6.handling_missing_data_replace.ipynb

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      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017       -99999          7  Sunny\n",
       "2  1/3/2017           28     -99999   Snow\n",
       "3  1/4/2017       -88888          7      0\n",
       "4  1/5/2017           32     -88888   Rain\n",
       "5  1/6/2017           31          2  Sunny\n",
       "6  1/6/2017           34          5      0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('weather_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
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       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
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       "      <th>3</th>\n",
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       "      <td>-88888.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>34.0</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017         32.0        6.0   Rain\n",
       "1  1/2/2017          NaN        7.0  Sunny\n",
       "2  1/3/2017         28.0        NaN   Snow\n",
       "3  1/4/2017     -88888.0        7.0      0\n",
       "4  1/5/2017         32.0   -88888.0   Rain\n",
       "5  1/6/2017         31.0        2.0  Sunny\n",
       "6  1/6/2017         34.0        5.0      0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.replace(-99999, value=np.NaN) # replace single value\n",
    "new_df"
   ]
  },
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   "execution_count": 13,
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       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017            0          7  Sunny\n",
       "2  1/3/2017           28          0   Snow\n",
       "3  1/4/2017            0          7      0\n",
       "4  1/5/2017           32          0   Rain\n",
       "5  1/6/2017           31          2  Sunny\n",
       "6  1/6/2017           34          5      0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.replace([-99999, -88888], value=0) # replace value with a list\n",
    "new_df"
   ]
  },
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   "cell_type": "code",
   "execution_count": 19,
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      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017         32.0        6.0   Rain\n",
       "1  1/2/2017     -99999.0        7.0  Sunny\n",
       "2  1/3/2017         28.0   -99999.0   Snow\n",
       "3  1/4/2017          NaN        7.0    NaN\n",
       "4  1/5/2017         32.0        NaN   Rain\n",
       "5  1/6/2017         31.0        2.0  Sunny\n",
       "6  1/6/2017         34.0        5.0    NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.replace({\n",
    "    'temperature':-88888,\n",
    "    'windspeed':-88888,\n",
    "    'event':'0'\n",
    "}, np.nan)\n",
    "new_df"
   ]
  },
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      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017         32.0        6.0   Rain\n",
       "1  1/2/2017          NaN        7.0  Sunny\n",
       "2  1/3/2017         28.0        NaN   Snow\n",
       "3  1/4/2017     -88888.0        7.0      0\n",
       "4  1/5/2017         32.0   -88888.0   Rain\n",
       "5  1/6/2017         31.0        2.0  Sunny\n",
       "6  1/6/2017         34.0        5.0      0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.replace({\n",
    "    -99999: np.nan,\n",
    "    'no event':'sunny'\n",
    "})\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
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      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017       -99999          7  Sunny\n",
       "2  1/3/2017           28     -99999   Snow\n",
       "3  1/4/2017       -88888          7      0\n",
       "4  1/5/2017           32     -88888   Rain\n",
       "5  1/6/2017           31          2  Sunny\n",
       "6  1/6/2017           34          5      0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.replace({\n",
    "    'temperature': '[A-Za-z]',\n",
    "    'windspeed': '[a-z]',\n",
    "},'', regex=True)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>student</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>exceptional</td>\n",
       "      <td>rob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>average</td>\n",
       "      <td>maya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good</td>\n",
       "      <td>parthiv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>poor</td>\n",
       "      <td>tom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>average</td>\n",
       "      <td>julian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>exceptional</td>\n",
       "      <td>erica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         score  student\n",
       "0  exceptional      rob\n",
       "1      average     maya\n",
       "2         good  parthiv\n",
       "3         poor      tom\n",
       "4      average   julian\n",
       "5  exceptional    erica"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'score': ['exceptional','average', 'good', 'poor', 'average', 'exceptional'],\n",
    "    'student': ['rob', 'maya', 'parthiv', 'tom', 'julian', 'erica']\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>score</th>\n",
       "      <th>student</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>rob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>maya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>parthiv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>tom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>julian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>erica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   score  student\n",
       "0      1      rob\n",
       "1      2     maya\n",
       "2      3  parthiv\n",
       "3      4      tom\n",
       "4      2   julian\n",
       "5      1    erica"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace(['exceptional','average', 'good', 'poor'], [1,2,3,4])"
   ]
  }
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