Klib is a Python library that provides amazing functionality for exploring your data in just a few lines of code. If you find that data exploration takes a lot of time, you can use this library as it gives you all the functions that will help you to explore, clean and prepare your data. If you’ve never used the Klib library in Python, this article is for you. In this article, I will introduce you to a tutorial on the Klib library in Python.
Introduction to Klib in Python
Most data scientists go through the same process while exploring the data they use to gain insight. Some of the common steps used by all data scientists while exploring a dataset are:
- check whether there are missing values or not
- understand the distribution of all the features
- understanding the categorical features
- understanding the correlation between the features of the data
After these steps, you may need to change the way you explore your datasets depending on the type of problem you are working on and the type of results you are looking for. But to get to this point, you need to explore your data to understand the type of data you are using. Sometimes it takes a long time to explore your dataset, this is where the Klib library in Python comes in. It helps you in exploring your data in just a few lines of code. In the section below, I’ll show you a tutorial on the Klib library in Python to explore your data.
Klib Tutorial in Python
Hope you now understand what the Klib library in Python is and what functionality it can provide you when exploring a dataset. If you’ve never used it before, you can easily install it using the pip command:
- pip install klib
Now let’s see how to use the Klib library in Python to explore your data. I will first start with importing the necessary Python libraries and the dataset:
import klib
import pandas as pd
data = pd.read_csv("https://raw.githubusercontent.com/amankharwal/Website-data/master/AER_credit_card_data.csv")
data.head()card reports age income ... dependents months majorcards active 0 yes 0 37.66667 4.5200 ... 3 54 1 12 1 yes 0 33.25000 2.4200 ... 3 34 1 13 2 yes 0 33.66667 4.5000 ... 4 58 1 5 3 yes 0 30.50000 2.5400 ... 0 25 1 7 4 yes 0 32.16667 9.7867 ... 2 64 1 5 ... ... ... ... ... ... ... ... ... ... 1314 yes 0 33.58333 4.5660 ... 0 94 1 19 1315 no 5 23.91667 3.1920 ... 3 12 1 5 1316 yes 0 40.58333 4.6000 ... 2 1 1 2 1317 yes 0 32.83333 3.7000 ... 0 60 1 7 1318 yes 0 48.25000 3.7000 ... 2 2 1 0 [1319 rows x 12 columns]
Now let’s see how we can get the report about the number and frequency of the categorical features in the dataset:
klib.cat_plot(data)
Now let’s have a look at the missing values:
klib.missingval_plot(data)
No missing values found in the dataset.
Fortunately, this dataset does not have any missing values, but you can use the same method on any data. Now let’s have a look at the correlation matrix to understand the correlation between the features of this dataset:
klib.corr_mat(data)
In the correlation matrix above, the red coloured values represent a negative correlation and the black coloured values represent a positive correlation. You can also visualize the correlation plot using this library as shown below:
klib.corr_plot(data)
Understanding the distribution of each column is also very important to understand what kind of data you are working with. So here is how you can visualize the distribution of each column in the data:
klib.dist_plot(data)
The figure above shows the distribution of the “reports” column in the dataset. You will see the distribution of each column like the figure above in the output. Hope you now understand how to use the Klib library in Python to explore your data. You can learn more about this library here.
Summary
So this is how you can use the Klib library to explore your datasets in just a few lines of code. Most data scientists go through the same process while exploring the data they use to gain insight. This is where this Python library can help you a lot. I hope you liked this article on a tutorial on the Klib library in Python. Feel free to ask your valuable questions in the comments section below.
