Python installation To install Python on your computer, you need to download the Python installer at https://www.python.org/downloads/ and install according to the instructions.
Install pip pip is a python package manager (Python package manager). With the use of pip, it is easy to install the packages needed while working with Python.
If you use Python version 2.7.9 or higher (for Python 2) or 3.4 and up (with Python 3), by default pip comes pre-installed with Python. In case you are worth using the lower version of Python as outlined above, you need to install pip following the instructions from the homepage https://pip.pypa.io/en/stable/installing/
To update current pip to the latest version, use the command
python -m pip install –upgrade pip Introducing some of the popular Python library packages NumPy Created by Travis Oliphant, NumPy is a true analysis horse of Python. It provides users with a way to work with multidimensional arrays, and a large number of functions for processing on multidimensional mathematical operators on those arrays. Arrays are data blocks arranged in multiple dimensions based on vectors and matrices in mathematics. Arrays are often useful not only for storing data, but also for the quick computation of matrices, which is indispensable when solving data science related problems.
Install: pip install numpy Homepage: http://www.numpy.org SciPy As an original project by Travis Oliphant, Pearu Peterson, and Eric Jones, SciPy completes NumPy’s features, in order to provide algorithms for linear algebra, matrix spaces, signal processing and image processing, optimization, Fourier transforms, …
Install: pip install scipy Homepage: http://www.scipy.org pandas pandas is a library that does everything that NymPy and SciPy cannot do. It works with data structure objects, DataFrames, and Series (Series). pandas allows you to handle complex data tables of different types (something NumPy’s arrays can’t do) and time series. You will easily load data from a variety of sources, then slide, dice, handle the missing components, add, rename, aggregate, reshape, and finally visualize the data at your disposal. friend.
Install: pip install pandas Homepage: http://pandas.pydata.org Scikit-learn Beginning as part of SciKits, Scikit-learn is the core of Python’s data science. It provides all you need for preprocessing, learning to monitor and unattended, model selection, validation and error metrics.
Installation: pip install scikit-learn Homepage: http://scikit-learn.org/stable IPython A scientific approach requires a quick test of different hypotheses over a period of time. IPython was created by Fernando Perez to address the need for a Shell Python command (shell-based, web browser, and application interface) with integrated graphics, customizable commands, rich history (below JSON format) and parallel computing capabilities to improve computational understanding.
Install: pip install “ipython [notebook]” Homepage: http://ipython.org Matplotlib Developed by John Hunte, matplotlib is a building blocks library needed to create quality graphs from arrays and visualize and interact with them.
Install: pip install matplotlib Homepage: http://matplotlib.org Statsmodels Formerly part of SciKits, Statsmodels adds statistical features to SciPy. It includes generalized linear models, discrete choice models, time series analysis, and a series of descriptive statistics such as the test. parametric and nonparametric tests.
Installation: http://statsmodels.sourceforge.net Homepage: http://statsmodels.sourceforge.net