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snippsat/gist:d77055030d82a57ffab6856e0ab64922

Created November 22, 2019 15:13
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gistfile1.txt
{
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{
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"metadata": {},
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{
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" </tr>\n",
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" <td>Marc D. Hamburg</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Senior Vice President/CFO</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
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" <td>2288500</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>2016</td>\n",
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" <td>1550000</td>\n",
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" <td>...</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>13250</td>\n",
" <td>(5)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1563250</td>\n",
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" </tbody>\n",
"</table>\n",
"<p>18 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" Name and Principal Position Unnamed: 1 Year Year.1 \\\n",
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"2 Chief Executive Officer/ NaN NaN 2017 \n",
"3 Chairman of the Board NaN NaN 2016 \n",
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"5 Charles T. Munger NaN NaN 2018 \n",
"6 Vice Chairman of the Board NaN NaN 2017 \n",
"7 NaN NaN NaN 2016 \n",
"8 NaN NaN NaN NaN \n",
"9 Gregory E. Abel(1) NaN NaN 2018 \n",
"10 Vice Chairman-Non Insurance Operations NaN NaN NaN \n",
"11 NaN NaN NaN NaN \n",
"12 Ajit Jain(1) NaN NaN 2018 \n",
"13 Vice Chairman-Insurance Operations NaN NaN NaN \n",
"14 NaN NaN NaN NaN \n",
"15 Marc D. Hamburg NaN NaN 2018 \n",
"16 Senior Vice President/CFO NaN NaN 2017 \n",
"17 NaN NaN NaN 2016 \n",
"\n",
" Unnamed: 4 Unnamed: 5 Annual Compensation Annual Compensation.1 \\\n",
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"1 NaN NaN $ 100000 \n",
"2 NaN NaN NaN 100000 \n",
"3 NaN NaN NaN 100000 \n",
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"15 NaN NaN NaN 2250000 \n",
"16 NaN NaN NaN 1775000 \n",
"17 NaN NaN NaN 1550000 \n",
"\n",
" Annual Compensation.2 Annual Compensation.3 ... Annual Compensation.5 \\\n",
"0 NaN NaN ... Bonus \n",
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"\n",
" Unnamed: 12 Unnamed: 13 AllOtherCompensation AllOtherCompensation.1 \\\n",
"0 NaN NaN AllOtherCompensation AllOtherCompensation \n",
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"\n",
" Unnamed: 16 Unnamed: 17 TotalCompensation TotalCompensation.1 Unnamed: 20 \n",
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"17 (5) NaN NaN 1563250 NaN \n",
"\n",
"[18 rows x 21 columns]"
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_html('https://www.sec.gov/Archives/edgar/data/1067983/000119312519076915/d684203ddef14a.htm',\n",
" match='Vice Chairman-Insurance Operations', header=1)\n",
"df = df[0]\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
"data": {
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" text-align: right;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th>Name and Principal Position</th>\n",
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" <td>$</td>\n",
" <td>288968</td>\n",
" <td>(2)</td>\n",
" <td>$</td>\n",
" <td>388968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Chief Executive Officer/</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>NaN</td>\n",
" <td>100000</td>\n",
" <td>NaN</td>\n",
" <td>—</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>375776</td>\n",
" <td>(2)</td>\n",
" <td>NaN</td>\n",
" <td>475776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Chairman of the Board</td>\n",
" <td>NaN</td>\n",
" <td>2016</td>\n",
" <td>NaN</td>\n",
" <td>100000</td>\n",
" <td>NaN</td>\n",
" <td>—</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>387881</td>\n",
" <td>(2)</td>\n",
" <td>NaN</td>\n",
" <td>487881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Charles T. Munger</td>\n",
" <td>NaN</td>\n",
" <td>2018</td>\n",
" <td>NaN</td>\n",
" <td>100000</td>\n",
" <td>NaN</td>\n",
" <td>—</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>—</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Vice Chairman of the Board</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>NaN</td>\n",
" <td>100000</td>\n",
" <td>NaN</td>\n",
" <td>—</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>—</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2016</td>\n",
" <td>NaN</td>\n",
" <td>100000</td>\n",
" <td>NaN</td>\n",
" <td>—</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>—</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Gregory E. Abel(1)</td>\n",
" <td>NaN</td>\n",
" <td>2018</td>\n",
" <td>NaN</td>\n",
" <td>16000000</td>\n",
" <td>NaN</td>\n",
" <td>2000000</td>\n",
" <td>(3)</td>\n",
" <td>NaN</td>\n",
" <td>13750</td>\n",
" <td>(5)</td>\n",
" <td>NaN</td>\n",
" <td>18013750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Vice Chairman-Non Insurance Operations</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Ajit Jain(1)</td>\n",
" <td>NaN</td>\n",
" <td>2018</td>\n",
" <td>NaN</td>\n",
" <td>16000000</td>\n",
" <td>NaN</td>\n",
" <td>2000000</td>\n",
" <td>(3)</td>\n",
" <td>NaN</td>\n",
" <td>13750</td>\n",
" <td>(5)</td>\n",
" <td>NaN</td>\n",
" <td>18013750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Vice Chairman-Insurance Operations</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Marc D. Hamburg</td>\n",
" <td>NaN</td>\n",
" <td>2018</td>\n",
" <td>NaN</td>\n",
" <td>2250000</td>\n",
" <td>NaN</td>\n",
" <td>—</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>13750</td>\n",
" <td>(5)</td>\n",
" <td>NaN</td>\n",
" <td>2263750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Senior Vice President/CFO</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>NaN</td>\n",
" <td>1775000</td>\n",
" <td>NaN</td>\n",
" <td>500000</td>\n",
" <td>(4)</td>\n",
" <td>NaN</td>\n",
" <td>13500</td>\n",
" <td>(5)</td>\n",
" <td>NaN</td>\n",
" <td>2288500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2016</td>\n",
" <td>NaN</td>\n",
" <td>1550000</td>\n",
" <td>NaN</td>\n",
" <td>—</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>13250</td>\n",
" <td>(5)</td>\n",
" <td>NaN</td>\n",
" <td>1563250</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name and Principal Position Year Year.1 Annual Compensation \\\n",
"0 Name and Principal Position Year Year Salary \n",
"1 Warren E. Buffett NaN 2018 $ \n",
"2 Chief Executive Officer/ NaN 2017 NaN \n",
"3 Chairman of the Board NaN 2016 NaN \n",
"4 NaN NaN NaN NaN \n",
"5 Charles T. Munger NaN 2018 NaN \n",
"6 Vice Chairman of the Board NaN 2017 NaN \n",
"7 NaN NaN 2016 NaN \n",
"8 NaN NaN NaN NaN \n",
"9 Gregory E. Abel(1) NaN 2018 NaN \n",
"10 Vice Chairman-Non Insurance Operations NaN NaN NaN \n",
"11 NaN NaN NaN NaN \n",
"12 Ajit Jain(1) NaN 2018 NaN \n",
"13 Vice Chairman-Insurance Operations NaN NaN NaN \n",
"14 NaN NaN NaN NaN \n",
"15 Marc D. Hamburg NaN 2018 NaN \n",
"16 Senior Vice President/CFO NaN 2017 NaN \n",
"17 NaN NaN 2016 NaN \n",
"\n",
" Annual Compensation.1 Annual Compensation.4 Annual Compensation.5 \\\n",
"0 Salary Bonus Bonus \n",
"1 100000 NaN $ — \n",
"2 100000 NaN — \n",
"3 100000 NaN — \n",
"4 NaN NaN NaN \n",
"5 100000 NaN — \n",
"6 100000 NaN — \n",
"7 100000 NaN — \n",
"8 NaN NaN NaN \n",
"9 16000000 NaN 2000000 \n",
"10 NaN NaN NaN \n",
"11 NaN NaN NaN \n",
"12 16000000 NaN 2000000 \n",
"13 NaN NaN NaN \n",
"14 NaN NaN NaN \n",
"15 2250000 NaN — \n",
"16 1775000 NaN 500000 \n",
"17 1550000 NaN — \n",
"\n",
" Unnamed: 12 AllOtherCompensation AllOtherCompensation.1 Unnamed: 16 \\\n",
"0 NaN AllOtherCompensation AllOtherCompensation NaN \n",
"1 NaN $ 288968 (2) \n",
"2 NaN NaN 375776 (2) \n",
"3 NaN NaN 387881 (2) \n",
"4 NaN NaN NaN NaN \n",
"5 NaN NaN — NaN \n",
"6 NaN NaN — NaN \n",
"7 NaN NaN — NaN \n",
"8 NaN NaN NaN NaN \n",
"9 (3) NaN 13750 (5) \n",
"10 NaN NaN NaN NaN \n",
"11 NaN NaN NaN NaN \n",
"12 (3) NaN 13750 (5) \n",
"13 NaN NaN NaN NaN \n",
"14 NaN NaN NaN NaN \n",
"15 NaN NaN 13750 (5) \n",
"16 (4) NaN 13500 (5) \n",
"17 NaN NaN 13250 (5) \n",
"\n",
" TotalCompensation TotalCompensation.1 \n",
"0 TotalCompensation TotalCompensation \n",
"1 $ 388968 \n",
"2 NaN 475776 \n",
"3 NaN 487881 \n",
"4 NaN NaN \n",
"5 NaN 100000 \n",
"6 NaN 100000 \n",
"7 NaN 100000 \n",
"8 NaN NaN \n",
"9 NaN 18013750 \n",
"10 NaN NaN \n",
"11 NaN NaN \n",
"12 NaN 18013750 \n",
"13 NaN NaN \n",
"14 NaN NaN \n",
"15 NaN 2263750 \n",
"16 NaN 2288500 \n",
"17 NaN 1563250 "
]
},
"execution_count": 190,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Start to clean up\n",
"df.dropna(axis=1, how='all')"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Name and Principal Position object\n",
"Unnamed: 1 float64\n",
"Year object\n",
"Year.1 object\n",
"Unnamed: 4 float64\n",
"Unnamed: 5 float64\n",
"Annual Compensation object\n",
"Annual Compensation.1 object\n",
"Annual Compensation.2 float64\n",
"Annual Compensation.3 float64\n",
"Annual Compensation.4 object\n",
"Annual Compensation.5 object\n",
"Unnamed: 12 object\n",
"Unnamed: 13 float64\n",
"AllOtherCompensation object\n",
"AllOtherCompensation.1 object\n",
"Unnamed: 16 object\n",
"Unnamed: 17 float64\n",
"TotalCompensation object\n",
"TotalCompensation.1 object\n",
"Unnamed: 20 float64\n",
"dtype: object"
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Look at types at start to clean up\n",
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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