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DataFrame Interpolator Tool

DataFrame Interpolator Tool is a python package that helps to solve the problem of missing data in pandas dataset. It uses machine learning models from scikit-learn package to fill in missing data in dataframe.

The algorithm:

• Pick column by column as target data
• Rest columns are explanatory data
• NaNs in explanatory columns are interpolated by mean value of the column
• Non-NaN values from target columns are used for training
• Estimator is trained on explanatory data to predict known values of target column
• This estimator is used to predict unknown (missing or NaN) values of target column
• New (predicted) values are inserted into the column
• All the steps from the above applied to next column
• Iteration is when all columns in data set are being processed by the estimator
• Several iterations allow updating the previously predicted values, as the explanatory data was also updated in previous steps.

As a user, you need to provide the sklearn model or the similar syntax model with fit, score and predict methods, likely regressor:

• RandomForestRegressor
• LinearRegression
• DecisionTreeRegressor
• etc

Script takes pandas DataFrame as an input. All values must be numerical, consider transforming your categorical data to numerical labels or one-hot encodings.

Install it from PyPI

Package is not yet released into PyPI, so the installation is performed through the GitHub.

pip install git+https://github.com/Katerunner/Interpolator

Usage

Basic scenario:

import numpy as np
import pandas as pd
from dataframe_interpolator.interpolator import Interpolator
# Load core model
from sklearn.ensemble import RandomForestRegressor

# Generate example data
data = np.random.rand(1000, 10)
data = pd.DataFrame(data)
i_range, j_range = data.shape

# With prob of 0.3 value is NaN
for i in range(i_range):
    for j in range(1, j_range):
        if np.random.rand() < 0.3:
            data.iloc[i, j] = np.nan

# With normalization
ip_model = Interpolator(model=RandomForestRegressor(),
                        normalize=True,
                        normalize_algorithm='minmax',
                        n_iter=10,
                        verbose=True)

df_result = ip_model.process(data.copy())
display(df_result)  # If in Jupyter

Interpolator also saves the models for each column. If the model was provided in __init__, then all the columns are trained using the copy of the same model and get_models() method will return each trained model for each column.

In addition to this, the separate models for each column can be specified in process() method models_list parameter. In this case the provided models will be trained corresponding to each column and return by get_models() method. The model specified in __init__ will be ignored in this case.

ip_model.get_models()

You can also see the example of usage in the example_usage.ipynb.