intial analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69ddef05-d25c-4f90-b068-2a92d9a2d998",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7dc5b82d-fe02-40e6-be58-54908f87a426",
   "metadata": {},
   "outputs": [],
   "source": [
    "glob = pd.read_csv(\"GlobalTemperatures.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7ca4b5b-72be-4499-8479-eee3b310a1d0",
   "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>dt</th>\n",
       "      <th>LandAverageTemperature</th>\n",
       "      <th>LandAverageTemperatureUncertainty</th>\n",
       "      <th>LandMaxTemperature</th>\n",
       "      <th>LandMaxTemperatureUncertainty</th>\n",
       "      <th>LandMinTemperature</th>\n",
       "      <th>LandMinTemperatureUncertainty</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1750-01-01</td>\n",
       "      <td>3.034</td>\n",
       "      <td>3.574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1750-02-01</td>\n",
       "      <td>3.083</td>\n",
       "      <td>3.702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1750-03-01</td>\n",
       "      <td>5.626</td>\n",
       "      <td>3.076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1750-04-01</td>\n",
       "      <td>8.490</td>\n",
       "      <td>2.451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1750-05-01</td>\n",
       "      <td>11.573</td>\n",
       "      <td>2.072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dt  LandAverageTemperature  LandAverageTemperatureUncertainty  \\\n",
       "0  1750-01-01                   3.034                              3.574   \n",
       "1  1750-02-01                   3.083                              3.702   \n",
       "2  1750-03-01                   5.626                              3.076   \n",
       "3  1750-04-01                   8.490                              2.451   \n",
       "4  1750-05-01                  11.573                              2.072   \n",
       "\n",
       "   LandMaxTemperature  LandMaxTemperatureUncertainty  LandMinTemperature  \\\n",
       "0                 NaN                            NaN                 NaN   \n",
       "1                 NaN                            NaN                 NaN   \n",
       "2                 NaN                            NaN                 NaN   \n",
       "3                 NaN                            NaN                 NaN   \n",
       "4                 NaN                            NaN                 NaN   \n",
       "\n",
       "   LandMinTemperatureUncertainty  LandAndOceanAverageTemperature  \\\n",
       "0                            NaN                             NaN   \n",
       "1                            NaN                             NaN   \n",
       "2                            NaN                             NaN   \n",
       "3                            NaN                             NaN   \n",
       "4                            NaN                             NaN   \n",
       "\n",
       "   LandAndOceanAverageTemperatureUncertainty  \n",
       "0                                        NaN  \n",
       "1                                        NaN  \n",
       "2                                        NaN  \n",
       "3                                        NaN  \n",
       "4                                        NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da3fb99c-ef67-4dfe-a8f3-71cb399ddbbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt                                              0\n",
       "LandAverageTemperature                         12\n",
       "LandAverageTemperatureUncertainty              12\n",
       "LandMaxTemperature                           1200\n",
       "LandMaxTemperatureUncertainty                1200\n",
       "LandMinTemperature                           1200\n",
       "LandMinTemperatureUncertainty                1200\n",
       "LandAndOceanAverageTemperature               1200\n",
       "LandAndOceanAverageTemperatureUncertainty    1200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf5dcb7e-cbf0-4742-bf41-f5cb61db35df",
   "metadata": {},
   "outputs": [],
   "source": [
    "glob.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d939b694-2a81-4971-b302-cd4dad4b03d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt                                           0\n",
       "LandAverageTemperature                       0\n",
       "LandAverageTemperatureUncertainty            0\n",
       "LandMaxTemperature                           0\n",
       "LandMaxTemperatureUncertainty                0\n",
       "LandMinTemperature                           0\n",
       "LandMinTemperatureUncertainty                0\n",
       "LandAndOceanAverageTemperature               0\n",
       "LandAndOceanAverageTemperatureUncertainty    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b55bcc9-b8c8-41a9-9475-a80518c415d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        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>dt</th>\n",
       "      <th>LandAverageTemperature</th>\n",
       "      <th>LandAverageTemperatureUncertainty</th>\n",
       "      <th>LandMaxTemperature</th>\n",
       "      <th>LandMaxTemperatureUncertainty</th>\n",
       "      <th>LandMinTemperature</th>\n",
       "      <th>LandMinTemperatureUncertainty</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>1850-01-01</td>\n",
       "      <td>0.749</td>\n",
       "      <td>1.105</td>\n",
       "      <td>8.242</td>\n",
       "      <td>1.738</td>\n",
       "      <td>-3.206</td>\n",
       "      <td>2.822</td>\n",
       "      <td>12.833</td>\n",
       "      <td>0.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>1850-02-01</td>\n",
       "      <td>3.071</td>\n",
       "      <td>1.275</td>\n",
       "      <td>9.970</td>\n",
       "      <td>3.007</td>\n",
       "      <td>-2.291</td>\n",
       "      <td>1.623</td>\n",
       "      <td>13.588</td>\n",
       "      <td>0.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>1850-03-01</td>\n",
       "      <td>4.954</td>\n",
       "      <td>0.955</td>\n",
       "      <td>10.347</td>\n",
       "      <td>2.401</td>\n",
       "      <td>-1.905</td>\n",
       "      <td>1.410</td>\n",
       "      <td>14.043</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>1850-04-01</td>\n",
       "      <td>7.217</td>\n",
       "      <td>0.665</td>\n",
       "      <td>12.934</td>\n",
       "      <td>1.004</td>\n",
       "      <td>1.018</td>\n",
       "      <td>1.329</td>\n",
       "      <td>14.667</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>1850-05-01</td>\n",
       "      <td>10.004</td>\n",
       "      <td>0.617</td>\n",
       "      <td>15.655</td>\n",
       "      <td>2.406</td>\n",
       "      <td>3.811</td>\n",
       "      <td>1.347</td>\n",
       "      <td>15.507</td>\n",
       "      <td>0.249</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3187</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>14.755</td>\n",
       "      <td>0.072</td>\n",
       "      <td>20.699</td>\n",
       "      <td>0.110</td>\n",
       "      <td>9.005</td>\n",
       "      <td>0.170</td>\n",
       "      <td>17.589</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3188</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>12.999</td>\n",
       "      <td>0.079</td>\n",
       "      <td>18.845</td>\n",
       "      <td>0.088</td>\n",
       "      <td>7.199</td>\n",
       "      <td>0.229</td>\n",
       "      <td>17.049</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3189</th>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>10.801</td>\n",
       "      <td>0.102</td>\n",
       "      <td>16.450</td>\n",
       "      <td>0.059</td>\n",
       "      <td>5.232</td>\n",
       "      <td>0.115</td>\n",
       "      <td>16.290</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3190</th>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>7.433</td>\n",
       "      <td>0.119</td>\n",
       "      <td>12.892</td>\n",
       "      <td>0.093</td>\n",
       "      <td>2.157</td>\n",
       "      <td>0.106</td>\n",
       "      <td>15.252</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3191</th>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>5.518</td>\n",
       "      <td>0.100</td>\n",
       "      <td>10.725</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.099</td>\n",
       "      <td>14.774</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1992 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              dt  LandAverageTemperature  LandAverageTemperatureUncertainty  \\\n",
       "1200  1850-01-01                   0.749                              1.105   \n",
       "1201  1850-02-01                   3.071                              1.275   \n",
       "1202  1850-03-01                   4.954                              0.955   \n",
       "1203  1850-04-01                   7.217                              0.665   \n",
       "1204  1850-05-01                  10.004                              0.617   \n",
       "...          ...                     ...                                ...   \n",
       "3187  2015-08-01                  14.755                              0.072   \n",
       "3188  2015-09-01                  12.999                              0.079   \n",
       "3189  2015-10-01                  10.801                              0.102   \n",
       "3190  2015-11-01                   7.433                              0.119   \n",
       "3191  2015-12-01                   5.518                              0.100   \n",
       "\n",
       "      LandMaxTemperature  LandMaxTemperatureUncertainty  LandMinTemperature  \\\n",
       "1200               8.242                          1.738              -3.206   \n",
       "1201               9.970                          3.007              -2.291   \n",
       "1202              10.347                          2.401              -1.905   \n",
       "1203              12.934                          1.004               1.018   \n",
       "1204              15.655                          2.406               3.811   \n",
       "...                  ...                            ...                 ...   \n",
       "3187              20.699                          0.110               9.005   \n",
       "3188              18.845                          0.088               7.199   \n",
       "3189              16.450                          0.059               5.232   \n",
       "3190              12.892                          0.093               2.157   \n",
       "3191              10.725                          0.154               0.287   \n",
       "\n",
       "      LandMinTemperatureUncertainty  LandAndOceanAverageTemperature  \\\n",
       "1200                          2.822                          12.833   \n",
       "1201                          1.623                          13.588   \n",
       "1202                          1.410                          14.043   \n",
       "1203                          1.329                          14.667   \n",
       "1204                          1.347                          15.507   \n",
       "...                             ...                             ...   \n",
       "3187                          0.170                          17.589   \n",
       "3188                          0.229                          17.049   \n",
       "3189                          0.115                          16.290   \n",
       "3190                          0.106                          15.252   \n",
       "3191                          0.099                          14.774   \n",
       "\n",
       "      LandAndOceanAverageTemperatureUncertainty  \n",
       "1200                                      0.367  \n",
       "1201                                      0.414  \n",
       "1202                                      0.341  \n",
       "1203                                      0.267  \n",
       "1204                                      0.249  \n",
       "...                                         ...  \n",
       "3187                                      0.057  \n",
       "3188                                      0.058  \n",
       "3189                                      0.062  \n",
       "3190                                      0.063  \n",
       "3191                                      0.062  \n",
       "\n",
       "[1992 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cb269ae7-8699-4ce7-a862-ec4de1c2a0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7490000000000001"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.loc[1200].iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "70dcf35b-4d30-44e4-beaa-0083b80e1ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = []\n",
    "l2 = []\n",
    "l3 = []\n",
    "l4 = []\n",
    "l5 = []\n",
    "l6 = []\n",
    "l7 = []\n",
    "l8 = []\n",
    "\n",
    "def convert1(param):\n",
    "    sum = 0\n",
    "    for i in range(0, 12):\n",
    "        sum += glob.loc[i+param].iloc[1]\n",
    "\n",
    "    return sum/12\n",
    "\n",
    "def convert2(param):\n",
    "    sum = 0\n",
    "    for i in range(0, 12):\n",
    "        sum += glob.loc[i+param].iloc[2]\n",
    "\n",
    "    return sum/12\n",
    "\n",
    "def convert3(param):\n",
    "    sum = 0\n",
    "    for i in range(0, 12):\n",
    "        sum += glob.loc[i+param].iloc[3]\n",
    "\n",
    "    return sum/12\n",
    "\n",
    "def convert4(param):\n",
    "    sum = 0\n",
    "    for i in range(0, 12):\n",
    "        sum += glob.loc[i+param].iloc[4]\n",
    "\n",
    "    return sum/12\n",
    "\n",
    "def convert5(param):\n",
    "    sum = 0\n",
    "    for i in range(0, 12):\n",
    "        sum += glob.loc[i+param].iloc[5]\n",
    "\n",
    "    return sum/12\n",
    "\n",
    "def convert6(param):\n",
    "    sum = 0\n",
    "    for i in range(0, 12):\n",
    "        sum += glob.loc[i+param].iloc[6]\n",
    "\n",
    "    return sum/12\n",
    "\n",
    "def convert7(param):\n",
    "    sum = 0\n",
    "    for i in range(0, 12):\n",
    "        sum += glob.loc[i+param].iloc[7]\n",
    "\n",
    "    return sum/12\n",
    "\n",
    "def convert8(param):\n",
    "    sum = 0\n",
    "    for i in range(0, 12):\n",
    "        sum += glob.loc[i+param].iloc[8]\n",
    "\n",
    "    return sum/12\n",
    "\n",
    "for i in range(1200, 3192, 12):\n",
    "    l1.append(convert1(i))\n",
    "    l2.append(convert2(i))\n",
    "    l3.append(convert3(i))\n",
    "    l4.append(convert4(i))\n",
    "    l5.append(convert5(i))\n",
    "    l6.append(convert6(i))\n",
    "    l7.append(convert7(i))\n",
    "    l8.append(convert8(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f7aba3c7-27b6-432e-9d81-d7f9d6831ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt                                           0\n",
       "LandAverageTemperature                       0\n",
       "LandAverageTemperatureUncertainty            0\n",
       "LandMaxTemperature                           0\n",
       "LandMaxTemperatureUncertainty                0\n",
       "LandMinTemperature                           0\n",
       "LandMinTemperatureUncertainty                0\n",
       "LandAndOceanAverageTemperature               0\n",
       "LandAndOceanAverageTemperatureUncertainty    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "69c2b922-a816-4925-b92e-3f9566b954d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 1850\n",
    "date = []\n",
    "for i in range(1200, 1366):\n",
    "    date.append(1850 + i - 1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a8e0ef12-d747-4594-a124-2054d14aedd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1850,\n",
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       " 2013,\n",
       " 2014,\n",
       " 2015]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2a0875f4-0438-43b3-987e-b1251f96d9dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dix = {\"date\":date, \"LandAverageTemperature\":l1, \"LandAverageTemperatureUncertainty\":l2, \"LandMaxTemperature\":l3, \"LandMaxTemperatureUncertainty\":l4,                \n",
    "       \"LandMinTemperature\":l5, \"LandMinTemperatureUncertainty\": l6, \"LandAndOceanAverageTemperature\":l7, \"LandAndOceanAverageTemperatureUncertainty\":l8}\n",
    "\n",
    "modified = pd.DataFrame(dix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "35910eb3-1bfe-4062-a3bf-6330ab5efaa1",
   "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>LandAverageTemperature</th>\n",
       "      <th>LandAverageTemperatureUncertainty</th>\n",
       "      <th>LandMaxTemperature</th>\n",
       "      <th>LandMaxTemperatureUncertainty</th>\n",
       "      <th>LandMinTemperature</th>\n",
       "      <th>LandMinTemperatureUncertainty</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1850</td>\n",
       "      <td>7.900667</td>\n",
       "      <td>0.876417</td>\n",
       "      <td>13.476667</td>\n",
       "      <td>2.394833</td>\n",
       "      <td>1.964333</td>\n",
       "      <td>1.571167</td>\n",
       "      <td>14.867167</td>\n",
       "      <td>0.308167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1851</td>\n",
       "      <td>8.178583</td>\n",
       "      <td>0.881917</td>\n",
       "      <td>13.081000</td>\n",
       "      <td>2.397250</td>\n",
       "      <td>2.203917</td>\n",
       "      <td>1.632417</td>\n",
       "      <td>14.991833</td>\n",
       "      <td>0.312083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1852</td>\n",
       "      <td>8.100167</td>\n",
       "      <td>0.918250</td>\n",
       "      <td>13.397333</td>\n",
       "      <td>2.619250</td>\n",
       "      <td>2.337000</td>\n",
       "      <td>1.382917</td>\n",
       "      <td>15.006500</td>\n",
       "      <td>0.316417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1853</td>\n",
       "      <td>8.041833</td>\n",
       "      <td>0.835000</td>\n",
       "      <td>13.886583</td>\n",
       "      <td>2.095083</td>\n",
       "      <td>1.892500</td>\n",
       "      <td>1.355583</td>\n",
       "      <td>14.955167</td>\n",
       "      <td>0.283833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1854</td>\n",
       "      <td>8.210500</td>\n",
       "      <td>0.825667</td>\n",
       "      <td>13.977417</td>\n",
       "      <td>1.783333</td>\n",
       "      <td>1.762167</td>\n",
       "      <td>1.357000</td>\n",
       "      <td>14.991000</td>\n",
       "      <td>0.276417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2011</td>\n",
       "      <td>9.516000</td>\n",
       "      <td>0.082000</td>\n",
       "      <td>15.284833</td>\n",
       "      <td>0.114333</td>\n",
       "      <td>3.827667</td>\n",
       "      <td>0.136583</td>\n",
       "      <td>15.769500</td>\n",
       "      <td>0.059000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2012</td>\n",
       "      <td>9.507333</td>\n",
       "      <td>0.083417</td>\n",
       "      <td>15.332833</td>\n",
       "      <td>0.107333</td>\n",
       "      <td>3.756167</td>\n",
       "      <td>0.145333</td>\n",
       "      <td>15.802333</td>\n",
       "      <td>0.061500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2013</td>\n",
       "      <td>9.606500</td>\n",
       "      <td>0.097667</td>\n",
       "      <td>15.373833</td>\n",
       "      <td>0.115500</td>\n",
       "      <td>3.911333</td>\n",
       "      <td>0.149833</td>\n",
       "      <td>15.854417</td>\n",
       "      <td>0.064667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2014</td>\n",
       "      <td>9.570667</td>\n",
       "      <td>0.090167</td>\n",
       "      <td>15.313583</td>\n",
       "      <td>0.111667</td>\n",
       "      <td>3.877750</td>\n",
       "      <td>0.139000</td>\n",
       "      <td>15.913000</td>\n",
       "      <td>0.063167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2015</td>\n",
       "      <td>9.831000</td>\n",
       "      <td>0.092167</td>\n",
       "      <td>15.572667</td>\n",
       "      <td>0.106500</td>\n",
       "      <td>4.148833</td>\n",
       "      <td>0.141750</td>\n",
       "      <td>16.058583</td>\n",
       "      <td>0.060833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     date  LandAverageTemperature  LandAverageTemperatureUncertainty  \\\n",
       "0    1850                7.900667                           0.876417   \n",
       "1    1851                8.178583                           0.881917   \n",
       "2    1852                8.100167                           0.918250   \n",
       "3    1853                8.041833                           0.835000   \n",
       "4    1854                8.210500                           0.825667   \n",
       "..    ...                     ...                                ...   \n",
       "161  2011                9.516000                           0.082000   \n",
       "162  2012                9.507333                           0.083417   \n",
       "163  2013                9.606500                           0.097667   \n",
       "164  2014                9.570667                           0.090167   \n",
       "165  2015                9.831000                           0.092167   \n",
       "\n",
       "     LandMaxTemperature  LandMaxTemperatureUncertainty  LandMinTemperature  \\\n",
       "0             13.476667                       2.394833            1.964333   \n",
       "1             13.081000                       2.397250            2.203917   \n",
       "2             13.397333                       2.619250            2.337000   \n",
       "3             13.886583                       2.095083            1.892500   \n",
       "4             13.977417                       1.783333            1.762167   \n",
       "..                  ...                            ...                 ...   \n",
       "161           15.284833                       0.114333            3.827667   \n",
       "162           15.332833                       0.107333            3.756167   \n",
       "163           15.373833                       0.115500            3.911333   \n",
       "164           15.313583                       0.111667            3.877750   \n",
       "165           15.572667                       0.106500            4.148833   \n",
       "\n",
       "     LandMinTemperatureUncertainty  LandAndOceanAverageTemperature  \\\n",
       "0                         1.571167                       14.867167   \n",
       "1                         1.632417                       14.991833   \n",
       "2                         1.382917                       15.006500   \n",
       "3                         1.355583                       14.955167   \n",
       "4                         1.357000                       14.991000   \n",
       "..                             ...                             ...   \n",
       "161                       0.136583                       15.769500   \n",
       "162                       0.145333                       15.802333   \n",
       "163                       0.149833                       15.854417   \n",
       "164                       0.139000                       15.913000   \n",
       "165                       0.141750                       16.058583   \n",
       "\n",
       "     LandAndOceanAverageTemperatureUncertainty  \n",
       "0                                     0.308167  \n",
       "1                                     0.312083  \n",
       "2                                     0.316417  \n",
       "3                                     0.283833  \n",
       "4                                     0.276417  \n",
       "..                                         ...  \n",
       "161                                   0.059000  \n",
       "162                                   0.061500  \n",
       "163                                   0.064667  \n",
       "164                                   0.063167  \n",
       "165                                   0.060833  \n",
       "\n",
       "[166 rows x 9 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2922eaed-76c4-4f81-a7e8-0c3bc2876036",
   "metadata": {},
   "outputs": [],
   "source": [
    "modified.to_csv(\"globalWarming.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "59b2b4df-5b6d-45ad-bc0b-6e8cdd365391",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(modified[\"date\"], modified[\"LandAverageTemperature\"], s = 50, c = \"red\")\n",
    "plt.xlabel(\"date\")\n",
    "plt.ylabel(\"temperature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "552d6d76-f6a7-4a68-874e-1323e1f83fa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                         1932.500000\n",
       "LandAverageTemperature                          8.571583\n",
       "LandAverageTemperatureUncertainty               0.276663\n",
       "LandMaxTemperature                             14.350601\n",
       "LandMaxTemperatureUncertainty                   0.479782\n",
       "LandMinTemperature                              2.743595\n",
       "LandMinTemperatureUncertainty                   0.431849\n",
       "LandAndOceanAverageTemperature                 15.212566\n",
       "LandAndOceanAverageTemperatureUncertainty       0.128532\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modified.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7e6ebc61-173b-4855-9c80-da09b2239df4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                         48.064193\n",
       "LandAverageTemperature                        0.473687\n",
       "LandAverageTemperatureUncertainty             0.208560\n",
       "LandMaxTemperature                            0.447741\n",
       "LandMaxTemperatureUncertainty                 0.549759\n",
       "LandMinTemperature                            0.614124\n",
       "LandMinTemperatureUncertainty                 0.406697\n",
       "LandAndOceanAverageTemperature                0.298629\n",
       "LandAndOceanAverageTemperatureUncertainty     0.071441\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modified.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e331e7e4-604c-41a1-ac0b-2d433d7f33a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}