Data Analytics

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

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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 Double Tap ♥️ For More

*Python Libraries You Should Know ✅* *🔹 1. 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)

*🔹 2. 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())

*🔹 3. 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()

*🔹 4. Seaborn: Statistical Plots 🎨* Seaborn builds on Matplotlib with beautiful, high-level charts. *Example:*

import seaborn as sns
import pandas as pd

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()

*🔹 5. 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'])

*🔹 6. 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 & Matplotlib *Double Tap ♥️ For More*

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