README.md

Novel grid-style programming

Excel-like Grid-style programming

Debugging SQL can be extremely cumbersome

Extract SQL to debug intermediate steps

Debugging Python is also tedious

Employ print method to output intermediate results

SPLUser-friendly Debugging IDE

High Interactivity for Exploratory Analysis

XLL Plugin Helps Excel

Write SPL code in Excel directly Finding periods during which stocks have risen consecutively for more than 5 days

= spl （“=E（？1）.sort（CODE，DT）.group @ i（CODE！=CODE [-1] || CL <CL [-1]）.select（~.len（）> = 5）.conj（）” ，A1：D253 ）

Concise and Powerful Code

Comprehensive and Simple Operations

Unique Set and Ordered Operations

calculate the longest consecutive rising days for each stock

• SPL

	A
2	StockRecords.xlsx
3	=T(A1).sort(DT)
4	=A2.group(CODE;~.group@i(CL < CL[-1]).max(~.len()):max_increase_days)

Especially skilled at complex scenarios such as order-related operations, sliding windows, and cross-row computations,much simpler than SQL or Python

• Python

import pandas as pd
stock_file = "StockRecords.txt"
stock_info = pd.read_csv(stock_file,sep="\t")
stock_info.sort_values(by=['CODE','DT'],inplace=True)
stock_group = stock_info.groupby(by='CODE')
stock_info['label'] = stock_info.groupby('CODE')['CL'].diff().fillna(0).le(0).astype(int).cumsum()
max_increase_days = {}
for code, group in stock_info.groupby('CODE'):
    max_increase_days[code] = group.groupby('label').size().max() – 1
max_rise_df = pd.DataFrame(list(max_increase_days.items()), columns=['CODE', 'max_increase_days'])

• SQL

SELECT CODE, MAX(con_rise) AS longest_up_days
FROM (
    SELECT CODE, COUNT(*) AS con_rise
    FROM (
        SELECT CODE, DT,  SUM(updown_flag) OVER (PARTITION BY CODE ORDER BY CODE, DT) AS no_up_days
        FROM (
            SELECT CODE, DT, 
                    CASE WHEN CL > LAG(CL) OVER (PARTITION BY CODE ORDER BY CODE, DT)  THEN 0
                    ELSE 1 END AS updown_flag
            FROM stock
        )
    )
    GROUP BY CODE, no_up_days
)
GROUP BY CODE

What to use for data analysis programming: SPL,Python or SPL?

Easy Big Data and Parallel Support

• Big Data

In Memory

	A
1	StockRecords.txt
2	=file(A1).import@t().sort(CODE,DT)
3	=A2.group(CODE;~.group@i(CL < CL[-1]).max(~.len()):mi)

External Storage

	A
1	StockRecords.txt
2	=file(A1).cursor@t().sort(CODE,DT)
3	=A2.group(CODE;~.group@i(CL < CL[-1]).max(~.len()):mi)

• Parallel Computation

In Memory

	A
1	StockRecords.txt
2	=file(A1).cursor@t().sortx(CODE,DT)
3	=A2.group@i(CODE!=CODE[-1]
4	=A3.select(~.len()>=5).conj()

External Storage

	A
1	StockRecords.txt
2	=file(A1).cursor@tm().sortx(CODE,DT)
3	=A2.group@i(CODE!=CODE[-1]
4	=A3.select(~.len()>=5).conj()

Lightweight and Portable