> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aryn.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Tutorial: Table Extraction

> Extract structured table data from PDFs using Aryn DocParse

In [this example](https://colab.research.google.com/drive/1Qpd-llPC-EPzuTwLfnguMnrQk0eclyqJ?usp=sharing), we'll use DocParse to extract a cash flow table (shown below) from the 10-K financial document  of 3M, and turn it into a pandas dataframe.

<Frame>
  <img src="https://mintcdn.com/aryn/NmC_FCDeIgvkLjlo/images/3m_10k_extract.png?fit=max&auto=format&n=NmC_FCDeIgvkLjlo&q=85&s=fd8c1ad26f1183a01d83a02021c15706" width="1740" height="480" data-path="images/3m_10k_extract.png" />
</Frame>

## Extracting Table Cell from DocParse

```python table_demo.py theme={null}
import aryn_sdk
from aryn_sdk.partition import partition_file, tables_to_pandas
import pandas as pd
from io import BytesIO

file = open('my-document.pdf', 'rb')
aryn_api_key = 'YOUR-KEY-HERE'

## Make a call to Aryn DocParse
## param extract_table_structure (boolean): extract tables and their structural content. default: False
## returns: JSON object with elements representing information inside the PDF
partitioned_file = partition_file(file, aryn_api_key, extract_table_structure=True)
```

If you inspect the partitioned\_file variable, you'll notice that it's a large JSON object with details about all the components in the PDF (check out [this page](../output_structure) to understand the schema of the returned JSON object in detail).  Below, we highlight the `table` element that contains the information about the table in the page.

```json output.json theme={null}
{'type': 'table',
   'bbox': [0.09049919577205882,
    0.35081939697265624,
    0.8906761259191176,
    0.5200832852450284],
   'properties': {'score': 0.9009878635406494,
    'title': None,
    'columns': None,
    'rows': None,
    '_element_index': 7,
    'page_number': 1},
   'text_representation': None,
   'table': {'cells': [{'content': 'Years ended December 31 (Millions)',
      'rows': [0],
      'cols': [0],
      'is_header': True,
      'bbox': {'x1': 0.09167566636029412,
       'y1': 0.3512739424272017,
       'x2': 0.355205078125,
       'y2': 0.37081939697265626},
      'properties': {}},
     {'content': '2018',
      'rows': [0],
      'cols': [1],
      'is_header': True,
      'bbox': {'x1': 0.5346168428308824,
       'y1': 0.3512739424272017,
       'x2': 0.6546168428308824,
       'y2': 0.37081939697265626},
      'properties': {}},
    ... ]}}
```

In particular let's look at the `cells` field  which is an array of cell objects that represent each of the cells in the table. Let's focus on the first element of that list.

```json cells.json theme={null}
{'cells': [{'content': 'Years ended December 31 (Millions)',
      'rows': [0],
      'cols': [0],
      'is_header': True,
      'bbox': {'x1': 0.09167566636029412,
       'y1': 0.3512739424272017,
       'x2': 0.355205078125,
       'y2': 0.37081939697265626},
      'properties': {}}, ... }

```

## Displaying the Table

Here we've detected the first cell, its bounding box (which indicates the coordinates of the cell in the PDF.), whether it's a header cell and its contents. You can then process this JSON however you'd like for further analysis. In [the notebook](https://colab.research.google.com/drive/1Qpd-llPC-EPzuTwLfnguMnrQk0eclyqJ?usp=sharing)  we use the `tables_to_pandas` function to turn the JSON into a pandas dataframe and then perform some analysis on it:

```python display_table.py theme={null}
pandas = tables_to_pandas(partitioned_file)

tables = []
#pull out the tables from the list of elements
for elt, dataframe in pandas:
    if elt['type'] == 'table':
        tables.append(dataframe)
        
supplemental_income = tables[0]
display(supplemental_income)
```

The output is given below:

|    | Years ended December 31 (Millions)                  | 2018     | 2017     | 2016     |
| -: | :-------------------------------------------------- | :------- | :------- | :------- |
|  0 | Major GAAP Cash Flow Categories                     |          |          |          |
|  1 | Net cash provided by operating activities           | \$ 6,439 | 6,240    | \$ 6,662 |
|  2 | Net cash provided by (used in) investing activities | 222      | (3,086)  | (1,403)  |
|  3 | Net cash used in financing activities.              | (6,701)  | (2,655)  | (4,626)  |
|  4 | Free Cash Flow (non-GAAP measure)                   |          |          |          |
|  5 | Net cash provided by operating activities           | \$ 6,439 | \$ 6,240 | 6,662    |
|  6 | Purchases of property, plant and equipment (PP\&E   | (1,577)  | (1,373)  | (1,420)  |
|  7 | Free cash flow                                      | \$ 4,862 | \$ 4,867 | \$ 5,242 |
|  8 | Net income attributable to 3M                       | \$ 5,349 | \$ 4,858 | \$ 5,050 |
|  9 | Free cash flow conversion                           | 91 %     | 100 %    | 104 %    |