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pandas Cheat Sheet.pdf
1.6 MB
๐Ÿ“• #pandas Cheat Sheet


๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป To easily read, inspect, clean, and manipulate data however you want, you need to master pandas!

โœ๏ธ To make learning and using pandas easier, this #cheatsheet covers almost all the important features you need for data-driven projects.

โœ”๏ธ Reading and writing data
โœ”๏ธ Data inspection
โœ”๏ธ Data transformation and cleaning
โœ”๏ธ Grouping and summarizing
โœ”๏ธ Combining datasets

๐ŸŒ #DataScience #DataScience

https://t.iss.one/DataAnalyticsX ๐Ÿ
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Important SQL concepts to master.pdf
3 MB
Important #SQL concepts to master:
- Joins (inner, left, right, full)
- Group By vs Where vs Having
- Window functions (ROW_NUMBER, RANK, DENSE_RANK)
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- Subqueries and nested queries
- Aggregations and filtering
- Indexing and performance basics
- NULL handling

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A comprehensive summary of the Seaborn Library.pdf
3.3 MB
๐Ÿ“Š A comprehensive summary of the ยซSeaborn Libraryยป

๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป 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.

โœ… 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.

โœ๏ธ 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
Enable notifications There are more surprises, don't miss them
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Pyspark Functions.pdf
4.1 MB
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Forwarded from Machine Learning with Python
Numpy @CodeProgrammer.pdf
2.4 MB
๐Ÿท Sections of the ยซNumPyยป library
โฌ…๏ธ From introductory to advanced


๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป 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.


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


โญ•๏ธ Introduction to NumPy
๐ŸŸ  NumPy arrays
โญ•๏ธ Introduction to array features
๐ŸŸ  Basic operations on arrays
โญ•๏ธ Functions for statistical and aggregative purposes
๐ŸŸ  And...

https://t.iss.one/CodeProgrammer โ›ˆโšก๏ธ
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SQL Ultimate Cheat Sheet

Standard #SQL, Queries & Management

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Forwarded from Machine Learning with Python
๐Ÿš€ #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
๐ŸŒŸ A new and comprehensive book "Mastering pandas"

๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป 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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Python Libraries You Should Know โœ…

โฆ NumPy: Numerical Computing โš™๏ธ
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 1โ€“10.
matrix = np.random.randint(1, 11, size=(3, 3))
print(matrix)


โฆ Pandas: Data Analysis ๐Ÿผ
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())


โฆ Matplotlib: Data Visualization ๐Ÿ“Š
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()


โฆ Seaborn: Statistical Plots ๐ŸŽจ
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()


โฆ Requests: HTTP for Humans ๐ŸŒ
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"])


โฆ Beautiful Soup: Web Scraping ๐Ÿœ
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:
โฆ Combine these libraries for real-world projects
โฆ Try scraping data and analyzing it with Pandas
โฆ Visualize insights with Seaborn and Matplotlib

Double Tap โ™ฅ๏ธ For More
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๐Ÿš€ Master Data Science & Programming!

Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!


๐Ÿ”ฐ Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.iss.one/CodeProgrammer

๐Ÿ”– Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.iss.one/DataScienceM

๐Ÿง  Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA โ€“ perfect for learning, coding, and mastering key programming skills.
https://t.iss.one/DataScience4

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Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ

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Your go-to hub for Kaggle datasets โ€“ explore, analyze, and leverage data for Machine Learning and Data Science projects.
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The first channel in Telegram that offers free Udemy coupons
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Advancing research in Machine Learning โ€“ practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT

๐Ÿ’ฌ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9

๐Ÿ Python Arab| ุจุงูŠุซูˆู† ุนุฑุจูŠ
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab

๐Ÿ–Š Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksโ€”insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.iss.one/DataScienceN

๐Ÿ“บ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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Dive into the world of Data Analytics โ€“ uncover insights, explore trends, and master data-driven decision making.
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Master Python with step-by-step courses โ€“ from basics to advanced projects and practical applications.
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Professional Academic Writing & Simulation Services
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These Python commands cover 90% of data cleaning tasks you'll ever need ๐Ÿ‘‡
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