       "      <td>2023.95342</td>\n",
       "      <td>417.840</td>\n",
       "      <td>0.178</td>\n",
       "      <td>3.92099</td>\n",
       "      <td>in situ</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-15</th>\n",
       "      <td>2024.03825</td>\n",
       "      <td>417.971</td>\n",
       "      <td>0.184</td>\n",
       "      <td>3.90701</td>\n",
       "      <td>in situ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-15</th>\n",
       "      <td>2024.12295</td>\n",
       "      <td>418.228</td>\n",
       "      <td>0.210</td>\n",
       "      <td>3.71201</td>\n",
       "      <td>in situ</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-15</th>\n",
       "      <td>2024.20219</td>\n",
       "      <td>418.608</td>\n",
       "      <td>0.215</td>\n",
       "      <td>3.78699</td>\n",
       "      <td>in situ</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-15</th>\n",
       "      <td>2024.28689</td>\n",
       "      <td>419.073</td>\n",
       "      <td>0.215</td>\n",
       "      <td>3.82199</td>\n",
       "      <td>in situ</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-15</th>\n",
       "      <td>2024.36885</td>\n",
       "      <td>419.514</td>\n",
       "      <td>0.199</td>\n",
       "      <td>3.64001</td>\n",
       "      <td>in situ</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-15</th>\n",
       "      <td>2024.45355</td>\n",
       "      <td>420.034</td>\n",
       "      <td>0.214</td>\n",
       "      <td>3.53702</td>\n",
       "      <td>in situ</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-15</th>\n",
       "      <td>2024.53552</td>\n",
       "      <td>420.463</td>\n",
       "      <td>0.148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-15</th>\n",
       "      <td>2024.62022</td>\n",
       "      <td>420.795</td>\n",
       "      <td>0.157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-15</th>\n",
       "      <td>2024.70492</td>\n",
       "      <td>421.180</td>\n",
       "      <td>0.139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-15</th>\n",
       "      <td>2024.78689</td>\n",
       "      <td>421.397</td>\n",
       "      <td>0.097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-15</th>\n",
       "      <td>2024.87158</td>\n",
       "      <td>421.414</td>\n",
       "      <td>0.156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-15</th>\n",
       "      <td>2024.95355</td>\n",
       "      <td>421.377</td>\n",
       "      <td>0.171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE  CO2(ppm)  SD(ppm)  GR(ppm/yr)   Source  month\n",
       "date                                                                 \n",
       "2022-07-15  2022.53425   414.547    0.184     1.87201  in situ      7\n",
       "2022-08-15  2022.61918   415.053    0.178     1.84100  in situ      8\n",
       "2022-09-15  2022.70411   415.279    0.122     1.84702  in situ      9\n",
       "2022-10-15  2022.78630   415.304    0.126     1.89099  in situ     10\n",
       "2022-11-15  2022.87123   415.329    0.117     1.99701  in situ     11\n",
       "2022-12-15  2022.95342   415.190    0.166     2.04501  in situ     12\n",
       "2023-01-15  2023.03836   414.958    0.189     2.00900  in situ      1\n",
       "2023-02-15  2023.12329   414.989    0.169     2.03000  in situ      2\n",
       "2023-03-15  2023.20000   415.174    0.173     2.11401  in situ      3\n",
       "2023-04-15  2023.28493   415.379    0.146     2.27103  in situ      4\n",
       "2023-05-15  2023.36712   415.713    0.164     2.44498  in situ      5\n",
       "2023-06-15  2023.45205   416.113    0.149     2.64999  in situ      6\n",
       "2023-07-15  2023.53425   416.556    0.195     3.01300  in situ      7\n",
       "2023-08-15  2023.61918   417.083    0.204     3.23898  in situ      8\n",
       "2023-09-15  2023.70411   417.393    0.137     3.43399  in situ      9\n",
       "2023-10-15  2023.78630   417.575    0.126     3.69400  in situ     10\n",
       "2023-11-15  2023.87123   417.774    0.185     3.80099  in situ     11\n",
       "2023-12-15  2023.95342   417.840    0.178     3.92099  in situ     12\n",
       "2024-01-15  2024.03825   417.971    0.184     3.90701  in situ      1\n",
       "2024-02-15  2024.12295   418.228    0.210     3.71201  in situ      2\n",
       "2024-03-15  2024.20219   418.608    0.215     3.78699  in situ      3\n",
       "2024-04-15  2024.28689   419.073    0.215     3.82199  in situ      4\n",
       "2024-05-15  2024.36885   419.514    0.199     3.64001  in situ      5\n",
       "2024-06-15  2024.45355   420.034    0.214     3.53702  in situ      6\n",
       "2024-07-15  2024.53552   420.463    0.148         NaN  in situ      7\n",
       "2024-08-15  2024.62022   420.795    0.157         NaN  in situ      8\n",
       "2024-09-15  2024.70492   421.180    0.139         NaN  in situ      9\n",
       "2024-10-15  2024.78689   421.397    0.097         NaN  in situ     10\n",
       "2024-11-15  2024.87158   421.414    0.156         NaN  in situ     11\n",
       "2024-12-15  2024.95355   421.377    0.171         NaN  in situ     12"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile_95 = df[\"CO2(ppm)\"].quantile(.95) # Calculate the 95th percentile\n",
    "df[df[\"CO2(ppm)\"] > percentile_95] # Filter the data for greater than the 95th percentile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86cc96e5-e532-496d-a6ba-b14c3afd4f57",
   "metadata": {},
   "source": [
    "This example shows how to use the `sort_values` method on a `DataFrame`. This method sorts values in a `DataFrame` by the column specified as an argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0c65a403-9835-4742-ab2a-177391876b90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>CO2(ppm)</th>\n",
       "      <th>SD(ppm)</th>\n",
       "      <th>GR(ppm/yr)</th>\n",
       "      <th>Source</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-05-15</th>\n",
       "      <td>1976.36890</td>\n",
       "      <td>328.861</td>\n",
       "      <td>0.165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-06-15</th>\n",
       "      <td>1976.45360</td>\n",
       "      <td>328.988</td>\n",
       "      <td>0.285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-07-15</th>\n",
       "      <td>1976.53550</td>\n",
       "      <td>329.653</td>\n",
       "      <td>0.282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-15</th>\n",
       "      <td>1977.03840</td>\n",
       "      <td>330.529</td>\n",
       "      <td>0.235</td>\n",
       "      <td>2.14697</td>\n",
       "      <td>in situ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-02-15</th>\n",
       "      <td>1977.12330</td>\n",
       "      <td>330.543</td>\n",
       "      <td>0.198</td>\n",
       "      <td>1.77701</td>\n",
       "      <td>in situ</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-15</th>\n",
       "      <td>2024.62022</td>\n",
       "      <td>420.795</td>\n",
       "      <td>0.157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-15</th>\n",
       "      <td>2024.70492</td>\n",
       "      <td>421.180</td>\n",
       "      <td>0.139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-15</th>\n",
       "      <td>2024.95355</td>\n",
       "      <td>421.377</td>\n",
       "      <td>0.171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-15</th>\n",
       "      <td>2024.78689</td>\n",
       "      <td>421.397</td>\n",
       "      <td>0.097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-15</th>\n",
       "      <td>2024.87158</td>\n",
       "      <td>421.414</td>\n",
       "      <td>0.156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>584 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE  CO2(ppm)  SD(ppm)  GR(ppm/yr)   Source  month\n",
       "date                                                                 \n",
       "1976-05-15  1976.36890   328.861    0.165         NaN  in situ      5\n",
       "1976-06-15  1976.45360   328.988    0.285         NaN  in situ      6\n",
       "1976-07-15  1976.53550   329.653    0.282         NaN  in situ      7\n",
       "1977-01-15  1977.03840   330.529    0.235     2.14697  in situ      1\n",
       "1977-02-15  1977.12330   330.543    0.198     1.77701  in situ      2\n",
       "...                ...       ...      ...         ...      ...    ...\n",
       "2024-08-15  2024.62022   420.795    0.157         NaN  in situ      8\n",
       "2024-09-15  2024.70492   421.180    0.139         NaN  in situ      9\n",
       "2024-12-15  2024.95355   421.377    0.171         NaN  in situ     12\n",
       "2024-10-15  2024.78689   421.397    0.097         NaN  in situ     10\n",
       "2024-11-15  2024.87158   421.414    0.156         NaN  in situ     11\n",
       "\n",
       "[584 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('CO2(ppm)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ae14f9-173a-4a38-af78-2e8ee758ffd3",
   "metadata": {},
   "source": [
    "You can also reverse the ordering of the sort by specifying the `ascending` keyword argument as `False`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bf05ed68-14d4-4393-9793-d64f7b805ce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>CO2(ppm)</th>\n",
       "      <th>SD(ppm)</th>\n",
       "      <th>GR(ppm/yr)</th>\n",
       "      <th>Source</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-11-15</th>\n",
       "      <td>2024.87158</td>\n",
       "      <td>421.414</td>\n",
       "      <td>0.156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-15</th>\n",
       "      <td>2024.78689</td>\n",
       "      <td>421.397</td>\n",
       "      <td>0.097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-15</th>\n",
       "      <td>2024.95355</td>\n",
       "      <td>421.377</td>\n",
       "      <td>0.171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-15</th>\n",
       "      <td>2024.70492</td>\n",
       "      <td>421.180</td>\n",
       "      <td>0.139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-15</th>\n",
       "      <td>2024.62022</td>\n",
       "      <td>420.795</td>\n",
       "      <td>0.157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-02-15</th>\n",
       "      <td>1977.12330</td>\n",
       "      <td>330.543</td>\n",
       "      <td>0.198</td>\n",
       "      <td>1.77701</td>\n",
       "      <td>in situ</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-15</th>\n",
       "      <td>1977.03840</td>\n",
       "      <td>330.529</td>\n",
       "      <td>0.235</td>\n",
       "      <td>2.14697</td>\n",
       "      <td>in situ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-07-15</th>\n",
       "      <td>1976.53550</td>\n",
       "      <td>329.653</td>\n",
       "      <td>0.282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-06-15</th>\n",
       "      <td>1976.45360</td>\n",
       "      <td>328.988</td>\n",
       "      <td>0.285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-15</th>\n",
       "      <td>1976.36890</td>\n",
       "      <td>328.861</td>\n",
       "      <td>0.165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>584 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE  CO2(ppm)  SD(ppm)  GR(ppm/yr)   Source  month\n",
       "date                                                                 \n",
       "2024-11-15  2024.87158   421.414    0.156         NaN  in situ     11\n",
       "2024-10-15  2024.78689   421.397    0.097         NaN  in situ     10\n",
       "2024-12-15  2024.95355   421.377    0.171         NaN  in situ     12\n",
       "2024-09-15  2024.70492   421.180    0.139         NaN  in situ      9\n",
       "2024-08-15  2024.62022   420.795    0.157         NaN  in situ      8\n",
       "...                ...       ...      ...         ...      ...    ...\n",
       "1977-02-15  1977.12330   330.543    0.198     1.77701  in situ      2\n",
       "1977-01-15  1977.03840   330.529    0.235     2.14697  in situ      1\n",
       "1976-07-15  1976.53550   329.653    0.282         NaN  in situ      7\n",
       "1976-06-15  1976.45360   328.988    0.285         NaN  in situ      6\n",
       "1976-05-15  1976.36890   328.861    0.165         NaN  in situ      5\n",
       "\n",
       "[584 rows x 6 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('CO2(ppm)', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ed1b83-446a-4240-ae7b-70a36305e291",
   "metadata": {},
   "source": [
    "### Resampling\n",
    "In these examples, we illustrate a process known as resampling. Using resampling, you can change the frequency of index data values, reducing so-called 'noise' in a data plot. This is especially useful when working with timeseries data; plots can be equally effective with resampled data in these cases. The resampling performed in these examples converts monthly values to yearly averages. This is performed by passing the value '1YE' to the `resample` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e14160b4-8585-4950-9a27-6ce4d78d771f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SjYuLY8uWLSQmJv7puczMTADCwsp/LxcWFsahQ4ccbby8vMqNvPze5vf9/2jOnDnMnj27sqWKiIiclw83HuL1hH0AzB0bQ782IU6uqP6p1AhKWloaDzzwAEuWLMHHx+es7f44/GUYxl8OiZ2rzaxZs8jNzXU80tLSKlO2iIhIha3ZlcXjy1MAeHBoO66JjXRyRfVTpQLK5s2bycrKIjY2Fg8PDzw8PEhISODll1/Gw8PDMXLyx5GQrKwsx3Ph4eFYrVays7PP2uaPvL29CQwMLPcQERGpbslHcpn80RbsBozrGcmUy9o4u6R6q1IBZfDgwSQnJ5OUlOR49OzZkxtvvJGkpCRatWpFeHg4q1atcuxjtVpJSEigX79+AMTGxuLp6VmuTUZGBikpKY42IiIiF9qR7CJufy+RIquNS9qG8MyYLpoQ60SVmoMSEBBAdHR0uW1+fn4EBwc7tk+dOpVnn32Wtm3b0rZtW5599lkaNGjAhAkTADCbzUyaNInp06cTHBxMUFAQM2bMoEuXLn+adCsiInIh5BaXctu7iRzPt9AhPIDXbuyBp+6p41TVvpLsQw89RHFxMffeey/Z2dn06dOH77//noCAAEebBQsW4OHhwbhx4yguLmbw4MEsXrwYd3f36i5HRETknKxldu7+YDN7sgoIC/Tm3dt6EeDj6eyy6j2TYRiGs4uorLy8PMxmM7m5uZqPIiIi580wDKZ/uo0vth7Fz8udT+/uS+cIs7PLqrMq8/mt8SsREam3Fqzewxdbj+LuZuK1m2IVTlyIAoqIiNRLH248xMv/2QPAM1dHM7BdqJMrkv+luxmLiEi9YhgGL/9nLwtWpwIw+dLWXN+7mZOrkj9SQBERkXqjzGbn8S9T+PjX0wt+3ndpG6YPa+fkquRMFFBERKReKLbamPLxFlbvzMLNBLNHRzPxoubOLkvOQgFFRETqvJMFFia9t4mktBy8Pdx4+YbuDO8c7uyy5BwUUEREpE47fLKIW979lQMnCjH7evLOLT3p2SLI2WXJX1BAERGROivlaC63vpvIiQILTRv68t7tvWjTOOCvdxSnU0AREZE6KSH1OPcu2Uyh1UbHJoEsvq0XYYE+zi5LKkgBRURE6pylm4/w8NLtlNkNLm4TzOs3xWr5+lpGAUVEROoMu93gX2v28uKq02ucXN0tguev7YqXh9YlrW0UUEREpE44UWBhxmfbWLv7OAB3DWzFw8M74OZmcnJlcj4UUEREpNbbsPcEUz9JIivfgreHG0+N6swNWh22VlNAERGRWqvMZmfh6j38a+1eDAPaNvbn1Qk9aB+uK3VqOwUUERGplY7mFPPAx1vZdCgbgBt6R/HEyM74erk7uTKpDgooIiJS68SnZPLw0u3kFpcS4O3Bs2O7cFXXCGeXJdVIAUVERGqNklIbz36zk/d/PgRA10gzr9zQg2bBDZxcmVQ3BRQREakV9mYVcN9HW9iVmQ/AXQNaMX1Ye11CXEcpoIiIiMtbv+cEd7y/ieJSG8F+Xrw4riuD2jd2dllSgxRQRETEpf2y/yR/ez+RklI7fVsF89L13WisJevrPAUUERFxWZsPneL2xafDyaXtQ3l9YizeHrpKpz7QF3ciIuKSth/J4dZ/J1JktdG/TQiLblI4qU8UUERExOXsSM9l4ju/km8po3fLIN66uSc+ngon9YkCioiIuJTUY/lMfOdXcotL6dGsIf++tZcWX6uHFFBERMRl7D9ewIS3NnKq0EpMpJnFt/fG31vTJesjBRQREXEJh04WMuGtjZwosNCxSSDv396bQB9PZ5clTqKAIiIiTncku4gJb20kM6+EdmH+LJnUm4YNvJxdljiRAoqIiDhVZm4JE97ayNGcYlqF+LHkb30I9vd2dlniZAooIiLiNFn5JUx46xcOnyqiWVADPrrjIhoHaBE20UJtIiLiJL8eOMX9H28lM6+Epg19+eiOPoSbFU7kNAUUERG5oGx2g9fW7GXB6lTsBrQK8ePd23oR2Uh3JJb/p4AiIiIXTFZeCVM/SWLDvpMAjO3elKevjsZPlxLLH+hfhIiIXBDrUo/z4KdJnCiw4uvpztNXR3NtbKSzyxIXpYAiIiI1qtRmZ/6qVBat3QdAh/AAXp3QgzaN/Z1cmbgyBRQREakxR3OKuf/jrWw+lA3AjX2a8fjITrqvjvwlBRQREakR3+/IZObn28ktLiXA24O518RwZUwTZ5cltYQCioiIVKtiq43n4nexeMNBALpGmnnlhh40C9ZVOlJxCigiIlJtthzOZsan29h/ohCAOy5pyczhHfDy0LqgUjkKKCIiUmWWMhsLV+/hjYR92A0IC/Rm7jUxXNq+sbNLk1pKAUVERKok5Wgu0z/dxu5j+QCM6d6Up67qjLmB7kQs508BRUREzkupzc5ra/bxyg97KLMbBPt58cyYLoyIDnd2aVIHKKCIiEilpR7LZ/qn20g+mgvAiM7hPDMmWnchlmqjgCIiIhVmsxu8/eN+Xvw+FavNjtnXk3+M7syorhGYTCZnlyd1iAKKiIhUyP7jBTz0+XY2/XfRtUvbhzL3mhjCAnUHYql+CigiInJOZTY776w/wPxVqVjK7Ph7e/D4yI6M6xmlUROpMQooIiJyVrsz83no821sO3J6rsklbUOYM7YLkY206JrULAUUERH5k1KbnUVrT1+hU2ozCPDx4PGRnbguNlKjJnJBKKCIiEg5KUdzmfn5dnZm5AEwpGNjnhnTRXNN5IJSQBEREeD0arCv/GcvixL2YbMbNGrgyVOjdIWOOIcCioiIsPVwNjM/387erAIAroxpwuxRnQnRuibiJAooIiL12KlCKwtXp7Lkl0PYDQjx9+afV3dmRHQTZ5cm9ZwCiohIPWQps/H+hkO8/MMe8kvKABjboylPjOxEwwZeTq5ORAFFRKReMQyD73ZkMufbXRw6WQRApyaBPDayI/1ahzi5OpH/p4AiIlJPJB/J5emVv/HrgVMAhAZ4M3N4e67pEYm7mybBimtRQBERqeMyc0t4/rtdfLHlKADeHm7cNaAVdw1sjZ+3PgbENelfpohIHVVkLeONhP28uW4/xaU2AMZ0b8rM4e2JaOjr5OpEzk0BRUSkDtp+JId7P9zCkexiAHo2b8RjIzvRLaqhcwsTqSAFFBGROsQwDD769TCzV/yG1WanaUNfHrmiI1d0Cddia1KrKKCIiNQRxVYbjy5L5outp+eaDOsUxrzrumL29XRyZSKVp4AiIlIH7D9ewD1LtrD7WD7ubiYeGt6eOwe00qiJ1FoKKCIitVx8SgYzPttOgaWMEH9vXp3QnYtaBTu7LJEqUUAREamlSm12nvt2F2+vPwBA7xZBvDqhO41112GpAxRQRERqoWN5Jdz30RYSD2YDcOeAVswc3h5PdzcnVyZSPRRQRERqmZ/3nWTKx1s4UWAlwNuDedd1ZUR0uLPLEqlWlYraixYtIiYmhsDAQAIDA+nbty/ffvut4/mCggLuu+8+IiMj8fX1pWPHjixatKjcMSwWC1OmTCEkJAQ/Pz9GjRrFkSNHqudsRETqMGuZnRe+282Nb//CiQIrHcIDWDGlv8KJ1EmVCiiRkZHMnTuXTZs2sWnTJi677DJGjx7Njh07AJg2bRrx8fEsWbKEnTt3Mm3aNKZMmcKXX37pOMbUqVNZtmwZcXFxrF+/noKCAkaOHInNZqveMxMRqUN2ZeZx9b9+4tU1e7EbcE2PSJbdezEtQ/ycXZpIjTAZhmFU5QBBQUHMmzePSZMmER0dzfjx43n88ccdz8fGxnLFFVfw9NNPk5ubS2hoKB988AHjx48HID09naioKL755huGDx9eodfMy8vDbDaTm5tLYGBgVcoXEXFpNrvBG+v2sWBVKqU2g0YNPHlmTBeu6NLE2aWJVFplPr/PezaVzWYjLi6OwsJC+vbtC0D//v1ZsWIFR48exTAM1qxZQ2pqqiN4bN68mdLSUoYNG+Y4TkREBNHR0WzYsOGsr2WxWMjLyyv3EBGp6w6cKOS61zfwfPxuSm0GQzo25rtpAxROpF6o9CTZ5ORk+vbtS0lJCf7+/ixbtoxOnToB8PLLL3PHHXcQGRmJh4cHbm5uvP322/Tv3x+AzMxMvLy8aNSoUbljhoWFkZmZedbXnDNnDrNnz65sqSIitZLdbrBk4yHmfLOL4lIb/t4ePHFVJ66LjdTCa1JvVDqgtG/fnqSkJHJycli6dCm33HILCQkJdOrUiZdffplffvmFFStW0Lx5c9atW8e9995LkyZNGDJkyFmPaRjGOX/pZs2axYMPPuj4OS8vj6ioqMqWLiLi8tJzinno8+2s33sCgL6tgpl3XQyRjRo4uTKRC6vSAcXLy4s2bdoA0LNnTxITE3nppZdYuHAhjzzyCMuWLePKK68EICYmhqSkJF544QWGDBlCeHg4VquV7OzscqMoWVlZ9OvX76yv6e3tjbe3d2VLFRGpNQzD4IstR3nqqx3kl5Th4+nG30d04Oa+LXBz06iJ1D9VXtHHMAwsFgulpaWUlpbi5lb+kO7u7tjtduD0hFlPT09WrVrleD4jI4OUlJRzBhQRkbrsZIGFu5dsZvpn28gvKaNbVEO+uf8Sbr24pcKJ1FuVGkF55JFHuPzyy4mKiiI/P5+4uDjWrl1LfHw8gYGBDBw4kJkzZ+Lr60vz5s1JSEjg/fffZ/78+QCYzWYmTZrE9OnTCQ4OJigoiBkzZtClS5dzfgUkIlJX/bDrGA99nsyJAgue7iamDmnHXQNa4aEVYaWeq1RAOXbsGBMnTiQjIwOz2UxMTAzx8fEMHToUgLi4OGbNmsWNN97IqVOnaN68Oc888wx333234xgLFizAw8ODcePGUVxczODBg1m8eDHu7u7Ve2YiIi6syFrGMyt38uHGwwC0bezPgvHdiG5qdnJlIq6hyuugOIPWQRGR2mzr4Wwe/HQbB04UAjCpf0tmDm+Pj6f+oyZ1W2U+v3UvHhGRC6TUZufVH/by6pq92OwGTcw+vHBdVy5uE+Ls0kRcjgKKiMgFsP94AdM+SWLbkVwARnWN4OnR0ZgbeDq5MhHXpIAiIlKDDMNgycbDPLPyN0pK7QT6ePD01dGM7tbU2aWJuDQFFBGRGnKiwMLMz7axZvdxAPq1DuaF67oS0dDXyZWJuD4FFBGRGrAu9TgPfrqNEwUWvDzceHhEB27rp0XXRCpKAUVEpBpZy+y88P1u3ly3H4B2Yf68fEN3OoTrikORylBAERGpJgdOFHL/x1tJPnp6IuxNFzXjsSs76fJhkfOggCIiUkWGYbB0y1Ge+DKFIquNhg08ee6aGIZ3Dnd2aSK1lgKKiEgV5JWU8tiyFFZsSwegT8sgFl7fjSZmTYQVqQoFFBGR87TlcDYPxG0l7VQx7m4mpg1pyz2D2uCuibAiVaaAIiJSSTa7wesJ+5i/KhWb3SCykS8vXd+d2OaNnF2aSJ2hgCIiUgmHThYy47NtJB7MBuCqrhE8MyaaQB+tCCtSnRRQREQq4PcVYZ9duZPiUhv+3h48eVUnro2NxGTSVzoi1U0BRUTkL2TkFvPQ59v5cc8JAC5qFcS8a7sSFdTAyZWJ1F0KKCIiZ2EYBsuTjvLklzvIKynD+78rwt6qFWFFapwCiojIGZwssPDoshTid2QC0DXSzIvjutGmsb+TKxOpHxRQRET+4PsdmTyyLJkTBVY83Ew8MLgt9wxqjYe7m7NLE6k3FFBERP4rr6SU2St+Y+mWIwC0DwvgxXFdiW5qdnJlIvWPAoqICLA3q4A73t/EgROFmExw54BWPDi0Hd4euo+OiDMooIhIvffDrmM88HES+ZYyIsw+vHRDd3q1CHJ2WSL1mgKKiNRbhmGwKGEf877bjWFArxaNWHRTLCH+3s4uTaTeU0ARkXqp2GrjoaXb+eq/N/m7oXczZo/qjJeHJsKKuAIFFBGpd47mFHPn+5vYkZ6Hh5uJp0Z15qaLmju7LBH5HwooIlKvJB48xT1LNnOiwEqQnxev3diDi1oFO7ssEfkDBRQRqTc+/vUwT3yZQqnNoGOTQN6cGKvl6kVclAKKiNR5pTY7//jqNz745RAAV3ZpwrzrYmjgpT+BIq5Kv50iUqedKLAw+cMtbDxwCoAZw9ox+dI2ugOxiItTQBGROmtbWg53L9lMRm4Jfl7uLLy+O0M7hTm7LBGpAAUUEamTPtuUxqPLU7CW2WkV4scbE2NpGxbg7LJEpIIUUESkTrGW2Xn66/+fbzKkY2Pmj+9GoI+nkysTkcpQQBGROiMrv4R7l2xh06FsAKYNaceUy9rg5qb5JiK1jQKKiNQJWw5nc8+SzRzLsxDg7cHC67sxuKPmm4jUVgooIlLrfbTxME+uOL2+SdvG/rwxMZZWof7OLktEqkABRURqLUuZjadW7ODjX9MAuDw6nHnXdcXfW3/aRGo7/RaLSK20OzOfh5duJyktB5MJZg5vzz0DW2t9E5E6QgFFRGqVzNwSFqxK5bPNadgNMPt68tL13RjUvrGzSxORaqSAIiK1Qn5JKW8k7Oft9fspKbUDp7/SeeSKjrqfjkgdpIAiIi7NWmbn418P89J/9nCq0ApAz+aNmHVFR2KbN3JydSJSUxRQRMQlGYbBtymZPB+/i4MniwBoFerH30d0YGinMM01EanjFFBExOX8euAUz36zk6S0HABC/L2ZNrQt43tG4eHu5tziROSCUEAREZdgGAbr957gzXX7+XHPCQAaeLlz54BW3HFJK/x06bBIvaLfeBFxqlKbna+2pfPmuv3syswHwN3NxPW9onhgSFsaB/g4uUIRcQYFFBFxirySUuJ+Pcy7Px0kI7cEOD1iMr5XFLdf3FJX5ojUcwooInJBpecUs3jDQT7aeJgCSxkAoQHe3NqvBTf2aUbDBl5OrlBEXIECiohcEKnH8lm0dh9fbUunzG4A0KaxP3de0orR3SPw9nB3coUi4koUUESkRpXZ7Ly2dh8v/2ePI5hc1CqIOwe0YlC7xri56XJhEfkzBRQRqTH7jxfw4KfbHJcLD+kYxv2D2xAT2dCpdYmI61NAEZFqZxgGSzYe5tmVOykutRHg48HTo6MZ3S1CC6yJSIUooIhItTqWV8JDn28nIfU4AP1aB/PCdV2JaOjr5MpEpDZRQBGRavP19nQeW55CTlEp3h5uPDyiA7f2a6F5JiJSaQooIlJluUWlPLEihS+T0gHo0tTMgvFdadM4wMmViUhtpYAiIlWyfs8JZny2jcy8EtzdTEwe1Jopg9viqXvmiEgVKKCIyHl7a91+nvlmJwAtghswf3w3ejRr5OSqRKQuUEARkUozDIO53+7ijXX7AbihdxSPj+xEAy/9SRGR6qG/JiJSKaU2O39fmszSLUcAmHV5B+4a2NrJVYlIXaOAIiIVVmy1MfmjLfywKwt3NxNzx3bhup5Rzi5LROogBRQRqZCcIiu3L05ky+EcfDzd+NeEHgzuGObsskSkjlJAEZG/lJFbzM3v/MqerAICfTx497ZexDYPcnZZIlKHKaCIyDntzcrn5nd+JT23hPBAH96f1Jt2YVrfRERqlgKKiJzV1sPZ3LY4kZyiUlqF+vHBpD401ZL1InIBKKCIyBmt3Z3FPUu2UFxqo2tUQ969tRdBfl7OLktE6gkFFBEpp9BSxns/H2T+96mU2Q0GtAtl0Y098PPWnwsRuXD0F0dEAMgvKeX9nw/x9o/7yS4qBWB0twjmXdsVLw8tWy8iF5YCikg9l1tcynsbDvLO+gPkFp8OJi2CGzD50jZc0yNSdyIWEaeo1H+LFi1aRExMDIGBgQQGBtK3b1++/fbbcm127tzJqFGjMJvNBAQEcNFFF3H48GHH8xaLhSlTphASEoKfnx+jRo3iyJEj1XM2IlJhOUVW5q9Kpf9zPzB/VSq5xacnwi4Y35XVDw7kup5RCici4jSVGkGJjIxk7ty5tGnTBoD33nuP0aNHs3XrVjp37sy+ffvo378/kyZNYvbs2ZjNZnbu3ImPj4/jGFOnTuWrr74iLi6O4OBgpk+fzsiRI9m8eTPu7u7Ve3Yi8ienCq28s34/7204RIGlDIC2jf2ZMrgtV3ZpgrtCiYi4AJNhGEZVDhAUFMS8efOYNGkS119/PZ6ennzwwQdnbJubm0toaCgffPAB48ePByA9PZ2oqCi++eYbhg8ffsb9LBYLFovF8XNeXh5RUVHk5uYSGBhYlfJF6o28klIWrd3HexsOUmS1AdAhPID7B7dlROdwjZaISI3Ly8vDbDZX6PP7vGe+2Ww24uLiKCwspG/fvtjtdlauXEm7du0YPnw4jRs3pk+fPixfvtyxz+bNmyktLWXYsGGObREREURHR7Nhw4azvtacOXMwm82OR1SU7v0hUlGlNjvvbTjIwOfXsGjtPoqsNjpHBPLGxFi+uf8SrujSROFERFxOpQNKcnIy/v7+eHt7c/fdd7Ns2TI6depEVlYWBQUFzJ07lxEjRvD9998zZswYxo4dS0JCAgCZmZl4eXnRqFGjcscMCwsjMzPzrK85a9YscnNzHY+0tLTKli1S7xiGwXc7Mhm2YB1PrthBdlEprUP9eHNiLF9P6c9wjZqIiAur9FU87du3JykpiZycHJYuXcott9xCQkICDRs2BGD06NFMmzYNgG7durFhwwZef/11Bg4ceNZjGoaByXT2P5Te3t54e3tXtlSReispLYdnV+7k14OnAAjx92LqkHZc3ysKD3ddMiwirq/SAcXLy8sxSbZnz54kJiby0ksv8corr+Dh4UGnTp3Kte/YsSPr168HIDw8HKvVSnZ2drlRlKysLPr161eV8xARIO1UEfO+282KbekAeHu4ccclrbhrYCsCfDydXJ2ISMVVeR0UwzCwWCx4eXnRq1cvdu/eXe751NRUmjdvDkBsbCyenp6sWrWKcePGAZCRkUFKSgrPP/98VUsRqbdyi0t5bc1e3v3pIFabHZMJxnaPZMbwdjQx6945IlL7VCqgPPLII1x++eVERUWRn59PXFwca9euJT4+HoCZM2cyfvx4BgwYwKWXXkp8fDxfffUVa9euBcBsNjNp0iSmT59OcHAwQUFBzJgxgy5dujBkyJBqPzmR+mDl9gweW57sWP21X+tgHrmiI9FNzU6uTETk/FUqoBw7doyJEyeSkZGB2WwmJiaG+Ph4hg4dCsCYMWN4/fXXmTNnDvfffz/t27dn6dKl9O/f33GMBQsW4OHhwbhx4yguLmbw4MEsXrxYa6CIVFKBpYzZK3bw2ebTCx22bezPI1d0ZFD70HPO6RIRqQ2qvA6KM1TmOmqRuigpLYcH4rZy6GQRJhNMHtSGB4a0xVMTYEXEhVXm81v34hGpRWx2g9cT9rFg1ek7DUeYfVgwvht9WgU7uzQRkWqlgCJSS6TnFDP1kyR+PXD60uErY5rw7NVdMDfQ1TkiUvcooIjUAl9vT+eRL5LJKymjgZc7s0d15trYSM01EZE6SwFFxIUVWMp4asUOPv/vRNiuUQ15aXw3WoT4ObkyEZGapYAi4qJ+3neSv3+xXRNhRaReUkARcTHJR3J5/rtd/LjnBIAmwopIvaSAIuIi9h0vYP73qaxMzgDAw83E9b2jmDmsgybCiki9o4Ai4mTpOcW8tHoPn285gs1uYDLB6K4RTBvajubBmmsiIvWTAoqIk5wssPDa2n188MshrGV2AIZ0DGPG8HZ0CNcChCJSvymgiFxg+SWlvP3jAd7+cT+FVhsAfVoG8dCIDsQ2b/QXe4uI1A8KKCIXwIkCCz/szGLVzmP8uOc4JaWnR0yimwYyc3gHBrQN0ZomIiL/QwFFpIbsO17A6t+Oseq3Y2w+nM3/3vWqdagfDw5tz+XR4bi5KZiIiPyRAopINbHZDbYezmbVztOhZP/xwnLPRzcNZGjHcIZ2CqNjkwCNmIiInIMCikgVWcvsfLjxEK+t3cfxfItju6e7iYtaBTOsUxiDO4YR0dDXiVWKiNQuCigi58kwDFYmZ/B8/G4OnyoCIMDHg8s6NGZIxzAGtg8l0Efrl4iInA8FFJHzsHH/SZ79dhfb0nIACA3wZtqQdlwbG4mXh5aiFxGpKgUUkUrYcyyf5+J3sXpnFgANvNy5a0Br/nZJS/y89eskIlJd9BdVpAKO5ZWwcHUqnySmYTfA3c3EDb2jeGBwO0IDvJ1dnohInaOAInIOBZYy3kzYx1s/HqC49PSiasM7h/HQiA60DvV3cnUiInWXAorIGRiGwbcpmcz+agfH8k5fmdOjWUMeuaIjPVsEObk6EZG6TwFF5A8OnSzkiS93kJB6HIBmQQ145IoODO8crrVLREQuEAUUkf+ylNl4I2E//1qzF0uZHS93N+4e1Jp7B7XGx9Pd2eWJiNQrCigiwE97T/D48hT2nzi9+mv/NiH8Y3RnWmmeiYiIUyigSL2WlV/CP7/eyYpt6cDp9UweH9mJq2Ka6OscEREnUkCReslmN1jyyyFe+G43+ZYy3Exwc98WPDisnVZ/FRFxAQooUq8YhsGPe07wXPwudqTnAdA10sw/r+5Cl0izk6sTEZHfKaBIvbEtLYfn4nexYd9J4PR9cx4a3p4JfZrj7qavc0REXIkCitR5+48X8ML3u/kmORMAL3c3brqoOZMvbU2wv1aBFRFxRQooUmedXp5+D59uSsNmNzCZYEz3pkwb0o6ooAbOLk9ERM5BAUXqnNyiUhYl7GPxhgOUlNoBGNyhMTNHtKdDeKCTqxMRkYpQQJE6I7+klA83HmbR2n3kFpcCENu8EX+/vAO9tDy9iEitooAitd6hk4Us3nCQzzYdocBSBkC7MH8eGt6BwR0baz0TEZFaSAFFaiXDMPh530n+/dNB/rPrGIZxenvrUD/uGdSGMd2b6socEZFaTAFFapWSUhtfJh3l3Z8Osisz37F9UPtQbru4JZe0CcFNwUREpNZTQJFaITO3hA9+OchHGw+TXXR6fomvpzvXxkZyS78WtGmse+aIiNQlCiji0spsdl74PpW3f9xPmf309zhNG/pya78WjOsVhdlXy9KLiNRFCijisk4UWJjy0VZ+3n965dfeLYO4/eIWDOkYhoe7m5OrExGRmqSAIi5p6+Fs7v1wCxm5Jfh5uTPvuq5c0aWJs8sSEZELRAFFXIphGHz062Fmr/gNq81Oq1A/3pwYS5vGAc4uTURELiAFFHEZJaU2Hl+ewmebjwAwonM4866LIcBH80xEROobBRRxCWmnirjnw82kHM3DzQQzh3fg7oGttMiaiEg9pYAiTrcu9Tj3x20lp6iUID8vXrmhOxe3CXF2WSIi4kQKKOI0drvBa2v38uKqVAwDYiLNLLoplqYNfZ1dmoiIOJkCijjFkewinvhyBz/sygLght5RPHlVZ3w83Z1cmYiIuAIFFLmgLGU23v7xAK/8sIeSUjteHm48Pboz43s1c3ZpIiLiQhRQ5IL5cc9xnvxyB/tPFALQp2UQT18dTbswXUIsIiLlKaBIjcvILeafX+9kZXIGACH+3jx2ZUdGd4vQVToiInJGCihSY6xldv790wFe/s8eiqw23ExwS78WTBvajkCtbSIiIueggCI1YsPeEzyxYgd7swoA6Nm8Ef8YHU2niEAnVyYiIrWBAopUq9Rj+bz8nz18vf301znBfl7MuqIjY7s3xc1NX+eIiEjFKKBIldntBmtTs3j3p4P8uOcEAG4muOmi5kwf1h6zr77OERGRylFAkfNWaClj6ZYjvPvTQQ7898ocNxOMiA7n3kFtiG5qdnKFIiJSWymgSKWlnSri/Z8PEpeYRn5JGQABPh7c0LsZN/dtTmSjBk6uUEREajsFFKkQwzDYdCibf68/wHc7MrEbp7e3DPHjtotbcE2PSPy89c9JRESqhz5R5C9t2HeC+d+nsulQtmNb/zYh3N6/BYPaNdbkVxERqXYKKHJWmw+d4sXvU9mw7yQAXh5uXNOjKbf2a0n7cK3+KiIiNUcBRf4k+UguL67azdrdxwHwdDdxQ+9mTL60DWGBPk6uTkRE6gMFFHHYlZnHglWpfLfjGADubiaui43kvsvaaOKriIhcUAoowr7jBSxcvYevt6djGGAywZhuTbl/cFtahPg5uzwREamHFFDqsdyiUuZ8u5NPN6U5rsq5sksTpg5pS1vdYVhERJxIAaWe+n5HJo8uT+F4vgWAIR3DmDa0LZ0jtLiaiIg4nwJKPXOq0MqTK3bw1bZ0AFqH+vHcNTH0bBHk5MpERET+nwJKPWEYBiuTM3jyyx2cLLTi7mbizgGteGBwW3w83Z1dnoiISDkKKPVAVn4JTyzfQfyOTAA6hAfw/LUxxEQ2dG5hIiIiZ+FWmcaLFi0iJiaGwMBAAgMD6du3L99+++0Z2951112YTCYWLlxYbrvFYmHKlCmEhITg5+fHqFGjOHLkyHmfgJydYRh8seUIQ+evI35HJh5uJh4Y3JYV9/VXOBEREZdWqYASGRnJ3Llz2bRpE5s2beKyyy5j9OjR7Nixo1y75cuXs3HjRiIiIv50jKlTp7Js2TLi4uJYv349BQUFjBw5EpvNVrUzkXIycouZ9N4mHvx0G7nFpXSOCGTFff2ZNrQdXh6V6nYREZELzmQYhlGVAwQFBTFv3jwmTZoEwNGjR+nTpw/fffcdV155JVOnTmXq1KkA5ObmEhoaygcffMD48eMBSE9PJyoqim+++Ybhw4ef8TUsFgsWi8Xxc15eHlFRUeTm5hIYGFiV8uuc06MmR3lqxQ7yLWV4ubvxwJC23DmgFZ7uCiYiIuI8eXl5mM3mCn1+n/cnls1mIy4ujsLCQvr27QuA3W5n4sSJzJw5k86dO/9pn82bN1NaWsqwYcMc2yIiIoiOjmbDhg1nfa05c+ZgNpsdj6ioqPMtu047WWDh7iWbmf7ZNvItZXSLasjK+/sz+dI2CiciIlKrVHqSbHJyMn379qWkpAR/f3+WLVtGp06dAHjuuefw8PDg/vvvP+O+mZmZeHl50ahRo3Lbw8LCyMzMPOtrzpo1iwcffNDx8+8jKPL/Vv12jFlfbOdEgRVPdxNTh7TjrgGt8FAwERGRWqjSAaV9+/YkJSWRk5PD0qVLueWWW0hISKC4uJiXXnqJLVu2YDKZKnVMwzDOuY+3tzfe3t6VLbVeyC8p5emvf+PTTacnGrcPC2D++K5acE1ERGq1SgcULy8v2rRpA0DPnj1JTEzkpZdeomPHjmRlZdGsWTNHW5vNxvTp01m4cCEHDx4kPDwcq9VKdnZ2uVGUrKws+vXrVw2nU7/8sv8kMz7bxpHsYkwmuPOSVkwb2k7rmoiISK1X5XVQDMPAYrEwceJEhgwZUu654cOHM3HiRG677TYAYmNj8fT0ZNWqVYwbNw6AjIwMUlJSeP7556taSr1RUmrjxe938/b6AxgGRAX58uJ13ejdUqvBiohI3VCpgPLII49w+eWXExUVRX5+PnFxcaxdu5b4+HiCg4MJDg4u197T05Pw8HDat28PgNlsZtKkSUyfPp3g4GCCgoKYMWMGXbp0+VO4kTNLOZrLtE+S2JNVAMD1vaJ4bGQn/L215p6IiNQdlfpUO3bsGBMnTiQjIwOz2UxMTAzx8fEMHTq0wsdYsGABHh4ejBs3juLiYgYPHszixYtxd9fXEn9kLbOz73gBuzPz2ZWZz+7MPH7cc4Iyu0GIvzfPXdOFwR3DnF2miIhItavyOijOUJnrqGsDwzA4kl3M7sx8dh/7/zCy/3ghZfY/d8+IzuE8MyaaYH9NHBYRkdqjMp/f+l7AidJzivl0UxqfJqaRnltyxjYBPh50CA+gfXgA7cMD6dLUTNdIc6WvlBIREalNFFAuMJvdYO3uLD7aeJg1u7P4fYDE091E61D//waRgP+GkkAizD4KIyIiUu8ooFwgGbnFfJKYxieJaWT8z2hJn5ZBTOjTjOGdw3V5sIiIyH8poNQgm90gIfX0aMkPu/5/tKRhA0+u7RHJ9b2b0aaxv3OLFBERcUEKKDUgv6SUuF/TWLzhIEdzih3bNVoiIiJSMQoo1ehYXgnv/nSQDzceIr+kDNBoiYiIyPlQQKkGe47l8+a6/SxPOkqp7fT3OK1D/bhzQCtGd2uq0RIREZFKUkA5T4ZhkHgwmzcS9vGfXVmO7b1aNOLOAa0Z3KExbm66+kZEROR8KKBUks1u8P2OTN5Yt5+ktBwATCYY1imMOwe0JrZ5o3MfQERERP6SAkol7MzI4+9Lt7PtSC4AXh5uXBsbyd/6t6RVqOaXiIiIVBcFlAqwlNl49Ye9LFq7jzK7QYC3B7de3IKb+7YgNEDLzYuIiFQ3BZS/sOngKR5eup19xwuB01/lPH11NGGBPk6uTEREpO5SQDmLAksZ8+J38f4vhzAMCPH35h+jO3N5dLiWnhcREalhCihnsGZ3Fo9+key4gd91sZE8emVHGjbwcnJlIiIi9YMCyv84VWjl6a9/Y9nWowBEBfkyZ0wM/duGOLkyERGR+kUBhdNrmny1PYPZK3ZwstCKmwluu7gl04e1o4GX3iIREZELrd5/+qbnFPP48hTHYmvtwwJ47toYukU1dG5hIiIi9Vi9DSh2u8GHGw/xXPxuCixleLqbuO/SttwzqDVeHm7OLk9ERKReq5cBZW9WAbO+2E7iwWwAejRryHPXxNA2LMDJlYmIiAjUs4BSarPzRsI+Xv7PXqw2O35e7jw0ogMTL2qu++aIiIi4kHoTULal5fDw0u3syswHYFD7UJ4Z04WmDX2dXJmIiIj8UZ0PKEXWMuZ/n8q/fzqA3YBGDTx58qrOjO4WoQXXREREXFSdDSiGYfDdjkye+WYnaaeKAbi6WwSPj+xEsL/unyMiIuLK6mRA2XI4m2dX7mTTodOTYCPMPjwzpguXdmjs5MpERESkIupUQDl0spDn43ezMjkDAB9PN+64pBV3DWyNv3edOlUREZE6rU58amcXWnn5hz0s+eUQpTYDk+n0/XMeHNqecLPuOiwiIlLb1OqAUlJq4+OEfby6Zi/5JWUADGgXyqzLO9CxSaCTqxMREZHzVasDylWvrOdYyelVXzs2CeSRKzpwSdtQJ1clIiIiVVWrA0pGbgkRoUHMGN6eMd2b4q7F1kREROqEWh1Q7h/chsnDYvD1cnd2KSIiIlKNavVd8e4c0FrhREREpA6q1QFFRERE6iYFFBEREXE5CigiIiLichRQRERExOUooIiIiIjLUUARERERl6OAIiIiIi5HAUVERERcjgKKiIiIuBwFFBEREXE5CigiIiLichRQRERExOUooIiIiIjLUUARERERl+Ph7ALOh2EYAOTl5Tm5EhEREamo3z+3f/8cP5daGVDy8/MBiIqKcnIlIiIiUln5+fmYzeZztjEZFYkxLsZut5Oenk5AQAAmk8nZ5ZxVXl4eUVFRpKWlERgY6OxyqqxXr14kJiY6u4xqUdf6BupO/6hvXFtd6x/1zYVlGAb5+flERETg5nbuWSa1cgTFzc2NyMhIZ5dRYYGBgS77j6Uy3N3d68R5/K+60jdQ9/pHfePa6kr/qG8uvL8aOfmdJslKhU2ePNnZJcg5qH9cl/rGdalvXFet/IqntsjLy8NsNpObm+vSabY+Ut+4LvWNa1P/uK661jcaQalB3t7ePPnkk3h7ezu7FPkD9Y3rUt+4NvWP66prfaMRFBEREXE5GkERERERl6OAIiIiIi5HAUVERERcjgKKiIiIuBwFlHNYt24dV111FREREZhMJpYvX17u+WPHjnHrrbcSERFBgwYNGDFiBHv27CnXJjMzk4kTJxIeHo6fnx89evTg888/L9cmOzubiRMnYjabMZvNTJw4kZycnBo+u9qvOvpn3759jBkzhtDQUAIDAxk3bhzHjh0r10b9U3lz5syhV69eBAQE0LhxY66++mp2795dro1hGDz11FNERETg6+vLoEGD2LFjR7k2FouFKVOmEBISgp+fH6NGjeLIkSPl2qh/Kqe6+ubNN99k0KBBBAYGYjKZzvieq28qpzr65tSpU0yZMoX27dvToEEDmjVrxv33309ubm6549SGvlFAOYfCwkK6du3Kq6+++qfnDMPg6quvZv/+/Xz55Zds3bqV5s2bM2TIEAoLCx3tJk6cyO7du1mxYgXJycmMHTuW8ePHs3XrVkebCRMmkJSURHx8PPHx8SQlJTFx4sQLco61WVX7p7CwkGHDhmEymfjhhx/46aefsFqtXHXVVdjtdsex1D+Vl5CQwOTJk/nll19YtWoVZWVlDBs2rNzvxvPPP8/8+fN59dVXSUxMJDw8nKFDhzrutQUwdepUli1bRlxcHOvXr6egoICRI0dis9kcbdQ/lVNdfVNUVMSIESN45JFHzvpa6pvKqY6+SU9PJz09nRdeeIHk5GQWL15MfHw8kyZNKvdataJvDKkQwFi2bJnj5927dxuAkZKS4thWVlZmBAUFGW+99ZZjm5+fn/H++++XO1ZQUJDx9ttvG4ZhGL/99psBGL/88ovj+Z9//tkAjF27dtXQ2dQ959M/3333neHm5mbk5uY62pw6dcoAjFWrVhmGof6pLllZWQZgJCQkGIZhGHa73QgPDzfmzp3raFNSUmKYzWbj9ddfNwzDMHJycgxPT08jLi7O0ebo0aOGm5ubER8fbxiG+qc6nE/f/K81a9YYgJGdnV1uu/qm6qraN7/79NNPDS8vL6O0tNQwjNrTNxpBOU8WiwUAHx8fxzZ3d3e8vLxYv369Y1v//v355JNPOHXqFHa7nbi4OCwWC4MGDQLg559/xmw206dPH8c+F110EWazmQ0bNlyYk6mDKtI/FosFk8lUblEjHx8f3NzcHG3UP9Xj9+HloKAgAA4cOEBmZibDhg1ztPH29mbgwIGO93Xz5s2UlpaWaxMREUF0dLSjjfqn6s6nbypCfVN11dU3v68s6+Fx+vZ7taVvFFDOU4cOHWjevDmzZs0iOzsbq9XK3LlzyczMJCMjw9Huk08+oaysjODgYLy9vbnrrrtYtmwZrVu3Bk7PUWncuPGfjt+4cWMyMzMv2PnUNRXpn4suugg/Pz8efvhhioqKKCwsZObMmdjtdkcb9U/VGYbBgw8+SP/+/YmOjgZwvHdhYWHl2oaFhTmey8zMxMvLi0aNGp2zjfrn/J1v31SE+qZqqqtvTp48ydNPP81dd93l2FZb+kYB5Tx5enqydOlSUlNTCQoKokGDBqxdu5bLL78cd3d3R7vHHnuM7OxsVq9ezaZNm3jwwQe57rrrSE5OdrQxmUx/Or5hGGfcLhVTkf4JDQ3ls88+46uvvsLf399xD4sePXqU60P1T9Xcd999bN++nY8//vhPz/3xPazI+/rHNuqf81fdffNXxzjf49RH1dE3eXl5XHnllXTq1Iknn3zynMc413GcxcPZBdRmsbGxJCUlkZubi9VqJTQ0lD59+tCzZ0/g9BUir776KikpKXTu3BmArl278uOPP/Kvf/2L119/nfDw8D9dNQJw/PjxP6VkqZy/6h+AYcOGsW/fPk6cOIGHhwcNGzYkPDycli1bAqh/qmjKlCmsWLGCdevWERkZ6dgeHh4OnP6fXJMmTRzbs7KyHO9reHg4VquV7OzscqMoWVlZ9OvXz9FG/XN+qtI3FaG+OX/V0Tf5+fmMGDECf39/li1bhqenZ7nj1Ia+0QhKNTCbzYSGhrJnzx42bdrE6NGjgdOz3AHc3Mq/ze7u7o6rRPr27Utubi6//vqr4/mNGzeSm5vr+CMsVXO2/vlfISEhNGzYkB9++IGsrCxGjRoFqH/Ol2EY3HfffXzxxRf88MMPjsD3u5YtWxIeHs6qVasc26xWKwkJCY73NTY2Fk9Pz3JtMjIySElJcbRR/1RedfRNRahvKq+6+iYvL49hw4bh5eXFihUrys3Fg1rUN06YmFtr5OfnG1u3bjW2bt1qAMb8+fONrVu3GocOHTIM4/TM6DVr1hj79u0zli9fbjRv3twYO3asY3+r1Wq0adPGuOSSS4yNGzcae/fuNV544QXDZDIZK1eudLQbMWKEERMTY/z888/Gzz//bHTp0sUYOXLkBT/f2qaq/WMYhvHvf//b+Pnnn429e/caH3zwgREUFGQ8+OCD5dqofyrvnnvuMcxms7F27VojIyPD8SgqKnK0mTt3rmE2m40vvvjCSE5ONm644QajSZMmRl5enqPN3XffbURGRhqrV682tmzZYlx22WVG165djbKyMkcb9U/lVFffZGRkGFu3bjXeeustAzDWrVtnbN261Th58qSjjfqmcqqjb/Ly8ow+ffoYXbp0Mfbu3VvuOLXt90YB5Rx+v3zuj49bbrnFMAzDeOmll4zIyEjD09PTaNasmfHYY48ZFoul3DFSU1ONsWPHGo0bNzYaNGhgxMTE/Omy45MnTxo33nijERAQYAQEBBg33njjny7Zkz+rjv55+OGHjbCwMMPT09No27at8eKLLxp2u71cG/VP5Z2pXwDj3XffdbSx2+3Gk08+aYSHhxve3t7GgAEDjOTk5HLHKS4uNu677z4jKCjI8PX1NUaOHGkcPny4XBv1T+VUV988+eSTf3kc9U3lVEffnO3vImAcOHDA0a429I3JMAyjpkZnRERERM6H5qCIiIiIy1FAEREREZejgCIiIiIuRwFFREREXI4CioiIiLgcBRQRERFxOQooIiIi4nIUUERERMTlKKCIyAU1aNAgpk6d6uwyRMTFKaCIiMtau3YtJpOJnJwcZ5ciIheYAoqIiIi4HAUUEakxhYWF3Hzzzfj7+9OkSRNefPHFcs8vWbKEnj17EhAQQHh4OBMmTCArKwuAgwcPcumllwLQqFEjTCYTt956K3D6tvTPP/88rVq1wtfXl65du/L5559f0HMTkZqlgCIiNWbmzJmsWbOGZcuW8f3337N27Vo2b97seN5qtfL000+zbds2li9fzoEDBxwhJCoqiqVLlwKwe/duMjIyeOmllwB47LHHePfdd1m0aBE7duxg2rRp3HTTTSQkJFzwcxSRmqG7GYtIjSgoKCA4OJj333+f8ePHA3Dq1CkiIyO58847Wbhw4Z/2SUxMpHfv3uTn5+Pv78/atWu59NJLyc7OpmHDhsDpUZmQkBB++OEH+vbt69j3b3/7G0VFRXz00UcX4vREpIZ5OLsAEamb9u3bh9VqLRcigoKCaN++vePnrVu38tRTT5GUlMSpU6ew2+0AHD58mE6dOp3xuL/99hslJSUMHTq03Har1Ur37t1r4ExExBkUUESkRvzV4GxhYSHDhg1j2LBhLFmyhNDQUA4fPszw4cOxWq1n3e/3ELNy5UqaNm1a7jlvb++qFy4iLkEBRURqRJs2bfD09OSXX36hWbNmAGRnZ5OamsrAgQPZtWsXJ06cYO7cuURFRQGwadOmcsfw8vICwGazObZ16tQJb29vDh8+zMCBAy/Q2YjIhaaAIiI1wt/fn0mTJjFz5kyCg4MJCwvj0Ucfxc3t9Nz8Zs2a4eXlxSuvvMLdd99NSkoKTz/9dLljNG/eHJPJxNdff80VV1yBr68vAQEBzJgxg2nTpmG32+nfvz95eXls2LABf39/brnlFmecrohUM13FIyI1Zt68eQwYMIBRo0YxZMgQ+vfvT2xsLAChoaEsXryYzz77jE6dOjF37lxeeOGFcvs3bdqU2bNn8/e//52wsDDuu+8+AJ5++mmeeOIJ5syZQ8eOHRk+fDhfffUVLVu2vODnKCI1Q1fxiIiIiMvRCIqIiIi4HAUUERERcTkKKCIiIuJyFFBERETE5SigiIiIiMtRQBERERGXo4AiIiIiLkcBRURERFyOAoqIiIi4HAUUERERcTkKKCIiIuJy/g8y/9dHbr8P+wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['CO2(ppm)'].resample('1YE').mean().plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4606311-7ed2-4683-92ea-402f89299f8a",
   "metadata": {},
   "source": [
    "### Applying operations to a DataFrame\n",
    "\n",
    "One of the most commonly used features in Pandas is the performing of calculations to multiple data values in a `DataFrame` simultaneously. Let's first look at a familiar concept: a function that converts single values.  The following example uses such a function to convert concentrations from parts ber million (ppm) to parts per billion (ppb):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "187f20c5-0a4c-4409-b097-7c4b9a7222a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_ppm_to_ppb(concentration_ppm):\n",
    "    \"\"\"\n",
    "    Converts from parts per million to parts per billion\n",
    "    \"\"\"\n",
    "\n",
    "    return concentration_ppm * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cc25744c-7af5-4dbf-b302-1822eb3d2436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert a single value\n",
    "convert_ppm_to_ppb(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd7b7c31-8e6b-44bb-9e5f-bf3625839f78",
   "metadata": {},
   "source": [
    "The following examples instead illustrate a new concept: using such functions with `DataFrames` and `Series`. For the first example, we start by creating a `Series`; in order to do so, we subset the `DataFrame` by the `CO2(ppm)` column. This has already been done earlier in this page; we do not need to create this `Series` again. We are using this particular `Series` for a reason: the data values are in parts per million."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c6fc0747-7a4b-461f-a849-3ca963d055fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1976-05-15    328.861\n",
       "1976-06-15    328.988\n",
       "1976-07-15    329.653\n",
       "1976-08-15    330.550\n",
       "1976-09-15    330.872\n",
       "               ...   \n",
       "2024-08-15    420.795\n",
       "2024-09-15    421.180\n",
       "2024-10-15    421.397\n",
       "2024-11-15    421.414\n",
       "2024-12-15    421.377\n",
       "Name: CO2(ppm), Length: 584, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0153329c-850d-458a-9a9a-be1aea345f1d",
   "metadata": {},
   "source": [
    "This example illustrates how to use the temperature-conversion function defined above on a `Series` object. Just as calling the function with a single value returns a single value, calling the function on a `Series` object returns another `Series` object. The function performs the temperature conversion on each data value in the `Series`, and returns a `Series` with all values converted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1dec9af7-6100-4def-b895-a80180674a75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1976-05-15    328861.0\n",
       "1976-06-15    328988.0\n",
       "1976-07-15    329653.0\n",
       "1976-08-15    330550.0\n",
       "1976-09-15    330872.0\n",
       "                ...   \n",
       "2024-08-15    420795.0\n",
       "2024-09-15    421180.0\n",
       "2024-10-15    421397.0\n",
       "2024-11-15    421414.0\n",
       "2024-12-15    421377.0\n",
       "Name: CO2(ppm), Length: 584, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_ppm_to_ppb(co2_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "785a8f10-1eaf-42d3-a029-69e5e2312528",
   "metadata": {},
   "source": [
    "If we call the `.values` method on the `Series` passed to the function, the `Series` is converted to a numpy array, as described above. The function then converts each value in the numpy array, and returns a new numpy array with all values sorted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f45b173-f81b-4a1e-bec9-0cd6a8393bc1",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Warning</p>\n",
    "    It is recommended to only convert <code>Series</code> to NumPy arrays when necessary; doing so removes the label information that enables much of the Pandas core functionality.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5bdbe610-09bc-493f-97ab-45f527100132",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       383962., 383925., 383598., 383502., 383728., 384019., 384418.,\n",
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       "       395948., 395889., 395922., 396062., 396203., 396527., 396943.,\n",
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       "       401589., 401765., 401803., 401542., 401349., 401284., 401228.,\n",
       "       401316., 401597., 401873., 402182., 402821., 403405., 403616.,\n",
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       "       404518., 404817., 405305., 405770., 405970., 405993., 405918.,\n",
       "       405872., 405843., 405758., 405895., 406308., 406847., 407432.,\n",
       "       408038., 408504., 408728., 408886., 408906., 408670., 408418.,\n",
       "       408494., 408750., 408984., 409236., 409660., 410236., 410700.,\n",
       "       410922., 410995., 410904., 410787., 410719., 410718., 410876.,\n",
       "       411005., 411197., 411617., 412127., 412633., 413017., 413156.,\n",
       "       413192., 413170., 413086., 413148., 413327., 413488., 413716.,\n",
       "       414068., 414547., 415053., 415279., 415304., 415329., 415190.,\n",
       "       414958., 414989., 415174., 415379., 415713., 416113., 416556.,\n",
       "       417083., 417393., 417575., 417774., 417840., 417971., 418228.,\n",
       "       418608., 419073., 419514., 420034., 420463., 420795., 421180.,\n",
       "       421397., 421414., 421377.])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_ppm_to_ppb(co2_series.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b7b7cb6-73d3-4b47-a732-ce1fb51888b4",
   "metadata": {},
   "source": [
    "As described above, when our temperature-conversion function accepts a `Series` as an argument, it returns a `Series`. We can directly assign this returned `Series` to a new column in our `DataFrame`, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5037b661-930f-4d8d-8c94-c5c4fe0faf3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1976-05-15    328861.0\n",
       "1976-06-15    328988.0\n",
       "1976-07-15    329653.0\n",
       "1976-08-15    330550.0\n",
       "1976-09-15    330872.0\n",
       "                ...   \n",
       "2024-08-15    420795.0\n",
       "2024-09-15    421180.0\n",
       "2024-10-15    421397.0\n",
       "2024-11-15    421414.0\n",
       "2024-12-15    421377.0\n",
       "Name: CO2(ppb), Length: 584, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CO2(ppb)'] =convert_ppm_to_ppb(co2_series)\n",
    "df['CO2(ppb)']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7217a122-68ef-408c-a41d-5ca495ab963c",
   "metadata": {},
   "source": [
    "In this final example, we demonstrate the use of the `to_csv` method to save a `DataFrame` as a `.csv` file. This example also demonstrates the `read_csv` method, which reads `.csv` files into Pandas `DataFrames`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d2d0c9be-f4f6-4c04-b966-a7da2aae9e20",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"CapeGrim_CO2_data_analyzed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "efe7cc4a-73ec-4c9c-bb31-52a0cd060276",
   "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>CO2(ppm)</th>\n",
       "      <th>SD(ppm)</th>\n",
       "      <th>GR(ppm/yr)</th>\n",
       "      <th>Source</th>\n",
       "      <th>month</th>\n",
       "      <th>CO2(ppb)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-05-15</th>\n",
       "      <td>1976.36890</td>\n",
       "      <td>328.861</td>\n",
       "      <td>0.165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>5</td>\n",
       "      <td>328861.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-06-15</th>\n",
       "      <td>1976.45360</td>\n",
       "      <td>328.988</td>\n",
       "      <td>0.285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>6</td>\n",
       "      <td>328988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-07-15</th>\n",
       "      <td>1976.53550</td>\n",
       "      <td>329.653</td>\n",
       "      <td>0.282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>7</td>\n",
       "      <td>329653.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-15</th>\n",
       "      <td>1976.62020</td>\n",
       "      <td>330.550</td>\n",
       "      <td>0.201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>8</td>\n",
       "      <td>330550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-09-15</th>\n",
       "      <td>1976.70490</td>\n",
       "      <td>330.872</td>\n",
       "      <td>0.234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>9</td>\n",
       "      <td>330872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-15</th>\n",
       "      <td>2024.62022</td>\n",
       "      <td>420.795</td>\n",
       "      <td>0.157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>8</td>\n",
       "      <td>420795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-15</th>\n",
       "      <td>2024.70492</td>\n",
       "      <td>421.180</td>\n",
       "      <td>0.139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>9</td>\n",
       "      <td>421180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-15</th>\n",
       "      <td>2024.78689</td>\n",
       "      <td>421.397</td>\n",
       "      <td>0.097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>10</td>\n",
       "      <td>421397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-15</th>\n",
       "      <td>2024.87158</td>\n",
       "      <td>421.414</td>\n",
       "      <td>0.156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>11</td>\n",
       "      <td>421414.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-15</th>\n",
       "      <td>2024.95355</td>\n",
       "      <td>421.377</td>\n",
       "      <td>0.171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in situ</td>\n",
       "      <td>12</td>\n",
       "      <td>421377.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>584 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE  CO2(ppm)  SD(ppm)  GR(ppm/yr)   Source  month  \\\n",
       "date                                                                    \n",
       "1976-05-15  1976.36890   328.861    0.165         NaN  in situ      5   \n",
       "1976-06-15  1976.45360   328.988    0.285         NaN  in situ      6   \n",
       "1976-07-15  1976.53550   329.653    0.282         NaN  in situ      7   \n",
       "1976-08-15  1976.62020   330.550    0.201         NaN  in situ      8   \n",
       "1976-09-15  1976.70490   330.872    0.234         NaN  in situ      9   \n",
       "...                ...       ...      ...         ...      ...    ...   \n",
       "2024-08-15  2024.62022   420.795    0.157         NaN  in situ      8   \n",
       "2024-09-15  2024.70492   421.180    0.139         NaN  in situ      9   \n",
       "2024-10-15  2024.78689   421.397    0.097         NaN  in situ     10   \n",
       "2024-11-15  2024.87158   421.414    0.156         NaN  in situ     11   \n",
       "2024-12-15  2024.95355   421.377    0.171         NaN  in situ     12   \n",
       "\n",
       "            CO2(ppb)  \n",
       "date                  \n",
       "1976-05-15  328861.0  \n",
       "1976-06-15  328988.0  \n",
       "1976-07-15  329653.0  \n",
       "1976-08-15  330550.0  \n",
       "1976-09-15  330872.0  \n",
       "...              ...  \n",
       "2024-08-15  420795.0  \n",
       "2024-09-15  421180.0  \n",
       "2024-10-15  421397.0  \n",
       "2024-11-15  421414.0  \n",
       "2024-12-15  421377.0  \n",
       "\n",
       "[584 rows x 7 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('CapeGrim_CO2_data_analyzed.csv', index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9446910-8708-4993-be2c-1e3610a8b37c",
   "metadata": {},
   "source": [
    "---\n",
    "## Summary\n",
    "* Pandas is a very powerful tool for working with tabular (i.e., spreadsheet-style) data\n",
    "* There are multiple ways of subsetting your pandas dataframe or series\n",
    "* Pandas allows you to refer to subsets of data by label, which generally makes code more readable and more robust\n",
    "* Pandas can be helpful for exploratory data analysis, including plotting and basic statistics\n",
    "* One can apply calculations to pandas dataframes and save the output via `csv` files\n",
    "\n",
    "### What's Next?\n",
    "In the next notebook, we will take a look at how to work with cloud radar/remote sensing data.\n",
    "\n",
    "## Resources and References\n",
    "1. [CSIRO Data Page](https://www.csiro.au/en/research/natural-environment/atmosphere/latest-greenhouse-gas-data)\n",
    "1. [Getting Started with Pandas](https://pandas.pydata.org/docs/getting_started/index.html#getting-started)\n",
    "1. [Pandas User Guide](https://pandas.pydata.org/docs/user_guide/index.html#user-guide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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