To manually find the current version of Python, open a new command window and type:
python --version
To manually find the current version of IPython, open a new command window and type:
ipython --version
To manually upgrade to the current version of IPython, type:
pip install --upgrade ipython
The Python Package Index (PyPI) is a repository of software for the Python programming language. Individual packages from PyPI can be installed easily from the Windows command shell (cmd
) or the terminal
window:
pip install [_package_]
To update a package:
pip install --upgrade [_package_]
To get a list of all Python packages on your computer:
pip list
Anaconda (Anaconda Navigator 1.8.7) is a package manager, and environment manager, a Python distribution, and a collection of over 1,000 most-commonly used open source packages. Anaconda uses conda
(conda 4.5.11) , a more powerful installation manager; however, pip
also works from the command prompt with Anaconda.
To update Anaconda, read this first: https://stackoverflow.com/questions/45197777/how-do-i-update-anaconda
script.stats
contains the algorithms for basic statistics.
Pandas (pandas 0.23.4): provides fast, flexible, and expressive data structures (called DataFrames) designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.
patsy (patsy 0.5.0): for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. Brings the convenience of R "formulas" to Python.
xlrd (xlrd 1.1.0): extracts data from MS Excel spreadsheets on any platform.
PyMC (pymc 2.3.6): for Bayesian statistics, including Markov chain Monte Carlo simulations.
scikit-learn (scikit-learn 0.19.2): machine learning tools for Python. Increasingly popular, contains all the main algorithms used in this field such as K-means clustering.
scikits.bootstrap (scikits.bootstrap 1.0.0): provides bootstrap confidence interval algorithms for SciPy.
scikit-image (scikit-image 0.14.0): a bunch of functionality for doing image analysis, including satellite images.
lifelines (lifelines 0.14.6): survival analysis in Python.
xarray (xarray 0.10.8): brings the labeled data power of Pandas to the physical sciences, by providing N-dimensional variants of the core Pandas data structures. Provides a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays.
Packages should be imported with their commonly used names:
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
import pandas as pd
import seaborn as sns
Python and IPython provide the foundation for conducting Statistical Analysis in Python.
The basic building blocks consists of the JuPyter, NumPy, SciPy, and Matplotlib packages.
Pandas enables the use of Series (1-dimensional) and DataFrames (2-dimensional) to conduct statistical analysis.
Statsmodels and Seaborn provide advanced statistical modeling, analysis, and visualization capabilities in Python.
Python packages for statistics can be managed using the package manager in Anaconda (preferred) or through the use of the Python Package Index (PyPi).
import numpy as np