Pandas Row by Row Logic Acceleration

Me

if I have a time series data, and for every timestamp, I need complex logic, which will create new variables in that timestamp, a lot of these complex logics.

then, what is the best way you can think of that I can do it in python?

background: before i have tried:

1. vectorize calculation, but it might be hard for complex logics

2. create class to loop each timestamp, grab variables and calculate, then save back to data frame: slow

Gemini

The best way to apply complex, row-by-row logic to time-series data in Python is to use Numba to accelerate a Python function that loops over the data.

This approach combines the flexibility of writing your complex logic in a standard Python loop with the near-C speed of Just-In-Time (JIT) compilation, directly solving the two problems you’ve faced: it’s not hard to write complex logic, and it’s not slow.

A powerful modern alternative is using the Polars DataFrame library, which has an expressive API for complex conditional logic that is highly optimized and parallelized.

---

## Recommended Approach: Numba 🚀

Numba is a Just-In-Time (JIT) compiler that translates a subset of Python and NumPy code into fast machine code. You simply write your logic in a Python function and add a Numba decorator (@numba.jit) to it.

Why it works for you:

• Performance: It compiles your Python loop into highly optimized machine code, making it orders of magnitude faster than a standard Python loop.

• Flexibility: You can write any complex if/elif/else logic, maintain state between iterations, or perform intricate calculations inside the function, just as you would in a normal loop.

• Ease of Use: It only requires adding a single line of code (the decorator) to your function.

You must pass NumPy arrays to the Numba function, not the entire Pandas DataFrame, as Numba is optimized to work on these arrays.

Python

Code

import pandas as pd
import numpy as np
import numba

# Sample time-series data
data = {
    'timestamp': pd.to_datetime(pd.date_range(start='2025-01-01', periods=1_000_000, freq='s')),
    'price': 100 + np.random.randn(1_000_000).cumsum(),
    'volume': np.random.randint(100, 1000, 1_000_000)
}
df = pd.DataFrame(data)

# Your complex logic in a standard Python function
# Note the @numba.jit decorator
@numba.jit(nopython=True)
def calculate_complex_variables(prices, volumes):
    n = len(prices)
    # Create empty arrays to store results
    new_var_1 = np.empty(n)
    new_var_2 = np.empty(n)
    
    # --- The Loop ---
    # This loop will be compiled to fast machine code
    for i in range(n):
        # Example complex logic for each timestamp 'i'
        
        # Logic 1: A conditional signal based on price and volume
        if prices[i] > prices[i-1] and volumes[i] > 500 and i > 0:
            new_var_1[i] = 1 # 'Buy Signal'
        elif prices[i] < prices[i-1] and i > 0:
            new_var_1[i] = -1 # 'Sell Signal'
        else:
            new_var_1[i] = 0 # 'Hold'
            
        # Logic 2: A stateful calculation (e.g., cumulative sum on a condition)
        if i == 0:
            new_var_2[i] = 0
        elif new_var_1[i] == 1: # If it was a buy signal
            new_var_2[i] = new_var_2[i-1] + volumes[i] # Add volume to previous state
        else:
            new_var_2[i] = new_var_2[i-1] # Carry previous state forward

    return new_var_1, new_var_2

# --- Execution ---
# Pass the underlying NumPy arrays (.values or .to_numpy()) to the function
price_array = df['price'].to_numpy()
volume_array = df['volume'].to_numpy()

signal, cumulative_volume = calculate_complex_variables(price_array, volume_array)

# Assign the fast results back to the DataFrame
df['signal'] = signal
df['cumulative_volume_on_buy'] = cumulative_volume

print(df.head())

---

## Powerful Alternative: Polars ⚙️

Polars is a blazingly fast DataFrame library written in Rust. Its strength is a powerful expression API that allows you to define complex logic declaratively. Polars’ query optimizer then figures out the fastest way to execute it, often in parallel.

Why it works for you:

• Highly Performant: It’s often even faster than vectorized NumPy/Pandas for complex queries.

• Expressive: The when/then/otherwise syntax makes it easy to chain together complex conditional logic.

• Scalable: It’s designed for out-of-core computation, meaning it can handle datasets larger than your computer’s RAM.

The main drawback is that it has a different API, so it requires learning a new way of thinking compared to Pandas.

Python

Code

import polars as pl
import numpy as np

# Create the same data, but as a Polars DataFrame
data = {
    'price': 100 + np.random.randn(1_000_000).cumsum(),
    'volume': np.random.randint(100, 1000, 1_000_000)
}
pl_df = pl.DataFrame(data)

# --- Execution using Polars Expressions ---
pl_df = pl_df.with_columns(
    # Logic 1: Conditional signal
    pl.when( (pl.col("price") > pl.col("price").shift(1)) & (pl.col("volume") > 500) )
      .then(pl.lit(1)) # 'Buy Signal'
      .when(pl.col("price") < pl.col("price").shift(1))
      .then(pl.lit(-1)) # 'Sell Signal'
      .otherwise(pl.lit(0)) # 'Hold'
      .alias("signal"),
).with_columns(
    # Logic 2: Stateful calculation using a window function
    pl.when(pl.col("signal") == 1)
      .then(pl.col("volume"))
      .otherwise(pl.lit(0))
      .cumsum()
      .alias("cumulative_volume_on_buy")
)

print(pl_df.head())

---

## Comparison and Summary

Method	Best For	Pros	Cons
Numba	Maximum flexibility with high performance. Best when logic is truly iterative or stateful.	🧑‍💻 Write plain Python loops. 🚀 Massive speedup. ✅ Easy to integrate with Pandas.	⚠️ Requires passing NumPy arrays. ⏳ Small compile time on first run.
Polars	Complex conditional logic on large datasets. Best for building a data pipeline from the ground up.	⚡️ Extremely fast & parallelized. 🧠 Expressive, readable API. 💪 Handles huge datasets.	📚 Learning curve (different from Pandas API).
Pandas Vectorization	Simple, arithmetic operations. (df['a'] * df['b'])	✅ Simple, idiomatic Pandas. 🏃‍♂️ Fast for simple math.	❌ Becomes clunky or impossible for complex if/else or stateful logic.
.apply() / Loop	Small datasets or prototyping.	✅ Easy to write and understand.	🐢 Extremely slow on large data; should be avoided in production.

grouping data

That’s an excellent question. You’re hitting on a key challenge of scaling up complex data processing: code organization and maintainability.

The most elegant and Numba-friendly way to manage groups of columns is to bundle them into 2D NumPy arrays (matrices) before passing them to your function.

Instead of your function signature being my_func(price, volume, x, y, z, a, b, c), it becomes a much cleaner my_func(market_data, vehicle_data, object_data). This approach mimics the organization of a class without the performance penalty inside the compiled code.

---

## The Best Practice: Group Columns into 2D Arrays ⚙️

Here is the step-by-step method to achieve this elegance.

Step 1: Organize Column Names in Lists

First, define your logical column groups as lists of strings. This is your central management point. If you need to add a new vehicle dynamics column, you only have to add it to one list.

Python

Code

# --- 1. Define column groups ---
vehicle_dynamic_cols = ['x_pos', 'y_pos', 'velocity', 'acceleration']
object_detection_cols = ['obj_a_dist', 'obj_b_dist', 'obj_c_confidence']

Step 2: Extract Groups into 2D NumPy Arrays

Use these lists to select the columns from your DataFrame and convert them into two separate 2D NumPy arrays. This is still a very fast, view-based operation.

Python

Code

# --- 2. Extract data into matrices ---
vehicle_data = df[vehicle_dynamic_cols].to_numpy()
object_data = df[object_detection_cols].to_numpy()

Step 3: Adapt the Numba Function to Accept 2D Arrays

Modify your Numba function to accept these 2D arrays. Inside the loop, you access specific columns using their index ([i, 0], [i, 1], etc.).

To keep it elegant and avoid “magic numbers” (like 0, 1, 2), define constants for the column indices. This makes your Numba code readable and easy to maintain.

Python

Code

import numba

# --- PRO TIP: Define column indices as constants for readability ---
# These constants will be compiled into the function
X_POS, Y_POS, VELOCITY, ACCELERATION = 0, 1, 2, 3
OBJ_A, OBJ_B, OBJ_C = 0, 1, 2

@numba.jit(nopython=True)
def calculate_new_features(vehicle_data, object_data):
    n = len(vehicle_data) # Number of timestamps
    new_feature_1 = np.empty(n)

    for i in range(n):
        # Access data using the readable constants
        current_velocity = vehicle_data[i, VELOCITY]
        obj_a_distance = object_data[i, OBJ_A]
        obj_b_distance = object_data[i, OBJ_B]

        # Your complex logic here...
        if current_velocity > 20.0 and (obj_a_distance < 50 or obj_b_distance < 50):
            # Calculate a "risk score" based on multiple columns
            new_feature_1[i] = current_velocity * (1 / (obj_a_distance + 1))
        else:
            new_feature_1[i] = 0.0

    return new_feature_1

# --- 3. Call the function with the grouped arrays ---
risk_score = calculate_new_features(vehicle_data, object_data)

# Assign result back
df['risk_score'] = risk_score

---

## Why This Method is Elegant ✨

1. Clean Function Signatures: Your function calls are simple and reflect the logical structure of your data.

2. Centralized Management: The lists of column names at the top of your script are the single source of truth. Adding a new column to a group is a one-line change.

3. Readable Numba Code: Using constants like VELOCITY and OBJ_A instead of vehicle_data[i, 2] makes your complex logic far easier to read, debug, and maintain.

4. High Performance: Passing 2D arrays is perfectly natural for Numba and introduces no performance overhead.

This pattern gives you the organizational benefits you like from classes while perfectly complementing the high-performance, array-based computing model that Numba requires.