Millennium Prize Problem: Yang-Mills and Mass Gap

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The Millennium prize problems are seven challenging problems in mathematics for which a solution results in a $1 million prize. In this post we will briefly discuss one of the Millennium prize problems, the Yang-Mills and Mass Gap problem.

All of the Millennium prize problems are listed on the Clay Mathematics Institutes’ website here.

The Yang-Mills theory describes elementary particles, which are particles with no substructure (quarks, leptons, Higgs boson), using algebraic objects. Specifically, non-abelian Lie groups are used to unify electromagnetic and weak forces. …


Building Machine Learning Models with Scikit-learn

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Scikit-learn is a powerful machine learning library in python. It provides many tools for classification, regression and clustering tasks. In this post we will discuss some popular tools for building classification models using scikit-learn.

Let’s get started!

For our purposes we will be working with the Bank Churn Modeling data set. The data can be found here.

To start, let’s import the Pandas library, relax display limits and print the first five rows of data:

import pandas as pddf = pd.read_csv("Bank_churn_modelling.csv")pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print(df.head())


Data selection, aggregation and statistics with Bank Churn Modeling data

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Pandas is a python library that is used for wrangling data, generating statistics, aggregating data and much more. In this post we will discuss how to perform data selection, aggregation and statistical analysis using the Pandas library.

Let’s get started!

For our purposes we will be working with the Bank Churn Modeling data set. The data can be found here.

To start, let’s import the Pandas library, relax display limits and print the first five rows of data:

import pandas as pddf = pd.read_csv("Bank_churn_modelling.csv")
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print(df.head())


Understanding Regular Expressions in Python

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Regular expressions are sequences of characters that define patterns which can be used for tasks such as pattern matching and text searching. In this post, we will discuss how to use the search method in the python regular expressions module.

Let’s get started!

Consider the following sentence:

sentence1 = 'Python is great'

We can use the ‘search()’ method from the ‘re’ module to search for patterns in this text. The syntax for searching for patterns in the beginning and end of text is as follows:

import re
result = re.search("^begin.*end$", text)

The ‘^’ is the character we use for finding a pattern at the start of a string and the ‘$’ is the character we use to find a pattern at the end of a string. Let’ s check if our sentence starts with ‘Python’ and ends with…


Understanding Class Inheritance in Python

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Inheritance is a concept in object oriented programming where existing classes can be modified by a new class. The existing class is called the base class and the new class is called the derived class. In this post, we will discuss class inheritance in python.

Let’s get started!

The syntax of python class inheritance is as follows:

class BaseClass: 
#body of BaseClass
class DerivedClass(BaseClass):
#body of DerivedClass

For our example we will consider a SpotifyUser derived class inheriting from FacebookUser base class. First let’s define our FacebookUser class:

class FacebookUser: 
pass

Now let’s consider some attributes of a Facebook user. Let’s add a first name, last name and a list of…


String Formatting in Python

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Python offers a variety of methods for string formatting. In this post, we will review three methods for formatting strings in python. Specifically, we will discuss %-formatting, str.format() and formatting strings with f-strings.

Let’s get started!

First, we will consider ‘%” formatting. Consider two variables that store a name and email address. We can write a function that takes the name and email and prints out a customized message using ‘%’ formatting:

def get_message_pct(name, email):
print("%s's email is %s."%(name, email))

We can call this function with values for name and email and get the following output:

get_message_pct('John', 'johnadams@gmail.com')


Understanding Python Lambda Expressions

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Function definition is an important part of software programming. In python, lambda expressions can be used to anonymously define simple functions. For example, a function that evaluates multiplication can be replaced with a lambda expression. In this post, we will discuss how to define anonymous functions in python using lambda expressions.

Suppose we have a one-line function that returns the product of two input integer values:

def product(x, y):
return x*y

If we call this function with integers 5 and 10 and print the return value we get:

print("Product:",product(5, 10))


Generating Summary Statistics with the Pandas Library

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Pandas is a python library used for data manipulation and statistical analysis. It is a fast and easy to use open-source library that enables several data manipulation tasks. These include merging, reshaping, wrangling, statistical analysis and much more. In this post, we will discuss how to calculate summary statistics using the Pandas library.

Let’s get started!

For our purposes we will be exploring the Movies on Netflix, Prime Video, Hulu and Disney Plus data set. The data can be found here.

Let’s get started!

First, let’s read the data into a Pandas data frame:

import pandas as pd 
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
df = pd.read_csv("MoviesOnStreamingPlatforms_updated.csv") …


Three Useful Pandas Functions for Generating Statistics

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Pandas is a useful python library that can be used for a variety of data tasks including statistical analysis, data imputation, data wrangling and much more. In this post, we will go over three useful custom functions that allow us to generate statistics from data.

Let’s get started!

For our purposes, we will be working with the Wines Reviews data set which can be found here.

To start, let’s import the pandas and numpy packages:

import pandas as pd 
import numpy as np

Next, let’s set the maximum number of display columns and rows to ‘None’:

pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

Now, let’s read in our…


Three Useful Functions for Data Filtering with Pandas

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Pandas is a python library used for generating statistics, wrangling data, analyzing data and more. In this post I will discuss three useful functions that allow us to easily filter data using Pandas.

Let’s get started!

For our purposes we will be working with the FIFA 19 data set which can be found here.

To start, let’s import the pandas package:

import pandas as pd 

Next, let’s set the maximum number of display columns and rows to ‘None’:

pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

Now, let’s read in our data:

df = pd.read_csv('fifa_data.csv')

Next we will print the first five rows of data to get an idea of the column types and their values (column results are…

About

Sadrach Pierre, Ph.D.

Data Scientist at WorldQuant Predictive. Writer for Built In & Towards Data Science. Cornell University Ph. D. in Chemical Physics.

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