EDA (Exploratory Data Analysis) is one of the first steps performed on a given dataset. It helps us to understand more about our data and gives us an idea of manipulations and cleaning we might have to do. EDA can take anywhere from a few lines to a few hundred lines. In this tutorial, we will look at libraries which help us perform EDA in a few lines

Dataset

We will use the Titanic Dataset provide by Kaggle. Using Panda’s describe() method, we get the below output

In yesterday’s article, we discussed the following

• Why you should learn JavaScript
• How to Run JavaScript/Python and show output
• Variables
• Comments

Today, we will talk about the following

• Conditional Statements
• Blocks
• Comparison Operators
• Logical Operators
• Truthy and Falsy Values
• Ternary Operators
• Switch Cases

Conditional Statements

Python

Python supports the following

• if statements
• else statements
• elif statements Below is an example

num = 10if num > 20:
  print("If statement")
elif num > 10:
  print("Elif statement")
else:
  print("Else statement")

Let’s try writing the same code snippet in JavaScript

JavaScript

JavaScript supports the following

• if statements
• else statements
• else if statements — else if statements in…

As of today, April 17th, I am on day 28 and although it’s been challenging, I am looking forward to the next 72 days.

My Challenge for the 100 days

• Code at least for an hour a day
• Post content every day related to Python, SQL, or programming in general

Why am I doing this?

• I started writing articles somewhat consistently on Medium in October 2020. I want to be more consistent and build a productive habit
• Writing an article/ blog post helps me understand concepts better and drives me to learn new things.
• Increasing online presence

My Stats when I started the challenge

• Twitter — 48
• Hashnode — 9
• Dev.to — 10
• Medium — 0 people following…

I have worked with JavaScript previously but it’s been almost 3 years since I have written any JavaScript Code. I have mostly been working with Python and I am all for Python. However, there is no denying that JavaScript is everywhere like EVERYWHERE. As I am re-learning JavaScript, I am going to document my experiences, in case anyone is in a similar boat (Learning JavaScript as a Python Developer).

We will be covering the following in this tutorial

• Why you should learn JavaScript
• How to Run JavaScript/Python and show output
• Variables
• Comments

Why JavaScript?

I have borrowed a Tweet from Danny…

We know the fastest way to combine lists. Today we will try to find the fastest way to combine dictionaries

We will be considering the following ways

• Using a for loop
• Using the update() method
• Using the ChainMap Function
• Using the unpacking operator
• Using the pipe operator ( | )
• Using the (|=) operator

How are we going to measure the performance?

Assert statements are a great tool for debugging. Given a boolean condition, they will raise an error if the condition evaluates to False. More specifically, it raises an “AssertionError”. We can also include our custom error messages.

General Syntax

# Without Error message
assert boolean Expression#With Error message
assert boolean Expression, "Error Message"

Standard If…….else

Let’s assume we have a function that calculates the Mass of an object. The Mass of an object must always be positive. We can have a check to ensure this

def calculate_mass(): # Some stuff to calculate the mass mass = 10 if mass > 0: pass else…

We will be using the library Faker

Install the library

pip install faker

Now let’s import the library and create an instance of “Faker”

from faker import Faker
fake = Faker()

Seeding

Seeding ensures that the same set of random data is returned during every run. This is very useful when you want to compare functions or models in the same environment without changes in the dataset.

from faker import Faker
Faker.seed(999)
fake = Faker()

Names

print(fake.name())
'''
Wesley Turner
'''print(fake.first_name())
'''
Alexandra
'''print(fake.last_name())
'''
Aguilar
'''print(fake.name_male())
'''
Charles Hanna
'''print(fake.name_female())
'''
Shannon Walker'''
print(fake.name_nonbinary())
'''
Christopher Brennan
'''

Address and Phone Numbers

from…

In yesterday’s article, we talked about getting started with Beautiful Soup. We discussed the following functions

• pretiffy()
• find()
• find_all()
• select()

Today we will try to scrape the data in the table of the worldometer website