Build a Data Science Query Language in Python using Lark

What if you could write something like this:

DATA [1, 2, 3, 4, 5]
SUM
MEAN
STD

…and have it behave like a mini data science engine?

In this tutorial, we’ll build a **Domain-Specific Language (DSL)** for data analysis using:

- Python   
- Lark (parser library)   
- NumPy   

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#  What Are We Building?

We are creating a **custom query language** that:

- Accepts a dataset
- Runs statistical commands
- Prints results

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#  Step 1: Install Dependencies

```
{% endraw %}
bash
pip install lark numpy
{% raw %}

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Step 2: Define the Grammar

The grammar defines how our language looks.

python
from lark import Lark, Transformer
import numpy as np

grammar = """
start: data command+

data: "DATA" list

command: "SUM" -> sum
       | "MEAN" -> mean
       | "STD" -> std
       | "MAX" -> max
       | "MIN" -> min

list: "[" NUMBER ("," NUMBER)* "]"

%import common.NUMBER
%import common.WS
%ignore WS
"""

---

Explanation

start: data command+

• Program must start with DATA
• Followed by one or more commands

---

data: "DATA" list

• Defines dataset input
• Example:

plaintext
  DATA [1, 2, 3]

---

Commands

plaintext
SUM → sum
MEAN → mean
STD → std
MAX → max
MIN → min

• These map text → function names
• -> sum means call sum() in Transformer

---

List Rule

plaintext
list: "[" NUMBER ("," NUMBER)* "]"

• Accepts:

  • [1]
  • [1, 2, 3]

• (, NUMBER)* means repeat

---

Ignore Spaces

plaintext
%ignore WS

• Allows flexible formatting

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⚙️ Step 3: Build the Interpreter

Now we convert parsed text into execution.

python
class DLangInterpreter(Transformer):

    def data(self, items):
        self.data = np.array([float(x) for x in items[0]])
        return self.data

---

Explanation

• items[0] → list of numbers
• Convert to NumPy array
• Store in self.data for reuse

---

Step 4: Add Operations

SUM

python
def sum(self, _):
    print(np.sum(self.data))

MEAN

python
def mean(self, _):
    print(np.mean(self.data))

STD

python
def std(self, _):
    print(np.std(self.data))

MAX

python
def max(self, _):
    print(np.max(self.data))

MIN

python
def min(self, _):
    print(np.min(self.data))

---

Explanation

• Each function matches grammar rule
• _ = unused input
• Uses NumPy for computation
• Prints result immediately

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Step 5: Parse List

python
def list(self, items):
    return items

---

Explanation

• Returns list of numbers
• Passed to data() method

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Step 6: Create the Parser

python
parser = Lark(grammar, parser="lalr", transformer=DLangInterpreter())

---

Explanation

• lalr → fast parsing algorithm
• transformer → auto-executes logic

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Step 7: Read Input File

python
with open("example.dl") as f:
    code = f.read()

parser.parse(code)

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Example example.dl

plaintext
DATA [10, 20, 30, 40]
SUM
MEAN
MAX

---

✅ Output

plaintext
100
25.0
40

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How It Works (Flow)

plaintext
Text Input
   ↓
Parser (Lark)
   ↓
Grammar Rules Match
   ↓
Transformer Methods Trigger
   ↓
NumPy Executes
   ↓
Output Printed

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✨ Why This Is Powerful

• You built a mini programming language

• Clean separation of:

  • Syntax (grammar)
  • Execution (Transformer)

• Easily extensible

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Next Features You Can Add

1. Filtering

plaintext
FILTER > 10

---

2. Sorting

plaintext
SORT ASC

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3. CSV Support

plaintext
DATA file.csv

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4. Chaining

plaintext
DATA [1,2,3,4]
FILTER > 2
MEAN

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Final Thought

This is how real systems like:

• SQL
• Pandas query engine
• Spark

…start at a basic level.

You just built the foundation of a data query engine

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If You Liked This

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And try extending this DSL yourself!