jalali-pandas 1.0.0

Full Jalali calendar support for pandas time series

Jalali Pandas Extension

Full-featured Jalali (Persian/Shamsi) calendar support for pandas — A complete pandas extension providing native Jalali datetime types, time series operations, and calendar-aware functionality.

✨ Features

🎯 Core Types

• JalaliTimestamp: Full-featured scalar type with all date/time properties
• JalaliDatetimeDtype: Registered pandas extension dtype (jalali_datetime64[ns])
• JalaliDatetimeArray: Extension array for Series storage with vectorized operations
• JalaliDatetimeIndex: Complete pandas Index implementation with Jalali awareness

📅 Date Range & Conversion

• jalali_date_range(): Generate date ranges with frequency support (daily, monthly, quarterly, yearly)
• to_jalali_datetime() / to_gregorian_datetime(): Bidirectional conversion for all input types
• String parsing with multiple formats: "1402-06-15", "1402/6/15", "1402-06", "1402"

🔄 Frequency Offsets

• JalaliMonthEnd/Begin: Handle Jalali month boundaries (31-day months 1-6, 30-day months 7-11, Esfand 29/30)
• JalaliQuarterEnd/Begin: Quarter boundaries respecting Jalali calendar
• JalaliYearEnd/Begin: Year boundaries (1 Farvardin start, 29/30 Esfand end)
• JalaliWeek: Saturday-based weeks with custom weekday support
• Frequency aliases: JME, JMS, JQE, JQS, JYE, JYS, JW

📊 Time Series Operations

• resample_jalali(): Jalali-aware resampling with proper calendar boundaries
• JalaliGrouper: Calendar-based grouping by year/month/quarter/day
• Enhanced Accessors: Full Series and DataFrame accessor support
  • Properties: year, month, day, quarter, week, weekday, is_leap_year, etc.
  • Methods: strftime(), normalize(), floor(), ceil(), round(), tz_localize(), tz_convert()

🧪 Quality & Performance

• 94% test coverage with 563+ passing tests
• Type hints throughout (PEP 561 compliant)
• Python 3.9-3.13 support
• pandas 2.0-2.2 compatibility

📦 Installation

Using pip

pip install jalali-pandas

For development:

pip install jalali-pandas[dev]

Using uv (recommended for faster installation)

uv add jalali-pandas

For development:

uv add --dev jalali-pandas

🚀 Quick Start

Basic Usage

import pandas as pd
import jalali_pandas
from jalali_pandas import jalali_date_range, to_jalali_datetime

# Create a Jalali date range
jdates = jalali_date_range("1402-01-01", periods=10, freq="D")
print(jdates)
# JalaliDatetimeIndex(['1402-01-01', '1402-01-02', ..., '1402-01-10'], dtype='jalali_datetime64[ns]', freq='D')

# Convert Gregorian to Jalali
gregorian_dates = pd.date_range("2023-01-01", periods=5)
jalali_dates = to_jalali_datetime(gregorian_dates)
print(jalali_dates)
# JalaliDatetimeIndex(['1401-10-11', '1401-10-12', ..., '1401-10-15'], dtype='jalali_datetime64[ns]')

Series Operations

import pandas as pd
import jalali_pandas

# Create a DataFrame with Gregorian dates
df = pd.DataFrame({
    "date": pd.date_range("2023-01-01", periods=10, freq="D"),
    "value": range(10)
})

# Convert to Jalali using accessor
df["jdate"] = df["date"].jalali.to_jalali()

# Access Jalali date components
df["year"] = df["jdate"].jalali.year
df["month"] = df["jdate"].jalali.month
df["day"] = df["jdate"].jalali.day
df["quarter"] = df["jdate"].jalali.quarter
df["weekday"] = df["jdate"].jalali.weekday
df["is_leap"] = df["jdate"].jalali.is_leap_year

# Format as Persian strings
df["persian_date"] = df["jdate"].jalali.strftime("%Y/%m/%d")

DataFrame Operations

import pandas as pd
import jalali_pandas
from jalali_pandas import jalali_date_range

# Create DataFrame with Jalali dates
df = pd.DataFrame({
    "date": jalali_date_range("1402-01-01", periods=100, freq="D"),
    "value": range(100)
})

# Group by Jalali year and month
monthly = df.jalali.groupby(["year", "month"]).sum()

# Use shortcuts for common groupings
yearly = df.jalali.groupby("year").mean()  # Group by year
quarterly = df.jalali.groupby("yq").sum()  # Group by year-quarter
daily = df.jalali.groupby("ymd").count()   # Group by year-month-day

# Resample with Jalali calendar awareness
monthly_resample = df.set_index("date").resample_jalali("JME").sum()
quarterly_resample = df.set_index("date").resample_jalali("JQE").mean()

Advanced Features

from jalali_pandas import JalaliTimestamp
from jalali_pandas.offsets import JalaliMonthEnd, JalaliYearEnd

# Create Jalali timestamps
jts = JalaliTimestamp(1402, 6, 15, 12, 30, 0)
print(jts.strftime("%Y/%m/%d %H:%M"))  # 1402/06/15 12:30

# Use frequency offsets
end_of_month = jts + JalaliMonthEnd()
end_of_year = jts + JalaliYearEnd()

# Timezone support
jts_tehran = jts.tz_localize("Asia/Tehran")
jts_utc = jts_tehran.tz_convert("UTC")

📚 Documentation

• Full Documentation: https://ghodsizadeh.github.io/jalali-pandas/
• API Reference: https://ghodsizadeh.github.io/jalali-pandas/en/api/
• Examples: 11 Python examples + 2 Jupyter notebooks
• Persian Documentation: مستندات فارسی

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide for details.

📝 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

🙏 Acknowledgments

• Built on top of pandas and jdatetime
• Inspired by the need for proper Jalali calendar support in data analysis

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راهنمای فارسی

برای مطالعه راهنمای فارسی استفاده از کتابخانه به این آدرس مراجعه کنید:

• مستندات کامل: https://ghodsizadeh.github.io/jalali-pandas/fa/
• راهنمای نصب: نصب و راه‌اندازی
• آموزش سریع: شروع سریع
• مقاله آموزشی: معرفی بسته pandas-jalali | آموزش کار با تاریخ شمسی در pandas

راهنمای ویدیویی