📘 PAPER 2: OBJECT ORIENTED PROGRAMMING WITH PYTHON UNIT 5 – NumPy and Pandas (university of allahabad)

 

🔴 UNIT 5 – NumPy and Pandas

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🟦 PART A: NUMPY (Numerical Python)

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1️⃣ Introduction to NumPy

✅ What is NumPy?

NumPy is a Python library used for:

• Numerical computation

• Scientific computing

• Working with arrays and matrices

✅ Features of NumPy

✔ Faster than Python lists
✔ Supports multi-dimensional arrays
✔ Efficient memory usage
✔ Used in ML, AI, Data Science

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2️⃣ ndarray (N-Dimensional Array)

Definition:

The main object of NumPy is ndarray, which represents a multi-dimensional array.

Example:

import numpy as np
a = np.array([1,2,3])
print(a)

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3️⃣ Data Types in NumPy

a = np.array([1,2,3], dtype=float)

Common Data Types:

• int

• float

• bool

• complex

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4️⃣ Array Attributes

Attribute	Description
ndim	Number of dimensions
shape	Size of array
size	Total elements
dtype	Data type

Example:

a = np.array([[1,2],[3,4]])
print(a.ndim)
print(a.shape)

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5️⃣ Array Creation Routines

🔹 From List

np.array([1,2,3])

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🔹 Zeros & Ones

np.zeros((2,2))
np.ones((3,3))

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🔹 Using arange()

np.arange(1,10,2)

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🔹 Using linspace()

np.linspace(1,10,5)

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6️⃣ Array from Existing Data

np.asarray([1,2,3])
np.frombuffer(b'hello', dtype='S1')

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7️⃣ Array Indexing & Slicing

a = np.array([10,20,30,40])
print(a[1])
print(a[1:3])

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8️⃣ Mathematical Operations

a + b
a * b
np.sqrt(a)
np.sum(a)
np.mean(a)

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🟩 PART B: PANDAS

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9️⃣ Introduction to Pandas

✅ What is Pandas?

Pandas is a Python library used for:

• Data analysis

• Data manipulation

• Handling structured data

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🔟 Pandas Data Structures

1️⃣ Series

A one-dimensional labeled array.

import pandas as pd
s = pd.Series([10,20,30])

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2️⃣ DataFrame

A two-dimensional table-like structure.

data = {
    "Name": ["Amit", "Rahul"],
    "Marks": [80, 90]
}
df = pd.DataFrame(data)

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1️⃣1️⃣ Creating Series

From List

pd.Series([1,2,3])

From Dictionary

pd.Series({'a':10, 'b':20})

From Scalar

pd.Series(5, index=[1,2,3])

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1️⃣2️⃣ Creating DataFrame

From List

pd.DataFrame([[1,2],[3,4]])

From Dictionary

pd.DataFrame({
    "Name":["A","B"],
    "Age":[20,22]
})

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1️⃣3️⃣ Manipulating DataFrames

Rename Column

df.rename(columns={"Name":"Student_Name"})

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Delete Column

df.drop("Age", axis=1)

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Delete Row

df.drop(0)

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1️⃣4️⃣ Handling Missing Values

Finding Missing Values

df.isnull()

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Filling Missing Values

df.fillna(0)

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Dropping Missing Values

df.dropna()

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1️⃣5️⃣ Advantages of Pandas

✔ Easy data handling
✔ Fast processing
✔ Data cleaning
✔ Used in ML & AI