Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import packages\n",
    "import pandas as pd\n",
    "\n",
    "#read csv document\n",
    "df = pd.read_csv('ChicagoResults.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 271 entries, 0 to 270\n",
      "Data columns (total 7 columns):\n",
      "year          271 non-null int64\n",
      "city          271 non-null object\n",
      "country       271 non-null object\n",
      "avg_temp      271 non-null float64\n",
      "sevenDayMA    265 non-null float64\n",
      "FiveYearMA    267 non-null float64\n",
      "TenYearMA     262 non-null float64\n",
      "dtypes: float64(4), int64(1), object(2)\n",
      "memory usage: 14.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_FiveYearMA = df['FiveYearMA'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         ...    \n",
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       "267    11.136000\n",
       "268    11.004000\n",
       "269    11.278000\n",
       "270    11.548000\n",
       "Name: FiveYearMA, Length: 271, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FiveYearMA'].fillna(mean_FiveYearMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>avg_temp</th>\n",
       "      <th>sevenDayMA</th>\n",
       "      <th>FiveYearMA</th>\n",
       "      <th>TenYearMA</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1743</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>5.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>1744</td>\n",
       "      <td>Chicago</td>\n",
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       "      <td>11.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1745</td>\n",
       "      <td>Chicago</td>\n",
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       "      <td>1.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1746</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1747</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.794</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1748</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.706</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1749</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.710000</td>\n",
       "      <td>0.360</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1750</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>10.49</td>\n",
       "      <td>3.431429</td>\n",
       "      <td>2.098</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1751</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>11.19</td>\n",
       "      <td>3.354286</td>\n",
       "      <td>4.336</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1752</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.740000</td>\n",
       "      <td>5.236</td>\n",
       "      <td>4.515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1753</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>10.04</td>\n",
       "      <td>5.174286</td>\n",
       "      <td>7.244</td>\n",
       "      <td>4.975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year     city        country  avg_temp  sevenDayMA  FiveYearMA  TenYearMA\n",
       "0   1743  Chicago  United States      5.44         NaN         NaN        NaN\n",
       "1   1744  Chicago  United States     11.73         NaN         NaN        NaN\n",
       "2   1745  Chicago  United States      1.80         NaN         NaN        NaN\n",
       "3   1746  Chicago  United States      0.00         NaN         NaN        NaN\n",
       "4   1747  Chicago  United States      0.00         NaN       3.794        NaN\n",
       "5   1748  Chicago  United States      0.00         NaN       2.706        NaN\n",
       "6   1749  Chicago  United States      0.00    2.710000       0.360        NaN\n",
       "7   1750  Chicago  United States     10.49    3.431429       2.098        NaN\n",
       "8   1751  Chicago  United States     11.19    3.354286       4.336        NaN\n",
       "9   1752  Chicago  United States      4.50    3.740000       5.236      4.515\n",
       "10  1753  Chicago  United States     10.04    5.174286       7.244      4.975"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['FiveYearMA'] = df['FiveYearMA'].fillna(mean_FiveYearMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>avg_temp</th>\n",
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       "      <th>1</th>\n",
       "      <td>1744</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1747</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.794000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>United States</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.706000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1749</td>\n",
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       "      <td>United States</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.710000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>7</th>\n",
       "      <td>1750</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>10.49</td>\n",
       "      <td>3.431429</td>\n",
       "      <td>2.098000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1751</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>11.19</td>\n",
       "      <td>3.354286</td>\n",
       "      <td>4.336000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1752</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.740000</td>\n",
       "      <td>5.236000</td>\n",
       "      <td>4.515</td>\n",
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      ],
      "text/plain": [
       "   year     city        country  avg_temp  sevenDayMA  FiveYearMA  TenYearMA\n",
       "0  1743  Chicago  United States      5.44         NaN    9.893401        NaN\n",
       "1  1744  Chicago  United States     11.73         NaN    9.893401        NaN\n",
       "2  1745  Chicago  United States      1.80         NaN    9.893401        NaN\n",
       "3  1746  Chicago  United States      0.00         NaN    9.893401        NaN\n",
       "4  1747  Chicago  United States      0.00         NaN    3.794000        NaN\n",
       "5  1748  Chicago  United States      0.00         NaN    2.706000        NaN\n",
       "6  1749  Chicago  United States      0.00    2.710000    0.360000        NaN\n",
       "7  1750  Chicago  United States     10.49    3.431429    2.098000        NaN\n",
       "8  1751  Chicago  United States     11.19    3.354286    4.336000        NaN\n",
       "9  1752  Chicago  United States      4.50    3.740000    5.236000      4.515"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_TenYearMA = df['TenYearMA'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['TenYearMA'].fillna(mean_TenYearMA, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1747</td>\n",
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       "      <td>United States</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>5</th>\n",
       "      <td>1748</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.706000</td>\n",
       "      <td>9.949863</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1749</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.710000</td>\n",
       "      <td>0.360000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1750</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>10.49</td>\n",
       "      <td>3.431429</td>\n",
       "      <td>2.098000</td>\n",
       "      <td>9.949863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1751</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>11.19</td>\n",
       "      <td>3.354286</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1752</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>United States</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.740000</td>\n",
       "      <td>5.236000</td>\n",
       "      <td>4.515000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     city        country  avg_temp  sevenDayMA  FiveYearMA  TenYearMA\n",
       "0  1743  Chicago  United States      5.44         NaN    9.893401   9.949863\n",
       "1  1744  Chicago  United States     11.73         NaN    9.893401   9.949863\n",
       "2  1745  Chicago  United States      1.80         NaN    9.893401   9.949863\n",
       "3  1746  Chicago  United States      0.00         NaN    9.893401   9.949863\n",
       "4  1747  Chicago  United States      0.00         NaN    3.794000   9.949863\n",
       "5  1748  Chicago  United States      0.00         NaN    2.706000   9.949863\n",
       "6  1749  Chicago  United States      0.00    2.710000    0.360000   9.949863\n",
       "7  1750  Chicago  United States     10.49    3.431429    2.098000   9.949863\n",
       "8  1751  Chicago  United States     11.19    3.354286    4.336000   9.949863\n",
       "9  1752  Chicago  United States      4.50    3.740000    5.236000   4.515000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "5      False\n",
       "6      False\n",
       "7      False\n",
       "8      False\n",
       "9      False\n",
       "10     False\n",
       "11     False\n",
       "12     False\n",
       "13     False\n",
       "14     False\n",
       "15     False\n",
       "16     False\n",
       "17     False\n",
       "18     False\n",
       "19     False\n",
       "20     False\n",
       "21     False\n",
       "22     False\n",
       "23     False\n",
       "24     False\n",
       "25     False\n",
       "26     False\n",
       "27     False\n",
       "28     False\n",
       "29     False\n",
       "       ...  \n",
       "241    False\n",
       "242    False\n",
       "243    False\n",
       "244    False\n",
       "245    False\n",
       "246    False\n",
       "247    False\n",
       "248    False\n",
       "249    False\n",
       "250    False\n",
       "251    False\n",
       "252    False\n",
       "253    False\n",
       "254    False\n",
       "255    False\n",
       "256    False\n",
       "257    False\n",
       "258    False\n",
       "259    False\n",
       "260    False\n",
       "261    False\n",
       "262    False\n",
       "263    False\n",
       "264    False\n",
       "265    False\n",
       "266    False\n",
       "267    False\n",
       "268    False\n",
       "269    False\n",
       "270    False\n",
       "Length: 271, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 271 entries, 0 to 270\n",
      "Data columns (total 7 columns):\n",
      "year          271 non-null int64\n",
      "city          271 non-null object\n",
      "country       271 non-null object\n",
      "avg_temp      271 non-null float64\n",
      "sevenDayMA    265 non-null float64\n",
      "FiveYearMA    271 non-null float64\n",
      "TenYearMA     271 non-null float64\n",
      "dtypes: float64(4), int64(1), object(2)\n",
      "memory usage: 16.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df['timestamp'] = pd.to_datetime(df['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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