In this short step-by-step guide, you will learn how to build a simple regex to match leading zeroes in text and how to use it in a real financial data example with yfinance.

Detecting leading zeroes is useful when you need to:

  • Validate numeric input
  • Clean financial datasets
  • Filter invalid values
  • Ensure numbers are non-zero
  • Process stock volume or price data

We will cover:

  • Regex pattern explanation
  • Python example
  • Practical yfinance use case
  • Full working example at the end

Step 1 — Understand the Regex Pattern for Leading Zeroes

To match a number that starts with zero, use:

^0+

Explanation

  • ^ → Start of string
  • 0+ → One or more zeroes

If you want to match only standalone zero values:

^0+$

If you want to match zero followed by space or word boundary (similar to the StackOverflow example):

^0\b

Step 2 — Simple Python Example

Let’s test this in Python:

import re

samples = [
    "3400",
    "0 fd",
    "df 0 f",
    "0.0",
    "df0",
    "-0-"
]

pattern = re.compile(r"^0\b")

for text in samples:
    if pattern.search(text):
        print(f"Matched: {text}")

Output

Matched: 0 fd
Matched: 0.0

This detects strings that start with zero.

Step 3 — Match Pure Zero Values Only

If you want to verify that a value is exactly zero:

pattern = re.compile(r"^0+$")

values = ["0", "00", "000", "10", "01"]

for value in values:
    if pattern.match(value):
        print(f"Pure zero value: {value}")

Step 4 — Real Practical Example Using yfinance

Now let’s use this in a real financial use case.

Imagine you download stock data and want to:

  • Detect rows where Volume starts with zero
  • Filter invalid or corrupted financial values
  • Validate imported CSV stock data

Example — Detect Zero Volume Days

import yfinance as yf
import re

ticker = yf.Ticker("AAPL")
df = ticker.history(period="1mo")

pattern = re.compile(r"^0+$")

df["Volume_str"] = df["Volume"].astype(str)

zero_volume_rows = df[df["Volume_str"].str.match(pattern)]

print(zero_volume_rows)

Why this is useful?

  • Detect missing trading data
  • Clean financial dashboards
  • Validate stock market datasets
  • Filter bad imports

Step 5 — Remove Leading Zeroes from Financial Data

If you imported numbers as strings and want to clean them:

df["Clean_Volume"] = df["Volume_str"].str.replace(r"^0+", "", regex=True)

This removes leading zeroes safely.

Full Working Example — Regex + yfinance

import yfinance as yf
import re
import pandas as pd

# Download stock data
ticker = yf.Ticker("AAPL")
df = ticker.history(period="1mo")

# Convert Volume to string
df["Volume_str"] = df["Volume"].astype(str)

# Regex pattern for pure zero values
zero_pattern = re.compile(r"^0+$")

# Find zero-volume days
zero_volume = df[df["Volume_str"].str.match(zero_pattern)]

print("Zero volume rows:")
print(zero_volume)

# Remove leading zeroes example
df["Clean_Volume"] = df["Volume_str"].str.replace(r"^0+", "", regex=True)

print("\nCleaned Volume Column:")
print(df[["Volume_str", "Clean_Volume"]].head())

When Should You Use This Regex?

Use leading zero regex when:

  • Validating numeric form input
  • Cleaning financial datasets
  • Checking non-zero values
  • Preprocessing stock or trading data
  • Cleaning scraped price data

Summary

To match leading zeroes in Python:

^0+

To match only pure zero values:

^0+$

To detect zero at start of string:

^0\b

Combining regex with yfinance makes it easy to clean and validate real stock market data in Python.

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