Data Analytics

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A comprehensive summary of the «Seaborn Library»

👨🏻‍💻 One of the best choices for any data scientist to convert data into clear and beautiful charts, so that they can better understand what the data is saying and also be able to present the results correctly and clearly to others, is the Seaborn library.

✅ A very user-friendly library for creating professional charts with minimal coding. It is built on top of Matplotlib but is simpler and easier to use than that.

✏️ With this summary, you will learn the syntax, see many examples and real applications of #Seaborn, and ultimately help you elevate your #datavisualization skills by several levels.

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A comprehensive summary of the Seaborn Library.pdf

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This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

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Pyspark Functions.pdf

4.1 MB

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• Core Ops: select(), withColumn(), filter(), dropDuplicates()
• Aggregations: groupBy(), countDistinct(), collect_list()
• Strings: concat(), split(), regexp_extract(), trim()
• Window: row_number(), rank(), lead(), lag()
• Date/Time: current_date(), date_add(), last_day(), months_between()
• Arrays/Maps: array(), array_union(), MapType

Just mastering these ~20 functions can simplify 70% of your transformations.

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Forwarded from Machine Learning with Python

Numpy @CodeProgrammer.pdf

2.4 MB

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Sections of the «NumPy» library
⬅️ From introductory to advanced

👨🏻‍💻 This is a long-term project to learn Python and NumPy from scratch. The main task is to handle numerical #data and #arrays in #Python using NumPy, and many other libraries are also used.

✏️ This section shows a structured and complete path for learning #NumPy; but the code examples and exercises help to practically memorize the concepts.

⭕️ Introduction to NumPy
🟠 NumPy arrays
⭕️ Introduction to array features
🟠 Basic operations on arrays
⭕️ Functions for statistical and aggregative purposes
🟠 And...

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SQL Ultimate Cheat Sheet

Standard #SQL, Queries & Management

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Forwarded from Machine Learning with Python

🚀 #Pandas Cheat Sheet for Everyday Data Work

This covers the essential functions we use in day to day work like inspecting data, selecting rows and columns, cleaning, manipulating and doing quick aggregations.

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Forwarded from Machine Learning with Python

Mastering pandas%22.pdf

1.6 MB

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A new and comprehensive book "Mastering pandas"

👨🏻‍💻 If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.

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And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.

🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:

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Python Libraries You Should Know ✅

⦁ NumPy: Numerical Computing ⚙️
NumPy is the foundation for numerical operations in Python. It provides fast arrays and math functions.

Example:

import numpy as np

arr = np.array([1, 2, 3])
print(arr * 2)  # [2 4 6]

Challenge: Create a 3x3 matrix of random integers from 1–10.

matrix = np.random.randint(1, 11, size=(3, 3))
print(matrix)

⦁ Pandas: Data Analysis 🐼
Pandas makes it easy to work with tabular data using DataFrames.

Example:

import pandas as pd

data = {"Name": ["Alice", "Bob"], "Age": [25, 30]}
df = pd.DataFrame(data)
print(df)

Challenge: Load a CSV file and show the top 5 rows.

df = pd.read_csv("data.csv")
print(df.head())

⦁ Matplotlib: Data Visualization 📊
Matplotlib helps you create charts and plots.

Example:

import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [2, 4, 1]

plt.plot(x, y)
plt.title("Simple Line Plot")
plt.show()

Challenge: Plot a bar chart of fruit sales.

fruits = ["Apples", "Bananas", "Cherries"]
sales = [30, 45, 25]

plt.bar(fruits, sales)
plt.title("Fruit Sales")
plt.show()

⦁ Seaborn: Statistical Plots 🎨
Seaborn builds on Matplotlib with beautiful, high-level charts.

Example:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

Challenge: Create a heatmap of correlation.

corr = tips.corr()
sns.heatmap(corr, annot=True, cmap="coolwarm")
plt.show()

⦁ Requests: HTTP for Humans 🌐
Requests makes it easy to send HTTP requests.

Example:

import requests

response = requests.get("https://api.github.com")
print(response.status_code)
print(response.json())

Challenge: Fetch and print your IP address.

res = requests.get("https://api.ipify.org?format=json")
print(res.json()["ip"])

⦁ Beautiful Soup: Web Scraping 🍜
Beautiful Soup helps you extract data from HTML pages.

Example:

from bs4 import BeautifulSoup
import requests

url = "https://example.com"
html = requests.get(url).text
soup = BeautifulSoup(html, "html.parser")

print(soup.title.text)

Challenge: Extract all links from a webpage.

links = soup.find_all("a")
for link in links:
    print(link.get("href"))

Next Steps:
⦁ Combine these libraries for real-world projects
⦁ Try scraping data and analyzing it with Pandas
⦁ Visualize insights with Seaborn and Matplotlib

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These Python commands cover 90% of data cleaning tasks you'll ever need 👇

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