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add camelot
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‎Chapter6/alternative_approach.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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"version": "3.11.4"
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},
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"toc": {
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"base_numbering": 1,

‎Chapter6/better_outputs.ipynb

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"source": [
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"[Link to Great Tables](https://bit.ly/3U58fvP)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "01df33c0",
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"metadata": {},
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"source": [
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"### Camelot: PDF Table Extraction for Humans"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "90e6b894",
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"metadata": {
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"tags": [
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"hide-cell"
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]
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},
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"outputs": [],
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"source": [
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"!pip install \"camelot-py[base]\" \"opencv-python\" \"pypdf2<3\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "ec590463",
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"metadata": {},
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"source": [
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"With Camelot, you can extract tables from PDFs using Python and convert the data into a more structured format, such as a pandas DataFrame or a CSV file for efficient analysis, manipulation, and integration."
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]
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},
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{
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"cell_type": "markdown",
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"id": "cce6ec0b",
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"metadata": {},
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"source": [
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"To see how Camelot works, start with reading the PDF file named 'foo.pdf' and extracts all the tables present in the file."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "60cbbdf3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<TableList n=1>"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import camelot\n",
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"tables = camelot.read_pdf('foo.pdf')\n",
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"tables"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f5125a51",
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"metadata": {},
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"source": [
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"The output shows that there is one table extracted from the PDF file."
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]
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},
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{
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"cell_type": "markdown",
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"id": "26edcb5c",
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"metadata": {},
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"source": [
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"Export the extracted tables to a CSV file named 'foo.csv'. Camelot also supports exporting tables to other formats like JSON, Excel, HTML, Markdown, and SQLite databases."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "611f33ff",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>0</th>\n",
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" <th>1</th>\n",
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" <th>2</th>\n",
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" <th>3</th>\n",
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" <th>4</th>\n",
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" <th>5</th>\n",
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" <th>6</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Cycle \\nName</td>\n",
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" <td>KI \\n(1/km)</td>\n",
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" <td>Distance \\n(mi)</td>\n",
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" <td>Percent Fuel Savings</td>\n",
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" <td></td>\n",
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" <td></td>\n",
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" <td></td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td></td>\n",
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" <td></td>\n",
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" <td></td>\n",
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" <td>Improved \\nSpeed</td>\n",
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" <td>Decreased \\nAccel</td>\n",
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" <td>Eliminate \\nStops</td>\n",
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" <td>Decreased \\nIdle</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2012_2</td>\n",
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" <td>3.30</td>\n",
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" <td>1.3</td>\n",
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" <td>5.9%</td>\n",
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" <td>9.5%</td>\n",
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" <td>29.2%</td>\n",
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" <td>17.4%</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>2145_1</td>\n",
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" <td>0.68</td>\n",
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" <td>11.2</td>\n",
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" <td>2.4%</td>\n",
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" <td>0.1%</td>\n",
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" <td>9.5%</td>\n",
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" <td>2.7%</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4234_1</td>\n",
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" <td>0.59</td>\n",
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" <td>58.7</td>\n",
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" <td>8.5%</td>\n",
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" <td>1.3%</td>\n",
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" <td>8.5%</td>\n",
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" <td>3.3%</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>2032_2</td>\n",
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" <td>0.17</td>\n",
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" <td>57.8</td>\n",
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" <td>21.7%</td>\n",
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" <td>0.3%</td>\n",
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" <td>2.7%</td>\n",
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" <td>1.2%</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>4171_1</td>\n",
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" <td>0.07</td>\n",
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" <td>173.9</td>\n",
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" <td>58.1%</td>\n",
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" <td>1.6%</td>\n",
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" <td>2.1%</td>\n",
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" <td>0.5%</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 0 1 2 3 \\\n",
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"0 Cycle \\nName KI \\n(1/km) Distance \\n(mi) Percent Fuel Savings \n",
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"1 Improved \\nSpeed \n",
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"2 2012_2 3.30 1.3 5.9% \n",
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"3 2145_1 0.68 11.2 2.4% \n",
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"4 4234_1 0.59 58.7 8.5% \n",
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"5 2032_2 0.17 57.8 21.7% \n",
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"6 4171_1 0.07 173.9 58.1% \n",
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"\n",
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" 4 5 6 \n",
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"0 \n",
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"1 Decreased \\nAccel Eliminate \\nStops Decreased \\nIdle \n",
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"2 9.5% 29.2% 17.4% \n",
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"3 0.1% 9.5% 2.7% \n",
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"4 1.3% 8.5% 3.3% \n",
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"5 0.3% 2.7% 1.2% \n",
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"6 1.6% 2.1% 0.5% "
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tables[0].parsing_report\n",
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"{\n",
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" 'accuracy': 99.02,\n",
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" 'whitespace': 12.24,\n",
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" 'order': 1,\n",
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" 'page': 1\n",
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"}\n",
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"tables[0].to_csv('foo.csv') # to_json, to_excel, to_html, to_markdown, to_sqlite\n",
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"tables[0].df # get a pandas DataFrame!"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9c914e50",
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"metadata": {},
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"source": [
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"[Link to Camelot](https://bit.ly/3xPBw6L)."
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]
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}
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],
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"metadata": {

‎Chapter6/foo.pdf

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‎Chapter6/workflow_automation.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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"version": "3.11.4"
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},
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"toc": {
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"base_numbering": 1,

‎docs/Chapter6/alternative_approach.html

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<li class="toctree-l2"><a class="reference internal" href="../Chapter2/dataclasses.html">3.7. Data Classes</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../Chapter2/typing.html">3.8. Typing</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../Chapter2/pathlib.html">3.9. pathlib</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../Chapter2/pydantic.html">3.10. Pydantic</a></li>
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</ul>
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</li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../Chapter3/Chapter3.html">4. Pandas</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>

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