What are Libraries in Python?

A Python library is a collection of related modules. It contains bundles of code that can be used repeatedly in different programs. It makes Python Programming simpler and convenient for the programmer. As we don’t need to write the same code again and again for different programs. Python libraries play a very vital role in fields of Machine Learning, Data Science, Data Visualization, etc.

What is Dataset in Python?

Dataset in Python has a lot of significance and is mostly used for dealing with a huge amount of data. These datasets have a certain resemblance with the packages present as part of Python 3.6 and more. Python datasets consist of dataset object which in turn comprises metadata as part of the dataset. Querying to these datasets may include dataset objects to return the required index based on rows and columns. The dataset object comes into the picture when the data gets loaded initially that also comprise the metadata consisting of other important information.

Here, Some of the Python Libraries that offer Datasets.

1. TensorFlow Datasets
2. Sklearn
3. nltk
4. statsmodel
5. pydataset
6. seaborn

1. TensorFlow Datasets

A collection of ready-to-use datasets. TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data.Datasets , enabling easy-to-use and high-performance input pipelines. To get started see the guide and our list of datasets.

2. S️klearn

Machine Learning package. This package also features helpers to fetch larger datasets commonly used by the machine learning community to benchmark algorithms on data that comes from the ‘real world’.

3. nltk

Natural Language Tool Kit package. Practical work in Natural Language Processing typically uses large bodies of linguistic data, or corpora.

4. statsmodel

Statistical Model package. Provides data sets (i.e., data and meta-data) for use in examples, tutorials, model testing, etc.

5. pydataset

Dataset for educational purposes, mainly. It tries to help those approaching Data Science in Python for the first time, who must deal with common (and time-consuming) data preparation tasks.

6. seaborn

Data Visualization package where you can also load an example dataset from the online repository (requires internet).

If there is any other important and authentic dataset or category, you’d want me to add to this list, feel free to respond to this story!

Thanks for reading and I will see you in my next blog. Also, you can follow me here Twitter and Instagram.