Jalali Pandas Extentsion

A pandas extension that solves all problems of Jalai/Iraninan/Shamsi dates

Features

Series Extenstion

• Convert string to Jalali date 1388/03/25 –> jdatetime(1388,3,25,0,0)
• Convert gregorian date to Jalali date datetime(2019,11,17,0,0) –> jdatetime(1398,8,26,0,0)
• Convert Jalali date to gregorian date jdatetime(1398,10,18,0,0) –> datetim(2020,1,8,6,19)

DataFrame extenstion

• Support grouping by Jalali date
• Group by year, month, days, …
• Shortcuts for groups: ymd for ['year','month','day'] and more
• Resampling: Convenience method for frequency conversion and resampling of time series but in Jalali dateformat. (comming soon)

Installation

pip install -U jalali-pandas

Usage

Just import jalali-pandas and use pandas just use .jalali as a method for series and dataframes. Nothin outside pandas.

jalali-pandas is an extentsion for pandas, that add a mehtod for series/columns and dataframes.

Series

import pandas as pd
import jalali_pandas

# create dataframe
df = pd.DataFrame({"date": pd.date_range("2019-01-01", periods=10, freq="D")})

# convert to jalali
df["jdate"] = df["date"].jalali.to_jalali()

# convert to gregorian
df["gdate"] = df["jdate"].jalali.to_gregorian()

# parse string to jalali
df1 = pd.DataFrame({"date": ["1399/08/02", "1399/08/03", "1399/08/04"]})
df1["jdate"] = df1["date"].jalali.parse_jalali("%Y/%m/%d")


# get access to jalali year,quarter ,month, day and weekday
df['year'] = df["jdate"].jalali.year
df['month'] = df["jdate"].jalali.month
df['quarter'] = df["jdate"].jalali.quarter
df['day'] = df["jdate"].jalali.day
df['weekday'] = df["jdate"].jalali.weekday

DataFrame

import pandas as pd
import jalali_pandas

df = pd.DataFrame(
    {
    "date": pd.date_range("2019-01-01", periods=10, freq="M"),
    "value": range(10),
    }
)
# make sure to create a column with jalali datetime format. (you can use any name)
df["jdate"] = df["date"].jalali.to_jalali()


# group by jalali year
gp = df.jalali.groupby("year")
gp.sum()

#group by month
mean = df.jalali.groupby('mean')

#groupby year and month and day
mean = df.jalali.groupby('ymd')
# or
mean = df.jalali.groupby(['year','month','day'])


#groupby year and quarter
mean = df.jalali.groupby('yq')
# or
mean = df.jalali.groupby(['year','quarter'])

---

راهنمای فارسی

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

[معرفی بسته pandas-jalali

آموزش کار با تاریخ شمسی در pandas](https://learnwithmehdi.ir/posts/jalali-pandas/)

راهنمای ویدیویی