Installation & Quick Start

Installation

uv (Recommended)pip

uv add centimators

pip install centimators

Quick Start

centimators transformers are dataframe-agnostic, powered by narwhals. You can use the same transformer (like RankTransformer) seamlessly with both Pandas and Polars DataFrames. This transformer calculates the normalized rank of features within each date group.

First, let's define some common data:

import pandas as pd
import polars as pl
# Create sample OHLCV data for two stocks over four trading days
data = {
    'date': ['2021-01-01', '2021-01-01', '2021-01-02', '2021-01-02', 
             '2021-01-03', '2021-01-03', '2021-01-04', '2021-01-04'],
    'ticker': ['AAPL', 'MSFT', 'AAPL', 'MSFT', 'AAPL', 'MSFT', 'AAPL', 'MSFT'],
    'open': [150.0, 280.0, 151.0, 282.0, 152.0, 283.0, 153.0, 284.0],    # Opening prices
    'high': [152.0, 282.0, 153.0, 284.0, 154.0, 285.0, 155.0, 286.0],    # Daily highs
    'low': [149.0, 278.0, 150.0, 280.0, 151.0, 281.0, 152.0, 282.0],     # Daily lows
    'close': [151.0, 281.0, 152.0, 283.0, 153.0, 284.0, 154.0, 285.0],   # Closing prices
    'volume': [1000000, 800000, 1200000, 900000, 1100000, 850000, 1050000, 820000]  # Trading volume
}

# Create both Pandas and Polars DataFrames
df_pd = pd.DataFrame(data)
df_pl = pl.DataFrame(data)

# Define the OHLCV features we want to transform
feature_cols = ['volume', 'close']

Now, let's use the transformer:

from centimators.feature_transformers import RankTransformer

transformer = RankTransformer(feature_names=feature_cols)
result_pd = transformer.fit_transform(df_pd[feature_cols], date_series=df_pd['date'])
result_pl = transformer.fit_transform(df_pl[feature_cols], date_series=df_pl['date'])

Both result_pd (from Pandas) and result_pl (from Polars) will contain the same transformed data in their native DataFrame formats. You may find significant performance gains using Polars for certain operations.