--- title: Parse Charts in PDFs and Analyze with Pandas | LlamaIndex OSS Documentation --- This tutorial shows how to parse a PDF with **specialized chart parsing** enabled, extract table data from a page that contains a chart, and run basic data science with **pandas**. We use the same [2024 Executive Summary PDF](https://fiscal.treasury.gov/files/reports-statements/financial-report/2024/executive-summary-2024.pdf) as in [Parse a PDF & Interpret Outputs](/python/cloud/llamaparse/tutorials/parse_pdf_outputs/index.md); the **third page** includes a chart that LlamaParse can turn into structured data. ## 1. Setup Install the LlamaParse SDK and pandas: Terminal window ``` pip install llama-cloud>=1.0 pandas ``` Set your API key and create a client: ``` import os from getpass import getpass os.environ["LLAMA_CLOUD_API_KEY"] = getpass("Llama Cloud API Key: ") ``` ``` from llama_cloud import AsyncLlamaCloud client = AsyncLlamaCloud() ``` ## 2. Parse with Specialized Chart Parsing [Specialized chart parsing](/python/cloud/llamaparse/processing_options/specialized_chart_parsing/index.md) tells LlamaParse to extract chart and graph data with higher fidelity. Enable it via `processing_options` and request the **items** view so you get structured tables (and figures) per page. We parse the same executive summary PDF and expand `items` so we can pull tables from page 3, which contains the following chart: ![Example Chart](/_astro/deficit_cost_chart.C3yNwn8N_Z1ENGnn.png) ``` # 1) Upload the file file_obj = await client.files.create( file="/content/executive-summary-2024.pdf", purpose="parse", ) # 2) Create a parse job (Agentic Plus tier, latest version) result = await client.parsing.parse( file_id=file_obj.id, tier="agentic_plus", version="latest", processing_options={ "specialized_chart_parsing": "agentic_plus", }, expand=["items"], ) ``` ## 3. Get Table Data from Page 3 The third page of the PDF (index `2` in `result.items.pages`) contains a chart. With chart parsing, LlamaParse often represents the chart’s data as a **table** in the items tree. We collect the first table on that page and use its `rows` for pandas. ``` page_three = result.items.pages[2] for item in page_three.items: print(item) tables = [item for item in page_three.items if getattr(item, "type", None) == "table" or hasattr(item, "rows")] if not tables: raise ValueError("No table found on page 3. Check the PDF or try agentic_plus tier.") table = tables[0] rows = table.rows ``` ## 4. Load the Fiscal-Year Chart into Pandas The chart on this page is a grouped bar chart showing **Budget Deficit** and **Net Operating Cost** (both in billions of dollars) for fiscal years 2020–2024. We turn its table into a clean time-series DataFrame: ``` import pandas as pd # First row as column names, rest as data header = rows[0] df = pd.DataFrame(rows[1:], columns=header) money_cols = [ "Budget Deficit (Billions of Dollars)", "Net Operating Cost (Billions of Dollars)", ] df["Fiscal Year"] = df["Fiscal Year"].astype(int) print("DataFrame:") print(df) ``` ``` DataFrame: Fiscal Year Budget Deficit (Billions of Dollars) \ 0 2020 $3,131.9 1 2021 $2,775.6 2 2022 $1,375.5 3 2023 $1,695.2 4 2024 $1,832.8 Net Operating Cost (Billions of Dollars) 0 $3,841.4 1 $3,094.9 2 $4,171.0 3 $3,417.2 4 $2,425.0 ``` ## 5. Analyze Deficit vs. Net Operating Cost With the data cleaned, we can reproduce the key relationships described under the chart text. **1. Year-over-year changes in both series** ``` df["Deficit YoY Change"] = df["Budget Deficit (Billions of Dollars)"].diff() df["Net Operating Cost YoY Change"] = df["Net Operating Cost (Billions of Dollars)"].diff() print(df[["Fiscal Year", "Budget Deficit (Billions of Dollars)", "Net Operating Cost (Billions of Dollars)", "Deficit YoY Change", "Net Operating Cost YoY Change"]]) ``` ``` Fiscal Year Budget Deficit (Billions of Dollars) \ 0 2020 3131.9 1 2021 2775.6 2 2022 1375.5 3 2023 1695.2 4 2024 1832.8 Net Operating Cost (Billions of Dollars) Deficit YoY Change \ 0 3841.4 NaN 1 3094.9 -356.3 2 4171.0 -1400.1 3 3417.2 319.7 4 2425.0 137.6 Net Operating Cost YoY Change 0 NaN 1 -746.5 2 1076.1 3 -753.8 ``` This highlights, for example, the **sharp spike** in net operating cost in 2022 and the **decline** through 2023–2024. **2. Gap between Net Operating Cost and Budget Deficit** ``` df["Gap (Net Operating Cost - Deficit)"] = ( df["Net Operating Cost (Billions of Dollars)"] - df["Budget Deficit (Billions of Dollars)"] ) print("\nGap between Net Operating Cost and Budget Deficit (billions):") print(df[["Fiscal Year", "Gap (Net Operating Cost - Deficit)"]]) print("\nYear with largest gap:") print(df.loc[df["Gap (Net Operating Cost - Deficit)"].idxmax()]) print("\nYear with smallest gap:") print(df.loc[df["Gap (Net Operating Cost - Deficit)"].idxmin()]) ``` ``` Gap between Net Operating Cost and Budget Deficit (billions): Fiscal Year Gap (Net Operating Cost - Deficit) 0 2020 709.5 1 2021 319.3 2 2022 2795.5 3 2023 1722.0 4 2024 592.2 Year with largest gap: Fiscal Year 2022.0 Budget Deficit (Billions of Dollars) 1375.5 Net Operating Cost (Billions of Dollars) 4171.0 Deficit YoY Change -1400.1 Net Operating Cost YoY Change 1076.1 Gap (Net Operating Cost - Deficit) 2795.5 Name: 2, dtype: float64 Year with smallest gap: Fiscal Year 2021.0 Budget Deficit (Billions of Dollars) 2775.6 Net Operating Cost (Billions of Dollars) 3094.9 Deficit YoY Change -356.3 Net Operating Cost YoY Change -746.5 Gap (Net Operating Cost - Deficit) 319.3 Name: 1, dtype: float64 ``` This reproduces the narrative that **2022** saw the largest divergence between the two metrics, while by **2024** the gap had **narrowed significantly**. **3. Quick visualization (optional)** ``` ax = df.plot( x="Fiscal Year", y=[ "Budget Deficit (Billions of Dollars)", "Net Operating Cost (Billions of Dollars)", ], kind="bar", title="U.S. Budget Deficit & Net Operating Cost (Billions of Dollars)", ) ax.set_ylabel("Billions of dollars") ``` This bar chart closely mirrors Chart 1 in the PDF, but now backed by a DataFrame that you can further slice, aggregate, or feed into downstream analytics. ![Pandas Plot](/_astro/pandas_plot.bko0DG5l_Z1xw6UV.png) ## Summary - Use **specialized chart parsing** (`processing_options.specialized_chart_parsing`: `"agentic"` or `"agentic_plus"`) when your PDF has charts you want as structured data. - Request **items** in `expand` to get per-page tables (and figures). - Pull **table rows** from the page that contains the chart (here, page 3), then build a **pandas DataFrame** from `rows` and run summaries, plots, or filters as needed. For more options (e.g. `efficient` vs `agentic`), see [Specialized Chart Parsing](/python/cloud/llamaparse/processing_options/specialized_chart_parsing/index.md).