In this example, we’ll use DocParse to extract a cash flow table (shown below) from the 10k financial document of 3M, and turn it into a pandas dataframe.

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Extracting Table Cell from DocParse

table_demo.py
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
## param use_ocr (boolean): extract text using an OCR model instead of extracting embedded text in PDF. default: False
## returns: JSON object with elements representing information inside the PDF
partitioned_file = partition_file(file, aryn_api_key, extract_table_structure=True, use_ocr=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 (checkout this page 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.

output.json
{'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.

cells.json
{'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': {}}, ... }

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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 we use the tables_to_pandas function to turn the JSON into a pandas dataframe and then perform some analysis on it:

display_table.py
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)201820172016
0Major GAAP Cash Flow Categories
1Net cash provided by operating activities$ 6,4396,240$ 6,662
2Net cash provided by (used in) investing activities222(3,086)(1,403)
3Net cash used in financing activities.(6,701)(2,655)(4,626)
4Free Cash Flow (non-GAAP measure)
5Net cash provided by operating activities$ 6,439$ 6,2406,662
6Purchases of property, plant and equipment (PP&E(1,577)(1,373)(1,420)
7Free cash flow$ 4,862$ 4,867$ 5,242
8Net income attributable to 3M$ 5,349$ 4,858$ 5,050
9Free cash flow conversion91 %100 %104 %

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