USD/INR Exchange Rate History: Rupee per Dollar Since 1973

The Indian rupee is the currency of the world’s most populous country and one of the clearest examples of a long structural depreciation against the dollar. This dataset tracks the Federal Reserve H.10 rate, expressed as Indian rupees per US dollar, distributed via FRED under the code DEXINUS, with daily coverage since 1973. A higher number means a stronger dollar and a weaker rupee.

Dataset: USD/INR Exchange Rate (1973–2026) · Updated 2026-05-29

Source: Federal Reserve Board · H.10 Foreign Exchange Rates (via FRED)

Macro Takeaway

Dataset Overview

Dataset Variables

The CSV and Excel files contain the following columns. Each row represents one business day.

Column names match the CSV headers exactly.

Download the Complete Dataset

The full dataset is available in CSV and Excel formats — daily observations spanning 1973–2026.

FRED Direct CSV Access

The underlying data is published in the Federal Reserve Economic Data (FRED) database under the series code DEXINUS, sourced from the Federal Reserve Board’s H.10 release:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=DEXINUS

The Eco3min dataset mirrors the same daily series, packaged in a stable, versionable CSV with consistent column names — designed for direct ingestion in Python, R, or any data pipeline. The URL never changes, making it suitable for automated scripts.

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/usd-inr-exchange-rate.csv

This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.

Using the Dataset in Python

import pandas as pd

url = "https://eco3min.fr/dataset/usd-inr-exchange-rate.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(df.head())
print(df["usd_inr"].describe())

df.plot(x="date", y="usd_inr", title="USD/INR Exchange Rate", legend=False)

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/usd-inr-exchange-rate.csv"
df <- read_csv(url)

head(df)
summary(df$usd_inr)

Both examples load the dataset directly from the URL — no download or API key required.

Methodology

The Federal Reserve reports the Indian rupees per US dollar noon rate in its H.10 release.

The Federal Reserve publishes these rates daily in its H.10 release. Values are indicative noon or end-of-day rates, not transactable quotes, and there are no observations on weekends or US holidays. The series begins in 1973.

This dataset is updated via an automated pull from the FRED API (series DEXINUS) by an Eco3min pipeline running on GitHub Actions, which regenerates the cleaned CSV and Excel files and refreshes the page metadata.

Data Quality & Provider Notes

The Federal Reserve H.10 rates are a standard, widely cited reference for bilateral exchange rates. A few provider-specific points matter when using this series.

• Indicative, not transactable. H.10 rates are reference rates collected at a set time of day. They will differ from the bid/ask a trader actually deals on, and from other fixings (ECB, WM/Refinitiv 4pm London).
• Gaps on non-business days. There are no observations on weekends or US public holidays, so the series is not strictly continuous in calendar time.
• Bilateral, not trade-weighted. This is a single currency pair. It is not the broad, trade-weighted dollar index, which aggregates many bilateral rates.
• Discontinued or restated quotes. The Federal Reserve has occasionally changed how a rate is reported; treat very long histories as broadly consistent rather than methodologically identical throughout.

Common Pitfalls When Using This Series

1. Reading the quotation direction backwards. This series is Indian rupees per US dollar, so the number rises when the dollar strengthens and falls when the rupee strengthens. The Reserve Bank of India actively manages the rupee, smoothing moves through intervention. Getting the direction wrong inverts every move and every regime described below.
2. Treating gaps as missing data. Weekends and holidays have no H.10 observation; this is by design, not a data error. Resample carefully before computing returns.
3. Confusing a bilateral rate with the dollar’s overall strength. One pair can move on currency-specific news while the broad, trade-weighted dollar barely moves, and vice versa.

Historical Regimes

1973–1991 — Managed and devalued. The rupee was managed and periodically devalued, notably in the 1991 balance-of-payments crisis.

1992–2002 — Liberalisation. India moved to a market-determined rate in the early 1990s; the rupee depreciated steadily.

2003–2012 — Range, then taper risk. The rupee traded in a band before weakening sharply heading into the 2013 taper tantrum.

2013–2019 — Structural depreciation. The rupee continued a slow structural slide on persistent inflation and current-account deficits.

2020–2026 — Record lows. The rupee drifted to successive record lows past 80, then 83 per dollar, smoothed by RBI intervention.

Related Macroeconomic Datasets

Emerging Markets Hub

This dataset is part of the Eco3min repository of exchange rates and policy rates for the major emerging-market economies, all sourced from the Federal Reserve and the OECD via FRED.

Explore the Emerging Markets Hub

Sources

• Federal Reserve Board — H.10 Foreign Exchange Rates, USD/INR
• Federal Reserve Bank of St. Louis — FRED database, series DEXINUS
• Federal Reserve H.10 noon rate, New York — basis underlying the FRED series

Dataset Reference

Eco3min Research. (2026). USD/INR Exchange Rate — Daily (1973-2026). Source: Federal Reserve Board H.10 via FRED.<br> https://eco3min.fr/en/usd-inr-exchange-rate-history-dataset/