5 Best Ways to Load Data Using the Scikit-learn Library in Python

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πŸ’‘ Problem Formulation: In the realm of data analysis and machine learning in Python, efficiently loading datasets into a workable format is often the first challenge. Scikit-learn, a go-to library for machine learning, provides streamlined methods for loading data. For instance, you may start with raw data in various formats and need to transform them into a DataFrame or a NumPy array for further processing and model training.

Method 1: Using load_* Functions for Built-in Datasets

Scikit-learn includes several built-in datasets accessible through functions starting with load_*. These datasets are useful for experimenting with algorithms and are loaded as Bunch objects, akin to dictionaries, containing data, target, and descriptive keys.

Here’s an example:

from sklearn.datasets import load_iris
iris_data = load_iris()
X, y = iris_data.data, iris_data.target

Output: Two arrays: one for features (X) and one for target values (y).

This snippet loads the Iris dataset, widely used in machine learning. The dataset is immediately split into features (X) and target (y) for convenient use in training machine learning models.

Method 2: Loading from External Datasets with fetch_* Functions

For larger datasets or ones not included in scikit-learn’s small standard repository, fetch_* functions retrieve data from the internet. These functions also return Bunch objects and are ideal for working with real-world data.

Here’s an example:

from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784', version=1)
X, y = mnist.data, mnist.target

Output: MNIST dataset loaded as features (X) and target (y) arrays.

This code accesses the MNIST dataset, a large collection of handwritten digits used for training image processing systems. We obtain the data and target arrays ready for preprocessing and model training.

Method 3: Importing Data Using load_svmlight_file for Sparse Data

The load_svmlight_file() function is tailored for loading datasets in the SVMlight format, advantageous when dealing with sparse datasets. This function returns data and target, which are suitable for feeding directly into an estimator’s fit() method.

Here’s an example:

from sklearn.datasets import load_svmlight_file
X, y = load_svmlight_file('my_dataset.txt')

Output: A sparse matrix (X) and an array of target values (y).

If you have a large and sparse dataset, especially suited for SVM classifiers, this method efficiently reads the data without unnecessarily increasing memory usage by keeping the data in a sparse format.

Method 4: Using load_files for Loading Text Files

The load_files() function suits loading text files organized into folders by class, commonly used for text classification tasks. It returns a Bunch object encapsulating training data and labels.

Here’s an example:

from sklearn.datasets import load_files
text_data = load_files('txt_dataset/')
X, y = text_data.data, text_data.target

Output: List of raw texts (X) and corresponding labels (y).

This approach is particularly useful for natural language processing tasks where text files are categorized into directories per class, making it convenient to load and vectorize textual data for classification algorithms.

Bonus One-Liner Method 5: Loading CSV with pandas.read_csv

Although not strictly a scikit-learn function, pandas.read_csv() offers an effective one-liner to load CSV data that can then be used with scikit-learn machine learning models.

Here’s an example:

import pandas as pd
df = pd.read_csv('my_data.csv')

Output: A Pandas DataFrame containing the CSV data.

By importing a CSV file into a DataFrame, you acquire a versatile data structure ready for preprocessing, exploration, and feeding into scikit-learn algorithms.


  • Method 1: Using load_* functions for built-in datasets. Strengths: Convenience and ease of use for standardized datasets. Weaknesses: Limited to a few, mostly small, datasets.
  • Method 2: Loading from external datasets with fetch_* functions. Strengths: Access to a wider range of real-world data. Weaknesses: Requires internet access and can be slow for large datasets.
  • Method 3: Importing data using load_svmlight_file for sparse data. Strengths: Efficient for sparse data, conserving memory. Weaknesses: Limited to the SVMlight format.
  • Method 4: Using load_files for loading text files. Strengths: Useful for NLP tasks with data organized by folders. Weaknesses: Only applicable for text files sorted into directories.
  • Bonus Method 5: Loading CSV with pandas.read_csv. Strengths: Easy to use and versatile for any CSV file. Weaknesses: Not a scikit-learn function, adding a dependency on Pandas.