10_minutes_to_pandas.ipynb

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   "execution_count": 1,
   "id": "d7586cb5-c091-47ab-ace7-f4860bc36837",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ca0c998-3a81-497f-a8fb-558a3be326d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(10,4))"
   ]
  },
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   "execution_count": 3,
   "id": "972fdf0a-5528-4b37-8bfc-a36252d1ea90",
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   "metadata": {},
   "outputs": [],
   "source": [
    "pieces = [df[:3], df[3:7], df[4:10]]"
   ]
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   "execution_count": 5,
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   "id": "70e12ac5-9f42-4a6d-9c94-b0846007c8c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({\"key\": [\"foo\", \"foo\"], \"lval\": [1, 2]})\n",
    "right = pd.DataFrame({\"key\": [\"foo\", \"foo\"], \"rval\": [4, 5]})"
   ]
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   "execution_count": 8,
   "id": "f56270c9-e7a6-4997-b98a-668a90c36e57",
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       "   key  lval  rval\n",
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   "id": "3f78e947-0454-407f-8c9c-a538a5e3ccdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({\"key\": [\"foo\", \"bar\", 'cin'], \"lval\": [1, 2, 9]})\n",
    "right = pd.DataFrame({\"key\": [\"foo\", \"bar\", 'cin'], \"rval\": [4, 5, np.nan]})"
   ]
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  {
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   "execution_count": 24,
   "id": "12e6d03a-e87b-4ef9-ac1b-a031131eb13d",
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       "   key  lval\n",
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       "2  cin     9"
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   "execution_count": 25,
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   "outputs": [
    {
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       "   key  lval  rval\n",
       "0  foo     1   4.0\n",
       "1  bar     2   5.0\n",
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     "execution_count": 26,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 27,
   "id": "cab03e88-fda8-4906-85fd-b03972b7f336",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "   ....:     {\n",
    "   ....:         \"A\": [\"foo\", \"bar\", \"foo\", \"bar\", \"foo\", \"bar\", \"foo\", \"foo\"],\n",
    "   ....:         \"B\": [\"one\", \"one\", \"two\", \"three\", \"two\", \"two\", \"one\", \"three\"],\n",
    "   ....:         \"C\": np.random.randn(8),\n",
    "   ....:         \"D\": np.random.randn(8),\n",
    "   ....:     }\n",
    "   ....: )"
   ]
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   "execution_count": 28,
   "id": "b4673b39-ff45-4455-a5dc-1460af0ce6f3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.220468</td>\n",
       "      <td>0.030112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>-0.029085</td>\n",
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       "      <td>0.294737</td>\n",
       "      <td>-2.767669</td>\n",
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       "      <th>7</th>\n",
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       "      <td>three</td>\n",
       "      <td>0.968136</td>\n",
       "      <td>1.047706</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  0.981623 -3.026943\n",
       "1  bar    one -0.353879  0.027991\n",
       "2  foo    two  0.036316  1.547072\n",
       "3  bar  three  0.422202  0.989217\n",
       "4  foo    two -0.220468  0.030112\n",
       "5  bar    two -0.029085 -1.309110\n",
       "6  foo    one  0.294737 -2.767669\n",
       "7  foo  three  0.968136  1.047706"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f1a16b47-d0f5-4e46-a3b1-4052d1b465c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    C          D        \n",
       "bar  -0.353879   0.027991    1\n",
       "     -0.029085  -1.309110    1\n",
       "      0.422202   0.989217    1\n",
       "foo  -0.220468   0.030112    1\n",
       "      0.036316   1.547072    1\n",
       "      0.294737  -2.767669    1\n",
       "      0.968136   1.047706    1\n",
       "      0.981623  -3.026943    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"A\")[['C','D']].value_counts()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ee192692-2caf-40da-9632-d8a0f22d5ba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            C         D\n",
       "A                      \n",
       "bar  0.039238 -0.291902\n",
       "foo  2.060343 -3.169722"
      ]
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     "execution_count": 38,
     "metadata": {},
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    "df.groupby(\"A\")[['C','D']].sum()"
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  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7ad5395b-1c2d-4063-8b25-9bb88ca311c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    B      C          D        \n",
       "bar  one    -0.353879   0.027991    1\n",
       "     three   0.422202   0.989217    1\n",
       "     two    -0.029085  -1.309110    1\n",
       "foo  one     0.294737  -2.767669    1\n",
       "             0.981623  -3.026943    1\n",
       "     three   0.968136   1.047706    1\n",
       "     two    -0.220468   0.030112    1\n",
       "             0.036316   1.547072    1\n",
       "Name: count, dtype: int64"
      ]
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     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.groupby([\"A\", \"B\"])[['C','D']].value_counts()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ae4f6f38-1af7-476c-bc02-c7517b823fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
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       "      <td>1.577184</td>\n",
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      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one   -0.353879  0.027991\n",
       "    three  0.422202  0.989217\n",
       "    two   -0.029085 -1.309110\n",
       "foo one    1.276360 -5.794612\n",
       "    three  0.968136  1.047706\n",
       "    two   -0.184153  1.577184"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"A\", \"B\"])[['C','D']].sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "061cb22b-054f-4adc-bfe5-6a1c25f364bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    2.099582\n",
       "D   -3.461625\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"A\", \"B\"])[['C','D']].sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a04e22db-af85-4c51-ad4f-aceee7c487fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "arrays = [\n",
    "   [\"bar\", \"bar\", \"baz\", \"baz\", \"foo\", \"foo\", \"qux\", \"qux\"],\n",
    "   [\"one\", \"two\", \"one\", \"two\", \"one\", \"two\", \"one\", \"two\"],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4820f664-752c-4a5e-8622-fdc2f57fc081",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_arrays(arrays, names=[\"first\", \"second\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ab096729-f840-4361-8317-8d6907ff245a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('bar', 'one'),\n",
       "            ('bar', 'two'),\n",
       "            ('baz', 'one'),\n",
       "            ('baz', 'two'),\n",
       "            ('foo', 'one'),\n",
       "            ('foo', 'two'),\n",
       "            ('qux', 'one'),\n",
       "            ('qux', 'two')],\n",
       "           names=['first', 'second'])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
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  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "551f4978-8421-4adf-b2c7-2c7369e06167",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=[\"A\", \"B\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7b078a34-452c-44d8-a873-44ed45079f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>two</th>\n",
       "      <td>0.446360</td>\n",
       "      <td>-0.043291</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>0.584238</td>\n",
       "      <td>-0.118922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.369576</td>\n",
       "      <td>1.663515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>-1.311074</td>\n",
       "      <td>1.852427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.296281</td>\n",
       "      <td>0.449877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">qux</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.245036</td>\n",
       "      <td>0.820063</td>\n",
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       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.237469</td>\n",
       "      <td>-0.307877</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -1.293205 -0.712895\n",
       "      two     0.446360 -0.043291\n",
       "baz   one     0.584238 -0.118922\n",
       "      two    -0.369576  1.663515\n",
       "foo   one    -1.311074  1.852427\n",
       "      two     1.296281  0.449877\n",
       "qux   one    -0.245036  0.820063\n",
       "      two     0.237469 -0.307877"
      ]
     },
     "execution_count": 48,
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       "first  second   \n",
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       "MultiIndex([('bar', 'one', 'A'),\n",
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    "df = pd.DataFrame(\n",
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    "        \"B\": [\"A\", \"B\", \"C\"] * 4,\n",
    "        \"C\": [\"foo\", \"foo\", \"foo\", \"bar\", \"bar\", \"bar\"] * 2,\n",
    "        \"D\": np.random.randn(12),\n",
    "        \"E\": np.random.randn(12),\n",
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    ")"
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   "execution_count": 70,
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       "        A  B    C         D         E\n",
       "0     one  A  foo -1.061470  0.350517\n",
       "1     one  B  foo  0.329323 -0.812136\n",
       "2     two  C  foo -1.105035  0.521999\n",
       "3   three  A  bar -0.012404  0.332078\n",
       "4     one  B  bar  0.820310  0.520874\n",
       "5     one  C  bar  0.770293  1.079676\n",
       "6     two  A  foo  1.210237 -1.322800\n",
       "7   three  B  foo -0.448982 -0.741048\n",
       "8     one  C  foo -0.182740 -1.240916\n",
       "9     one  A  bar -0.495412 -1.072328\n",
       "10    two  B  bar  0.605505  0.694294\n",
       "11  three  C  bar -0.555842  0.233356"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
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    "df"
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   "execution_count": 71,
   "id": "600b6eef-4380-453c-a361-c9c7e2c3f0ae",
   "metadata": {},
   "outputs": [
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       "C             bar       foo\n",
       "A     B                    \n",
       "one   A -0.495412 -1.061470\n",
       "      B  0.820310  0.329323\n",
       "      C  0.770293 -0.182740\n",
       "three A -0.012404       NaN\n",
       "      B       NaN -0.448982\n",
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       "      C       NaN -1.105035"
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     "execution_count": 71,
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    "pd.pivot_table(df, values=\"D\", index=[\"A\", \"B\"], columns=[\"C\"])\n"
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   "execution_count": 73,
   "id": "a5943992-d4f1-47c3-b22a-a3b03b1186de",
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   "outputs": [
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       "      <td>0.521999</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                D                   E          \n",
       "C             bar       foo       bar       foo\n",
       "A     B                                        \n",
       "one   A -0.495412 -1.061470 -1.072328  0.350517\n",
       "      B  0.820310  0.329323  0.520874 -0.812136\n",
       "      C  0.770293 -0.182740  1.079676 -1.240916\n",
       "three A -0.012404       NaN  0.332078       NaN\n",
       "      B       NaN -0.448982       NaN -0.741048\n",
       "      C -0.555842       NaN  0.233356       NaN\n",
       "two   A       NaN  1.210237       NaN -1.322800\n",
       "      B  0.605505       NaN  0.694294       NaN\n",
       "      C       NaN -1.105035       NaN  0.521999"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values=[\"D\", \"E\"], index=[\"A\", \"B\"], columns=[\"C\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d5370297-1bb3-46c8-8a92-ffd544af5562",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range(\"1/1/2012\", periods=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "40ebfe56-4b7a-4ec1-adb9-3b94064bcab1",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04',\n",
       "               '2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08',\n",
       "               '2012-01-09', '2012-01-10', '2012-01-11', '2012-01-12',\n",
       "               '2012-01-13', '2012-01-14', '2012-01-15', '2012-01-16',\n",
       "               '2012-01-17', '2012-01-18', '2012-01-19', '2012-01-20',\n",
       "               '2012-01-21', '2012-01-22', '2012-01-23', '2012-01-24',\n",
       "               '2012-01-25', '2012-01-26', '2012-01-27', '2012-01-28',\n",
       "               '2012-01-29', '2012-01-30', '2012-01-31', '2012-02-01',\n",
       "               '2012-02-02', '2012-02-03', '2012-02-04', '2012-02-05',\n",
       "               '2012-02-06', '2012-02-07', '2012-02-08', '2012-02-09',\n",
       "               '2012-02-10', '2012-02-11', '2012-02-12', '2012-02-13',\n",
       "               '2012-02-14', '2012-02-15', '2012-02-16', '2012-02-17',\n",
       "               '2012-02-18', '2012-02-19', '2012-02-20', '2012-02-21',\n",
       "               '2012-02-22', '2012-02-23', '2012-02-24', '2012-02-25',\n",
       "               '2012-02-26', '2012-02-27', '2012-02-28', '2012-02-29',\n",
       "               '2012-03-01', '2012-03-02', '2012-03-03', '2012-03-04',\n",
       "               '2012-03-05', '2012-03-06', '2012-03-07', '2012-03-08',\n",
       "               '2012-03-09', '2012-03-10', '2012-03-11', '2012-03-12',\n",
       "               '2012-03-13', '2012-03-14', '2012-03-15', '2012-03-16',\n",
       "               '2012-03-17', '2012-03-18', '2012-03-19', '2012-03-20',\n",
       "               '2012-03-21', '2012-03-22', '2012-03-23', '2012-03-24',\n",
       "               '2012-03-25', '2012-03-26', '2012-03-27', '2012-03-28',\n",
       "               '2012-03-29', '2012-03-30', '2012-03-31', '2012-04-01',\n",
       "               '2012-04-02', '2012-04-03', '2012-04-04', '2012-04-05',\n",
       "               '2012-04-06', '2012-04-07', '2012-04-08', '2012-04-09'],\n",
       "              dtype='datetime64[ns]', freq='D')"
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     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "018b6a47-44df-45d5-8bba-7e27084ea4a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range(\"1/1/2012\", periods=100, freq=\"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e5245ada-250f-43fd-a128-bce170355911",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',\n",
       "               '2012-01-01 00:00:02', '2012-01-01 00:00:03',\n",
       "               '2012-01-01 00:00:04', '2012-01-01 00:00:05',\n",
       "               '2012-01-01 00:00:06', '2012-01-01 00:00:07',\n",
       "               '2012-01-01 00:00:08', '2012-01-01 00:00:09',\n",
       "               '2012-01-01 00:00:10', '2012-01-01 00:00:11',\n",
       "               '2012-01-01 00:00:12', '2012-01-01 00:00:13',\n",
       "               '2012-01-01 00:00:14', '2012-01-01 00:00:15',\n",
       "               '2012-01-01 00:00:16', '2012-01-01 00:00:17',\n",
       "               '2012-01-01 00:00:18', '2012-01-01 00:00:19',\n",
       "               '2012-01-01 00:00:20', '2012-01-01 00:00:21',\n",
       "               '2012-01-01 00:00:22', '2012-01-01 00:00:23',\n",
       "               '2012-01-01 00:00:24', '2012-01-01 00:00:25',\n",
       "               '2012-01-01 00:00:26', '2012-01-01 00:00:27',\n",
       "               '2012-01-01 00:00:28', '2012-01-01 00:00:29',\n",
       "               '2012-01-01 00:00:30', '2012-01-01 00:00:31',\n",
       "               '2012-01-01 00:00:32', '2012-01-01 00:00:33',\n",
       "               '2012-01-01 00:00:34', '2012-01-01 00:00:35',\n",
       "               '2012-01-01 00:00:36', '2012-01-01 00:00:37',\n",
       "               '2012-01-01 00:00:38', '2012-01-01 00:00:39',\n",
       "               '2012-01-01 00:00:40', '2012-01-01 00:00:41',\n",
       "               '2012-01-01 00:00:42', '2012-01-01 00:00:43',\n",
       "               '2012-01-01 00:00:44', '2012-01-01 00:00:45',\n",
       "               '2012-01-01 00:00:46', '2012-01-01 00:00:47',\n",
       "               '2012-01-01 00:00:48', '2012-01-01 00:00:49',\n",
       "               '2012-01-01 00:00:50', '2012-01-01 00:00:51',\n",
       "               '2012-01-01 00:00:52', '2012-01-01 00:00:53',\n",
       "               '2012-01-01 00:00:54', '2012-01-01 00:00:55',\n",
       "               '2012-01-01 00:00:56', '2012-01-01 00:00:57',\n",
       "               '2012-01-01 00:00:58', '2012-01-01 00:00:59',\n",
       "               '2012-01-01 00:01:00', '2012-01-01 00:01:01',\n",
       "               '2012-01-01 00:01:02', '2012-01-01 00:01:03',\n",
       "               '2012-01-01 00:01:04', '2012-01-01 00:01:05',\n",
       "               '2012-01-01 00:01:06', '2012-01-01 00:01:07',\n",
       "               '2012-01-01 00:01:08', '2012-01-01 00:01:09',\n",
       "               '2012-01-01 00:01:10', '2012-01-01 00:01:11',\n",
       "               '2012-01-01 00:01:12', '2012-01-01 00:01:13',\n",
       "               '2012-01-01 00:01:14', '2012-01-01 00:01:15',\n",
       "               '2012-01-01 00:01:16', '2012-01-01 00:01:17',\n",
       "               '2012-01-01 00:01:18', '2012-01-01 00:01:19',\n",
       "               '2012-01-01 00:01:20', '2012-01-01 00:01:21',\n",
       "               '2012-01-01 00:01:22', '2012-01-01 00:01:23',\n",
       "               '2012-01-01 00:01:24', '2012-01-01 00:01:25',\n",
       "               '2012-01-01 00:01:26', '2012-01-01 00:01:27',\n",
       "               '2012-01-01 00:01:28', '2012-01-01 00:01:29',\n",
       "               '2012-01-01 00:01:30', '2012-01-01 00:01:31',\n",
       "               '2012-01-01 00:01:32', '2012-01-01 00:01:33',\n",
       "               '2012-01-01 00:01:34', '2012-01-01 00:01:35',\n",
       "               '2012-01-01 00:01:36', '2012-01-01 00:01:37',\n",
       "               '2012-01-01 00:01:38', '2012-01-01 00:01:39'],\n",
       "              dtype='datetime64[ns]', freq='S')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "39750868-785d-4e82-8306-e4cef3ab4007",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range(\"1/1/2012\", periods=100, freq=\"W\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4962b6c9-b332-4bf0-80a8-d0dbbc25e035",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01', '2012-01-08', '2012-01-15', '2012-01-22',\n",
       "               '2012-01-29', '2012-02-05', '2012-02-12', '2012-02-19',\n",
       "               '2012-02-26', '2012-03-04', '2012-03-11', '2012-03-18',\n",
       "               '2012-03-25', '2012-04-01', '2012-04-08', '2012-04-15',\n",
       "               '2012-04-22', '2012-04-29', '2012-05-06', '2012-05-13',\n",
       "               '2012-05-20', '2012-05-27', '2012-06-03', '2012-06-10',\n",
       "               '2012-06-17', '2012-06-24', '2012-07-01', '2012-07-08',\n",
       "               '2012-07-15', '2012-07-22', '2012-07-29', '2012-08-05',\n",
       "               '2012-08-12', '2012-08-19', '2012-08-26', '2012-09-02',\n",
       "               '2012-09-09', '2012-09-16', '2012-09-23', '2012-09-30',\n",
       "               '2012-10-07', '2012-10-14', '2012-10-21', '2012-10-28',\n",
       "               '2012-11-04', '2012-11-11', '2012-11-18', '2012-11-25',\n",
       "               '2012-12-02', '2012-12-09', '2012-12-16', '2012-12-23',\n",
       "               '2012-12-30', '2013-01-06', '2013-01-13', '2013-01-20',\n",
       "               '2013-01-27', '2013-02-03', '2013-02-10', '2013-02-17',\n",
       "               '2013-02-24', '2013-03-03', '2013-03-10', '2013-03-17',\n",
       "               '2013-03-24', '2013-03-31', '2013-04-07', '2013-04-14',\n",
       "               '2013-04-21', '2013-04-28', '2013-05-05', '2013-05-12',\n",
       "               '2013-05-19', '2013-05-26', '2013-06-02', '2013-06-09',\n",
       "               '2013-06-16', '2013-06-23', '2013-06-30', '2013-07-07',\n",
       "               '2013-07-14', '2013-07-21', '2013-07-28', '2013-08-04',\n",
       "               '2013-08-11', '2013-08-18', '2013-08-25', '2013-09-01',\n",
       "               '2013-09-08', '2013-09-15', '2013-09-22', '2013-09-29',\n",
       "               '2013-10-06', '2013-10-13', '2013-10-20', '2013-10-27',\n",
       "               '2013-11-03', '2013-11-10', '2013-11-17', '2013-11-24'],\n",
       "              dtype='datetime64[ns]', freq='W-SUN')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "rng"
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  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "55908a4a-f559-4cb3-b238-abcc6142d03f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range(\"1/1/2012\", periods=100, freq=\"M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0a610a27-7091-41cc-8121-eb38af1416b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-31', '2012-02-29', '2012-03-31', '2012-04-30',\n",
       "               '2012-05-31', '2012-06-30', '2012-07-31', '2012-08-31',\n",
       "               '2012-09-30', '2012-10-31', '2012-11-30', '2012-12-31',\n",
       "               '2013-01-31', '2013-02-28', '2013-03-31', '2013-04-30',\n",
       "               '2013-05-31', '2013-06-30', '2013-07-31', '2013-08-31',\n",
       "               '2013-09-30', '2013-10-31', '2013-11-30', '2013-12-31',\n",
       "               '2014-01-31', '2014-02-28', '2014-03-31', '2014-04-30',\n",
       "               '2014-05-31', '2014-06-30', '2014-07-31', '2014-08-31',\n",
       "               '2014-09-30', '2014-10-31', '2014-11-30', '2014-12-31',\n",
       "               '2015-01-31', '2015-02-28', '2015-03-31', '2015-04-30',\n",
       "               '2015-05-31', '2015-06-30', '2015-07-31', '2015-08-31',\n",
       "               '2015-09-30', '2015-10-31', '2015-11-30', '2015-12-31',\n",
       "               '2016-01-31', '2016-02-29', '2016-03-31', '2016-04-30',\n",
       "               '2016-05-31', '2016-06-30', '2016-07-31', '2016-08-31',\n",
       "               '2016-09-30', '2016-10-31', '2016-11-30', '2016-12-31',\n",
       "               '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30',\n",
       "               '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',\n",
       "               '2017-09-30', '2017-10-31', '2017-11-30', '2017-12-31',\n",
       "               '2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',\n",
       "               '2018-05-31', '2018-06-30', '2018-07-31', '2018-08-31',\n",
       "               '2018-09-30', '2018-10-31', '2018-11-30', '2018-12-31',\n",
       "               '2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',\n",
       "               '2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',\n",
       "               '2019-09-30', '2019-10-31', '2019-11-30', '2019-12-31',\n",
       "               '2020-01-31', '2020-02-29', '2020-03-31', '2020-04-30'],\n",
       "              dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "95cb671b-769c-4307-a70e-453ccf260298",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range(\"1/1/2012\", periods=100, freq=\"Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e270e0d4-2d14-4487-bad0-81066a1dd86a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31', '2015-12-31',\n",
       "               '2016-12-31', '2017-12-31', '2018-12-31', '2019-12-31',\n",
       "               '2020-12-31', '2021-12-31', '2022-12-31', '2023-12-31',\n",
       "               '2024-12-31', '2025-12-31', '2026-12-31', '2027-12-31',\n",
       "               '2028-12-31', '2029-12-31', '2030-12-31', '2031-12-31',\n",
       "               '2032-12-31', '2033-12-31', '2034-12-31', '2035-12-31',\n",
       "               '2036-12-31', '2037-12-31', '2038-12-31', '2039-12-31',\n",
       "               '2040-12-31', '2041-12-31', '2042-12-31', '2043-12-31',\n",
       "               '2044-12-31', '2045-12-31', '2046-12-31', '2047-12-31',\n",
       "               '2048-12-31', '2049-12-31', '2050-12-31', '2051-12-31',\n",
       "               '2052-12-31', '2053-12-31', '2054-12-31', '2055-12-31',\n",
       "               '2056-12-31', '2057-12-31', '2058-12-31', '2059-12-31',\n",
       "               '2060-12-31', '2061-12-31', '2062-12-31', '2063-12-31',\n",
       "               '2064-12-31', '2065-12-31', '2066-12-31', '2067-12-31',\n",
       "               '2068-12-31', '2069-12-31', '2070-12-31', '2071-12-31',\n",
       "               '2072-12-31', '2073-12-31', '2074-12-31', '2075-12-31',\n",
       "               '2076-12-31', '2077-12-31', '2078-12-31', '2079-12-31',\n",
       "               '2080-12-31', '2081-12-31', '2082-12-31', '2083-12-31',\n",
       "               '2084-12-31', '2085-12-31', '2086-12-31', '2087-12-31',\n",
       "               '2088-12-31', '2089-12-31', '2090-12-31', '2091-12-31',\n",
       "               '2092-12-31', '2093-12-31', '2094-12-31', '2095-12-31',\n",
       "               '2096-12-31', '2097-12-31', '2098-12-31', '2099-12-31',\n",
       "               '2100-12-31', '2101-12-31', '2102-12-31', '2103-12-31',\n",
       "               '2104-12-31', '2105-12-31', '2106-12-31', '2107-12-31',\n",
       "               '2108-12-31', '2109-12-31', '2110-12-31', '2111-12-31'],\n",
       "              dtype='datetime64[ns]', freq='A-DEC')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "525b2cb1-f5cb-4fba-8cab-b4418918a87e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range(\"1/1/2012\", periods=100, freq=\"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8816deb6-c734-4712-b07d-db221fd0d770",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',\n",
       "               '2012-01-01 00:00:02', '2012-01-01 00:00:03',\n",
       "               '2012-01-01 00:00:04', '2012-01-01 00:00:05',\n",
       "               '2012-01-01 00:00:06', '2012-01-01 00:00:07',\n",
       "               '2012-01-01 00:00:08', '2012-01-01 00:00:09',\n",
       "               '2012-01-01 00:00:10', '2012-01-01 00:00:11',\n",
       "               '2012-01-01 00:00:12', '2012-01-01 00:00:13',\n",
       "               '2012-01-01 00:00:14', '2012-01-01 00:00:15',\n",
       "               '2012-01-01 00:00:16', '2012-01-01 00:00:17',\n",
       "               '2012-01-01 00:00:18', '2012-01-01 00:00:19',\n",
       "               '2012-01-01 00:00:20', '2012-01-01 00:00:21',\n",
       "               '2012-01-01 00:00:22', '2012-01-01 00:00:23',\n",
       "               '2012-01-01 00:00:24', '2012-01-01 00:00:25',\n",
       "               '2012-01-01 00:00:26', '2012-01-01 00:00:27',\n",
       "               '2012-01-01 00:00:28', '2012-01-01 00:00:29',\n",
       "               '2012-01-01 00:00:30', '2012-01-01 00:00:31',\n",
       "               '2012-01-01 00:00:32', '2012-01-01 00:00:33',\n",
       "               '2012-01-01 00:00:34', '2012-01-01 00:00:35',\n",
       "               '2012-01-01 00:00:36', '2012-01-01 00:00:37',\n",
       "               '2012-01-01 00:00:38', '2012-01-01 00:00:39',\n",
       "               '2012-01-01 00:00:40', '2012-01-01 00:00:41',\n",
       "               '2012-01-01 00:00:42', '2012-01-01 00:00:43',\n",
       "               '2012-01-01 00:00:44', '2012-01-01 00:00:45',\n",
       "               '2012-01-01 00:00:46', '2012-01-01 00:00:47',\n",
       "               '2012-01-01 00:00:48', '2012-01-01 00:00:49',\n",
       "               '2012-01-01 00:00:50', '2012-01-01 00:00:51',\n",
       "               '2012-01-01 00:00:52', '2012-01-01 00:00:53',\n",
       "               '2012-01-01 00:00:54', '2012-01-01 00:00:55',\n",
       "               '2012-01-01 00:00:56', '2012-01-01 00:00:57',\n",
       "               '2012-01-01 00:00:58', '2012-01-01 00:00:59',\n",
       "               '2012-01-01 00:01:00', '2012-01-01 00:01:01',\n",
       "               '2012-01-01 00:01:02', '2012-01-01 00:01:03',\n",
       "               '2012-01-01 00:01:04', '2012-01-01 00:01:05',\n",
       "               '2012-01-01 00:01:06', '2012-01-01 00:01:07',\n",
       "               '2012-01-01 00:01:08', '2012-01-01 00:01:09',\n",
       "               '2012-01-01 00:01:10', '2012-01-01 00:01:11',\n",
       "               '2012-01-01 00:01:12', '2012-01-01 00:01:13',\n",
       "               '2012-01-01 00:01:14', '2012-01-01 00:01:15',\n",
       "               '2012-01-01 00:01:16', '2012-01-01 00:01:17',\n",
       "               '2012-01-01 00:01:18', '2012-01-01 00:01:19',\n",
       "               '2012-01-01 00:01:20', '2012-01-01 00:01:21',\n",
       "               '2012-01-01 00:01:22', '2012-01-01 00:01:23',\n",
       "               '2012-01-01 00:01:24', '2012-01-01 00:01:25',\n",
       "               '2012-01-01 00:01:26', '2012-01-01 00:01:27',\n",
       "               '2012-01-01 00:01:28', '2012-01-01 00:01:29',\n",
       "               '2012-01-01 00:01:30', '2012-01-01 00:01:31',\n",
       "               '2012-01-01 00:01:32', '2012-01-01 00:01:33',\n",
       "               '2012-01-01 00:01:34', '2012-01-01 00:01:35',\n",
       "               '2012-01-01 00:01:36', '2012-01-01 00:01:37',\n",
       "               '2012-01-01 00:01:38', '2012-01-01 00:01:39'],\n",
       "              dtype='datetime64[ns]', freq='S')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2b6ec221-6042-40ba-bf10-f965a97e5ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b2ccb95b-6ed4-49f5-94e7-73405f2f1755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01 00:00:00    410\n",
       "2012-01-01 00:00:01    352\n",
       "2012-01-01 00:00:02    187\n",
       "2012-01-01 00:00:03    325\n",
       "2012-01-01 00:00:04    124\n",
       "                      ... \n",
       "2012-01-01 00:01:35    210\n",
       "2012-01-01 00:01:36    275\n",
       "2012-01-01 00:01:37    462\n",
       "2012-01-01 00:01:38    485\n",
       "2012-01-01 00:01:39    171\n",
       "Freq: S, Length: 100, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "17042c22-ea36-4331-b320-01ec69cd56c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    26581\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample(\"5Min\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "59bb46f1-bc44-4695-a08b-6caabc599a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    26581\n",
       "Freq: 10T, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample(\"10Min\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "eb4abba9-5a39-4c9a-84b8-3c578bc33492",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range(\"3/6/2012\", periods=5, freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "57760be9-4f7a-45f7-9c18-bc7581a4ffc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09',\n",
       "               '2012-03-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "4a255160-c46d-4ba0-89cd-4fc1084ab245",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c2d71afa-1367-460e-9710-ffdb6498937c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06   -1.231218\n",
       "2012-03-07   -1.186152\n",
       "2012-03-08   -0.378533\n",
       "2012-03-09   -0.054723\n",
       "2012-03-10    1.144039\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "acb14fcb-4ec7-40ef-9420-96add94ec0ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), index=rng, name=\"ts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "5560e957-8041-4d55-ac15-4a8cb082e627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06   -2.289220\n",
       "2012-03-07    1.149095\n",
       "2012-03-08    0.789448\n",
       "2012-03-09   -1.424680\n",
       "2012-03-10   -0.738940\n",
       "Freq: D, Name: ts, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "bca6a185-d713-4d08-8969-8fdafdde58be",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_utc = ts.tz_localize(\"UTC\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "146463c0-097f-487d-9720-a3636089b5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 00:00:00+00:00   -2.289220\n",
       "2012-03-07 00:00:00+00:00    1.149095\n",
       "2012-03-08 00:00:00+00:00    0.789448\n",
       "2012-03-09 00:00:00+00:00   -1.424680\n",
       "2012-03-10 00:00:00+00:00   -0.738940\n",
       "Freq: D, Name: ts, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "068a51af-6723-48a1-b0b2-625b818ba741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-05 19:00:00-05:00   -2.289220\n",
       "2012-03-06 19:00:00-05:00    1.149095\n",
       "2012-03-07 19:00:00-05:00    0.789448\n",
       "2012-03-08 19:00:00-05:00   -1.424680\n",
       "2012-03-09 19:00:00-05:00   -0.738940\n",
       "Freq: D, Name: ts, dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert(\"US/Eastern\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "4c16a87a-e4dd-4ce6-9dc5-596141b21795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09',\n",
       "               '2012-03-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "8e5c454d-f709-416f-aa21-162f9d0ec747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-13', '2012-03-14', '2012-03-15', '2012-03-16',\n",
       "               '2012-03-16'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng + pd.offsets.BusinessDay(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "e3730f2c-6894-4594-8600-49ef3043d718",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\"id\": [1, 2, 3, 4, 5, 6], \"raw_grade\": [\"a\", \"b\", \"b\", \"a\", \"a\", \"e\"]}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "f1d72fa5-ede1-49dc-9f99-902e11904aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>id</th>\n",
       "      <th>raw_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>a</td>\n",
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       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade\n",
       "0   1         a\n",
       "1   2         b\n",
       "2   3         b\n",
       "3   4         a\n",
       "4   5         a\n",
       "5   6         e"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "6a6e72fc-0ce9-4c9b-8ab2-300b6f30ba57",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "02dad89c-1d20-4fd0-8d8f-7833434ede8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "   id raw_grade grade\n",
       "0   1         a     a\n",
       "1   2         b     b\n",
       "2   3         b     b\n",
       "3   4         a     a\n",
       "4   5         a     a\n",
       "5   6         e     e"
      ]
     },
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     "metadata": {},
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    }
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    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "586a9f18-8963-4897-a075-639cf6b83ff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    e\n",
       "Name: raw_grade, dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['raw_grade']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "af44cefd-8113-4638-8894-22d247a48f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    e\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): ['a', 'b', 'e']"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['grade']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "f4bf6bb0-08d7-496a-b92b-799e535b8906",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_categories = [\"very good\", \"good\", \"very bad\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "539e87b3-1520-4bf3-bebb-b49c9ce9fbfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"grade\"] = df[\"grade\"].cat.rename_categories(new_categories)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c277d8b9-efa7-4291-8446-1e9db3d8974d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade      grade\n",
       "0   1         a  very good\n",
       "1   2         b       good\n",
       "2   3         b       good\n",
       "3   4         a  very good\n",
       "4   5         a  very good\n",
       "5   6         e   very bad"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "676a6809-61b3-42e7-8a10-643db113d0c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"grade\"] = df[\"grade\"].cat.set_categories(\n",
    "    [\"very bad\", \"bad\", \"medium\", \"good\", \"very good\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "72d6c65b-afcf-428b-af0e-ebd2341586b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade      grade\n",
       "0   1         a  very good\n",
       "1   2         b       good\n",
       "2   3         b       good\n",
       "3   4         a  very good\n",
       "4   5         a  very good\n",
       "5   6         e        NaN"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "c4893e88-050f-43c7-9d59-cb3d812b8957",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "e8470641-9843-4167-b03d-f218f5a4997b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close(\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "9d560b00-16a2-4300-92c0-06d8903ca13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range(\"1/1/2000\", periods=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "927c3008-fab3-42f6-9080-4c879041bcac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -0.230422\n",
       "2000-01-02   -0.067247\n",
       "2000-01-03    0.418113\n",
       "2000-01-04   -1.940695\n",
       "2000-01-05    0.481252\n",
       "                ...   \n",
       "2002-09-22    2.080511\n",
       "2002-09-23    1.089469\n",
       "2002-09-24    0.444998\n",
       "2002-09-25    0.069345\n",
       "2002-09-26    0.000417\n",
       "Freq: D, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "2abbc063-1556-4847-b13d-263225ebe0a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "3cf4bb60-cb40-4c87-af29-ddd64cb48d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ts.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "2d174943-0c42-4491-8c99-53931ca40ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    -0.230422\n",
       "2000-01-02    -0.297669\n",
       "2000-01-03     0.120445\n",
       "2000-01-04    -1.820250\n",
       "2000-01-05    -1.338999\n",
       "                ...    \n",
       "2002-09-22   -19.164716\n",
       "2002-09-23   -18.075247\n",
       "2002-09-24   -17.630250\n",
       "2002-09-25   -17.560904\n",
       "2002-09-26   -17.560488\n",
       "Freq: D, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "15a6d2a3-5699-4b1a-a6f0-a3b7b2a52358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "d0487cac-edf5-4f69-b5cb-ca0a7ae2ca92",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    np.random.randn(1000, 4), index=ts.index, columns=[\"A\", \"B\", \"C\", \"D\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "a697f55f-3ef7-4fb1-a5ec-dfb804b20926",
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-1.059840</td>\n",
       "      <td>-0.048059</td>\n",
       "      <td>0.790629</td>\n",
       "      <td>-0.068302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>0.237354</td>\n",
       "      <td>2.205644</td>\n",
       "      <td>0.737123</td>\n",
       "      <td>-1.136563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-1.698962</td>\n",
       "      <td>1.954039</td>\n",
       "      <td>0.963892</td>\n",
       "      <td>-0.349676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-1.137210</td>\n",
       "      <td>-0.788457</td>\n",
       "      <td>0.057160</td>\n",
       "      <td>0.717402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>0.876871</td>\n",
       "      <td>-1.748605</td>\n",
       "      <td>-1.429354</td>\n",
       "      <td>0.378444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>1.905433</td>\n",
       "      <td>0.887962</td>\n",
       "      <td>0.389925</td>\n",
       "      <td>0.971564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>0.754643</td>\n",
       "      <td>-2.451467</td>\n",
       "      <td>-1.386089</td>\n",
       "      <td>-0.527915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>-0.417225</td>\n",
       "      <td>-0.383555</td>\n",
       "      <td>0.246189</td>\n",
       "      <td>-0.814934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>1.086829</td>\n",
       "      <td>0.478131</td>\n",
       "      <td>1.273341</td>\n",
       "      <td>-1.436775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>-0.629808</td>\n",
       "      <td>1.115297</td>\n",
       "      <td>-0.819631</td>\n",
       "      <td>1.127358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -1.059840 -0.048059  0.790629 -0.068302\n",
       "2000-01-02  0.237354  2.205644  0.737123 -1.136563\n",
       "2000-01-03 -1.698962  1.954039  0.963892 -0.349676\n",
       "2000-01-04 -1.137210 -0.788457  0.057160  0.717402\n",
       "2000-01-05  0.876871 -1.748605 -1.429354  0.378444\n",
       "...              ...       ...       ...       ...\n",
       "2002-09-22  1.905433  0.887962  0.389925  0.971564\n",
       "2002-09-23  0.754643 -2.451467 -1.386089 -0.527915\n",
       "2002-09-24 -0.417225 -0.383555  0.246189 -0.814934\n",
       "2002-09-25  1.086829  0.478131  1.273341 -1.436775\n",
       "2002-09-26 -0.629808  1.115297 -0.819631  1.127358\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "69654bfd-8892-4a3b-95bf-9aa0cd218966",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "6040f0c6-976b-4589-a099-f77753ae1bd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-1.059840</td>\n",
       "      <td>-0.048059</td>\n",
       "      <td>0.790629</td>\n",
       "      <td>-0.068302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-0.822486</td>\n",
       "      <td>2.157584</td>\n",
       "      <td>1.527752</td>\n",
       "      <td>-1.204865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-2.521448</td>\n",
       "      <td>4.111624</td>\n",
       "      <td>2.491644</td>\n",
       "      <td>-1.554541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-3.658658</td>\n",
       "      <td>3.323167</td>\n",
       "      <td>2.548803</td>\n",
       "      <td>-0.837139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-2.781786</td>\n",
       "      <td>1.574562</td>\n",
       "      <td>1.119449</td>\n",
       "      <td>-0.458696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>-5.212772</td>\n",
       "      <td>34.597399</td>\n",
       "      <td>-13.495744</td>\n",
       "      <td>-30.390655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>-4.458129</td>\n",
       "      <td>32.145932</td>\n",
       "      <td>-14.881833</td>\n",
       "      <td>-30.918570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>-4.875354</td>\n",
       "      <td>31.762377</td>\n",
       "      <td>-14.635644</td>\n",
       "      <td>-31.733504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>-3.788525</td>\n",
       "      <td>32.240508</td>\n",
       "      <td>-13.362302</td>\n",
       "      <td>-33.170279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>-4.418333</td>\n",
       "      <td>33.355805</td>\n",
       "      <td>-14.181933</td>\n",
       "      <td>-32.042921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A          B          C          D\n",
       "2000-01-01 -1.059840  -0.048059   0.790629  -0.068302\n",
       "2000-01-02 -0.822486   2.157584   1.527752  -1.204865\n",
       "2000-01-03 -2.521448   4.111624   2.491644  -1.554541\n",
       "2000-01-04 -3.658658   3.323167   2.548803  -0.837139\n",
       "2000-01-05 -2.781786   1.574562   1.119449  -0.458696\n",
       "...              ...        ...        ...        ...\n",
       "2002-09-22 -5.212772  34.597399 -13.495744 -30.390655\n",
       "2002-09-23 -4.458129  32.145932 -14.881833 -30.918570\n",
       "2002-09-24 -4.875354  31.762377 -14.635644 -31.733504\n",
       "2002-09-25 -3.788525  32.240508 -13.362302 -33.170279\n",
       "2002-09-26 -4.418333  33.355805 -14.181933 -32.042921\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "5bb4db50-972e-40d4-9b82-f4fa03e4543f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "7a7dc2e6-eb84-4484-8afa-e9f41a3dc7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "d9532c1d-6f1d-41d5-b2c9-1b9f26f3cad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "c3a854b1-7b5c-48b5-b461-f69af6fa6206",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randint(0, 5, (10, 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "0ace0bec-e730-4a24-89be-a9644dbddc15",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2  3  4\n",
       "0  2  1  3  0  2\n",
       "1  0  2  3  4  3\n",
       "2  3  1  1  0  1\n",
       "3  3  1  3  3  1\n",
       "4  2  3  1  2  4\n",
       "5  2  1  0  1  3\n",
       "6  1  1  4  0  2\n",
       "7  1  4  3  2  0\n",
       "8  0  0  1  0  4\n",
       "9  3  1  4  3  4"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "e9c76734-2ea3-45ee-8250-c02764a9420f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"foo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "036e7904-778e-4ecc-a6b5-a04811413a90",
   "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>Unnamed: 0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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   "execution_count": 195,
   "id": "d343605e-c68a-44c1-8742-c701742ca21d",
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   "execution_count": 196,
   "id": "5eda1b80-258a-4b2f-a853-01e0d0f47ed3",
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   "execution_count": 200,
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   "id": "9c0854d6-8993-40a0-b079-dfa4a6d05c6d",
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