Data Science Libraries Python

Native Types:

Native Collections

list, tuple, dict, set

Native String Types

str, bytes. The Python 3 str type represents human text (elements are Unicode characters), whereas bytes are sequences of integers with values from 0 to 255.

Integers

int()

Floats

float(). You can convert a numeric value into float format in Python by using the float() command.

Data Science Libraries:

Data Wrangling

Pandas. Python is particularly strong in this area, with the Pandas library being very extensive in this regard.

Database Collections

mysql-connector-python, psycopg2, SQLAlchemy. Both Python and R have several libraries available to connect to a SQL database, import data, and commit queries, among other common tasks.

Machine Learning

PyBrain, PyLearn2, scikit-learn, statsmodels. scikit-learn in Python is quite popular for running machine learning algorithms, and the faster processing speed of Python makes it more suitable for this purpose.

Regression Analysis

Numpy, scikit-learn, SciPy, statsmodels

Time Series

Prophet, PyFlux, statsmodels

Visualization

matplotlib is the dominant plotting library in Python. Others include Plotly, Pygal, Bokeh, and Seaborn.

How to Choose

What is your background?

Your choice of language will be highly dependent on your background. If you are a programmer who has used other low-level languages such as C++, using Python will prove a much more seamless transition. However, if you come from an academic background or have previously used statistical programs such as SAS, SPSS, and others, R will likely be easier to come to grips with compared to Python.

What types of tasks do you wish to accomplish?

Are you looking to conduct a high degree of statistical modeling, or is data manipulation and machine learning your goal? If it’s the former, the packages in R are specifically geared toward statistics and regression analysis, which would make R a better choice in this regard. However, Pandas and scikit-learn are highly renowned for their use in data manipulation and machine learning, respectively, and Python is therefore a better choice in these areas. Moreover, even though the TensorFlow machine learning framework—which was developed by researchers working on the Google Brain Team—is available in both Python and R, the environment works much more seamlessly with Python’s Anaconda environment.

What environment are you operating in?

As mentioned in the introductory section, Python is much more flexible for integrating with other programming languages. In this regard, if you are working with developers or in an environment where there is an emphasis on production, Python is the better choice. However, if you are conducting statistical analysis for research purposes, R is a more efficient choice as implementation of statistical algorithms are, in many cases, easier than Python thanks to R’s many libraries designed for this purpose.