5 Lesser-Known Python Libraries Everyone should try!

One of the best things about using Python is its infinity of open-source libraries. There is a library for basically anything. If a library can solve a problem, why not save your precious time and give it a try? Today, I will introduce you to 5 libraries that you probably have never heard about but you should add to your pipeline. Let’s get started!


How to use it

Just type pip install pyforest and you are good to go. To import it to your notebook, type from pyforest import * and you can start using your libraries. To check which libraries were imported, type lazy_imports().

All the libraries above are good to use. Technically, they will only be imported if you use them. Otherwise, they will not. You can see libraries such as Pandas, Matplotlib, Seaborn, Tensorflow, Sklearn, NLTK, XGBoost, Plotly, Keras, Numpy, and many others.

I mostly use PyForest for my personal projects or projects that will not be reviewed by other people. If your code will be reviewed by other people, PyForest is not recommended for not making clear that these libraries are being imported.


How to use

To install it, you can type pip install emot, and you are good to go. Then you will need to import it into your notebook by typing import emot. You will need to decide if you want to figure out the meaning of emojis or emoticons. For emojis, the code is emot.emoji(your_text). Let's check it out with an example:

You can see above that I added the sentence I ❤️ Python 🙂 and used Emot to figure it out. It returned a dictionary with the values, the description, and the location. Like any dictionary, you can slice it and focus on the information that you need. If I type ans['mean'], it will return only the emoji description.


How to use it

You can install it by typing pip install geemap in your Terminal. To import it to your notebook, you can type import geemap. For demonstration purposes, I will create a folium-based interactive map using the following code:

import geemap.eefolium as geemap
Map = geemap.Map(center=[40,-100], zoom=4)

As I mentioned, I haven’t explored it as much as it deserves, but they have a complete GitHub README talking more about how it works and what it can do.


How to use

First, to install it, you can just type pip install dabl in your terminal. Then, you can import Dabl to your notebook by typing import dabl. You are good to go from here. You can use dabl.clean(data) to get information about features, such as if there is any useless features. It also shows continuous, categorical, and high-cardinality features.

You can use dabl.plot(data) to generate visualizations about a specific feature:

And finally, you can create multiple models with one line of code using dabl.AnyClassifier or dabl.Simplefier() just like you would do using Scikit-Learn. However, in this step, you will have to take some of the steps you would usually take, such as creating training and testing dataset, calling, fitting and predicting the model. Then, you can use Scikit-Learn to evaluate the model.

# Setting X and y variables
X, y = load_digits(return_X_y=True)# Splitting the dataset into train and test setsX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)# Calling the model
sc = dabl.SimpleClassifier().fit(X_train, y_train)# Evaluating accuracy score
print(“Accuracy score”, sc.score(X_test, y_test))

As we can see, Dabl iterated through multiple models, including Dummy Classifier, GaussianNB, Decision Trees with different depths, and Logistic Regression. At the end, it shows the best model. All these models in about 10 seconds. Cool, right? I decided to test the final model using Scikit-Learn to make sure that this result was trustworthy. Here is the result:

I got 0.968 accuracy using the conventional way to predict and 0.971 with Dabl. That’s close enough for me! Note that I didn’t have to import the Logistic Regression model from the Scikit-Learn library because it was already imported with PyForest. I need to confess that I prefer LazyPredict, but Dabl is worth trying. There is much more to show about Dabl, and I will work on a blog exclusively for it with more details. Stay tuned!


How to use it

my_report = sv.analyze(dataframe)

Did you see that? Sweetviz was able to create an EDA HTML file with information about the entire dataset and break it down so that you can analyze each feature individually. You can get the numerical and categorical association to other features, largest, smallest, and most frequent values. The visualization also changes depending on the data type. You can do so much more with Sweetviz, but I will keep it for another blog. In the meantime, I highly recommend you trying it out.


I recommend you try them out and explore their functionalities that I didn’t mention here. If you do, let me know what you found out about them. Thank you for reading!

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