In this post, you will learn how to convert Sklearn.datasets to Pandas Dataframe. It will be useful to know this technique (code example) if you are comfortable working with Pandas Dataframe. You will be able to perform several operations faster with the dataframe.
Sklearn datasets class comprises of several different types of datasets including some of the following:
- Breast cancer
The code sample below is demonstrated with IRIS data set. Before looking into the code sample, recall that IRIS dataset when loaded has data in form of “data” and labels present as “target”.
import pandas as pd import matplotlib.pyplot as plt from sklearn import datasets # Load the IRIS dataset iris = datasets.load_iris() X = iris.data y = iris.target # Create dataframe using iris.data df = pd.DataFrame(data=iris.data, columns=["sepal_length", "sepal_width", "petal_length", "petal_width"]) # Append class / label data df["class"] = iris.target # Print the data and check for yourself df.head()
Executing the above code will print the following dataframe.
In case, you don’t want to explicitly assign column name, you could use the following commands:
# Create dataframe using iris.data df = pd.DataFrame(data=iris.data) # Append class / label data df["class"] = iris.target # Print the data and check for yourself df.head()
In this post, you learned about how to convert the SKLearn dataset to Pandas DataFrame.
- Standard Deviation of Population & Sample – Python - August 3, 2020
- Machine Learning – Feature Selection vs Feature Extraction - August 2, 2020
- Sklearn SelectFromModel for Feature Importance - August 2, 2020