Revised 29 October 2026 · v1.0.0 · what changed

Code: github.com/herrrickshaw/ppac-ready-reckoner-data

Watch the walkthrough

Watch on YouTube: youtu.be/nL78kDD0QSQ

Why it is useful

• Energy analysts, students and journalists skip the PDF copy-paste: refining, gas, ethanol and sales tables are already CSV.
• It replaces manual retyping of PPAC's Ready Reckoner, and the extraction script (extract_rr.py) can be pointed at the next edition.
• The tables cover Table 1.1 economy indicators through Table 6.10 ethanol blending, including gross refining margins by refinery and state-wise fuel sales.
• Evidence from the run: all 100 CSVs loaded in pandas (3,465 rows). It also surfaced real clean-up work: spaces stripped from state names, '**' markers for missing values, Indian-style numbers like 13,61,343, and one shifted Grand Total row.

What it is for

The repo holds the tables of the PPAC Ready Reckoner (annual, FY2025-26 H1 edition) as CSVs under annual_fy2025-26_h1/csv/, with index.csv mapping each file to its table number, caption and page. extract_rr.py rebuilds the CSVs from a Ready Reckoner PDF using pdfplumber.

The tables are the publisher's figures as printed; this repo extracts them but does not correct them.

Who it is for

• Energy and fuels analysts who need Indian refining, gas, ethanol and sales data in tabular form
• Students building dashboards or models on public petroleum statistics
• Researchers who want a starting point for extracting the next Ready Reckoner edition

What you need

Get the code and an AI assistant

You do not have to be a programmer. Get the repository, give it to an AI assistant, and ask in plain language. Any of these routes works:

git clone https://github.com/herrrickshaw/ppac-ready-reckoner-data.git
cd ppac-ready-reckoner-data
claude      # or: gemini

Install

1. Clone the repository

   git clone https://github.com/herrrickshaw/ppac-ready-reckoner-data.git
   cd ppac-ready-reckoner-data

2. Install pandas in a virtual environment

   python3 -m venv .venv && source .venv/bin/activate
   pip install pandas

Quick start: commands and what you should see

Load every table and count rows

python3 -c "import pandas as pd,glob;fs=sorted(glob.glob('annual_fy2025-26_h1/csv/*.csv'));print(len(fs),sum(len(pd.read_csv(f,header=None,dtype=str)) for f in fs))"

100 3465

Read one table (gross refining margins)

python3 -c "import pandas as pd;print(pd.read_csv('annual_fy2025-26_h1/csv/Table_4.7_grossrefiningmargins_grm_ofrefineries.csv',skiprows=2).head(8))"

Refiners by row and financial years by column, with $/bbl values (see sample output).

Sample outputs

Real output from a run on the date shown in the sources line; trimmed to fit.

Table 4.7 Gross refining margins ($/bbl), first rows (run 19 October 2026)

pd.read_csv('Table_4.7_grossrefiningmargins_grm_ofrefineries.csv', skiprows=2).head(8)
      Company  2020- 21  2021- 22 2022- 23 2023- 24 2024- 25 2024-25 (Q1) 2025-26 (Q1)
PSUrefineries       NaN       NaN      NaN      NaN      NaN          NaN          NaN
        IOCL#      5.64     11.25    19.52    12.05     4.80         6.39         2.15
        BPCL       4.06      9.09    20.24    14.14     6.82         7.86         4.88
        HPCL       3.86      7.19    12.09     9.08     5.74         5.03         3.08
        CPCL       7.14      8.85    12.48     8.64     4.22         6.33         3.22
        MRPL       3.71      8.72     9.88    10.36     4.45         4.70         3.88
        NRL#       37.23     43.46    35.82    29.72    19.95        21.50        21.41
        BORL       6.20     11.00       **       **       **           **           **

Values as extracted; '#' and '**' are the source's footnote markers (meaning is in the PDF). NRL's much higher figures are as printed in the extract; check the source footnote before using them.

Table 6.10 Ethanol blending programme, first rows

pd.read_csv('Table_6.10_ethanol_blending_programme.csv', skiprows=3, header=None, names=[...])
sr  state              qty_2425_cr_l  blend_2425  qty_2324_cr_l  blend_2324
 1  AndhraPradesh              40.84       19.3%          29.30       14.3%
 2  Andaman&Nicobar             0.00        7.2%           0.28        7.2%
 4  Assam                      29.03       19.2%          12.51       15.2%
11  Gujarat                    43.02       19.2%          39.57       14.3%
12  Haryana                    39.60       19.5%          25.98       14.8%
36  WestBengal                 35.01       19.5%          26.07       15.1%
    GrandTotal                   NaN      904.80          19.2%      707.40  (shifted)

Two extraction artefacts are visible: state names lost their spaces, and the Grand Total row is shifted one column to the right. Period 1 Nov 2024 to 30 Sep 2025 for the 2024-25 columns.

Table 1.1 Selected indicators (raw header and first rows)

head -6 Table_1.1_selected_indicators_of_the_indian_economy.csv
Table 1.1 : Selected indicators of the Indian economy,,,,,,,,,
General,,,,,,,,,
Sl. no.,Particulars,Source,Unit/ Base,2020-21,2021-22,2022-23,2023-24,2024-25,2025-26 (H1)
1,Projected Total Population ...,Report of the Technical Group ...,('000),"13,61,343","13,73,761",...
2,GDP,MoSPI,Rs Crores,"1,36,94,869","1,50,21,846",...,"47,89,000 (Q1)"
,GDP Growth,MoC&I,Growth %,-5.8,9.7,7.6,9.2,6.5,7.8 (Q1)

Titles and numbers carry Indian digit grouping and annotation suffixes like '(Q1)'; strip them before converting to numbers. Text trimmed with '...'.

Ways to use it

Sample prompts for your AI assistant

Copy, paste and edit the parts in [brackets].

• Load annual_fy2025-26_h1/index.csv and list the tables about ethanol, with their file names.
• Write a pandas function that cleans Indian-style numbers like '13,61,343' and values such as '47,89,000 (Q1)' and '**' into floats or NaN.
• Fix the Table 6.10 Grand Total row that is shifted one column, and add the space back into state names.
• Plot gross refining margins by refinery for 2020-21 to 2024-25 from Table 4.7, and flag values marked with ** or #.
• Read extract_rr.py and explain how it finds table captions and joins wrapped header cells.

Limits and honest caveats

Sources: the ppac-ready-reckoner-data repo (index.csv, 100 CSVs, extract_rr.py docstring), loaded with pandas in a throwaway virtualenv on 19 October 2026 from a fresh clone.

About this article: Researched, written and edited by Umashankar Triplicane Dwarakanathan, with AI research assistance; every figure is meant to trace to the primary source cited. See the Editorial Policy for how sourcing, AI use and corrections work.