05-Machine-Learning-Code/数据分析工具/Pandas/5_update_rows_columns.ipynb

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       "      <th>email</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>CoreyMSchafer@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JohnDoe@email.com</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  CoreyMSchafer@gmail.com\n",
       "1   Jane      Doe        JaneDoe@email.com\n",
       "2   John      Doe        JohnDoe@email.com"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "people = {\n",
    "    \"first\": [\"Corey\", 'Jane', 'John'], \n",
    "    \"last\": [\"Schafer\", 'Doe', 'Doe'], \n",
    "    \"email\": [\"CoreyMSchafer@gmail.com\", 'JaneDoe@email.com', 'JohnDoe@email.com']\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(people)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['first', 'last', 'email'], dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['first_name','last_name','email']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['first_name', 'last_name', 'email'], dtype='object')"
      ]
     },
     "execution_count": 5,
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   "source": [
    "df.columns"
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   "execution_count": 7,
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       "      <td>John</td>\n",
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       "      <td>JohnDoe@email.com</td>\n",
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      "text/plain": [
       "  first_name last_name                    email\n",
       "0      Corey   Schafer  CoreyMSchafer@gmail.com\n",
       "1       Jane       Doe        JaneDoe@email.com\n",
       "2       John       Doe        JohnDoe@email.com"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>FIRST_NAME</th>\n",
       "      <th>LAST_NAME</th>\n",
       "      <th>EMAIL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>CoreyMSchafer@gmail.com</td>\n",
       "    </tr>\n",
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       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JohnDoe@email.com</td>\n",
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      "text/plain": [
       "  FIRST_NAME LAST_NAME                    EMAIL\n",
       "0      Corey   Schafer  CoreyMSchafer@gmail.com\n",
       "1       Jane       Doe        JaneDoe@email.com\n",
       "2       John       Doe        JohnDoe@email.com"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = [x.upper() for x in df.columns]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.replace('_',' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Corey</td>\n",
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       "      <td>CoreyMSchafer@gmail.com</td>\n",
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       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
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       "      <td>2</td>\n",
       "      <td>John</td>\n",
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      "text/plain": [
       "  FIRST NAME LAST NAME                    EMAIL\n",
       "0      Corey   Schafer  CoreyMSchafer@gmail.com\n",
       "1       Jane       Doe        JaneDoe@email.com\n",
       "2       John       Doe        JohnDoe@email.com"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>first_name</th>\n",
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       "      <td>Corey</td>\n",
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       "      <td>CoreyMSchafer@gmail.com</td>\n",
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       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
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       "      <td>2</td>\n",
       "      <td>John</td>\n",
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      "text/plain": [
       "  first_name last_name                    email\n",
       "0      Corey   Schafer  CoreyMSchafer@gmail.com\n",
       "1       Jane       Doe        JaneDoe@email.com\n",
       "2       John       Doe        JohnDoe@email.com"
      ]
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     "execution_count": 12,
     "metadata": {},
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   "source": [
    "df.columns = [x.lower() for x in df.columns]\n",
    "df.columns = df.columns.str.replace(' ','_')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'first_name':'first','last_name':'last'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "      <td>Corey</td>\n",
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       "      <td>CoreyMSchafer@gmail.com</td>\n",
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       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
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       "      <td>John</td>\n",
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       "      <td>JohnDoe@email.com</td>\n",
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      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  CoreyMSchafer@gmail.com\n",
       "1   Jane      Doe        JaneDoe@email.com\n",
       "2   John      Doe        JohnDoe@email.com"
      ]
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     "metadata": {},
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first                 John\n",
       "last                   Doe\n",
       "email    JohnDoe@email.com\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[2] = ['John','Smith','JohnSmith@email.com']"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "      <td>JohnSmith@email.com</td>\n",
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      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  CoreyMSchafer@gmail.com\n",
       "1   Jane      Doe        JaneDoe@email.com\n",
       "2   John    Smith      JohnSmith@email.com"
      ]
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     "execution_count": 20,
     "metadata": {},
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>CoreyMSchafer@gmail.com</td>\n",
       "    </tr>\n",
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       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
       "    </tr>\n",
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       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JohnDoe@gmail.com</td>\n",
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      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  CoreyMSchafer@gmail.com\n",
       "1   Jane      Doe        JaneDoe@email.com\n",
       "2   John      Doe        JohnDoe@gmail.com"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2,['last','email']] = ['Doe','JohnDoe@gmail.com']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Corey</td>\n",
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       "      <td>CoreyMSchafer@gmail.com</td>\n",
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       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
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       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Smith</td>\n",
       "      <td>JohnDoe@gmail.com</td>\n",
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      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  CoreyMSchafer@gmail.com\n",
       "1   Jane      Doe        JaneDoe@email.com\n",
       "2   John    Smith        JohnDoe@gmail.com"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2,'last'] = 'Smith'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.at[2,'last'] = 'Doe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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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       "      <th></th>\n",
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       "      <th>email</th>\n",
       "    </tr>\n",
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       "      <td>0</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>CoreyMSchafer@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JohnDoe@gmail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  CoreyMSchafer@gmail.com\n",
       "1   Jane      Doe        JaneDoe@email.com\n",
       "2   John      Doe        JohnDoe@gmail.com"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JohnDoe@gmail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first last              email\n",
       "2  John  Doe  JohnDoe@gmail.com"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt = (df['email'] == 'JohnDoe@gmail.com')\n",
    "df[filt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    Doe\n",
       "Name: last, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[filt]['last']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>first</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>CoreyMSchafer@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Smith</td>\n",
       "      <td>JohnDoe@gmail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  CoreyMSchafer@gmail.com\n",
       "1   Jane      Doe        JaneDoe@email.com\n",
       "2   John    Smith        JohnDoe@gmail.com"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[filt,'last'] = 'Smith'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    coreymschafer@gmail.com\n",
       "1          janedoe@email.com\n",
       "2          johndoe@gmail.com\n",
       "Name: email, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['email'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>last</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>coreymschafer@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>janedoe@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Smith</td>\n",
       "      <td>johndoe@gmail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  coreymschafer@gmail.com\n",
       "1   Jane      Doe        janedoe@email.com\n",
       "2   John    Smith        johndoe@gmail.com"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['email'] = df['email'].str.lower()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23\n",
       "1    17\n",
       "2    17\n",
       "Name: email, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['email'].apply(len) # function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_email(email):\n",
    "    return email.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    COREYMSCHAFER@GMAIL.COM\n",
       "1          JANEDOE@EMAIL.COM\n",
       "2          JOHNDOE@GMAIL.COM\n",
       "Name: email, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['email'].apply(update_email)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>COREYMSCHAFER@GMAIL.COM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JANEDOE@EMAIL.COM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Smith</td>\n",
       "      <td>JOHNDOE@GMAIL.COM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  COREYMSCHAFER@GMAIL.COM\n",
       "1   Jane      Doe        JANEDOE@EMAIL.COM\n",
       "2   John    Smith        JOHNDOE@GMAIL.COM"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['email'] = df['email'].apply(update_email)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>coreymschafer@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>janedoe@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Smith</td>\n",
       "      <td>johndoe@gmail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  coreymschafer@gmail.com\n",
       "1   Jane      Doe        janedoe@email.com\n",
       "2   John    Smith        johndoe@gmail.com"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['email'] = df['email'].apply(lambda x : x.lower())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23\n",
       "1    17\n",
       "2    17\n",
       "Name: email, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['email'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first    3\n",
       "last     3\n",
       "email    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(len) # length function to each series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['email'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    3\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(len,axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first                      Corey\n",
       "last                         Doe\n",
       "email    coreymschafer@gmail.com\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.apply(pd.Series.min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first                      Corey\n",
       "last                         Doe\n",
       "email    coreymschafer@gmail.com\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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",
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       "    }\n",
       "\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>first</th>\n",
       "      <th>last</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first  last  email\n",
       "0      5     7     23\n",
       "1      4     3     17\n",
       "2      4     5     17"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.applymap(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>first</th>\n",
       "      <th>last</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>corey</td>\n",
       "      <td>schafer</td>\n",
       "      <td>coreymschafer@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>jane</td>\n",
       "      <td>doe</td>\n",
       "      <td>janedoe@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>john</td>\n",
       "      <td>smith</td>\n",
       "      <td>johndoe@gmail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first     last                    email\n",
       "0  corey  schafer  coreymschafer@gmail.com\n",
       "1   jane      doe        janedoe@email.com\n",
       "2   john    smith        johndoe@gmail.com"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.applymap(str.lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chris\n",
       "1     Mary\n",
       "2      NaN\n",
       "Name: first, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['first'].map({'Corey':'Chris','Jane':'Mary'}) # map all cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chris\n",
       "1     Mary\n",
       "2     John\n",
       "Name: first, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['first'].replace({'Corey':'Chris','Jane':'Mary'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>first</th>\n",
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       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Chris</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>coreymschafer@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Mary</td>\n",
       "      <td>Doe</td>\n",
       "      <td>janedoe@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Smith</td>\n",
       "      <td>johndoe@gmail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first     last                    email\n",
       "0  Chris  Schafer  coreymschafer@gmail.com\n",
       "1   Mary      Doe        janedoe@email.com\n",
       "2   John    Smith        johndoe@gmail.com"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['first'] = df['first'].replace({'Corey':'Chris','Jane':'Mary'})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('survey_results_public.csv',index_col='Respondent')\n",
    "schema_df = pd.read_csv('survey_results_schema.csv',index_col='Column')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',10)\n",
    "pd.set_option('display.max_rows',10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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       "      <td>No</td>\n",
       "      <td>Employed part-time</td>\n",
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       "      <td>25 - 34 years old</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Very easy</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</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",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>35 - 44 years old</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Yes, part-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>18 - 24 years old</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hobby OpenSource         Country         Student  \\\n",
       "Respondent                                                    \n",
       "1            Yes         No           Kenya              No   \n",
       "3            Yes        Yes  United Kingdom              No   \n",
       "4            Yes        Yes   United States              No   \n",
       "5             No         No   United States              No   \n",
       "7            Yes         No    South Africa  Yes, part-time   \n",
       "\n",
       "                    Employment  ...                Age Dependents MilitaryUS  \\\n",
       "Respondent                      ...                                            \n",
       "1           Employed part-time  ...  25 - 34 years old        Yes        NaN   \n",
       "3           Employed full-time  ...  35 - 44 years old        Yes        NaN   \n",
       "4           Employed full-time  ...                NaN        NaN        NaN   \n",
       "5           Employed full-time  ...  35 - 44 years old         No         No   \n",
       "7           Employed full-time  ...  18 - 24 years old        Yes        NaN   \n",
       "\n",
       "                                   SurveyTooLong     SurveyEasy  \n",
       "Respondent                                                       \n",
       "1           The survey was an appropriate length      Very easy  \n",
       "3           The survey was an appropriate length  Somewhat easy  \n",
       "4                                            NaN            NaN  \n",
       "5           The survey was an appropriate length  Somewhat easy  \n",
       "7           The survey was an appropriate length  Somewhat easy  \n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>The survey was an appropriate length</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>101513</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>101531</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Not employed, but looking for work</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>101541</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>101544</td>\n",
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       "      <td>Russian Federation</td>\n",
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       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>98855 rows × 128 columns</p>\n",
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      ],
      "text/plain": [
       "           Hobby OpenSource             Country          People  \\\n",
       "Respondent                                                        \n",
       "1            Yes         No               Kenya              No   \n",
       "3            Yes        Yes      United Kingdom              No   \n",
       "4            Yes        Yes       United States              No   \n",
       "5             No         No       United States              No   \n",
       "7            Yes         No        South Africa  Yes, part-time   \n",
       "...          ...        ...                 ...             ...   \n",
       "101513       Yes        Yes       United States             NaN   \n",
       "101531        No        Yes               Spain  Yes, full-time   \n",
       "101541       Yes        Yes               India  Yes, full-time   \n",
       "101544       Yes         No  Russian Federation              No   \n",
       "101548       Yes        Yes            Cambodia             NaN   \n",
       "\n",
       "                                                   Employment  ...  \\\n",
       "Respondent                                                     ...   \n",
       "1                                          Employed part-time  ...   \n",
       "3                                          Employed full-time  ...   \n",
       "4                                          Employed full-time  ...   \n",
       "5                                          Employed full-time  ...   \n",
       "7                                          Employed full-time  ...   \n",
       "...                                                       ...  ...   \n",
       "101513                                                    NaN  ...   \n",
       "101531                     Not employed, but looking for work  ...   \n",
       "101541                                     Employed full-time  ...   \n",
       "101544      Independent contractor, freelancer, or self-em...  ...   \n",
       "101548                                                    NaN  ...   \n",
       "\n",
       "                          Age Dependents MilitaryUS  \\\n",
       "Respondent                                            \n",
       "1           25 - 34 years old        Yes        NaN   \n",
       "3           35 - 44 years old        Yes        NaN   \n",
       "4                         NaN        NaN        NaN   \n",
       "5           35 - 44 years old         No         No   \n",
       "7           18 - 24 years old        Yes        NaN   \n",
       "...                       ...        ...        ...   \n",
       "101513                    NaN        NaN        NaN   \n",
       "101531                    NaN        NaN        NaN   \n",
       "101541                    NaN        NaN        NaN   \n",
       "101544                    NaN        NaN        NaN   \n",
       "101548                    NaN        NaN        NaN   \n",
       "\n",
       "                                   SurveyTooLong     SurveyEasy  \n",
       "Respondent                                                       \n",
       "1           The survey was an appropriate length      Very easy  \n",
       "3           The survey was an appropriate length  Somewhat easy  \n",
       "4                                            NaN            NaN  \n",
       "5           The survey was an appropriate length  Somewhat easy  \n",
       "7           The survey was an appropriate length  Somewhat easy  \n",
       "...                                          ...            ...  \n",
       "101513                                       NaN            NaN  \n",
       "101531                                       NaN            NaN  \n",
       "101541                                       NaN            NaN  \n",
       "101544                                       NaN            NaN  \n",
       "101548                                       NaN            NaN  \n",
       "\n",
       "[98855 rows x 128 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'Student':'People'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>35 - 44 years old</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</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",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>35 - 44 years old</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Yes, part-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>18 - 24 years old</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101513</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>101531</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Not employed, but looking for work</td>\n",
       "      <td>...</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",
       "      <td>101541</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>India</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</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",
       "      <td>101544</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>No</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>...</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",
       "      <td>101548</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "<p>98855 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hobby OpenSource             Country          People  \\\n",
       "Respondent                                                        \n",
       "1            Yes         No               Kenya              No   \n",
       "3            Yes        Yes      United Kingdom              No   \n",
       "4            Yes        Yes       United States              No   \n",
       "5             No         No       United States              No   \n",
       "7            Yes         No        South Africa  Yes, part-time   \n",
       "...          ...        ...                 ...             ...   \n",
       "101513       Yes        Yes       United States             NaN   \n",
       "101531        No        Yes               Spain  Yes, full-time   \n",
       "101541       Yes        Yes               India  Yes, full-time   \n",
       "101544       Yes         No  Russian Federation              No   \n",
       "101548       Yes        Yes            Cambodia             NaN   \n",
       "\n",
       "                                                   Employment  ...  \\\n",
       "Respondent                                                     ...   \n",
       "1                                          Employed part-time  ...   \n",
       "3                                          Employed full-time  ...   \n",
       "4                                          Employed full-time  ...   \n",
       "5                                          Employed full-time  ...   \n",
       "7                                          Employed full-time  ...   \n",
       "...                                                       ...  ...   \n",
       "101513                                                    NaN  ...   \n",
       "101531                     Not employed, but looking for work  ...   \n",
       "101541                                     Employed full-time  ...   \n",
       "101544      Independent contractor, freelancer, or self-em...  ...   \n",
       "101548                                                    NaN  ...   \n",
       "\n",
       "                          Age Dependents MilitaryUS  \\\n",
       "Respondent                                            \n",
       "1           25 - 34 years old        Yes        NaN   \n",
       "3           35 - 44 years old        Yes        NaN   \n",
       "4                         NaN        NaN        NaN   \n",
       "5           35 - 44 years old         No         No   \n",
       "7           18 - 24 years old        Yes        NaN   \n",
       "...                       ...        ...        ...   \n",
       "101513                    NaN        NaN        NaN   \n",
       "101531                    NaN        NaN        NaN   \n",
       "101541                    NaN        NaN        NaN   \n",
       "101544                    NaN        NaN        NaN   \n",
       "101548                    NaN        NaN        NaN   \n",
       "\n",
       "                                   SurveyTooLong     SurveyEasy  \n",
       "Respondent                                                       \n",
       "1           The survey was an appropriate length      Very easy  \n",
       "3           The survey was an appropriate length  Somewhat easy  \n",
       "4                                            NaN            NaN  \n",
       "5           The survey was an appropriate length  Somewhat easy  \n",
       "7           The survey was an appropriate length  Somewhat easy  \n",
       "...                                          ...            ...  \n",
       "101513                                       NaN            NaN  \n",
       "101531                                       NaN            NaN  \n",
       "101541                                       NaN            NaN  \n",
       "101544                                       NaN            NaN  \n",
       "101548                                       NaN            NaN  \n",
       "\n",
       "[98855 rows x 128 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'Student':'People'},inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Respondent\n",
       "1                     No\n",
       "3                     No\n",
       "4                     No\n",
       "5                     No\n",
       "7         Yes, part-time\n",
       "               ...      \n",
       "101513               NaN\n",
       "101531    Yes, full-time\n",
       "101541    Yes, full-time\n",
       "101544                No\n",
       "101548               NaN\n",
       "Name: People, Length: 98855, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['People']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Respondent\n",
       "1         Yes\n",
       "3         Yes\n",
       "4         Yes\n",
       "5          No\n",
       "7         Yes\n",
       "         ... \n",
       "101513    Yes\n",
       "101531     No\n",
       "101541    Yes\n",
       "101544    Yes\n",
       "101548    Yes\n",
       "Name: Hobby, Length: 98855, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hobby']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Respondent\n",
       "1          True\n",
       "3          True\n",
       "4          True\n",
       "5         False\n",
       "7          True\n",
       "          ...  \n",
       "101513     True\n",
       "101531    False\n",
       "101541     True\n",
       "101544     True\n",
       "101548     True\n",
       "Name: Hobby, Length: 98855, dtype: bool"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hobby'].map({'Yes':True,'No':False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hobby'] = df['Hobby'].map({'Yes':True,'No':False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Hobby</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Country</th>\n",
       "      <th>People</th>\n",
       "      <th>Employment</th>\n",
       "      <th>...</th>\n",
       "      <th>Age</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>MilitaryUS</th>\n",
       "      <th>SurveyTooLong</th>\n",
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       "    <tr>\n",
       "      <th>Respondent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed part-time</td>\n",
       "      <td>...</td>\n",
       "      <td>25 - 34 years old</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Very easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>35 - 44 years old</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</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",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>35 - 44 years old</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Yes, part-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>18 - 24 years old</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101513</td>\n",
       "      <td>True</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>101531</td>\n",
       "      <td>False</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Not employed, but looking for work</td>\n",
       "      <td>...</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",
       "      <td>101541</td>\n",
       "      <td>True</td>\n",
       "      <td>Yes</td>\n",
       "      <td>India</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</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",
       "      <td>101544</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>No</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>...</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",
       "      <td>101548</td>\n",
       "      <td>True</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "<p>98855 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Hobby OpenSource             Country          People  \\\n",
       "Respondent                                                         \n",
       "1            True         No               Kenya              No   \n",
       "3            True        Yes      United Kingdom              No   \n",
       "4            True        Yes       United States              No   \n",
       "5           False         No       United States              No   \n",
       "7            True         No        South Africa  Yes, part-time   \n",
       "...           ...        ...                 ...             ...   \n",
       "101513       True        Yes       United States             NaN   \n",
       "101531      False        Yes               Spain  Yes, full-time   \n",
       "101541       True        Yes               India  Yes, full-time   \n",
       "101544       True         No  Russian Federation              No   \n",
       "101548       True        Yes            Cambodia             NaN   \n",
       "\n",
       "                                                   Employment  ...  \\\n",
       "Respondent                                                     ...   \n",
       "1                                          Employed part-time  ...   \n",
       "3                                          Employed full-time  ...   \n",
       "4                                          Employed full-time  ...   \n",
       "5                                          Employed full-time  ...   \n",
       "7                                          Employed full-time  ...   \n",
       "...                                                       ...  ...   \n",
       "101513                                                    NaN  ...   \n",
       "101531                     Not employed, but looking for work  ...   \n",
       "101541                                     Employed full-time  ...   \n",
       "101544      Independent contractor, freelancer, or self-em...  ...   \n",
       "101548                                                    NaN  ...   \n",
       "\n",
       "                          Age Dependents MilitaryUS  \\\n",
       "Respondent                                            \n",
       "1           25 - 34 years old        Yes        NaN   \n",
       "3           35 - 44 years old        Yes        NaN   \n",
       "4                         NaN        NaN        NaN   \n",
       "5           35 - 44 years old         No         No   \n",
       "7           18 - 24 years old        Yes        NaN   \n",
       "...                       ...        ...        ...   \n",
       "101513                    NaN        NaN        NaN   \n",
       "101531                    NaN        NaN        NaN   \n",
       "101541                    NaN        NaN        NaN   \n",
       "101544                    NaN        NaN        NaN   \n",
       "101548                    NaN        NaN        NaN   \n",
       "\n",
       "                                   SurveyTooLong     SurveyEasy  \n",
       "Respondent                                                       \n",
       "1           The survey was an appropriate length      Very easy  \n",
       "3           The survey was an appropriate length  Somewhat easy  \n",
       "4                                            NaN            NaN  \n",
       "5           The survey was an appropriate length  Somewhat easy  \n",
       "7           The survey was an appropriate length  Somewhat easy  \n",
       "...                                          ...            ...  \n",
       "101513                                       NaN            NaN  \n",
       "101531                                       NaN            NaN  \n",
       "101541                                       NaN            NaN  \n",
       "101544                                       NaN            NaN  \n",
       "101548                                       NaN            NaN  \n",
       "\n",
       "[98855 rows x 128 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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