Watch Chuck Severance’s videos from Python for Everybody to understand how to find your path directory, as well as how to download and install Python and Atom. Learn how to write your first script and execute py files in the terminal.

Click here to download the latest version of Python.

Click here to download Atom.

Anaconda is a data science toolkit that contains many Python packages, including numpy, pandas, matplotlib, seaborn, scipy, and scikit-learn.

Click here to download Anaconda.

Spyder is included with Anaconda and is a great…

While I was learning SQL, I was unsure of which database management system to use. I was advised to first learn the fundamentals, as they are mostly the same for relational database management systems.

Out of 388 votes in a LinkedIn poll on which RDBMS to use, 64% of participants said they use Microsoft SQL Server, while 16% use PostgreSQL, 15% use MySQL, and 4% said other. The choice for which RDBMS to use also depends on the use case, type, and amount of data being processed and analyzed.

Based on the results, I definitely think it is import to…

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I first became a Python Developer

**I enrolled college as a physics major**

I started college at the age of 17 at Stevens Institute of Technology in Hoboken, NJ. I graduated high school in three years and had not gotten a chance to take Calc 1 or any AP courses. I enrolled as a physics major, hoping to minor in philosophy and ultimately pursue a career in theoretical physics. …

Python lists are data structures used to store and change items. If you’re just learning Python or want to learn some neat tricks when using lists, this post is for you! The documentation on python lists can be found here.

Today I tackled plotting both probability density functions and kernel density estimations in Python.

In order to first understand probability density functions or PDF’s, we need to first look at the docs for scipy.stats.norm.

A normal continuous random variable.

The location (

`loc`

) keyword specifies the mean. The scale (`scale`

) keyword specifies the standard deviation.

Given a dataframe and a column in that dataframe, we can calculate the probability density function of a variable using the following:

from scipy import statsdata = df['column']

loc = data.mean()

scale = data.std()pdf = stats.norm.pdf(data, loc=loc, scale=scale)

We can also use stats.norm to…