The post Hello world! appeared first on Christian Manuel Pena.

Hello world!

Welcome to my side of the internet. My name is Christian Pena. I’m a marketing aficionado and aspiring data analyst.

Thank you for taking time out of your day to join me on my journey into the world of data analytics and data science.

Finding Inspiration in Unexpected Places

Now, I know what you’re thinking “What’s so special about this guy’s data blog?“

Well, the answer might surprise you.

I recently played a video game called Celeste that has inspired me to do something I’ve never done before . . . face my fears publicly.

You see, in the game Celeste, you play as a girl named Madeline whose goal is to climb up the titular Mt. Celeste. The mountain has spiritual powers that force Madeline to confront her inner fears. She comes face-to-face with her anxiety and insecurities as she climbs the mountain.

However, the part of the game that inspires me the most is a feature that counts how many times you’ve died in the game and have had to restart.

Embracing the Journey

In the +20 hours it took me to complete the first run-through of the game, I died over 3,700 times. By the time I completed the game, I wore my death count like a badge of honor. I’m not the only one that feels this way. You can find threads all over Reddit dedicated to the death counts required to complete the game.

“What does this have to do with data analytics?”

It has everything to do with it. Every time you fall and get back up, you prove that success comes after many attempts and a willingness to learn from your mistakes.

You see, as a beginner, it’s difficult to relate to other people’s success stories because they’re so far ahead of you. It can feel like their success is difficult to replicate because they’ve already “made it”.

I, like Madeline from Celeste, have a very ambitious goal. My Mt. Celeste is to break into the field of data analytics field by taking the self-learning route. More importantly, my goal is to document the ups and downs of my journey into the world of data science.

What to Expect

Throughout my journey, I plan to share resources, lessons, and projects. I hope you find my blog useful and you have as much fun following along as I am putting this together.

If you’re interested in following along, please feel free to sign up for my free newsletter so you don’t miss a beat!

PS: Can you believe this is my first blog post ever? How did I do?

The post My Journey into Data Analytics appeared first on Christian Manuel Pena.

Greetings,

Step into my digital domain. I warmly welcome those joining me on this exploration into the world of data analytics and marketing. I’m Christian Pena, a marketing enthusiast and data aficionado. Thanks for dropping by and joining me on this escapade into the realms of data analytics and science.

Embarking on the Code Adventure: The Task at Hand

In pursuing a comprehensive hotel list from a hospitality management company’s portfolio webpage, I found the need for a strategic and accurate approach that wasn’t manual. I was tasked to gather basic hotel information like hotel name, address, and phone number, among other details.

This led me to develop a web scraper using Python, a tool that proves instrumental in data analytics.

Naturally, I thought, “How can I develop a tool to do this for me rather than by hand”?

Fueled by a hunger for efficiency and spot-on data, I decided to make a cup of coffee, turn on my favorite playlist, and build a web scraper.

In this blog post, I will guide you through the technical aspects of my coding journey and showcase the effectiveness of the web scraping tool.

Setting the Digital Stage

# Loading the Tools
from bs4 import BeautifulSoup
import requests

# Loading the URL 
url = 'https://xeniareit.com/portfolio/'
page = requests.get(url)

# Crafting a Digital Map with BeautifulSoup
soup = BeautifulSoup(page.text, 'html')

# Mining through Table Data
hotel_headers = soup.find_all('th')[0:7]

# Stripping HTML Tags from the Data
headers = [header.text for header in hotel_headers]

To kickstart this web scraping process, I summoned the BeautifulSoup library and enlisted the help of requests to prepare my notebook to read HTML data. The portfolio page was none other than that of the eminent hospitality management company Xenia REIT.

Utilizing BeautifulSoup, I efficiently extracted table headings, ensuring a clean dataset by eliminating unnecessary HTML tags.

Delving into Data Extraction

# Creating a Data Table
import pandas as pd
df = pd.DataFrame(columns=headers)

# Collecting and Merging Hotel Intel
hotel_data = soup.find_all('tr')
hotel_data_1 = hotel_data[1:13]
hotel_data_2 = hotel_data[14:]
all_hotel_data = hotel_data_1 + hotel_data_2

# Loading Data into the DataFrame
for row in all_hotel_data:
    row_data = row.find_all('td')
    individual_hotel_data = [data.text.strip() for data in row_data]
    df.loc[len(df)] = individual_hotel_data

# Polishing and Amplifying the Data
df['City/State'].str.rsplit(' ', expand=True)

# Creating a CSV from DataFrame
df.to_csv(r'/Users/christianpena/Desktop/Jupyter Webscraping/hotel_data.csv')

Taking it up a notch involved a meticulous extraction of hotel data from two tables on the website, culminating in a comprehensive list of hotels. The data was organized into a DataFrame using the Pandas library, allowing for smoother manipulation and analysis.

Finally, the code exported the enriched dataset into a CSV file named ‘hotel_data.csv’ and used to update our CRM with the correct portfolio data.

The Conclusion

This web scraping project significantly enhanced my coding skills and provided me with a robust tool for data analytics in the hospitality management sector. As I continue refining and expanding my skill set, I aim to leverage this project as a stepping stone toward securing a rewarding position in the data analytics field.

Stay tuned for more insights into my dynamic journey through the world of data analytics!

The post Building My First Python Webscraper appeared first on Christian Manuel Pena.