Top Python Libraries for Data Science
By: Peace Ikeoluwa Adegbite
Tags: Data Analysis, Python Programming, Data Visualization, Python, Data Science

Python is the most commonly used and one of the best programming languages for data science. This is because Python is an easy to use language and it has many open-source libraries that can be used for data science. Since Python is open-source, it is free and has an active community which makes it regularly updated. Here, I have discussed some of the top Python libraries for data science. I have divided these libraries into two: those used for data processing and modelling, and those used for data visualizations. Let’s go!

Libraries Used for Data Processing and Modelling

1. NumPy

NumPy means Numeric Python. It is used for scientific computations which include linear algebra, n-dimensional arrays and matrices vectorization and processing, Fourier transformation, random number processing. The main objects in Numpy are the n-dimensional or multidimensional arrays or matrices. Several operations can be carried out on these arrays, some of which are: Arithmetic Operations, Reshaping, Transposition, Flattening, Slicing, Stacking, Splitting, Broadcasting and many more. NumPy is a fundamental Python library as many other libraries e.g Scipy were built on it.

2. SciPy

SciPy library is different from SciPy stack. SciPy library is built on NumPy and is one of the main packages in SciPy stack. It has several submodules which can be used for statistics, linear algebra, integration, interpolation, optimization etc. Because SciPy is built on NumPy, its main objects are multidimensional matrices as well. SciPy has great documentation which makes it easy to use.

3. Pandas

Pandas is called Python Data Analysis Library. It has two main data structures which are:

i. Series (a 1-D array which can hold any data type)

ii. Data Frame (a 2-D tabular data structure with rows and columns)

Pandas is a great tool for data analysis, data wrangling, data manipulation, data aggregation, handling missing values, simple visualizations and many more. Pandas are also used for reading and writing datasets or files of various formats such as SQL, CSV, Excel, Text etc.

4. Scikit-learn

Scikit-learn is primarily used for Machine Learning operations such as Regression, Classification, Decision Clustering, Model Selection, Dimensionality Reduction and Preprocessing. It can also be used for data analysis and mining. Scikit-learn is built on Numpy, Pandas, Scipy and Matplotlib libraries and so it interoperates with them.

Libraries Used for Data Visualization

5. Matplotlib

Matplotlib is used for data visualization and 2-D plotting. It is the most widely used Python plotting library. Matplotlib can be used on several platforms such as Jupyter notebook, Python and IPython shells, Web application servers etc., and also for embedding plots into applications through its object-oriented API. Matplotlib library is used to produce line plots, histograms, pie charts, bar charts, scatterplots, stem plots and many other visualizations.

6. Seaborn

Seaborn provides a high-level interface for creating informatory and appealing statistical graphics. It is based on Matplotlib library and is closely integrated with the NumPy and Pandas data structures (arrays, series and data frames). Seaborn helps in data exploration and understanding. Seaborn has a broad gallery of visualizations which include histograms, bar charts, pair plots, heatmaps, violin plots, boxplots, cluster maps etc.

7. Plotly

Plotly can be used to create sophisticated visualizations like contour plots, tenary plots and 3-D charts which are rare in other visualization libraries. Plotly provides a large number of unique graphs e.g heatmaps, histograms, area charts, scatter plots, multiple-axes, subplots, polar charts, bubble charts and many more.