expand - Method Usage

Complex Extraction of Law Enforcement Complaints

This PDF contains a set of complaint records from a local law enforcement agency. Challenges include its relational data structure, unusual formatting common in the region, and redactions that disrupt automatic parsing.

    .find_all('text:contains(Complaint #)')
    .right(include_source=True)
    .merge()
    .expand(top=5, bottom=7)
    .show(crop=section)
)

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    .find_all('text:contains(Complaint #)')
    .right(include_source=True)
    .merge()
    .expand(top=5, bottom=7)
    .extract_table()
    .to_df(header=['Type of Complaint', 'Description', 'Complaint Disposition'])
)

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    .find_all('text:contains(Complaint #)')
    .right(include_source=True)
    .merge()
    .expand(top=5, bottom=7)
)

# Build vertical guidelines from lines

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    .find_all('text:contains(Officer #)')
    .right(include_source=True)
    .merge()
    .expand(top=5, bottom=7)
)

guides = Guides(table)

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      section
      .find('text:contains(Date Assigned)')
      .below(width='element')
      .expand(left=5, right=5)
      .find('text')
    )
    completed = (

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      section
      .find('text:contains(Completed)')
      .below(width='element')
      .expand(left=5, right=5)
      .find('text')
    )
    recorded = (

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      section
      .find('text:contains(Recorded)')
      .below(until='text', width='element')
      .expand(left=5, right=5)
    )
    
    row = {}

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        .find_all('text:contains(Officer #)')
        .right(include_source=True)
        .merge()
        .expand(top=3, bottom=6)
    )
    
    # Use the guides to extract the table

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Extracting Economic Data from Brazil's Central Bank PDF

This PDF is the weekly “Focus” report from Brazil’s central bank with economic projections and statistics. Challenges include commas instead of decimal points, images showing projection changes, and tables without border lines that merge during extraction.

(
    sections[0]
    .expand(top=-50)
    .show()
)

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(
    sections[0]
    .expand(top=-50, right=0)
    .extract_table('stream')
    .to_df(header=False)
    .dropna(axis=0, how='all')

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dataframes = sections.apply(lambda section: (
    section
        .expand(top=-50, right=0)
        .extract_table('stream')
        .to_df(header=False)
        .dropna(axis=0, how='all')

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        include_endpoint=False
    )
    .below(width='element')
    .expand(top=-20)
    .clip(data)
)

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df_2025 = table.expand(top=-5).extract_table('stream').to_df(header=False)
df_2025

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        include_endpoint=False
    )
    .below(width='element')
    .expand(top=-20)
    .clip(data)
)
table.show()

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df_2026 = table.expand(top=-5).extract_table('stream').to_df(header=False).dropna(axis=0, how='all')
df_2026.insert(0, 'year', 2026)
df_2026.insert(0, 'value', headers)
df_2026

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        include_endpoint=False
    )
    .below(width='element')
    .expand(top=-20)
    .clip(data)
)
df_2027 = table.expand(top=-5).extract_table('stream').to_df(header=False).dropna(axis=0, how='all')

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    .expand(top=-20)
    .clip(data)
)
df_2027 = table.expand(top=-5).extract_table('stream').to_df(header=False).dropna(axis=0, how='all')
df_2027.insert(0, 'year', 2027)
df_2027.insert(0, 'value', headers)
df_2027

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    .find('text:contains(2028)')
    .right(include_source=True)
    .below(width='element')
    .expand(top=-20)
    .clip(data)
)
df_2028 = table.expand(top=-5).extract_table('stream').to_df(header=False).dropna(axis=0, how='all')

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    .expand(top=-20)
    .clip(data)
)
df_2028 = table.expand(top=-5).extract_table('stream').to_df(header=False).dropna(axis=0, how='all')
df_2028.insert(0, 'year', 2028)
df_2028.insert(0, 'value', headers)
df_2028

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Extracting State Agency Call Center Wait Times from FOIA PDF

This PDF contains data on wait times at a state agency call center. The main focus is on the data on the first two pages, which matches other states' submission formats. The later pages provide granular breakdowns over several years. Challenges include it being heavily pixelated, making it hard to read numbers and text, with inconsistent and unreadable charts.

        until='text:contains(Please use the comments)',
        include_endpoint=False
    )
    .expand(
        right=-(page.width * 0.58),
        left=-30,
        bottom=3

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