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    Day 7: Pandas Series & DataFrame Basics – Python Class for ML & Data Science

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    📘 Day 7 — Pandas Series & DataFrame Basics (Hindi + English)

    Easy input → output Python examples, simple explanations, and downloadable diagrams (SVG/PNG). Paste into Blogger (HTML mode). Title removed as requested.

    Table of Contents
    1. Introduction to Pandas
    2. Pandas Series
      1. Create Series
      2. Accessing Elements
      3. Series Operations
    3. Pandas DataFrame
      1. Creating DataFrame
      2. Selecting Data
      3. Modifying DataFrame
    4. Useful DataFrame Functions (Quick EDA)
    5. Real Dataset Hands-On (Titanic)
    6. Diagrams & Downloads
    7. Homework / Practice

    1) 🔹 Introduction to Pandas — क्या है, क्यों ज़रूरी

    क्या है? Pandas एक Python library है जो table-like data (CSV, Excel, SQL) को आसानी से read, manipulate और analyze करने देती है.
    क्यों जरूरी? Machine Learning में data cleaning और transformation का बड़ा हिस्सा होता है — Pandas ये काम आसान और fast बना देता है.
    कैसे शुरू करें? Install & import नीचे देखें।

    Install / Import (Input → Output style)

    # Input (run in terminal or notebook)
!pip install pandas

# Input (in Python)
import pandas as pd
import numpy as np
print('pandas version:', pd.__version__)

# Output (example)
# pandas version: 2.2.0

    2) 🔹 Pandas Series — क्या है (1D)

    Series = 1D labeled array → index + values. Example: daily temperature list with date labels.

    2.1 Create Series — 5 Examples (Input → Output)

    हर example में पहले input दिखाया गया है, फिर expected/typical output.
    # Example 1 — From List
# Input:
s = pd.Series([10,20,30])
print(s)
# Output:
# 0    10
# 1    20
# 2    30
# dtype: int64

    # Example 2 — With custom index (labels)
# Input:
s = pd.Series([100,200,300], index=['a','b','c'])
print(s)
# Output:
# a    100
# b    200
# c    300
# dtype: int64

    # Example 3 — From dictionary
# Input:
s = pd.Series({'x':5,'y':10,'z':15})
print(s)
# Output:
# x     5
# y    10
# z    15
# dtype: int64

    # Example 4 — From scalar (same value for each index)
# Input:
s = pd.Series(7, index=[0,1,2,3])
print(s)
# Output:
# 0    7
# 1    7
# 2    7
# 3    7
# dtype: int64

    # Example 5 — From NumPy array
# Input:
arr = np.array([1,2,3,4])
s = pd.Series(arr)
print(s)
# Output:
# 0    1
# 1    2
# 2    3
# 3    4
# dtype: int64

    2.2 Accessing Elements — practical examples

    Use indexing, slicing, labels and boolean filters.
    # Input:
s = pd.Series([5,10,15,20], index=['a','b','c','d'])

# By position
print('s[0] ->', s[0])
# Output: s[0] -> 5

# By label
print(\"s['c'] ->\", s['c'])
# Output: s['c'] -> 15

# Slice
print('s[1:3] ->\\n', s[1:3])
# Output:
# s[1:3] ->
# b    10
# c    15
# dtype: int64

# Boolean filter
print('s[s > 10] ->\\n', s[s > 10])
# Output:
# c    15
# d    20
# dtype: int64

    2.3 Series Operations — vectorized & aligned ops

    # Input:
s = pd.Series([2,4,6])

# Scalar op
print('s*3 ->', s*3)
# Output: s*3 -> 0    6 1   12 2   18 dtype: int64

# Add two Series (alignment by index)
s1 = pd.Series([1,2,3], index=['a','b','c'])
s2 = pd.Series([10,20,30], index=['b','c','d'])
print('s1 + s2 ->\\n', s1 + s2)
# Output:
# a     NaN
# b    12.0
# c    23.0
# d     NaN
# dtype: float64

# Use numpy functions
print('np.sqrt(s) ->', np.sqrt(s))
# Output: np.sqrt(s) -> [1.41421356 2.         2.44948974]

    3) 🔹 Pandas DataFrame — 2D table

    DataFrame = rows (records) + columns (features). Real world datasets mostly DataFrame format.

    3.1 Creating DataFrame — 5 Examples (Input → Output)

    # Example 1 — From dictionary
# Input:
data = {'Name':['Aman','Ravi','Priya'],'Age':[22,25,24],'Marks':[85,78,92]}
df = pd.DataFrame(data)
print(df)
# Output:
#     Name  Age  Marks
# 0   Aman   22     85
# 1   Ravi   25     78
# 2  Priya   24     92

    # Example 2 — From list of dicts
# Input:
data = [{'a':1,'b':2},{'a':3,'b':4}]
df = pd.DataFrame(data)
print(df)
# Output:
#    a  b
# 0  1  2
# 1  3  4

    # Example 3 — From list of lists (with columns)
# Input:
data = [[1,'Aman'],[2,'Ravi']]
df = pd.DataFrame(data, columns=['ID','Name'])
print(df)
# Output:
#    ID  Name
# 0   1  Aman
# 1   2  Ravi

    # Example 4 — From NumPy array
# Input:
arr = np.arange(6).reshape(2,3)
df = pd.DataFrame(arr, columns=['A','B','C'])
print(df)
# Output:
#    A  B  C
# 0  0  1  2
# 1  3  4  5

    # Example 5 — From Series
# Input:
s1 = pd.Series([1,2,3])
s2 = pd.Series([4,5,6])
df = pd.DataFrame({'col1':s1,'col2':s2})
print(df)
# Output:
#    col1  col2
# 0     1     4
# 1     2     5
# 2     3     6

    3.2 Selecting Data — practical cases

    # Input:
df = pd.DataFrame({'Name':['A','B','C'],'Age':[20,21,22],'Marks':[80,85,90]})

# Single column
print('df[\"Name\"] ->\\n', df['Name'])
# Output:
# 0    A
# 1    B
# 2    C
# Name: Name, dtype: object

# Multiple columns
print('df[[\"Name\",\"Marks\"]] ->\\n', df[['Name','Marks']])
# Output: table with Name and Marks columns

# Row by label (loc)
print('df.loc[1] ->\\n', df.loc[1])
# Output: Series for row index 1

# Row by position (iloc)
print('df.iloc[2] ->\\n', df.iloc[2])
# Output: Series for third row

# Conditional selection
print('df[df[\"Marks\"] > 80] ->\\n', df[df['Marks'] > 80])
# Output: rows where Marks > 80 (rows 1 and 2)

    3.3 Modifying DataFrame — add/update/drop

    # Input:
df = pd.DataFrame({'Name':['A','B','C'],'Marks':[70,80,90]})

# Add column
df['Passed'] = df['Marks'] > 75
print('After add ->\\n', df)
# Output: column 'Passed' with True/False

# Update value
df.loc[0,'Marks'] = 75
print('After update ->\\n', df)

# Drop column
df = df.drop('Passed', axis=1)
print('After drop ->\\n', df)

# Add row
df.loc[3] = ['D', 65]
print('After add row ->\\n', df)

# Drop row
df = df.drop(3, axis=0)
print('After drop row ->\\n', df)

    4) 🔹 Useful DataFrame Functions (Quick EDA)

    Data samajhne के लिए common commands: head(), info(), describe(), value_counts().

    # Example EDA (Input -> Output)
print(df.head())      # first 5 rows
print(df.shape)       # (rows, cols)
print(df.info())      # dtypes & non-null counts
print(df.describe())  # numerical summary
# Output: respectively head table, shape tuple, info text, describe table

    5) 🔹 Real Dataset Hands-On — Titanic (practical)

    Step-by-step: load CSV → inspect → simple analysis.

    # Input:
titanic = pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')
print(titanic.head())

# Example 1 — Average Age
print('Average Age ->', titanic['Age'].mean())
# Example 2 — Total Survived
print('Total Survived ->', titanic['Survived'].sum())
# Example 3 — Male vs Female count
print('Sex counts ->\\n', titanic['Sex'].value_counts())
# Example 4 — Top fares
print(titanic.sort_values('Fare', ascending=False)[['Name','Fare']].head())
# Example 5 — Survival rate by Sex
print('Survival Rate by Sex ->\\n', titanic.groupby('Sex')['Survived'].mean())

# Output: See printed tables & numbers in console / notebook


    6) 🎯 Homework / Practice

    • Series बनाइए — IPL scores — Find max, min और average (input→output).
    • DataFrame बनाइए — 10 students (Name, Age, Marks) — Find top 3 marks and average marks.
    • Titanic practice: Find minimum age, avg fare by Pclass, survival rate for children (<18).

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