4장_판다스_데이터_분석_(4.2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a7abf5e",
   "metadata": {},
   "source": [
    "# 데이터 입출력"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800b3d77",
   "metadata": {},
   "source": [
    "Pandas는 다음 여러 포맷 지원해 데이터프레임 생성\n",
    "\n",
    "* CSV\n",
    "\n",
    "* Excel\n",
    "\n",
    "* HTML\n",
    "\n",
    "* JSON\n",
    "\n",
    "* HDF5\n",
    "\n",
    "* SAS\n",
    "\n",
    "* STATA\n",
    "\n",
    "* SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f7873a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing sample1.csv\n"
     ]
    }
   ],
   "source": [
    "%%writefile sample1.csv\n",
    "c1, c2, c3\n",
    "1, 1.11, one\n",
    "2, 2.22, two\n",
    "3, 3.33, three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1665f127",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.11</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.22</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.33</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   c1    c2      c3\n",
       "0   1  1.11     one\n",
       "1   2  2.22     two\n",
       "2   3  3.33   three"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.read_csv('sample1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86222803",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.11</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.22</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.33</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      c2      c3\n",
       "c1              \n",
       "1   1.11     one\n",
       "2   2.22     two\n",
       "3   3.33   three"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('sample1.csv', index_col = 'c1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee8749ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing sample3.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile sample3.txt\n",
    "c1        c2        c3        c4\n",
    "0.179181 -1.538472  1.347553  0.43381\n",
    "1.024209  0.087307 -1.281997  0.49265\n",
    "0.417899 -2.002308  0.255245 -1.10515"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9017c767",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.179181</td>\n",
       "      <td>-1.538472</td>\n",
       "      <td>1.347553</td>\n",
       "      <td>0.43381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.024209</td>\n",
       "      <td>0.087307</td>\n",
       "      <td>-1.281997</td>\n",
       "      <td>0.49265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.417899</td>\n",
       "      <td>-2.002308</td>\n",
       "      <td>0.255245</td>\n",
       "      <td>-1.10515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         c1        c2        c3       c4\n",
       "0  0.179181 -1.538472  1.347553  0.43381\n",
       "1  1.024209  0.087307 -1.281997  0.49265\n",
       "2  0.417899 -2.002308  0.255245 -1.10515"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_table('sample3.txt', sep = '\\s+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa2e3bf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing sample4.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile sample4.txt\n",
    "파일 제목: sample4.txt\n",
    "데이터 포맷의 설명:\n",
    "c1, c2, c3\n",
    "1, 1.11, one\n",
    "2, 2.22, two\n",
    "3, 3.33, three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d7fea00e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.11</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.22</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.33</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   c1    c2      c3\n",
       "0   1  1.11     one\n",
       "1   2  2.22     two\n",
       "2   3  3.33   three"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('sample4.txt', skiprows=[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb6351d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing sample5.csv\n"
     ]
    }
   ],
   "source": [
    "%%writefile sample5.csv\n",
    "c1, c2, c3\n",
    "1, 1.11, one\n",
    "2, , two\n",
    "누락, 3.33, three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c8c6e826",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.11</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td></td>\n",
       "      <td>two</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.33</td>\n",
       "      <td>three</td>\n",
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      "text/plain": [
       "    c1     c2      c3\n",
       "0  1.0   1.11     one\n",
       "1  2.0            two\n",
       "2  NaN   3.33   three"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('sample5.csv', na_values=['누락'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d18ee475",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('sample7.txt', sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "56451525",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('sample8.csv', na_rep='누락')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc63a0a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/datascienceschool/docker_rpython/master/data/titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "91696fea",
   "metadata": {},
   "outputs": [
    {
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       "      <td>22.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\", 20)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4b4355ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "be07d979",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
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       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>13.00</td>\n",
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       "      <th>887</th>\n",
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       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
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       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0baa1063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas-datareader in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (0.10.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas-datareader) (2.27.1)\n",
      "Requirement already satisfied: lxml in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas-datareader) (4.8.0)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas-datareader) (1.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
      "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (1.22.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2022.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (1.26.9)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2021.10.8)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2.0.12)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python-dateutil>=2.8.1->pandas>=0.23->pandas-datareader) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install pandas-datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e13e50f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "dt_start = datetime.datetime(2015, 1, 1)\n",
    "dt_end = \"2016, 6, 30\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81d03bbc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas-datareader in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (0.10.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas-datareader) (2.27.1)\n",
      "Requirement already satisfied: lxml in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas-datareader) (4.8.0)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas-datareader) (1.4.2)\n",
      "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (1.22.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2022.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (1.26.9)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (3.3)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2.0.12)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2021.10.8)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\admin\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python-dateutil>=2.8.1->pandas>=0.23->pandas-datareader) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas-datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00c1dbdc",
   "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>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>18193.707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>18306.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>18332.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>18425.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>18611.617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  GDP\n",
       "DATE                 \n",
       "2015-04-01  18193.707\n",
       "2015-07-01  18306.960\n",
       "2015-10-01  18332.079\n",
       "2016-01-01  18425.306\n",
       "2016-04-01  18611.617"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader as pdr\n",
    "\n",
    "gdp = pdr.get_data_fred('GDP', dt_start, dt_end)\n",
    "gdp.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6c5f8e59",
   "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>CPIAUCSL</th>\n",
       "      <th>CPILFESL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>237.336</td>\n",
       "      <td>245.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>238.080</td>\n",
       "      <td>245.913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>238.992</td>\n",
       "      <td>246.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>239.557</td>\n",
       "      <td>247.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>240.222</td>\n",
       "      <td>247.540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CPIAUCSL  CPILFESL\n",
       "DATE                          \n",
       "2016-02-01   237.336   245.510\n",
       "2016-03-01   238.080   245.913\n",
       "2016-04-01   238.992   246.551\n",
       "2016-05-01   239.557   247.137\n",
       "2016-06-01   240.222   247.540"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation = pdr.get_data_fred([\"CPIAUCSL\", \"CPILFESL\"], dt_start, dt_end)\n",
    "inflation.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7b0fc79",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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