In this post, you will learn about which data structure to use between Pandas Dataframe and Numpy Array when working with Scikit Learn libraries. As a data scientist, it is very important to understand the difference between Numpy array and Pandas Dataframe and when to use which data structure.
Here are some facts:
- Scikit learn was originally developed to work well with Numpy array
- Numpy Ndarray provides a lot of convenient and optimized methods for performing several mathematical operations on vectors. Numpy array can be instantiated using the following manner:
np.array([4, 5, 6])
- Pandas Dataframe is an in-memory 2-dimensional tabular representation of data. In simpler words, it can be seen as a spreadsheet having rows and columns. One can see Pandas Dataframe as SQL tables as well while Numpy array as C array. Due to this very fact, it found to be more convenient, at times, for data preprocessing due to some of the following useful methods it provides.
- Row and columns operations such as addition / removal of columns, extracting rows / columns information etc. Some of the most commonly used methods are head(), tail(), summary(), describe() etc.
- Group operations (Here the Pandas dataframe is the winner due to ease of use)
- Storing items of different types
- Creation of pivot tables
- Ease of creation of plots using Matplotlib
- Time-series functionalities
- Pandas dataframe columns gets stored as Numpy arrays and dataframe operations are thin wrappers around numpy operations.
- It is recommended to use Numpy array, whenever possible, with Scikit learn libraries due to mature data handling.
How to Convert Dataframe to Numpy Array?
Here is the code which can be used to convert Pandas dataframe to Numpy array:
import pandas as pd # Load data as Pandas Dataframe df = pd.read_csv("...") # Convert dataframe to Numpy array df.values
Here is what will get printed:
In this post, you learned about difference between Numpy array and Pandas Dataframe. Simply speaking, use Numpy array when there are complex mathematical operations to be performed. Use Pandas dataframe for ease of usage of data preprocessing including performing group operations, creation of Matplotlib plots, rows and columns operations. As a matter of fact, one could use both Pandas Dataframe and Numpy array based on the data preprocessing and data processing needs.