Data Visualization

visualization** — NOT on algorithms or web development.

🧠 Hinglish mein samjho: Python data analyst ka Swiss Army knife hai — data laana, saaf karna, analyze karna, graph banana — sab ek hi language mein. DecisionTree mein Pandas aur Seaborn roz use hote hain. DSA ya web development BILKUL nahi poochenge — sirf data manipulation aur analysis poochenge.

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CHAPTER 1: PYTHON BASICS (Quick Review)

🧠 Agar Python bilkul nahi aati: Yeh chapter pehle 2-3 baar practice karo. Agar thodi bahut aati hai toh seedha Chapter 3 (Pandas) pe jaao — wahi interview mein poochha jaata hai.

1.1 Variables & Data Types

# Variables — no need to declare type (Python figures it out)
name = "DecisionTree"        # str (text)
employees = 160              # int (whole number)
revenue = 45.5               # float (decimal)
is_profitable = True         # bool (True/False)
cities = ["Delhi", "Mumbai"] # list (ordered, changeable)
info = {"name": "DT", "year": 2004}  # dict (key-value pairs)

Type	Example	Mutable?	Ordered?
str	"hello"	❌	✅
int	42	❌	N/A
float	3.14	❌	N/A
bool	True/False	❌	N/A
list	[1, 2, 3]	✅	✅
tuple	(1, 2, 3)	❌	✅
set	{1, 2, 3}	✅	❌
dict	{"a": 1}	✅	✅ (Python 3.7+)

Mutable = bana ke baad change kar sakte ho (jaise list). Immutable = ek baar bana di toh change nahi hogi (jaise string, tuple). Interview mein zaroor poochte hain!

1.2 Lists — The Most Common Data Structure

fruits = ["apple", "banana", "cherry"]

# Accessing elements (0-indexed)
fruits[0]     # "apple" (first)
fruits[-1]    # "cherry" (last)
fruits[1:3]   # ["banana", "cherry"] (slice: start inclusive, end exclusive)

# Modifying
fruits.append("date")        # Add to end → ["apple", "banana", "cherry", "date"]
fruits.insert(1, "avocado")  # Insert at position 1
fruits.remove("banana")      # Remove by value
fruits.pop(0)                # Remove by index, returns removed item

# Useful operations
len(fruits)          # Number of items
sorted(fruits)       # Returns new sorted list
fruits.sort()        # Sorts in-place
"apple" in fruits    # True/False — membership test

1.3 Dictionaries — Key-Value Lookup

customer = {
    "name": "Rajesh",
    "city": "Delhi",
    "orders": 5
}

# Access
customer["name"]               # "Rajesh"
customer.get("phone", "N/A")   # "N/A" (safe access — no error if key missing)

# Modify
customer["orders"] = 6         # Update value
customer["email"] = "r@dt.com" # Add new key

# Loop through
for key, value in customer.items():
    print(f"{key}: {value}")

1.4 Loops

# For loop — iterate over a sequence
for i in range(5):       # 0, 1, 2, 3, 4
    print(i)

for city in ["Delhi", "Mumbai", "Bangalore"]:
    print(city)

# While loop — repeat until condition is false
count = 0
while count < 5:
    print(count)
    count += 1  # Don't forget to update!

# List comprehension — ek line mein loop + list banana (BAHUT use hota hai!)
# Socho: "Har x ka square nikalo, x = 1 se 5 tak"
squares = [x**2 for x in range(1, 6)]  # [1, 4, 9, 16, 25]

# Condition ke saath — "sirf even numbers ka square do"
even_squares = [x**2 for x in range(1, 11) if x % 2 == 0]  # [4, 16, 36, 64, 100]
# 🧠 Yaad rakho: [RESULT for ITEM in LIST if CONDITION]

1.5 Functions

def calculate_profit(revenue, cost):
    """Calculate profit and profit percentage."""
    profit = revenue - cost
    profit_pct = (profit / cost) * 100
    return profit, profit_pct  # Return multiple values as a tuple

# Call the function
p, pct = calculate_profit(10000, 8000)
print(f"Profit: ₹{p}, Profit%: {pct}%")  # Profit: ₹2000, Profit%: 25.0%

Lambda Functions (One-line functions)

# Lambda = chhoti si bina-naam-ki function — ek line mein kaam ho jaaye
double = lambda x: x * 2   # "x lo, x*2 return karo"
double(5)  # 10

# 🧠 Lambda Pandas mein BAHUT use hota hai:
# df['revenue'].apply(lambda x: x / 1000)  → har value ko 1000 se divide karo
# Jab chhota kaam hai toh function banana bekar hai — lambda lagao

1.6 Exception Handling

try:
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero!")
except Exception as e:
    print(f"Error: {e}")
finally:
    print("This always runs")

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CHAPTER 2: NumPy — Numerical Computing

2.1 What is NumPy?

NumPy (Numerical Python) is the foundation for numerical computing in Python. Pandas is built on top of it.

import numpy as np

# Creating arrays
arr = np.array([1, 2, 3, 4, 5])
zeros = np.zeros(5)           # [0, 0, 0, 0, 0]
ones = np.ones((3, 4))        # 3×4 matrix of 1s
sequence = np.arange(0, 10, 2) # [0, 2, 4, 6, 8]

2.2 Why NumPy Over Lists?

Feature	Python List	NumPy Array
Speed	Slow	10–100× faster
Element-wise operations	❌ Need loops	✅ Vectorized
Memory	More	Less
Math operations	Manual	Built-in

# Element-wise operations (impossible with plain lists)
arr = np.array([10, 20, 30, 40, 50])
arr * 2         # [20, 40, 60, 80, 100]
arr + 5         # [15, 25, 35, 45, 55]
arr > 25        # [False, False, True, True, True]
arr[arr > 25]   # [30, 40, 50] — boolean filtering

2.3 Essential NumPy Functions for Data Analysis

data = np.array([23, 45, 12, 67, 34, 56, 78, 89, 21, 43])

np.mean(data)      # 46.8 — Average
np.median(data)    # 44.0 — Middle value
np.std(data)       # 23.3 — Standard deviation
np.var(data)       # 543.0 — Variance
np.min(data)       # 12
np.max(data)       # 89
np.sum(data)       # 468
np.percentile(data, 75)  # 75th percentile (Q3)
np.sort(data)      # Sorted array
np.unique(data)    # Unique values

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CHAPTER 3: PANDAS — The Heart of Data Analysis

🧠 YEH CHAPTER SABSE IMPORTANT HAI! Interview mein 70% Python questions Pandas se aate hain. Isko ache se padho aur practice karo.

3.1 What is Pandas?

Pandas ek Python library hai jo DataFrames deti hai — 2D tables (Excel sheets jaisi) — jinpe powerful data manipulation kar sakte ho.

🧠 Socho DataFrame ko Excel sheet samjho — rows hain, columns hain, filter kar sakte ho, sort kar sakte ho, formulas laga sakte ho. Bas Python mein likha jaata hai Excel clicks ki jagah.

import pandas as pd  # pd = chhota naam, hamesha aise hi import karte hain

# Dictionary se DataFrame banana (interview mein karwa sakte hain)
data = {
    'name': ['Rajesh', 'Priya', 'Amit', 'Sneha', 'Vikram'],
    'city': ['Delhi', 'Mumbai', 'Ahm