डेटा एनालिसिस - संपूर्ण कोर्स (विस्तृत सामग्री)

Complete Data Analysis Course with Detailed Answers

मॉड्यूल 1: डेटा एनालिसिस की बुनियादी बातें

अध्याय 1.1: डेटा एनालिसिस क्या है?

डेटा एनालिसिस की परिभाषा

डेटा एनालिसिस एक systematic process है जिसमें हम raw data को collect करते हैं, उसे clean करते हैं, analyze करते हैं और meaningful insights निकालते हैं। यह process businesses और organizations को better decisions लेने में help करती है।

डेटा एनालिसिस के मुख्य चरण:

1. Data Collection: विभिन्न sources से data gather करना

2. Data Cleaning: Errors, duplicates, और missing values को handle करना

3. Data Processing: Data को analysis के लिए suitable format में convert करना

4. Data Analysis: Statistical methods और algorithms का use करके patterns find करना

5. Data Interpretation: Results को business context में समझना

6. Reporting: Findings को stakeholders के साथ share करना

महत्व और Applications

Business में Applications:

• Sales Analysis: कौन से products ज्यादा sell हो रहे हैं

• Customer Segmentation: Different customer groups की identification

• Market Research: Market trends और consumer behavior की समझ

• Financial Analysis: Revenue, profit, और cost optimization

• Risk Assessment: Potential risks की identification और mitigation

विभिन्न Industries में उपयोग:

• Healthcare: Patient data analysis, drug effectiveness studies

• Finance: Credit scoring, fraud detection, investment strategies

• E-commerce: Recommendation systems, price optimization

• Manufacturing: Quality control, supply chain optimization

• Education: Student performance analysis, curriculum improvement

• Government: Policy making, resource allocation

Career Opportunities

Job Roles:

1. Data Analyst: Entry-level position, basic analysis और reporting
   
   

   • Average Salary: ₹3-6 लाख per annum

   • Skills Required: Excel, SQL, Basic statistics

2. Business Analyst: Business problems को data से solve करना
   
   

   • Average Salary: ₹5-10 लाख per annum

   • Skills Required: Domain knowledge, communication skills

3. Data Scientist: Advanced analytics और machine learning
   
   

   • Average Salary: ₹8-20 लाख per annum

   • Skills Required: Python/R, ML algorithms, statistics

4. Data Engineer: Data infrastructure और pipelines
   
   

   • Average Salary: ₹6-15 लाख per annum

   • Skills Required: Programming, databases, cloud platforms

Data vs Information vs Knowledge

Data: Raw facts और figures

• Example: Sales figure "₹50,000"

• यह isolated fact है जिसका कोई context नहीं है

Information: Processed data with context

• Example: "Last month sales were ₹50,000, which is 20% increase from previous month"

• यह data को meaningful context में present करता है

Knowledge: Information + Experience + Insight

• Example: "Sales increase is due to festive season demand, we should increase inventory for next month"

• यह actionable insights provide करता है

अध्याय 1.2: डेटा के प्रकार

Quantitative Data (संख्यात्मक डेटा)

Continuous Data (लगातार डेटा):

• यह data किसी भी value ले सकता है एक range में

• Decimal points हो सकते हैं

• Examples: Height (170.5 cm), Weight (65.3 kg), Temperature (25.7°C)

• Measurement: Precise measurements possible

• Analysis: Mean, median, standard deviation calculate कर सकते हैं

Discrete Data (असतत डेटा):

• यह data केवल specific values ले सकता है

• Usually whole numbers

• Examples: Number of students (25), Number of cars (5), Number of orders (150)

• Characteristics: Countable और finite values

• Analysis: Frequency distributions, percentages

Qualitative Data (गुणात्मक डेटा)

Nominal Data:

• Categories हैं जिनका कोई order नहीं है

• Examples: Gender (Male, Female), Colors (Red, Blue, Green), Marital Status (Married, Single)

• Analysis: Frequency counts, percentages, mode

• Visualization: Pie charts, bar charts

Ordinal Data:

• Categories हैं जिनका एक specific order है

• Examples: Education Level (High School, Bachelor's, Master's), Satisfaction Rating (Poor, Average, Good, Excellent)

• Analysis: Median, percentiles, correlation

• Visualization: Ordered bar charts, stacked charts

Structured vs Unstructured Data

Structured Data:

• Well-organized format में होता है

• Easily searchable और analyzable

• Examples: Database tables, Excel spreadsheets, CSV files

• Characteristics: Rows और columns में organized

• Analysis: SQL queries, statistical analysis

Unstructured Data:

• No predefined format

• Text, images, videos, audio files

• Examples: Social media posts, emails, documents, images

• Challenges: Requires special processing techniques

• Analysis: Natural Language Processing, image recognition

Primary vs Secondary Data

Primary Data:

• Directly collected for specific research purpose

• Collection Methods: Surveys, interviews, observations, experiments

• Advantages: Highly relevant, current, reliable

• Disadvantages: Time-consuming, expensive

• Examples: Customer survey data, sales transactions, website analytics

Secondary Data:

• Already collected by someone else for different purpose

• Sources: Government reports, industry studies, academic research

• Advantages: Quick access, cost-effective, large datasets

• Disadvantages: May not be exactly relevant, could be outdated

• Examples: Census data, market research reports, financial statements

अध्याय 1.3: डेटा एनालिसिस प्रक्रिया

Data Collection (डेटा संग्रह)

Data Collection Methods:

1. Surveys और Questionnaires:

• Online surveys (Google Forms, SurveyMonkey)

• Phone interviews

• Face-to-face interviews

• Best Practices: Clear questions, unbiased language, proper sampling

2. Observational Studies:

• Direct observation

• Hidden observation

• Participant observation

• Applications: Customer behavior, usability testing

3. Experiments:

• Controlled experiments

• A/B testing

• Randomized controlled trials

• Applications: Product testing, website optimization

4. Secondary Data Sources:

• Government databases

• Industry reports

• Academic research

• Examples: Census data, economic indicators

Data Cleaning (डेटा सफाई)

Common Data Quality Issues:

1. Missing Values:

• Identification: Check for blank cells, null values, "N/A" entries

• Handling Strategies:

  • Delete rows with missing values (if small percentage)

  • Fill with mean/median (for numerical data)

  • Fill with mode (for categorical data)

  • Use interpolation for time series data

2. Duplicate Records:

• Identification: Look for identical rows या similar entries

• Handling: Remove duplicates, keep most recent या most complete record

3. Inconsistent Data:

• Examples: "Male" vs "M" vs "male"

• Solutions: Standardize formats, create data dictionaries

4. Outliers:

• Identification: Values that are significantly different from others

• Detection Methods: Box plots, Z-scores, IQR method

• Handling: Remove, transform, या separately analyze

Data Cleaning Process:

1. Data Profiling: Understand data structure और quality

2. Data Validation: Check for completeness और accuracy

3. Data Standardization: Consistent formats और units

4. Data Transformation: Convert to suitable formats

Data Exploration (डेटा अन्वेषण)

Exploratory Data Analysis (EDA) Steps:

1. Univariate Analysis:

• Analyze each variable individually

• For Numerical Variables: Histograms, box plots, summary statistics

• For Categorical Variables: Frequency tables, bar charts

2. Bivariate Analysis:

• Relationship between two variables

• Numerical vs Numerical: Scatter plots, correlation analysis

• Categorical vs Numerical: Box plots, group comparisons

• Categorical vs Categorical: Cross-tabulation, chi-square tests

3. Multivariate Analysis:

• Analyze multiple variables together

• Techniques: Correlation matrices, heatmaps, parallel coordinates

Key Questions to Answer:

• What is the distribution of each variable?

• Are there any patterns या trends?

• What are the relationships between variables?

• Are there any anomalies या outliers?

Data Analysis Techniques

Descriptive Analysis:

• Summarize और describe data characteristics

• Measures: Mean, median, mode, standard deviation

• Purpose: Understanding current state

Diagnostic Analysis:

• Understand why something happened

• Techniques: Drill-down analysis, root cause analysis

• Purpose: Identify causes of problems या trends

Predictive Analysis:

• Forecast future outcomes

• Techniques: Regression analysis, time series forecasting

• Purpose: Planning और decision making

Prescriptive Analysis:

• Recommend actions to take

• Techniques: Optimization algorithms, simulation

• Purpose: Actionable insights और recommendations

Data Visualization

Chart Selection Guidelines:

For Comparisons:

• Bar charts: Compare categories

• Column charts: Compare over time

• Horizontal bar charts: Long category names

For Relationships:

• Scatter plots: Correlation between two variables

• Line charts: Trends over time

• Bubble charts: Three-dimensional relationships

For Compositions:

• Pie charts: Parts of a whole (limited categories)

• Stacked bar charts: Components over time

• Area charts: Cumulative values

For Distributions:

• Histograms: Frequency distribution

• Box plots: Outliers और quartiles

• Violin plots: Distribution shape

Visualization Best Practices:

• Choose appropriate chart type

• Use clear और descriptive titles

• Label axes properly

• Use consistent colors

• Avoid 3D effects unnecessarily

• Consider color-blind accessibility

Interpretation और Reporting

Key Elements of Good Analysis:

1. Context Setting:

• Business background

• Objectives और goals

• Data sources और limitations

2. Methodology:

• Analysis approach

• Tools और techniques used

• Assumptions made

3. Findings:

• Key insights discovered

• Statistical significance

• Practical significance

4. Recommendations:

• Actionable steps

• Expected outcomes

• Implementation timeline

5. Limitations:

• Data quality issues

• Methodological constraints

• Scope limitations

Reporting Formats:

• Executive summary for leadership

• Detailed technical report for analysts

• Dashboard for ongoing monitoring

• Presentation for stakeholders

मॉड्यूल 2: सांख्यिकी की बुनियादी बातें

अध्याय 2.1: Descriptive Statistics

Measures of Central Tendency

Mean (औसत):

Definition: सभी values का sum divided by number of values

Formula: Mean = (Sum of all values) / (Number of values) Mean = (x₁ + x₂ + x₃ + ... + xₙ) / n

Example: Marks of 5 students: 85, 90, 78, 92, 85 Mean = (85 + 90 + 78 + 92 + 85) / 5 = 430 / 5 = 86

Types of Mean:

1. Arithmetic Mean: Regular average

2. Geometric Mean: Used for growth rates

   • Formula: ⁿ√(x₁ × x₂ × ... × xₙ)

3. Harmonic Mean: Used for rates

   • Formula: n / (1/x₁ + 1/x₂ + ... + 1/xₙ)

When to Use Mean:

• Data is normally distributed

• No extreme outliers present

• Need to use all data points

Advantages:

• Uses all data points

• Algebraically defined

• Unique value

Disadvantages:

• Affected by outliers

• May not represent actual data point

Median (मध्यक):

Definition: Middle value when data is arranged in ascending order

Calculation Steps:

1. Arrange data in ascending order

2. If n is odd: Median = middle value

3. If n is even: Median = average of two middle values

Example 1 (Odd number of values): Data: 12, 15, 18, 20, 25 Median = 18 (middle value)

Example 2 (Even number of values): Data: 10, 15, 20, 25, 30, 35 Median = (20 + 25) / 2 = 22.5

When to Use Median:

• Data has outliers

• Skewed distribution

• Ordinal data

• Want typical value

Advantages:

• Not affected by outliers

• Easy to understand

• Works with ordinal data

Disadvantages:

• Ignores extreme values

• Less stable than mean

Mode (बहुलक):

Definition: Most frequently occurring value in dataset

Examples:

• Data: 2, 3, 3, 4, 5, 5, 5, 6

• Mode = 5 (appears 3 times)

Types:

1. Unimodal: One mode

2. Bimodal: Two modes

3. Multimodal: More than two modes

4. No Mode: All values appear once

When to Use Mode:

• Categorical data

• Want most common value

• Discrete data

• Quality control

Advantages:

• Easy to identify

• Not affected by outliers

• Useful for categorical data

Disadvantages:

• May not exist

• May not be unique

• Ignores other values

Comparison और Selection:

• Normal Distribution: Mean = Median = Mode

• Right Skewed: Mean > Median > Mode

• Left Skewed: Mean < Median < Mode

Measures of Variability

Range (परिसर):

Definition: Difference between maximum और minimum values

Formula: Range = Maximum value - Minimum value

Example: Data: 10, 15, 20, 25, 30 Range = 30 - 10 = 20

Advantages:

• Easy to calculate

• Quick measure of spread

• Intuitive understanding

Disadvantages:

• Only uses two values

• Affected by outliers

• Doesn't show distribution shape

Variance (प्रसरण):

Definition: Average of squared differences from mean

Population Variance Formula: σ² = Σ(xi - μ)² / N

Sample Variance Formula: s² = Σ(xi - x̄)² / (n-1)

Calculation Steps:

1. Calculate mean

2. Find difference of each value from mean

3. Square each difference

4. Sum all squared differences

5. Divide by n (population) या n-1 (sample)

Example: Data: 2, 4, 6, 8, 10 Mean = 6

Value

Difference

Squared Difference

2

-4

16

4

-2

4

6

0

0

8

2

4

10

4

16

Variance = (16 + 4 + 0 + 4 + 16) / 5 = 40 / 5 = 8

Standard Deviation (मानक विचलन):

Definition: Square root of variance

Formula:

• Population: σ = √σ²

• Sample: s = √s²

From above example: Standard Deviation = √8 = 2.83

Interpretation:

• About 68% values within 1 SD of mean

• About 95% values within 2 SD of mean

• About 99.7% values within 3 SD of mean

When to Use Standard Deviation:

• Normal distribution

• Comparing variability

• Quality control

• Risk assessment

Coefficient of Variation:

Definition: Relative measure of variability

Formula: CV = (Standard Deviation / Mean) × 100%

Example: If Mean = 50, SD = 10 CV = (10/50) × 100% = 20%

Uses:

• Compare variability between different datasets

• Different units या scales

• Relative risk assessment

Interpretation:

• CV < 15%: Low variability

• CV 15-35%: Moderate variability

• CV > 35%: High variability

अध्याय 2.2: Probability Basics

Probability की परिभाषा

Definition: Probability is measure of likelihood of an event occurring

Mathematical Definition: P(Event) = Number of favorable outcomes / Total number of possible outcomes

Range: 0 ≤ P(Event) ≤ 1

• P = 0: Impossible event

• P = 1: Certain event

• P = 0.5: Equally likely

Example: Rolling a die, probability of getting 3: P(3) = 1/6 = 0.167 या 16.7%

Types of Events

Independent Events:

• Outcome of one doesn't affect the other

• Example: Coin flips, die rolls

• P(A and B) = P(A) × P(B)

Dependent Events:

• Outcome of one affects the other

• Example: Drawing cards without replacement

• P(A and B) = P(A) × P(B|A)

Mutually Exclusive Events:

• Cannot occur simultaneously

• Example: Getting heads या tails

• P(A and B) = 0

• P(A or B) = P(A) + P(B)

Complementary Events:

• One या the other must occur

• Example: Pass या fail

• P(A) + P(A') = 1

• P(A') = 1 - P(A)

Probability Rules

Addition Rule:

For Mutually Exclusive Events: P(A or B) = P(A) + P(B)

For Non-Mutually Exclusive Events: P(A or B) = P(A) + P(B) - P(A and B)

Example: Drawing a card: P(King or Heart) P(King) = 4/52, P(Heart) = 13/52, P(King of Hearts) = 1/52 P(King or Heart) = 4/52 + 13/52 - 1/52 = 16/52

Multiplication Rule:

For Independent Events: P(A and B) = P(A) × P(B)

For Dependent Events: P(A and B) = P(A) × P(B|A)

Example: Two coin flips: P(Heads and Heads) P(H and H) = P(H) × P(H) = 0.5 × 0.5 = 0.25

Conditional Probability

Definition: Probability of event A given that event B has occurred

Formula: P(A|B) = P(A and B) / P(B)

Example: In a class: 60% students pass math, 40% pass both math and science P(Science|Math) = P(Math and Science) / P(Math) = 0.4 / 0.6 = 0.67

Tree Diagrams:

• Visual representation of conditional probabilities

• Branches represent different outcomes

• Probabilities multiply along branches

Bayes' Theorem

Formula: P(A|B) = P(B|A) × P(A) / P(B)

Components:

• P(A|B): Posterior probability

• P(B|A): Likelihood

• P(A): Prior probability

• P(B): Marginal probability

Medical Test Example:

• Disease prevalence: 1% (prior)

• Test accuracy: 95% (sensitivity)

• False positive rate: 5%

Question: If test is positive, what's probability of having disease?

Solution: P(Disease|Positive) = P(Positive|Disease) × P(Disease) / P(Positive)

P(Positive) = P(Positive|Disease) × P(Disease) + P(Positive|No Disease) × P(No Disease) P(Positive) = 0.95 × 0.01 + 0.05 × 0.99 = 0.0095 + 0.0495 = 0.059

P(Disease|Positive) = 0.95 × 0.01 / 0.059 = 0.161 या 16.1%

Applications:

• Medical diagnosis

• Spam filtering

• Machine learning

• Business decision making

अध्याय 2.3: Distributions

Normal Distribution

Characteristics:

• Bell-shaped curve

• Symmetrical around mean

• Mean = Median = Mode

• 68-95-99.7 rule

Parameters:

• μ (mu): Population mean

• σ (sigma): Population standard deviation

Standard Normal Distribution:

• Mean = 0, Standard Deviation = 1

• Any normal distribution can be standardized

• Z-score = (X - μ) / σ

Z-Score Interpretation:

• Z = 0: Value equals mean

• Z = 1: Value is 1 SD above mean

• Z = -1: Value is 1 SD below mean

Example: Height follows normal distribution: μ = 170 cm, σ = 10 cm Person's height = 185 cm Z-score = (185 - 170) / 10 = 1.5

Applications:

• Quality control

• Test scores

• Measurement errors

• Natural phenomena

Binomial Distribution

Definition: Probability distribution for number of successes in fixed number of trials

Conditions:

• Fixed number of trials (n)

• Each trial has two outcomes (success/failure)

• Probability of success (p) remains constant

• Trials are independent

Parameters:

• n: Number of trials

• p: Probability of success

• q = 1-p: Probability of failure

Formula: P(X = k) = C(n,k) × p^k × q^(n-k)

where C(n,k) = n! / (k!(n-k)!)

Mean: μ = n × p Variance: σ² = n × p × q

Example: Coin flip 10 times, probability of getting exactly 6 heads: n = 10, p = 0.5, k = 6 P(X = 6) = C(10,6) × (0.5)^6 × (0.5)^4 = 210 × 0.0156 × 0.0625 = 0.205

Applications:

• Quality control

• Survey sampling

• Medical trials

• Marketing campaigns

Poisson Distribution

Definition: Probability of number of events in fixed time interval

Conditions:

• Events occur randomly

• Average rate is constant

• Events are independent

Parameter:

• λ (lambda): Average number of events

Formula: P(X = k) = (e^(-λ) × λ^k) / k!

Mean: μ = λ Variance: σ² = λ

Example: Average 3 customers per hour at bank Probability of exactly 5 customers in next hour: λ = 3, k = 5 P(X = 5) = (e^(-3) × 3^5) / 5! = (0.0498 × 243) / 120 = 0.101

Applications:

• Call center arrivals

• Manufacturing defects

• Website traffic

• Natural disasters

Central Limit Theorem

Statement: Distribution of sample means approaches normal distribution as sample size increases

Key Points:

• Works regardless of population distribution

• Sample size ≥ 30 generally sufficient

• Sampling distribution has same mean as population

• Standard error = σ / √n

Implications:

• Can use normal distribution for inference

• Larger samples give more accurate estimates

• Foundation for hypothesis testing

Example: Population: Any distribution, μ = 100, σ = 15 Sample size = 36 Sample mean distribution: Normal with μ = 100, σ = 15/√36 = 2.5

Applications:

• Quality control

• Market research

• Political polling

• A/B testing

मॉड्यूल 3: Microsoft Excel में डेटा एनालिसिस

अध्याय 3.1: Excel Basics for Data Analysis

Data Entry Best Practices

Column Headers:

• Use clear, descriptive names

• Avoid spaces (use underscores: first_name)

• Keep headers in first row

• Use consistent naming convention

Data Types:

• Numbers: Right-aligned, no text mixing

• Dates: Use standard date format (DD/MM/YYYY)

• Text: Left-aligned, consistent spelling

• Boolean: TRUE/FALSE या Yes/No

Data Organization:

• One data type per column

• No merged cells in data area

• No blank rows या columns within data

• Keep raw data separate from analysis

Common Mistakes to Avoid:

• Mixing data types in same column

• Using formatting instead of data

• Leaving blank rows

• Not backing up original data

Formatting और Organization

Table Creation:

1. Select data range

2. Insert → Table

3. Check "My table has headers"

4. Benefits: Auto-filtering, structured references

Conditional Formatting:

• Highlight cells based on values

• Data bars for quick visualization

• Color scales for trends

• Icon sets for categories

Data Validation:

• Restrict input to specific values

• Create dropdown lists

• Set number ranges

• Prevent invalid entries

Examples:

• Age validation: Whole number between 0-120

• Gender dropdown: Male, Female, Other

• Date validation: Between specific dates

Named Ranges

Benefits:

• Make formulas easier to read

• Reduce errors

• Easier to maintain

Creating Named Ranges:

1. Select range

2. Formulas → Define Name

3. Enter descriptive name

4. Verify scope (Workbook या Worksheet)

Example: Instead of: =SUM(B2:B100) Use: =SUM(Sales_Data)

अध्याय 3.2: Excel Functions और Formulas

Statistical Functions

AVERAGE Function:

• Syntax: =AVERAGE(range)

• Example: =AVERAGE(A1:A10)

• Ignores text और empty cells

• Alternative: =AVERAGEA() includes text as 0

MEDIAN Function:

• Syntax: =MEDIAN(range)

• Example: =MEDIAN(A1:A10)

• Returns middle value

• Better for skewed data

MODE Functions:

• MODE.SNGL(): Single mode

• MODE.MULT(): Multiple modes

• Example: =MODE.SNGL(A1:A10)

Standard Deviation:

• STDEV.S(): Sample standard deviation

• STDEV.P(): Population standard deviation

• Example: =STDEV.S(A1:A10)

Variance:

• VAR.S(): Sample variance

• VAR.P(): Population variance

• Example: =VAR.S(A1:A10)

COUNT Functions:

• COUNT(): Numbers only

• COUNTA(): Non-empty cells

• COUNTBLANK(): Empty cells

• COUNTIF(): Conditional counting

COUNTIF Examples:

• =COUNTIF(A1:A10,">50") - Count values > 50

• =COUNTIF(A1:A10,"Male") - Count "Male" entries

• =COUNTIF(A1:A10,">=100") - Count values ≥ 100

Logical Functions

IF Function:

• Syntax: =IF(condition, value_if_true, value_if_false)

• Example: =IF(A1>50, "Pass", "Fail")

Multiple Conditions:

• AND(): All conditions must be true

• OR(): At least one condition must be true

• NOT(): Reverse the logic

Examples:

• =IF(AND(A1>50, B1>60), "Both Pass", "Fail")

• =IF(OR(A1="A", A1="B"), "Good Grade", "Poor Grade")

Nested IF:

• =IF(A1>=90, "A", IF(A1>=80, "B", IF(A1>=70, "C", "F")))

IFS Function (Excel 2016+):

• =IFS(A1>=90, "A", A1>=80, "B", A1>=70, "C", TRUE, "F")

Lookup Functions

VLOOKUP:

• Searches in leftmost column

• Returns value from specified column

• Syntax: =VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])

Example: Employee table with ID, Name, Department =VLOOKUP(101, A2:C100, 2, FALSE) - Returns name for ID 101

HLOOKUP:

• Searches in top row

• Returns value from specified row

• Useful for horizontally organized data

INDEX-MATCH Combination:

• More flexible than VLOOKUP

• Can look left या right

• Syntax: =INDEX(return_array, MATCH(lookup_value, lookup_array, 0))

Example: =INDEX(C2:C100, MATCH(101, A2:A100, 0))

XLOOKUP (Excel 365):

• Modern replacement for VLOOKUP

• Can search in any direction

• Better error handling

अध्याय 3.3: Data Analysis Tools

Pivot Tables

Creating Pivot Tables:

1. Select data range

2. Insert → PivotTable

3. Choose destination (new worksheet recommended)

4. Drag fields to appropriate areas

Pivot Table Areas:

• Rows: Categories to group by

• Columns: Sub-categories

• Values: Numbers to summarize

• Filters: Criteria to filter data

Common Aggregations:

• Sum: Total values

• Count: Number of records

• Average: Mean values

• Max/Min: Extreme values

• Percentage: Proportion of total

Example Setup: Sales data with columns: Date, Product, Region, Sales

• Rows: Product

• Columns: Region

• Values: Sum of Sales

• Filters: Date

Grouping Data:

• Dates: Group by month, quarter, year

• Numbers: Create ranges (0-100, 101-200)

• Text: Manual grouping

Calculated Fields:

• Create new metrics

• Example: Profit = Sales - Cost

• Formula: =Sales-Cost

Pivot Charts:

• Visual representation of pivot data

• Updates automatically with pivot table

• Insert → PivotChart

Data Analysis Toolpak

Enabling Toolpak:

1. File → Options → Add-ins

2. Manage Excel Add-ins → Go

3. Check Analysis ToolPak

4. Access via Data → Data Analysis

Descriptive Statistics:

• Input range: Select data

• Output: Summary statistics table

• Includes: Mean, median, mode, standard deviation

Histogram:

• Creates frequency distribution

• Input range: Data values

• Bin range: Intervals (optional)

• Output: Frequency table और chart

Regression Analysis:

• Linear relationship between variables

• Input Y range: Dependent variable

• Input X range: Independent variable(s)

• Output: Regression statistics

Correlation Analysis:

• Measures relationship strength between variables

• Output: Correlation matrix

• Values range from -1 to +1

• Interpretation: -1 (perfect negative), 0 (no relation), +1 (perfect positive)

Sampling:

• Random sampling from dataset

• Periodic sampling (every nth value)

• Useful for large datasets

Charts और Graphs

Chart Types और Usage:

Column Charts:

• Compare categories

• Best for: Discrete categories, limited number of series

• Example: Sales by product category

Bar Charts:

• Horizontal version of column charts

• Best for: Long category names, ranking data

• Example: Top 10 customers by revenue

Line Charts:

• Show trends over time

• Best for: Continuous data, time series

• Example: Monthly sales trends

Scatter Plot:

• Relationship between two variables

• Best for: Correlation analysis, outlier detection

• Example: Height vs Weight relationship

Pie Charts:

• Parts of a whole

• Best for: Few categories (≤7), percentage composition

• Example: Market share by company

Area Charts:

• Cumulative values over time

• Best for: Multiple series, showing total and components

• Example: Revenue breakdown by product lines

Chart Formatting Best Practices:

• Clear, descriptive titles

• Labeled axes with units

• Legend placement

• Consistent colors

• Remove unnecessary gridlines

• Appropriate chart size

Dashboard Creation:

• Combine multiple charts

• Use consistent formatting

• Add slicers for interactivity

• Include key metrics

• Logical layout flow

मॉड्यूल 4: SQL Database Analysis

अध्याय 4.1: SQL Fundamentals

Database Concepts

Database Definition: A database is organized collection of structured information stored electronically in a computer system.

Key Components:

Tables:

• Store data in rows और columns

• Each table represents an entity (customers, products, orders)

• Example: Customer table with columns: ID, Name, Email, Phone

Records (Rows):

• Individual entries in table

• Each record represents one instance

• Example: One customer's complete information

Fields (Columns):

• Attributes of entity

• Define data type और constraints

• Example: Customer_Name field stores text data

Primary Key:

• Unique identifier for each record

• Cannot be NULL या duplicate

• Example: Customer_ID, Product_ID

Foreign Key:

• Links tables together

• References primary key of another table

• Maintains referential integrity

Relationships:

• One-to-One: Each record in Table A relates to one record in Table B

• One-to-Many: One record in Table A relates to multiple records in Table B

• Many-to-Many: Multiple records in both tables can relate to each other

SQL Syntax Basics

SQL Structure:

• Structured Query Language

• Declarative language (what, not how)

• Case-insensitive (convention: UPPERCASE for keywords)

Basic Syntax Rules:

• Statements end with semicolon (;)

• String values in single quotes ('text')

• Comments: -- single line, /* multi-line */

SQL Categories:

• DDL (Data Definition Language): CREATE, ALTER, DROP

• DML (Data Manipulation Language): INSERT, UPDATE, DELETE

• DQL (Data Query Language): SELECT

• DCL (Data Control Language): GRANT, REVOKE

Data Types

Numeric Types:

• INT: Whole numbers (-2,147,483,648 to 2,147,483,647)

• BIGINT: Large whole numbers

• DECIMAL(p,s): Fixed-point numbers (p=precision, s=scale)

• FLOAT: Approximate numbers

String Types:

• CHAR(n): Fixed length strings

• VARCHAR(n): Variable length strings

• TEXT: Large text data

Date/Time Types:

• DATE: Date only (YYYY-MM-DD)

• TIME: Time only (HH:MM:SS)

• DATETIME: Date and time combined

• TIMESTAMP: Date and time with timezone

Other Types:

• BOOLEAN: TRUE/FALSE values

• BLOB: Binary large objects (images, files)

Database Design Principles

Normalization: Process of organizing data to reduce redundancy

First Normal Form (1NF):

• Each column contains atomic values

• No repeating groups

• Each row is unique

Second Normal Form (2NF):

• Must be in 1NF

• No partial dependencies on primary key

• Non-key attributes depend on entire primary key

Third Normal Form (3NF):

• Must be in 2NF

• No transitive dependencies

• Non-key attributes depend only on primary key

Example: Instead of: | Order_ID | Customer_Name | Customer_City | Product_Name | Price |

Better design: Customers: Customer_ID, Name, City Products: Product_ID, Name, Price Orders: Order_ID, Customer_ID, Product_ID

अध्याय 4.2: Basic SQL Queries

SELECT Statements

Basic SELECT:

SELECT column1, column2

FROM table_name;

Select All Columns:

SELECT *

FROM customers;

Select Specific Columns:

SELECT customer_name, email, city

FROM customers;

Column Aliases:

SELECT customer_name AS name,

       email AS contact_email

FROM customers;

WHERE Clause Conditions

Comparison Operators:

-- Equal to

SELECT * FROM products WHERE price = 100;

-- Not equal to

SELECT * FROM products WHERE price != 100;

SELECT * FROM products WHERE price <> 100;

-- Greater than

SELECT * FROM products WHERE price > 100;

-- Less than or equal to

SELECT * FROM products WHERE price <= 100;

Logical Operators:

-- AND condition

SELECT * FROM customers 

WHERE city = 'Mumbai' AND age > 25;

-- OR condition

SELECT * FROM customers 

WHERE city = 'Delhi' OR city = 'Mumbai';

-- NOT condition

SELECT * FROM customers 

WHERE NOT city = 'Chennai';

Pattern Matching with LIKE:

-- Starts with 'A'

SELECT * FROM customers WHERE customer_name LIKE 'A%';

-- Ends with 'son'

SELECT * FROM customers WHERE customer_name LIKE '%son';

-- Contains 'john'

SELECT * FROM customers WHERE customer_name LIKE '%john%';

-- Exactly 5 characters

SELECT * FROM customers WHERE customer_name LIKE '_____';

Range Conditions:

-- BETWEEN operator

SELECT * FROM products 

WHERE price BETWEEN 100 AND 500;

-- IN operator

SELECT * FROM customers 

WHERE city IN ('Mumbai', 'Delhi', 'Bangalore');

-- IS NULL

SELECT * FROM customers 

WHERE phone IS NULL;

-- IS NOT NULL

SELECT * FROM customers 

WHERE email IS NOT NULL;

Sorting with ORDER BY

Ascending Order (Default):

SELECT * FROM customers 

ORDER BY customer_name;

SELECT * FROM customers 

ORDER BY customer_name ASC;

Descending Order:

SELECT * FROM products 

ORDER BY price DESC;

Multiple Column Sorting:

SELECT * FROM customers 

ORDER BY city ASC, customer_name DESC;

Sorting by Column Position:

SELECT customer_name, city, age 

FROM customers 

ORDER BY 2, 1;  -- Sort by city, then by name

Aggregate Functions

COUNT Function:

-- Count all rows

SELECT COUNT(*) FROM customers;

-- Count non-null values

SELECT COUNT(email) FROM customers;

-- Count distinct values

SELECT COUNT(DISTINCT city) FROM customers;

SUM Function:

-- Total sales

SELECT SUM(amount) FROM orders;

-- Sum with condition

SELECT SUM(amount) FROM orders 

WHERE order_date >= '2024-01-01';

AVG Function:

-- Average price

SELECT AVG(price) FROM products;

-- Average with rounding

SELECT ROUND(AVG(price), 2) FROM products;

MIN और MAX Functions:

-- Minimum and maximum prices

SELECT MIN(price) AS lowest_price,

       MAX(price) AS highest_price

FROM products;

GROUP BY Clause

Basic Grouping:

-- Count customers by city

SELECT city, COUNT(*) AS customer_count

FROM customers

GROUP BY city;

Multiple Column Grouping:

-- Sales by region and product category

SELECT region, category, SUM(sales) AS total_sales

FROM sales_data

GROUP BY region, category;

Grouping with Calculations:

-- Average order value by customer

SELECT customer_id, 

       COUNT(*) AS order_count,

       AVG(amount) AS avg_order_value,

       SUM(amount) AS total_spent

FROM orders

GROUP BY customer_id;

HAVING Clause

Difference from WHERE:

• WHERE filters rows before grouping

• HAVING filters groups after aggregation

Examples:

-- Cities with more than 10 customers

SELECT city, COUNT(*) AS customer_count

FROM customers

GROUP BY city

HAVING COUNT(*) > 10;

-- Customers with total purchases > 10000

SELECT customer_id, SUM(amount) AS total_purchases

FROM orders

GROUP BY customer_id

HAVING SUM(amount) > 10000;

अध्याय 4.3: Advanced SQL

Joins

INNER JOIN: Returns only matching records from both tables

-- Customer orders with customer names

SELECT c.customer_name, o.order_id, o.amount

FROM customers c

INNER JOIN orders o ON c.customer_id = o.customer_id;

LEFT JOIN (LEFT OUTER JOIN): Returns all records from left table, matching records from right

-- All customers, including those without orders

SELECT c.customer_name, o.order_id, o.amount

FROM customers c

LEFT JOIN orders o ON c.customer_id = o.customer_id;

RIGHT JOIN (RIGHT OUTER JOIN): Returns all records from right table, matching records from left

-- All orders, including those without customer details

SELECT c.customer_name, o.order_id, o.amount

FROM customers c

RIGHT JOIN orders o ON c.customer_id = o.customer_id;

FULL OUTER JOIN: Returns all records when there's a match in either table

-- All customers and orders

SELECT c.customer_name, o.order_id, o.amount

FROM customers c

FULL OUTER JOIN orders o ON c.customer_id = o.customer_id;

Multiple Table Joins:

-- Orders with customer and product details

SELECT c.customer_name, p.product_name, o.quantity, o.amount

FROM orders o

INNER JOIN customers c ON o.customer_id = c.customer_id

INNER JOIN products p ON o.product_id = p.product_id;

Self Join: Join table with itself

-- Employees and their managers

SELECT e1.employee_name AS employee,

       e2.employee_name AS manager

FROM employees e1

LEFT JOIN employees e2 ON e1.manager_id = e2.employee_id;

Subqueries

Single Value Subqueries:

-- Customers who spent more than average

SELECT customer_name, total_spent

FROM customers

WHERE total_spent > (SELECT AVG(total_spent) FROM customers);

Multiple Value Subqueries:

-- Products in categories with high sales

SELECT product_name, price

FROM products

WHERE category_id IN (

    SELECT category_id 

    FROM sales_summary 

    WHERE total_sales > 100000

);

Correlated Subqueries: Subquery references outer query

-- Customers with above-average orders for their city

SELECT customer_name, city, order_amount

FROM orders o1

WHERE order_amount > (

    SELECT AVG(order_amount)

    FROM orders o2

    WHERE o2.city = o1.city

);

EXISTS Operator:

-- Customers who have placed orders

SELECT customer_name

FROM customers c

WHERE EXISTS (

    SELECT 1 

    FROM orders o 

    WHERE o.customer_id = c.customer_id

);

Window Functions

ROW_NUMBER(): Assigns unique number to each row

-- Rank customers by total spending

SELECT customer_name, total_spent,

       ROW_NUMBER() OVER (ORDER BY total_spent DESC) AS rank

FROM customers;

RANK() और DENSE_RANK():

-- Rank with ties

SELECT customer_name, total_spent,

       RANK() OVER (ORDER BY total_spent DESC) AS rank,

       DENSE_RANK() OVER (ORDER BY total_spent DESC) AS dense_rank

FROM customers;

PARTITION BY:

-- Rank customers within each city

SELECT customer_name, city, total_spent,

       ROW_NUMBER() OVER (PARTITION BY city ORDER BY total_spent DESC) AS city_rank

FROM customers;

Aggregate Window Functions:

-- Running total of sales

SELECT order_date, daily_sales,

       SUM(daily_sales) OVER (ORDER BY order_date) AS running_total

FROM daily_sales_summary;

LAG और LEAD:

-- Compare with previous month

SELECT month, sales,

       LAG(sales) OVER (ORDER BY month) AS prev_month_sales,

       sales - LAG(sales) OVER (ORDER BY month) AS growth

FROM monthly_sales;

Case Statements

Simple CASE:

-- Categorize customers by spending

SELECT customer_name, total_spent,

       CASE 

           WHEN total_spent > 10000 THEN 'High Value'

           WHEN total_spent > 5000 THEN 'Medium Value'

           ELSE 'Low Value'

       END AS customer_category

FROM customers;

Searched CASE:

-- Complex conditions

SELECT product_name, price, stock_quantity,

       CASE 

           WHEN stock_quantity = 0 THEN 'Out of Stock'

           WHEN stock_quantity < 10 THEN 'Low Stock'

           WHEN price > 1000 AND stock_quantity > 50 THEN 'Premium Available'

           ELSE 'In Stock'

       END AS stock_status

FROM products;

CASE in Aggregations:

-- Conditional counting

SELECT 

    COUNT(CASE WHEN age < 30 THEN 1 END) AS young_customers,

    COUNT(CASE WHEN age BETWEEN 30 AND 50 THEN 1 END) AS middle_age,

    COUNT(CASE WHEN age > 50 THEN 1 END) AS senior_customers

FROM customers;

मॉड्यूल 5: Python में डेटा एनालिसिस

अध्याय 5.1: Python Basics

Python Installation और Setup

Python Installation:

1. Download from python.org

2. Choose Python 3.8+ version

3. Check "Add Python to PATH"

4. Verify installation: python --version

IDE Options:

• Jupyter Notebook: Interactive development

• PyCharm: Professional IDE

• VS Code: Lightweight, extensible

• Spyder: Scientific computing focus

Package Management:

# Install pip (usually included)

python -m pip install --upgrade pip

# Install packages

pip install pandas numpy matplotlib seaborn

# Install from requirements file

pip install -r requirements.txt

# Create virtual environment

python -m venv data_analysis_env

Basic Syntax और Data Types

Variables:

# Variable assignment

name = "John"

age = 25

height = 5.8

is_student = True

# Multiple assignment

x, y, z = 1, 2, 3

Data Types:

# Numeric types

integer_num = 42

float_num = 3.14

complex_num = 2 + 3j

# String

text = "Hello, World!"

multiline = """This is

a multiline

string"""

# Boolean

is_true = True

is_false = False

# None type

empty_value = None

Type Checking:

print(type(age))          # <class 'int'>

print(isinstance(age, int))  # True

String Operations:

# String methods

name = "  John Doe  "

print(name.lower())       # "  john doe  "

print(name.upper())       # "  JOHN DOE  "

print(name.strip())       # "John Doe"

print(name.replace("John", "Jane"))  # "  Jane Doe  "

# String formatting

age = 25

message = f"I am {age} years old"  # f-string

message = "I am {} years old".format(age)  # format method

Lists:

# List creation

numbers = [1, 2, 3, 4, 5]

mixed_list = [1, "hello", 3.14, True]

empty_list = []

# List operations

numbers.append(6)         # Add element

numbers.insert(0, 0)      # Insert at position

numbers.remove(3)         # Remove specific value

popped = numbers.pop()    # Remove and return last element

# List slicing

print(numbers[0])         # First element

print(numbers[-1])        # Last element

print(numbers[1:4])       # Elements from index 1 to 3

print(numbers[:3])        # First 3 elements

print(numbers[2:])        # From index 2 to end

Dictionaries:

# Dictionary creation

student = {

    "name": "John",

    "age": 25,

    "grades": [85, 90, 78]

}

# Dictionary operations

print(student["name"])           # Access value

student["city"] = "Mumbai"       # Add new key-value

student.update({"phone": "123"}) # Update multiple

# Dictionary methods

print(student.keys())            # All keys

print(student.values())          # All values

print(student.items())           # Key-value pairs

Control Structures

Conditional Statements:

# if-elif-else

score = 85

if score >= 90:

    grade = "A"

elif score >= 80:

    grade = "B"

elif score >= 70:

    grade = "C"

else:

    grade = "F"

# Ternary operator

grade = "Pass" if score >= 60 else "Fail"

# Multiple conditions

if score >= 80 and attendance >= 75:

    print("Eligible for scholarship")

Loops:

# for loop with range

for i in range(5):          # 0 to 4

    print(i)

for i in range(1, 11):      # 1 to 10

    print(i)

for i in range(0, 10, 2):   # 0, 2, 4, 6, 8

    print(i)

# for loop with lists

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

for fruit in fruits:

    print(fruit)

# for loop with enumerate

for index, fruit in enumerate(fruits):

    print(f"{index}: {fruit}")

# while loop

count = 0

while count < 5:

    print(count)

    count += 1

# Loop control

for i in range(10):

    if i == 3:

        continue    # Skip iteration

    if i == 7:

        break       # Exit loop

    print(i)

List Comprehensions:

# Basic list comprehension

squares = [x**2 for x in range(10)]

# With condition

even_squares = [x**2 for x in range(10) if x % 2 == 0]

# Nested comprehension

matrix = [[i*j for j in range(3)] for i in range(3)]

Functions और Modules

Function Definition:

# Basic function

def greet(name):

    return f"Hello, {name}!"

# Function with default parameters

def calculate_area(length, width=1):

    return length * width

# Function with multiple return values

def get_name_age():

    return "John", 25

name, age = get_name_age()

# Function with variable arguments

def sum_numbers(*args):

    return sum(args)

result = sum_numbers(1, 2, 3, 4, 5)

# Function with keyword arguments

def create_profile(**kwargs):

    return kwargs

profile = create_profile(name="John", age=25, city="Mumbai")

Lambda Functions:

# Lambda function

square = lambda x: x**2

print(square(5))  # 25

# Lambda with map

numbers = [1, 2, 3, 4, 5]

squared = list(map(lambda x: x**2, numbers))

# Lambda with filter

even_numbers = list(filter(lambda x: x % 2 == 0, numbers))

Modules:

# Importing modules

import math

from datetime import datetime

import pandas as pd  # Alias

# Using imported functions

print(math.sqrt(16))

print(datetime.now())

# Creating custom module

# File: my_functions.py

def add(a, b):

    return a + b

# Using custom module

# from my_functions import add

# result = add(5, 3)

अध्याय 5.2: NumPy Library

Arrays Creation और Manipulation

NumPy Installation:

import numpy as np

Array Creation:

# From list

arr1 = np.array([1, 2, 3, 4, 5])

arr2 = np.array([[1, 2, 3], [4, 5, 6]])

# Built-in functions

zeros = np.zeros(5)           # Array of zeros

ones = np.ones((3, 4))        # 3x4 array of ones

full = np.full((2, 3), 7)     # Array filled with 7

empty = np.empty((2, 2))      # Uninitialized array

# Range arrays

range_arr = np.arange(0, 10, 2)    # [0, 2, 4, 6, 8]

linspace = np.linspace(0, 1, 5)    # 5 evenly spaced values

# Random arrays

random_arr = np.random.random(5)         # Random floats 0-1

random_int = np.random.randint(1, 10, 5) # Random integers

normal_dist = np.random.normal(0, 1, 100) # Normal distribution

Array Properties:

arr = np.array([[1, 2, 3], [4, 5, 6]])

print(arr.shape)      # (2, 3)

print(arr.size)       # 6

print(arr.ndim)       # 2

print(arr.dtype)      # int64

print(arr.itemsize)   # 8 bytes per element

Mathematical Operations

Element-wise Operations:

arr1 = np.array([1, 2, 3, 4])

arr2 = np.array([5, 6, 7, 8])

# Arithmetic operations

addition = arr1 + arr2        # [6, 8, 10, 12]

subtraction = arr1 - arr2     # [-4, -4, -4, -4]

multiplication = arr1 * arr2   # [5, 12, 21, 32]

division = arr2 / arr1        # [5.0, 3.0, 2.33, 2.0]

power = arr1 ** 2            # [1, 4, 9, 16]

# Scalar operations

scalar_mult = arr1 * 3       # [3, 6, 9, 12]

scalar_add = arr1 + 10       # [11, 12, 13, 14]

Mathematical Functions:

arr = np.array([1, 4, 9, 16, 25])

# Square root

sqrt_arr = np.sqrt(arr)      # [1, 2, 3, 4, 5]

# Trigonometric functions

angles = np.array([0, np.pi/2, np.pi])

sin_values = np.sin(angles)   # [0, 1, 0]

cos_values = np.cos(angles)   # [1, 0, -1]

# Logarithmic functions

log_values = np.log(arr)      # Natural log

log10_values = np.log10(arr)  # Base 10 log

# Exponential

exp_values = np.exp([1, 2, 3]) # [e, e^2, e^3]

Aggregate Functions:

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Basic aggregations

print(np.sum(arr))           # Sum of all elements

print(np.mean(arr))          # Average

print(np.median(arr))        # Median

print(np.std(arr))           # Standard deviation

print(np.var(arr))           # Variance

print(np.min(arr))           # Minimum

print(np.max(arr))           # Maximum

# Axis-wise operations

print(np.sum(arr, axis=0))   # Sum along columns [12, 15, 18]

print(np.sum(arr, axis=1))   # Sum along rows [6, 15, 24]

print(np.mean(arr, axis=0))  # Column means [4, 5, 6]

Array Indexing और Slicing

Basic Indexing:

arr = np.array([10, 20, 30, 40, 50])

# Single element

print(arr[0])    # 10

print(arr[-1])   # 50

# Slicing

print(arr[1:4])  # [20, 30, 40]

print(arr[:3])   # [10, 20, 30]

print(arr[2:])   # [30, 40, 50]

print(arr[::2])  # [10, 30, 50] - every 2nd element

2D Array Indexing:

arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Element access

print(arr_2d[0, 1])          # 2

print(arr_2d[1][2])          # 6

# Row/column access

print(arr_2d[0, :])          # First row [1, 2, 3]

print(arr_2d[:, 1])          # Second column [2, 5, 8]

# Subarray

print(arr_2d[0:2, 1:3])      # [[2, 3], [5, 6]]

Boolean Indexing:

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

# Boolean condition

condition = arr > 5

print(condition)             # [False, False, False, False, False, True, True, True, True, True]

print(arr[condition])        # [6, 7, 8, 9, 10]

# Direct boolean indexing

print(arr[arr > 5])          # [6, 7, 8, 9, 10]

print(arr[(arr > 3) & (arr < 8)])  # [4, 5, 6, 7]

# Multiple conditions

print(arr[(arr < 3) | (arr > 8)])  # [1, 2, 9, 10]

Fancy Indexing:

arr = np.array([10, 20, 30, 40, 50])

# Index array

indices = [0, 2, 4]

print(arr[indices])          # [10, 30, 50]

# 2D fancy indexing

arr_2d = np.array([[1, 2], [3, 4], [5, 6]])

rows = [0, 2]

cols = [1, 0]

print(arr_2d[rows, cols])    # [2, 5]

Broadcasting

Broadcasting Rules: NumPy automatically broadcasts arrays of different shapes during arithmetic operations.

# Scalar with array

arr = np.array([1, 2, 3, 4])

result = arr + 10            # [11, 12, 13, 14]

# 1D with 2D

arr_1d = np.array([1, 2, 3])

arr_2d = np.array([[10], [20], [30]])

result = arr_1d + arr_2d     # [[11, 12, 13], [21, 22, 23], [31, 32, 33]]

# Different shapes

a = np.array([[1, 2, 3]])    # Shape: (1, 3)

b = np.array([[4], [5]])     # Shape: (2, 1)

result = a + b               # Shape: (2, 3)

print(result)

# [[5, 6, 7],

#  [6, 7, 8]]

Broadcasting Examples:

# Normalize columns

data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

column_means = np.mean(data, axis=0)  # [4, 5, 6]

normalized = data - column_means      # Broadcasting subtraction

# Distance calculation

points = np.array([[1, 2], [3, 4], [5, 6]])

center = np.array([3, 4])

distances = np.sqrt(np.sum((points - center)**2, axis=1))

अध्याय 5.3: Pandas Library

DataFrames और Series

Pandas Installation:

import pandas as pd

import numpy as np

Series Creation:

# From list

series1 = pd.Series([1, 2, 3, 4, 5])

# With custom index

series2 = pd.Series([10, 20, 30], index=['a', 'b', 'c'])

# From dictionary

data_dict = {'A': 1, 'B': 2, 'C': 3}

series3 = pd.Series(data_dict)

# Series properties

print(series2.values)        # Array of values

print(series2.index)         # Index labels

print(series2.dtype)         # Data type

print(series2.shape)         # Dimensions

DataFrame Creation:

# From dictionary

data = {

    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],

    'Age': [25, 30, 35, 28],

    'City': ['Mumbai', 'Delhi', 'Bangalore', 'Chennai'],

    'Salary': [50000, 60000, 70000, 55000]

}

df = pd.DataFrame(data)

# From list of lists

data_list = [

    ['Alice', 25, 'Mumbai', 50000],

    ['Bob', 30, 'Delhi', 60000],

    ['Charlie', 35, 'Bangalore', 70000]

]

df2 = pd.DataFrame(data_list, columns=['Name', 'Age', 'City',