docs: add validation examples for all 5 recently added South Asian countries (ID, BD, PK, LK, NP)

README.md

@@ -13,52 +13,16 @@ Ideal for **form validation, data cleaning, and big data applications**.
 
 ## Table of Contents
 
-- [Features](#features)
 - [Installation](#installation)
-- [Usage](#usage)
+- [Quick Start](#quick-start)
+- [Features](#features)
+- [Command-Line Usage](#command-line-usage)
 - [Big Data Support](#big-data-support)
 - [Contributing](#contributing)
 - [License](#license)
 
 ---
 
-## Features
-
-- ✅ 50+ countries included, with postal code regex patterns  
-- ✅ Validate postal codes by **country code** or **country name**
-```python
-from postal_regex.core import validate
-
-validate("IN", "110001")      # True
-validate("India", "110001")   # True
-validate("US", "12345-6789")  # True
-````
-
-* ✅ Normalize country identifiers
-
-```python
-from postal_regex.core import normalize
-
-normalize("United States")  # "US"
-normalize("India")          # "IN"
-```
-
-* ✅ Works with **Pandas and Spark DataFrames**
-
-```python
-import pandas as pd
-from postal_regex.bulk import validate_dataframe
-
-df = pd.DataFrame({"country": ["US", "FR"], "postal_code": ["90210", "75001"]})
-df_validated = validate_dataframe(df, country_col="country", postal_col="postal_code")
-print(df_validated)
-```
-
-* ✅ JSON schema ensures consistent data structure
-* ✅ Precompiled regex for fast Python validation
-
----
-
 ## Installation
 
 ```bash
@@ -75,37 +39,61 @@ pip install -e .
 
 ---
 
-## Big Data Support
+## Quick Start
 
-Validate postal codes in **large datasets** with Spark or Pandas.
+```python
+from postal_regex.core import validate
 
-### Spark Example
+# Validate by country code
+validate("IN", "110001")      # True
+validate("US", "12345-6789")  # True
+
+# By country name
+validate("India", "110001")   # True
+
+# Invalid returns False
+validate("US", "ABCDE")       # False
+```
+
+---
+
+## Features
+
+- ✅ 50+ countries included, with postal code regex patterns
+- ✅ Validate postal codes by **country code** or **country name**
+- ✅ Normalize country identifiers
 
 ```python
-from pyspark.sql import SparkSession
-from postal_regex.bulk import validate_spark_dataframe
+from postal_regex.core import normalize
 
-spark = SparkSession.builder.getOrCreate()
-df = spark.createDataFrame([
-    {"country": "FR", "postal_code": "75001"},
-    {"country": "DE", "postal_code": "10115"}
-])
-df_validated = validate_spark_dataframe(df, country_col="country", postal_col="postal_code")
-df_validated.show()
+normalize("United States")  # "US"
+normalize("India")          # "IN"
 ```
 
-### Pandas Example
+- ✅ Works with **Pandas and Spark DataFrames**
+- ✅ JSON schema ensures consistent data structure
+- ✅ Precompiled regex for fast Python validation
+
+### Examples for Recently Added Countries (Indonesia, Bangladesh, Pakistan, Sri Lanka, Nepal)
 
 ```python
-import pandas as pd
-from postal_regex.bulk import validate_dataframe
+from postal_regex.core import validate
 
-df = pd.DataFrame({
-    "country": ["FR", "DE"],
-    "postal_code": ["75001", "10115"]
-})
-df_validated = validate_dataframe(df, country_col="country", postal_col="postal_code")
-print(df_validated)
+# Indonesia (ID)
+validate("ID", "12345")           # **Expected: True** (valid 5-digit code)
+validate("Indonesia", "12345")    # **Expected: True**
+
+# Bangladesh (BD)
+validate("BD", "1205")            # **Expected: True** (valid 4-digit code)
+
+# Pakistan (PK)
+validate("PK", "44000")           # **Expected: True** (valid 5-digit code)
+
+# Sri Lanka (LK)
+validate("LK", "00300")           # **Expected: True** (valid 5-digit code, e.g., Colombo)
+
+# Nepal (NP)
+validate("NP", "44600")           # **Expected: True** (valid 5-digit code, e.g., Kathmandu)
 ```
 
 ---
@@ -125,7 +113,7 @@ python -m postal_regex.cli validate <postal_code1> <postal_code2> ... <country>
 **Examples:**
 
 Validate a single code for India:
-```
+```sh
 python -m postal_regex.cli validate 110001 IN
 ```
 Output:
@@ -134,7 +122,7 @@ Output:
 ```
 
 Validate multiple codes for the United States:
-```
+```sh
 python -m postal_regex.cli validate 12345 90210 US
 ```
 Output:
@@ -144,30 +132,77 @@ Output:
 ```
 
 You can also use country names:
-```
+```sh
 python -m postal_regex.cli validate 110001 India
 ```
 
 ### View Validation Statistics
 
 To view local validation statistics:
-```
+```sh
 python -m postal_regex.cli stats
 ```
 
 To reset statistics:
-```
+```sh
 python -m postal_regex.cli stats --reset
 ```
 
 ---
 
+## Big Data Support
+
+Validate postal codes in **large datasets** with Spark or Pandas.
+
+### Spark Example
+
+```python
+from pyspark.sql import SparkSession
+from postal_regex.bulk import validate_spark_dataframe
+
+spark = SparkSession.builder.getOrCreate()
+df = spark.createDataFrame([
+    {"country": "FR", "postal_code": "75001"},
+    {"country": "DE", "postal_code": "10115"}
+])
+df_validated = validate_spark_dataframe(df, country_col="country", postal_col="postal_code")
+df_validated.show()
+```
+
+### Pandas Example
+
+```python
+import pandas as pd
+from postal_regex.bulk import validate_dataframe
+
+df = pd.DataFrame({
+    "country": ["FR", "DE"],
+    "postal_code": ["75001", "10115"]
+})
+df_validated = validate_dataframe(df, country_col="country", postal_col="postal_code")
+print(df_validated)
+```
+
+---
+
 ## Contributing
 
-We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
+We ❤️ contributions! Help expand coverage or improve docs.
+
+1. Fork the repo and create a feature branch: `git checkout -b feat/new-country`.
+2. Add/update patterns in `postal_regex/data/` (follow JSON schema).
+3. Run tests: `pytest`.
+4. Commit and push: `git commit -m "Add postal patterns for [Country]"`.
+5. Open a Pull Request—reference any related issue.
+
+See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines, including testing new patterns and updating examples.
 
 ---
 
 ## License
 
-MIT License. See [LICENSE](LICENSE) for details.
+MIT License. See [LICENSE](LICENSE) for details.
+
+---
+
+⭐ **Star this repo** if it's useful! Found a bug or missing country? [Open an issue](https://github.com/ankitgadling/postal-regex/issues). Questions? Join the discussion!