BDAA

We need to know about Java. So, java has two things as follows: - 

Java has JRE (Java Runtime Environment) for running Java applications and JDK (Java Development Kit) for developing them, which includes JRE, compiler, and development tools.

So, JDK = JRE + JDK, so always install JDK then.

We can check if JDK is installed by running this command in the command prompt (CMD or terminal):

for JRE version

java -version

for JDK version

javac -version

In order to know to run the commands, hold on windows button + R 

then a black screen comes, then type above one command and enter then second command then enter, if you see response in both then JRE as well as JDK version both are installed. 

In case you want to know where Java is installed like :C Drive or :D drive then

Using Command Prompt

Type this command in CMD and press Enter:

where java

or

where javac

1. This will show the exact path where Java (JDK) is installed.

Method 2: Using Environment Variables

In CMD, type:

echo %JAVA_HOME%

1. If JAVA_HOME is set, it will display the installation path.

Method 3: Manually Check in C Drive

1. Open File Explorer (Win + E).

2. Go to C:\Program Files\Java (for 64-bit Java).

3. Or check C:\Program Files (x86)\Java (for 32-bit Java).

4. Inside, you’ll find a folder like jdk-22.0.2, which is your JDK installation directory.

1. java.exe (Tea Cup Icon) ☕

• This is the Java Runtime Environment (JRE) executable.

• It runs Java applications.

• The tea cup icon represents Java's branding.

2. javac.exe (Blue Rectangle with Lines) 📄

• This is the Java Compiler (javac stands for "Java Compiler").

• It compiles .java files into .class files (bytecode).

• The blue rectangle with lines is the default Windows icon for executables that don’t have a custom icon.

Why No Tea Cup Icon on javac.exe?

• Java assigns the tea cup icon only to java.exe since it is used to run Java programs.

• javac.exe is just a command-line tool for compiling Java code, so it does not have a fancy icon.

Summary:

• java.exe ☕ → Runs Java programs.

• javac.exe 📄 → Compiles Java code (but no tea cup icon).

Set the JAVA_HOME Environment Variable

1. Open Environment Variables:

   • Press Win + R, type sysdm.cpl, and hit Enter.

   • Go to the Advanced tab and click Environment Variables.

2. Set JAVA_HOME:

   • Under System Variables, click New (or Edit if JAVA_HOME exists).

   • Enter:

     • Variable Name: JAVA_HOME

     • Variable Value: The path to your correct Java installation (e.g., C:\Java or C:\Program Files\Common Files\Oracle\Java\javapath).

3. Update the PATH Variable:

   • In the System Variables section, find Path and click Edit.

   • Click New and add %JAVA_HOME%\bin.

Verify the Configuration

Close and reopen the command prompt, then run:

echo %JAVA_HOME%

java -version

Ensure it displays the correct Java path and version.

Question - What are system variables in simple terms?

Ans. System variables are global environment variables in Windows that define system-wide settings, such as paths to important software like Java, Python, and Hadoop. They help programs locate required executables and libraries. or 

System variables are like a toolbox label for a plumber. They tell the system where to find the right tools (software) so it can do its job properly. 

Download and Install Java 11

• Download Java 11 (LTS) from:

  • Adoptium

  • Oracle

• Install it in a separate folder, e.g.,
  C:\Program Files\Java\jdk-11

2. Set Up Environment Variables for Java 11

1. Open Environment Variables:

   • Press Win + R, type sysdm.cpl, and hit Enter.

   • Go to the Advanced tab → Click Environment Variables.

2. Modify JAVA_HOME for Java 11:

   • Find JAVA_HOME in System Variables and edit it.

Set the value to Java 11's installation path:
makefile
CopyEdit
C:\Program Files\Java\jdk-11

2. • 
     

3. Modify Path Variable:

   • In System Variables, find Path and click Edit.

Add this new entry at the top:
%JAVA_HOME%\bin

3. • 
     

   • This ensures that the system picks Java 11 first when needed.

3. Switch Between Java Versions Manually

Whenever you want to switch between Java 11 and Java 22, use these commands:

Switch to Java 11 (for Hadoop)

set JAVA_HOME=C:\Program Files\Java\jdk-11

set PATH=%JAVA_HOME%\bin;%PATH%

Switch to Java 22 (if needed)

set JAVA_HOME=C:\java

set PATH=%JAVA_HOME%\bin;%PATH%

💡 These settings apply only to the current Command Prompt session.
For a permanent switch, update JAVA_HOME in Environment Variables.

4. Verify the Setup

After switching, check which Java version is active:

java -version

echo %JAVA_HOME%

It should now show Java 11 when set for Hadoop

• binary-aarch64: Hadoop compiled for ARM-based processors (e.g., Apple M1/M2, Raspberry Pi, some servers).

• binary-lean: A lighter version of Hadoop with fewer dependencies, useful for minimal installations.

• binary (default): The standard, full-featured Hadoop binary for most systems (use this one for Windows).

• checksum/signature: Files used to verify integrity and ensure the download isn’t corrupted or tampered with.

What to do?

✅ Download the "binary" version (without -aarch64 or -lean) for your Windows setup.

Next Step:

Once the download is complete, extract the ZIP/TAR file to:

C:\Hadoop\Hadoop

Set HADOOP_HOME

1. Open Command Prompt (Win + R, type sysdm.cpl, and press Enter).

2. Go to the Advanced tab → Click Environment Variables.

3. Under System Variables, click New:

   • Variable Name: HADOOP_HOME

   • Variable Value: C:\Hadoop\Hadoop

4. Click OK to save.

✅ Confirm once done, and we’ll move to the next step! 🚀

You said:

done

ChatGPT said:

ChatGPT

Awesome! ✅ Now, let’s add Hadoop’s bin directory to the Path variable.

Step 2: Add Hadoop to System Path

1. Open Environment Variables again (Win + R → type sysdm.cpl → Enter).

2. Under System Variables, find and select Path, then click Edit.

Click New and add:

C:\Hadoop\Hadoop\bin

Click OK to save and close all windows.

✅ Confirm once done, and we’ll proceed to verifying the setup! 🚀

Great! ✅ Now, let's verify the Hadoop setup.

Step 3: Verify Hadoop Installation

1. Open Command Prompt (cmd).

Run the following command to check if Hadoop is recognized:

hadoop version

2. If everything is set up correctly, it should display the Hadoop version (e.g., Hadoop 3.4.1).

Answer from command prompt is that of the following: -


Hadoop 3.4.1

Source code repository https://github.com/apache/hadoop.git -r 4d7825309348956336b8f06a08322b78422849b1

Compiled by mthakur on 2024-10-09T14:57Z

Compiled on platform linux-x86_64

Compiled with protoc 3.23.4

From source with checksum 7292fe9dba5e2e44e3a9f763fce3e680

This command was run using /C:/Hadoop/Hadoop/share/hadoop/common/hadoop-common-3.4.1.jar

Next Step: Configure Hadoop

Now, we need to set up Hadoop for standalone mode and configure essential files.

Step 4: Configure HADOOP_CONF_DIR

1. Open Environment Variables (sysdm.cpl).

2. Under System Variables, click New and add:

   • Variable Name: HADOOP_CONF_DIR

   • Variable Value: C:\Hadoop\Hadoop\etc\hadoop

3. Click OK and close all windows.

Edit core-site.xml

Navigate to:
makefile
CopyEdit
C:\Hadoop\Hadoop\etc\hadoop

1. 
   

2. Open the file core-site.xml in Notepad or any text editor.

Replace the content with the following configuration:

<configuration>

    <property>

        <name>fs.defaultFS</name>

        <value>hdfs://localhost:9000</value>

    </property>

</configuration>

Step 6: Edit hdfs-site.xml

C:\Hadoop\Hadoop\etc\hadoop

1. Open hdfs-site.xml in Notepad or any text editor.

<configuration>

    <property>

        <name>dfs.replication</name>

        <value>1</value>

    </property>

    <property>

        <name>dfs.namenode.name.dir</name>

        <value>C:\Hadoop\data\namenode</value>

    </property>

    <property>

        <name>dfs.datanode.data.dir</name>

        <value>C:\Hadoop\data\datanode</value>

    </property>

</configuration>

The response from the command will be as follows: - 

C:\Users\DEV>mkdir C:\Hadoop\data\namenode

A subdirectory or file C:\Hadoop\data\namenode already exists.

C:\Users\DEV>mkdir C:\Hadoop\data\datanode

A subdirectory or file C:\Hadoop\data\datanode already exists.

Edit mapred-site.xml

Navigate to:
makefile
CopyEdit
C:\Hadoop\Hadoop\etc\hadoop

1. 
   

2. Open mapred-site.xml in Notepad or any text editor.

Make sure it contains this configuration:
xml
CopyEdit
<configuration>

    <property>

        <name>mapreduce.framework.name</name>

        <value>yarn</value>

    </property>

</configuration>

3. 
   

4. Save and close the file.

Edit yarn-site.xml

Navigate to:
makefile
CopyEdit
C:\Hadoop\Hadoop\etc\hadoop

1. 
   

2. Open yarn-site.xml in Notepad or any text editor.

Replace its content with:
xml
CopyEdit
<configuration>

    <property>

        <name>yarn.nodemanager.aux-services</name>

        <value>mapreduce_shuffle</value>

    </property>

</configuration>

3. 
   

4. Save and close the file

import pandas as pd

# Create a DataFrame (your digital recipe book)

df = pd.DataFrame({

    "Dish": ["Pizza", "Burger", "Pasta"],

    "Ingredients": ["Cheese, Dough", "Patty, Bun", "Noodles, Sauce"],

    "Time to Cook (mins)": [20, 15, 25],

    "Calories": [500, 400, 450]

})

# View the DataFrame

print(df) 

What is a Library?

A library in Python is like a set of specialized kitchen tools for a chef.

• Imagine you’re a chef, and you have a regular kitchen with basic tools like knives and pans (Python’s built-in tools).

• But what if you want to make fancy dishes, like sushi or soufflé? You’d need special tools like a rice cooker, pastry blender, or sous-vide machine.

In Python, libraries are those specialized tools that help you do specific tasks:

• Pandas: Your tool for organizing and managing recipes (data).

• Matplotlib: Your tool for presenting your dishes beautifully (visualizations).

What is Pandas (to a Chef)?

Pandas is like your recipe organizer.

• It’s your kitchen assistant who helps you keep all your recipes (data) in a neat and tidy table (DataFrame).

• You can:

  • Add recipes (rows of data).

  • Edit ingredients (columns of data).

  • Find and filter recipes (querying data).

  • Sort recipes (organize data by time or calories).

Example for a Chef

Let’s say you’re working with a digital recipe book:

import pandas as pd # Create a DataFrame (recipe book) recipes = pd.DataFrame({ "Dish": ["Pizza", "Burger", "Pasta"], "Calories": [500, 400, 450], "Cooking Time (mins)": [20, 15, 25] }) # View your recipes print(recipes)

Output:

Dish Calories Cooking Time (mins) 0 Pizza 500 20 1 Burger 400 15 2 Pasta 450 25

With Pandas, you can:

• Sort by calories:

• recipes.sort_values(by="Calories")

• Find dishes with less than 20 minutes cooking time:

• recipes[recipes["Cooking Time (mins)"] < 20]

Pandas = your digital sous-chef!

What is Matplotlib (to a Chef)?

Matplotlib is like your presentation toolkit.

• After you cook your dishes, you want to present them beautifully.

• Matplotlib is your tool for:

  • Making beautiful food presentations (bar charts, pie charts, line charts).

  • Highlighting which dishes are the most popular or healthiest.

Example for a Chef

Let’s visualize the calories of your dishes:

import matplotlib.pyplot as plt # Dishes and their calories dishes = ["Pizza", "Burger", "Pasta"] calories = [500, 400, 450] # Create a bar chart plt.bar(dishes, calories, color="lightgreen", edgecolor="black") plt.title("Calories in Each Dish") plt.xlabel("Dish") plt.ylabel("Calories") plt.show()

Output:

• A bar chart showing the calories of each dish.

Matplotlib = your plating and presentation artist!

Summary

• Library: Your set of specialized tools (like kitchen gadgets).

• Pandas: Your assistant for managing data (like a digital recipe book).

• Matplotlib: Your presentation expert for showcasing data beautifully (like plating dishes).

The burden of Big Data analysis is poised to fall heavily on administrative officers due to the increasing adoption of data-driven decision-making in governance. Training them now can preemptively equip them with the necessary skills to effectively leverage data for public administration. Here are the reasons, examples, and solutions:

Reasons for Increased Burden

1. Data-Driven Policy Making:

   • Governments are adopting evidence-based policies to address complex issues like poverty, health, and urban planning.

   • Example: The Indian government’s Aspirational Districts Programme uses data to track development metrics in backward districts.

   • Implication: Administrative officers will need to interpret data to ensure accurate tracking and intervention.

2. Emergence of Digital Infrastructure:

   • Initiatives like Digital India and Smart Cities Mission generate vast amounts of data through IoT, sensors, and citizen feedback systems.

   • Example: Smart Cities dashboards provide real-time data on traffic, water usage, and waste management.

   • Implication: Officers will need to extract actionable insights from these dashboards.

3. Increased Accountability and Transparency:

   • Right to Information (RTI) Act demands data transparency, increasing the need for organized data analysis.

   • Example: Officers managing e-Governance portals must analyze citizen grievance trends and ensure quick redressal.

   • Implication: Without proper training, officers might struggle with compliance and responsiveness.

4. Integration of Emerging Technologies:

   • Technologies like AI, ML, and Big Data analytics are being integrated into government systems for predictive modeling.

   • Example: AI is used in crop yield predictions to support farmers.

   • Implication: Officers must understand the basics of these technologies to collaborate effectively with technical teams.

5. Rapid Expansion of Citizen Databases:

   • The use of massive databases like Aadhaar, PM Kisan, and Ujjwala Yojana requires in-depth understanding of data handling.

   • Implication: Officers will increasingly manage, analyze, and safeguard sensitive citizen data.

Examples of Big Data in Governance

1. Health:

   • Example: During the COVID-19 pandemic, Big Data helped analyze infection trends, vaccination progress, and resource allocation.

   • Challenge: Many officers were unprepared to handle the scale and complexity of data during emergencies.

2. Education:

   • Example: Data from Diksha Portal and UDISE+ is used to monitor student outcomes and teacher performance.

   • Challenge: Lack of trained officers delays effective interventions.

3. Environment:

   • Example: Pollution monitoring systems in Delhi use data from sensors for air quality management.

   • Challenge: Interpreting this data to enforce actionable measures requires analytical skills.

Solutions

1. Comprehensive Training Programs:

   • Organize workshops on Big Data fundamentals:

     • Data cleaning and visualization.

     • Tools like Tableau, Power BI, or Python basics.

   • Example: ISTM or state administrative institutes can lead these efforts.

2. Hands-On Practice:

   • Provide real-world datasets (e.g., rainfall, health indicators) for officers to analyze in training.

   • Example: Create simulated governance scenarios for decision-making.

3. Collaborations with Technical Experts:

   • Foster partnerships with institutions like IITs, NITs, and NIC to upskill officers in data analytics.

   • Example: Data analytics courses tailored for administrative officers.

4. Creation of Dedicated Data Teams:

   • Establish Big Data cells in each department with trained officers supported by IT professionals.

   • Example: Data-driven cells in Smart City projects.

5. Policy-Level Integration:

   • Mandate Big Data proficiency as a required skill in promotion exams for officers.

   • Example: Incorporate data-related case studies in administrative training.

6. Incentivizing Continuous Learning:

   • Offer certifications or rewards for officers excelling in Big Data analysis.

   • Example: Certification programs on Coursera, or government-recognized courses.

Benefits of Training Now

1. Proactive Problem Solving: Officers can anticipate trends and take timely actions.

2. Efficient Resource Allocation: Data insights will improve governance efficiency.

3. Enhanced Career Progression: Trained officers will be better equipped for senior roles.

4. Public Trust: Data-driven governance fosters transparency and trust.

Conclusion

Big Data is reshaping governance. By training administrative officers now, the government can ensure smoother transitions into data-driven frameworks, more efficient public service delivery, and better policy outcomes. Investing in their capabilities will have a long-lasting impact on governance quality and citizen satisfaction.

• • Definition: Big Data refers to large, complex datasets that cannot be processed using traditional methods.

  • Characteristics (5 Vs):

    1. Volume: Massive amounts of data (e.g., citizen records, economic data).

    2. Velocity: Data generated at high speed (e.g., real-time traffic data).

    3. Variety: Different types (structured: spreadsheets; unstructured: emails, images).

    4. Veracity: Reliability of data (e.g., social media rumors vs. authentic data).

    5. Value: Insights derived (e.g., better policymaking).

• Example:

  • "Imagine analyzing millions of Aadhaar transactions to find patterns in healthcare access."

• Monitoring welfare schemes (e.g., MNREGA fund usage).

• Analyzing traffic data for urban planning.

• Detecting fraud in subsidies using data patterns.

• Using weather data to prepare for disasters.

Exercise:

• Data: Provide a mock dataset of 1,000 citizen transactions with fields like Transaction ID, Amount, Date, Region, and Scheme.

• Task: Ask participants to identify anomalies (e.g., unusually high claims in a specific region).

Microsoft Excel (basic), Tableau, Hadoop, Spark. 

Region, Scheme, Beneficiaries, Funds Utilized (in Lakhs)

North, MNREGA, 5000, 300

South, MNREGA, 4000, 250

East, MNREGA, 4500, 275

West, MNREGA, 4800, 290

• Task:

  • Create a pivot table to summarize total beneficiaries and funds by region.

  • Identify which region utilized funds most efficiently.

• COVID-19 Management: Explain how the Aarogya Setu app used Big Data to track infections and allocate resources.

• Example: "Data collected from millions of app users helped in identifying infection clusters and planning containment zones."

https://ndap.niti.gov.in/

Several departments within the Government of India actively utilize big data analytics to enhance decision-making and service delivery:

• Ministry of Finance: Employs data analytics to improve tax administration and compliance. Both direct and indirect tax departments utilize big data and AI/ML techniques to make tax administration more effective and taxpayer-friendly. 

• Ministry of Electronics and Information Technology (MeitY): Under the Digital India programme, MeitY has launched numerous e-services and initiatives that leverage big data to promote innovation and improve governance. 

• NITI Aayog: Through its Data Management and Analysis Vertical, NITI Aayog addresses issues related to streamlining data usage for public policy. A key initiative is the National Data and Analytics Platform (NDAP), which democratizes access to public government data by making it accessible, interoperable, and interactive. 

• National Informatics Centre (NIC): Provides data analytics services to various government departments, aiding in the development of e-Government applications and managing the National Knowledge Network. 

To learn more about big data in governance and the Government of India's initiatives in big data analysis, the following platforms offer valuable resources:

• Open Government Data (OGD) Platform India: A single-point access to datasets, documents, services, tools, and applications published by various government ministries and departments. It facilitates data sharing and promotes innovation over non-personal data. 

• National Data and Analytics Platform (NDAP): Developed by NITI Aayog, NDAP aims to democratize access to public government data by making it accessible, interoperable, and interactive. It hosts datasets from various government agencies and provides tools for analytics and visualization.

These platforms serve as comprehensive resources for understanding and engaging with the Government of India's big data initiatives.

Several departments within the Government of India actively utilize big data analytics to enhance decision-making and service delivery:

• Ministry of Finance: Employs data analytics to improve tax administration and compliance. Both direct and indirect tax departments utilize big data and AI/ML techniques to make tax administration more effective and taxpayer-friendly. 

• Ministry of Electronics and Information Technology (MeitY): Under the Digital India programme, MeitY has launched numerous e-services and initiatives that leverage big data to promote innovation and improve governance. 

• NITI Aayog: Through its Data Management and Analysis Vertical, NITI Aayog addresses issues related to streamlining data usage for public policy. A key initiative is the National Data and Analytics Platform (NDAP), which democratizes access to public government data by making it accessible, interoperable, and interactive. 

• National Informatics Centre (NIC): Provides data analytics services to various government departments, aiding in the development of e-Government applications and managing the National Knowledge Network. 

To learn more about big data in governance and the Government of India's initiatives in big data analysis, the following platforms offer valuable resources:

• Open Government Data (OGD) Platform India: A single-point access to datasets, documents, services, tools, and applications published by various government ministries and departments. It facilitates data sharing and promotes innovation over non-personal data. 

• National Data and Analytics Platform (NDAP): Developed by NITI Aayog, NDAP aims to democratize access to public government data by making it accessible, interoperable, and interactive. It hosts datasets from various government agencies and provides tools for analytics and visualization. 

These platforms serve as comprehensive resources for understanding and engaging with the Government of India's big data initiatives.

Access Government Data

• Platforms to Access Data:

  1. Open Government Data (OGD) Platform India: data.gov.in

  2. National Data and Analytics Platform (NDAP): ndap.niti.gov.in

• Steps:

  1. Visit the website.

  2. Browse or search for datasets by categories (e.g., education, health, finance).

  3. Download datasets in formats like CSV, JSON, or Excel.

Understand the Data

• Steps:

  • Read the metadata or data dictionary provided with the dataset.

  • Understand key variables, data types, and the context of the dataset.

  • Clean and preprocess the data for analysis using tools like Excel or Python libraries.

• Recommended Tools:

  • Spreadsheet Software: Microsoft Excel, Google Sheets

  • Python Libraries: Pandas, NumPy

Explore the Data

• Steps:

  • Perform descriptive statistics to summarize the data.

  • Create visualizations to identify patterns and trends.

  • Use tools to filter and query data for specific insights.

• Recommended Tools:

  • Visualization: Tableau Public, Power BI, Matplotlib, Seaborn (Python)

  • Querying Data: SQL, Python (Pandas)

Perform Advanced Analysis

• Steps:

  • Use statistical methods for deeper insights.

  • Apply machine learning models for predictions or classifications.

  • Use geographic data for spatial analysis.

• Recommended Tools:

  • Statistical Analysis: R, Python (Statsmodels, Scikit-learn)

  • GIS and Spatial Analysis: QGIS, ArcGIS, Google Earth Engine

Generate Insights

• Steps:

  • Compile insights into reports or dashboards.

  • Share findings with stakeholders or the public.

• Recommended Tools:

  • Dashboards: Power BI, Tableau, Streamlit (Python)

  • Documenting: Jupyter Notebooks, Google Docs

Download a Dataset

1. Go to the Open Government Data (OGD) Platform India: https://data.gov.in.

2. Search for a dataset. For example:

   • Dataset: "State-wise Monthly Rainfall Data (2004-2020)"

   • Link: State-wise Rainfall Data

3. Download the dataset as a CSV file.

Set Up Tools

• Use Google Colab (no installation required) or Jupyter Notebook.

• Google Colab: Open Google Colab and create a new notebook.

• Jupyter Notebook: If installed, launch it from your computer.

Upload the Dataset

1. Open your notebook in Google Colab or Jupyter.

2. Upload the CSV file.

   • Google Colab: Use the upload button in the sidebar or write the code to upload.

from google.colab import files

uploaded = files.upload()

3. Save the file in your working directory if using Jupyter.

Write Python Code

Here is a complete script to showcase your big data analysis. Copy and paste it into your notebook.

Python Code:

# Importing necessary libraries

import pandas as pd

import matplotlib.pyplot as plt

# Step 1: Load the dataset

# Replace 'rainfall.csv' with the name of your downloaded file

file_name = "rainfall.csv"

data = pd.read_csv(file_name)

# Step 2: Display the first few rows of the dataset

print("Dataset Overview:")

print(data.head())

# Step 3: Summary of data

print("\nDataset Summary:")

print(data.describe())

# Step 4: Clean the data (e.g., handling missing values)

print("\nChecking for missing values:")

print(data.isnull().sum())

# Dropping rows with missing values

data_cleaned = data.dropna()

# Step 5: Analyze data

# Example: Calculate average annual rainfall per state

average_rainfall = data_cleaned.groupby("State")["Annual Rainfall"].mean()

print("\nAverage Annual Rainfall per State:")

print(average_rainfall)

# Step 6: Visualize the data

# Example: Bar plot of average rainfall

average_rainfall.plot(kind="bar", figsize=(12, 6))

plt.title("Average Annual Rainfall by State")

plt.xlabel("State")

plt.ylabel("Average Rainfall (mm)")

plt.tight_layout()

plt.show()

Explain Your Steps During the Session

1. Load Data: Show participants how you loaded the dataset into the notebook.

2. Overview: Explain the dataset structure using head() and describe().

3. Data Cleaning: Discuss why handling missing values is crucial.

4. Analysis: Show how you calculated the average rainfall for each state.

5. Visualization: Explain how the bar chart represents the results

Practice the Steps

• Run this practical multiple times before the session to ensure smooth execution.

• Familiarize yourself with the dataset and outputs so you can confidently explain the results.

1. Value in Big Data

Value refers to the usefulness and insights derived from analyzing Big Data. It focuses on how the data can be transformed into meaningful and actionable information, leading to better decision-making, innovation, and business opportunities. Not all data is valuable, so the challenge is to extract value from the massive amounts of data being collected.

Examples of Value in Big Data:

• Healthcare: Predictive Analytics for Patient Care

  • Example: Hospitals collect massive amounts of patient data (medical history, diagnostics, lab results). Using Big Data analytics, healthcare providers can identify patterns in this data to predict patient health outcomes. For instance, by analyzing vital signs and historical records, hospitals can predict when patients might need readmission or when certain complications might arise. This improves patient care and reduces costs.

  • Value: The value here is derived from saving lives, improving care quality, and reducing hospital readmissions.

• E-commerce: Personalized Recommendations

  • Example: Platforms like Amazon collect vast amounts of data about customer preferences, browsing behavior, and purchase history. By analyzing this data, Amazon can provide personalized product recommendations to customers, increasing sales and improving the user experience.

  • Value: The value for the business is in increased revenue and customer satisfaction through more targeted marketing and recommendations.

• Finance: Fraud Detection

  • Example: Banks and financial institutions collect data on millions of transactions daily. By analyzing patterns in this data, Big Data tools can detect fraudulent activities in real-time, such as unusual transaction amounts, locations, or behavior.

  • Value: The value lies in protecting customers from fraud, saving money for the bank, and maintaining trust.

• Supply Chain Optimization

  • Example: A retail company can use Big Data to monitor its entire supply chain in real-time, adjusting inventory levels based on consumer demand, sales data, and delivery patterns.

  • Value: Optimizing supply chains leads to reduced costs, faster deliveries, and improved customer satisfaction.

The main takeaway is that Value in Big Data is about turning raw data into insights that can help businesses or organizations make smarter decisions.

2. Velocity in Big Data

Velocity refers to the speed at which data is generated, collected, and processed. In Big Data, data is produced at an extremely fast rate, often in real-time, and it needs to be processed quickly to extract timely insights and take action.

Examples of Velocity in Big Data:

• Social Media Monitoring (Twitter, Facebook)

  • Example: Twitter processes around 500 million tweets per day (about 6,000 tweets per second). Social media platforms must analyze data in real-time to detect trends, monitor user behavior, and respond to emerging topics or crises (e.g., viral trends, breaking news).

  • Velocity: The velocity here is high because the data is being generated and analyzed in real-time, often requiring instant processing to capitalize on trends or prevent the spread of misinformation.

• Stock Market Trading

  • Example: Financial markets generate an enormous amount of data in real-time, with millions of transactions happening every second. Big Data algorithms analyze this information in real-time to enable high-frequency trading (HFT), where traders make buying and selling decisions based on market fluctuations in milliseconds.

  • Velocity: In this case, velocity is extremely high, as data must be processed in real-time to gain a competitive edge and make split-second financial decisions.

• Smart Cities and IoT

  • Example: In a smart city, IoT sensors collect real-time data about traffic flow, air quality, electricity usage, and water systems. To manage city services efficiently, this data must be analyzed in real-time to adjust traffic lights, optimize energy usage, or predict maintenance needs.

  • Velocity: The velocity is high because the data is continuously streaming, and decisions must be made in real-time to keep city systems running smoothly.

• Real-Time Fraud Detection

  • Example: Credit card companies like Visa and Mastercard process thousands of transactions per second. To detect and prevent fraud, they need to analyze these transactions in real-time, identifying suspicious activities as they occur.

  • Velocity: Fraud detection systems rely on high-velocity data processing to identify and stop fraudulent transactions as they happen.

• Streaming Services (Netflix, YouTube)

  • Example: Platforms like Netflix and YouTube collect data in real-time about user behavior, such as what people are watching, how long they stay on the platform, and what content they interact with. This data is analyzed in real-time to provide personalized recommendations and to improve user experience.

  • Velocity: The velocity is high because data is generated continuously while users are interacting with the platform, requiring immediate processing.

Summary of Value and Velocity in Big Data

• Value: Refers to how useful the data is in generating insights that lead to better decision-making and outcomes. Examples include personalized recommendations (e-commerce), predictive analytics (healthcare), and fraud detection (finance).

• Velocity: Refers to the speed at which data is generated and processed, often in real-time. Examples include social media monitoring (Twitter), financial trading (stock markets), and IoT data streams (smart cities).

Both Value and Velocity are crucial in Big Data analysis. Value is what you get from data insights, while Velocity is how fast you need to process the data to get those insights in time for them to be useful.

In the context of the 5 V’s of Big Data, Veracity refers to the quality and reliability of the data. It emphasizes the importance of ensuring that the data being processed is trustworthy, accurate, and meaningful. In Big Data, since data comes from multiple sources and is generated at a high speed, there is often uncertainty or inconsistency regarding its quality. Not all the data collected is accurate, complete, or reliable, which can make analysis difficult.

Explaining "Veracity" in Big Data

Veracity in Big Data highlights:

• Data quality: Is the data clean and accurate, or does it have errors, noise, or inconsistencies?

• Trustworthiness: Can the data be trusted to make decisions? Is it coming from reliable sources?

• Uncertainty: How confident can you be in the insights derived from the data, given its possible flaws or bias?

The veracity of Big Data is often one of the most challenging aspects because inaccurate or low-quality data can lead to faulty conclusions or poor decision-making.

Examples to Illustrate Veracity

Here are some practical examples of Veracity to make the concept clearer:

1. Social Media Data (Low Veracity)

• Example: Consider a social media platform like Twitter, where millions of posts are generated every minute. While a lot of valuable insights can be derived from social media data (e.g., public sentiment, trends), not all data is reliable.

  • Issues: Some posts may contain spam, fake news, irrelevant content, or even misinformation. For example, bots generating fake comments, or biased opinions from people trying to manipulate trends, can affect the trustworthiness of the analysis.

  • Challenge: The platform needs to filter out this low-quality data (noise) and focus on authentic interactions to derive meaningful insights. This uncertainty about the reliability of user-generated content is an example of low veracity in Big Data.

2. IoT Devices (Sensor Data)

• Example: A smart city relies on sensors for monitoring traffic, pollution, weather, and more. These sensors generate large amounts of data, but sometimes, the data might be incomplete or inaccurate.

  • Issues: Sensors can malfunction, sending inconsistent or corrupted data (e.g., temperature sensors recording faulty readings due to weather conditions). Different sensors might also produce conflicting data for the same location.

  • Challenge: When analyzing the data, it’s critical to filter out incorrect or inconsistent readings to make accurate decisions. The uncertainty about the correctness of the data captured from IoT devices reflects veracity issues.

3. Customer Data in E-commerce

• Example: An online retailer collects data about customers, including purchasing habits, preferences, and demographics.

  • Issues: Some customers might enter incorrect information (such as wrong email addresses or fake phone numbers). Additionally, duplicate or incomplete records (e.g., customers having multiple accounts or missing information in their profiles) can reduce data quality.

  • Challenge: Ensuring that the customer data is clean, deduplicated, and accurate is essential to make trustworthy business decisions, like personalized recommendations or targeted marketing. The reliability and accuracy of customer profiles reflect the veracity of the data.

4. Financial Data (High Veracity Needed)

• Example: In the financial industry, banks and trading platforms collect vast amounts of data about transactions, stock prices, and economic indicators.

  • Issues: Small errors in this data (e.g., incorrect transaction records, duplicate entries, or delays in real-time updates) can have major financial consequences. If inaccurate data is used for trading algorithms or investment decisions, it can lead to significant losses.

  • Challenge: Financial data needs to be precise, accurate, and real-time to ensure reliable decisions. This is why the veracity of data is critical in the financial sector, where even minor errors can lead to large-scale impacts.

Dealing with Veracity Issues

To deal with veracity in Big Data, companies often implement:

• Data cleansing techniques: Removing duplicates, correcting errors, and filling in missing information to improve data quality.

• Filtering unreliable sources: Detecting and eliminating data from low-trust sources, such as fake accounts or spam.

• Consistency checks: Ensuring that data coming from different sources or sensors is consistent with expected ranges or known facts.

For example, in e-commerce, customer data can be cross-checked with third-party services to verify accuracy, and social media platforms may use algorithms to flag or remove misleading or irrelevant content.

Summary of Veracity in Big Data

Veracity in Big Data refers to the quality, reliability, and trustworthiness of the data. It's about ensuring that the data you’re analyzing is accurate and clean because poor-quality data leads to faulty insights. High veracity means data is consistent, error-free, and can be trusted, while low veracity means the data is uncertain, inconsistent, or potentially misleading.

Examples:

• Social media data often suffers from low veracity due to misinformation or spam.

• IoT sensors can produce inconsistent or faulty readings that need to be filtered.

• Customer data in e-commerce may contain errors or duplicates, affecting data quality.

• Financial data requires high veracity to avoid costly mistakes in real-time decision-making.

Ensuring high veracity is a critical step in any Big Data project to guarantee accurate and valuable insights.

In the context of the 5 V’s of Big Data, the Volume refers to the sheer amount of data being generated, stored, and processed. It highlights the vast quantity of data that comes from various sources, often measured in terabytes (TB), petabytes (PB), or even zettabytes (ZB). Volume is often considered the defining characteristic of Big Data, as traditional databases and systems struggle to handle the large scale.

Explaining "Volume" in Big Data

Volume in Big Data means "how much" data is generated. This data can come from a variety of sources:

• Social media posts: Every day, platforms like Facebook, Twitter, and Instagram generate billions of posts, likes, comments, and shares.

• Sensor data: In a smart city, millions of sensors on traffic lights, buildings, and streets constantly send streams of data about temperature, traffic, air quality, etc.

• Transactional data: E-commerce platforms like Amazon process millions of transactions per minute, logging customer details, purchases, reviews, and more.

These examples show the massive volume that Big Data systems handle daily. The critical challenge is storing and processing this data efficiently.

Example to Illustrate Volume

Let’s say you’re explaining the Volume aspect to someone:

1. Example 1: Social Media (Facebook)

   • Volume: Facebook generates 4 petabytes of data daily, including likes, shares, posts, photos, and videos.

   • Explanation: Imagine how much data that is—it's equivalent to 1 million HD movies being stored every day. Traditional databases wouldn’t be able to handle this volume, but Big Data systems like Hadoop or Spark can process such a massive amount of data efficiently.

2. Example 2: E-commerce (Amazon)

   • Volume: Amazon generates around 1 million transactions per second during peak times, producing terabytes of customer purchase data, product reviews, recommendations, and delivery information.

   • Explanation: To make it relatable, you could say that in one minute, Amazon handles as much data as a small company might handle in an entire year!

3. Example 3: Internet of Things (IoT)

   • Volume: In a smart city, IoT devices (smart meters, traffic sensors, air quality monitors) generate terabytes of data per day. Each sensor continuously sends data on energy usage, vehicle movement, or environmental conditions.

   • Explanation: Think of this as the city "talking" to itself—millions of sensors collecting information simultaneously, and all this data needs to be processed in real-time.

Summary of Volume in Big Data

The volume in Big Data refers to the massive amount of data generated by various digital processes, devices, and interactions. As technology advances, this volume continues to grow exponentially.

For citing examples of volume:

• Social media generates 4 petabytes of data daily.

• E-commerce platforms like Amazon handle 1 million transactions per second.

• Smart cities produce terabytes of sensor data every day from IoT devices.

These examples clearly demonstrate how the volume of Big Data is far beyond the capabilities of traditional data systems, necessitating specialized tools and techniques for managing such enormous datasets.

To define Big Data Analysis and perform a practical demonstration of it on any computer with an internet connection, you can leverage cloud-based services, open-source tools, and datasets available online. Here’s how you can approach this:

1. Defining Big Data Analysis

Big Data Analysis refers to the process of examining large and complex datasets, often involving millions or billions of records. These datasets are so large that traditional data processing tools struggle to handle them. Big Data Analysis involves extracting useful information from this data to identify patterns, trends, correlations, and insights. It typically involves:

• Volume: Huge amounts of data.

• Variety: Different types of data (text, video, images, structured/unstructured data).

• Velocity: Speed at which data is generated and processed.

• Veracity: Trustworthiness of data.

• Value: Insights and actionable information extracted from the data.

2. Tools and Technologies for Big Data Analysis

To perform a practical Big Data analysis, you can use the following tools and coding environments that are accessible via the internet:

a) Python and Libraries for Big Data Analysis

• Python is a versatile language for Big Data analysis with its rich ecosystem of libraries.

  • Pandas: For data manipulation and analysis.

  • NumPy: For numerical data processing.

  • Dask: For parallel computing and handling larger-than-memory datasets.

  • PySpark: Python API for Apache Spark, which is a popular distributed computing framework.

  • Matplotlib/Seaborn: For data visualization.

• You can run Python code locally or via cloud-based environments (e.g., Google Colab or Jupyter Notebooks).

b) Apache Spark (via PySpark)

• Apache Spark is one of the most powerful tools for Big Data processing. It's fast, and built for distributed computing. You can use PySpark to work with Spark in Python. This is suitable for analyzing huge datasets using a distributed computing model.
  You can use cloud services like Google Colab, which allows running PySpark code without needing local setup.

c) Cloud Platforms

If you want to explore more serious Big Data analysis:

• Google Cloud (BigQuery): Offers a managed data warehouse with massive scale and SQL-based querying.

• Amazon Web Services (AWS S3 + EMR): Store data in S3 and use Elastic MapReduce for Big Data analysis using Hadoop or Spark.

• Azure Data Lake Analytics: For scalable cloud-based analysis.

3. Performing a Practical Big Data Analysis

Here’s how you can run a Big Data Analysis practical using publicly available datasets and tools accessible from any internet-connected computer.

Step 1: Get a Dataset

You need a large dataset to define Big Data. Some useful sources:

• Kaggle: Free datasets related to various domains.

  • Example: Kaggle Datasets

• Google Dataset Search: Search engine for finding large datasets.

• Open Data Portals: Websites like data.gov or European Union Open Data Portal provide free public datasets.

For example, you could use:

• New York City Taxi Trip Data: Over 1 billion records of taxi rides in NYC.

• Global Temperature Data: Huge datasets for climate research.

• COVID-19 Data: Large-scale, globally collected datasets.

Step 2: Set Up an Environment (Google Colab for Example)

• Go to Google Colab. This is a free, cloud-based Python environment with access to many Big Data libraries.

• Install necessary libraries (Pandas, PySpark, Dask, etc.).

Step 3: Analyze the Data

Here’s an example of Big Data analysis using PySpark in Google Colab.

Install PySpark on Colab:
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!pip install pyspark

Load and Analyze Data (using NYC taxi data as an example):
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from pyspark.sql import SparkSession

# Initialize Spark session

spark = SparkSession.builder.appName("BigDataAnalysis").getOrCreate()

# Load data (assuming NYC taxi data is available as a CSV on cloud storage or local drive)

df = spark.read.csv("/path-to-your-nyc-taxi-data.csv", header=True, inferSchema=True)

# Show data schema

df.printSchema()

# Example: Calculate average trip distance

df.select('trip_distance').summary('mean').show()

2. 
   

3. Run Queries and Visualize

   • Filter data based on conditions (e.g., find all trips over 10 miles).

   • Visualize patterns such as trip duration vs distance using matplotlib/seaborn.

4. Summarize Findings: You can summarize the findings in terms of trends or anomalies discovered, such as peak travel times, average fare per distance, or other insights.

Step 4: Interpretation and Reporting

After running the practical analysis, you can report:

• The size and variety of the dataset (to demonstrate the "Big" part of Big Data).

• The velocity at which insights were derived using PySpark.

• The veracity of the data and whether any cleaning was required.

• The value in terms of insights (e.g., how the data analysis can help optimize services like taxis or resource allocation).

1. Data Source

The data source is where the data originates. It can come from multiple sources like databases, APIs, sensors, or publicly available datasets.

Example: NYC Taxi Dataset

• Source: The NYC Taxi & Limousine Commission provides an open dataset that records every trip made by a taxi in NYC. You can download data from NYC Taxi Data.

Practical Task:

1. Go to the website and download a month's worth of NYC Taxi data (e.g., January 2023).

You can also directly import it into Google Colab:
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!wget https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet

2. Data Lake

A data lake is a centralized repository that allows you to store structured and unstructured data at scale. A popular tool for this is AWS S3, but you can also use Google Cloud Storage or even local storage on your system or Google Colab environment for simplicity.

Example with Google Colab or AWS S3:

1. Local Data Lake (Colab): The dataset you downloaded can be considered part of a local "data lake" in your Google Colab environment.

2. AWS S3 for Data Lake:

   • Create an AWS S3 bucket.

   • Upload the NYC Taxi dataset (or other datasets).

   • Use AWS CLI or Boto3 to access the data from your data lake programmatically.

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import boto3

# Access your AWS S3 bucket

s3 = boto3.resource('s3')

bucket = s3.Bucket('your-bucket-name')

# Download the dataset

bucket.download_file('your-file.csv', 'local-filename.csv')

3. Data Processing

Data Processing is the step where raw data is cleaned and transformed into a more usable format. This typically involves removing errors, missing values, and outliers, as well as transforming the data into formats that are more suitable for analysis.

Example: Data Cleaning and Transformation with Pandas:

Data Cleaning: Use Pandas to handle missing data and remove irrelevant columns from the dataset.
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import pandas as pd

# Load the dataset

df = pd.read_parquet("yellow_tripdata_2023-01.parquet")

# Check for missing data

print(df.isnull().sum())

# Remove missing values

df_cleaned = df.dropna()

# Remove irrelevant columns

df_cleaned = df_cleaned[['tpep_pickup_datetime', 'tpep_dropoff_datetime', 'passenger_count', 'trip_distance', 'total_amount']]

Data Transformation: Convert columns (e.g., timestamps) into useful formats and create new columns if necessary (e.g., trip duration).
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# Convert pickup and dropoff times to datetime

df_cleaned['pickup_datetime'] = pd.to_datetime(df_cleaned['tpep_pickup_datetime'])

df_cleaned['dropoff_datetime'] = pd.to_datetime(df_cleaned['tpep_dropoff_datetime'])

# Create a new column for trip duration in minutes

df_cleaned['trip_duration'] = (df_cleaned['dropoff_datetime'] - df_cleaned['pickup_datetime']).dt.total_seconds() / 60

4. Data Analysis

Data Analysis involves applying statistical and machine learning techniques to discover patterns or trends in the data.

Example: Basic Descriptive Statistics and Visualizations:

Descriptive Statistics: Calculate basic statistics such as mean, median, and distribution of trips and fares.
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# Descriptive statistics for trip distance and total amount

df_cleaned[['trip_distance', 'total_amount']].describe()

Data Visualization: Use Matplotlib and Seaborn to visualize data, like the distribution of trip distances or total fare amounts.
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import matplotlib.pyplot as plt

import seaborn as sns

# Plot distribution of trip distances

plt.figure(figsize=(10,6))

sns.histplot(df_cleaned['trip_distance'], bins=50, kde=False)

plt.title('Distribution of Trip Distances')

plt.xlabel('Trip Distance (miles)')

plt.ylabel('Frequency')

plt.show()

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3. Advanced Analysis: You can apply machine learning models to predict the fare based on trip distance, passenger count, etc., using libraries like Scikit-Learn.

5. Data Interpretation

Data Interpretation involves understanding the results from the data analysis and drawing meaningful conclusions. This step is key to turning raw data into insights that can drive decision-making.

Example: Insights from NYC Taxi Data:

Insight 1: Discover the busiest hours for NYC taxis based on pickup times.
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df_cleaned['hour'] = df_cleaned['pickup_datetime'].dt.hour

hourly_trips = df_cleaned.groupby('hour').size()

hourly_trips.plot(kind='bar', figsize=(10,6), title='Number of Taxi Trips per Hour')

plt.xlabel('Hour of Day')

plt.ylabel('Number of Trips')

plt.show()

• Interpretation: The plot might show a peak during rush hours (8-9 AM, 5-6 PM), indicating the busiest periods for taxi drivers.

Insight 2: Determine the correlation between trip distance and total fare.
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# Calculate correlation

correlation = df_cleaned[['trip_distance', 'total_amount']].corr()

print("Correlation between trip distance and total fare:\n", correlation)

• Interpretation: A positive correlation would suggest that longer trips tend to generate higher fares, but analyzing outliers (like very short trips with high fares) can offer deeper insights.

6. Data Use

Data Use refers to applying the insights to solve real-world problems or improve decision-making. This could be in business strategy, operations, customer service, etc.

Example: Taxi Fleet Optimization:

• Problem: A taxi company might want to optimize its fleet based on the busiest times and locations.

• Use Case: Using the insights from the analysis, the company could:

  1. Increase the number of taxis available during peak hours.

  2. Position taxis near high-demand drop-off locations.

  3. Adjust fare pricing dynamically based on demand.

Sharing the Insights:

You can export your cleaned dataset or analysis to a CSV for sharing or further analysis.

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# Save the processed and analyzed data to a CSV

df_cleaned.to_csv('processed_nyc_taxi_data.csv', index=False)

# Download the CSV file

from google.colab import files

files.download('processed_nyc_taxi_data.csv')

Resources:

Here are some useful resources for each stage:

1. Data Sources:

   • NYC Taxi Data: NYC Taxi & Limousine Commission Data

   • Kaggle Datasets: Kaggle

2. Data Lake:

   • AWS S3: Amazon S3

   • Google Cloud Storage: Google Cloud Storage

3. Data Processing:

   • Pandas Documentation: Pandas

   • AWS Glue (Data Processing Service): AWS Glue

4. Data Analysis:

   • Matplotlib Documentation: Matplotlib

   • Seaborn Documentation: Seaborn

5. Data Interpretation & Use:

   • Data Science for Business: Data Science for Business Book

By following these steps, you can practically demonstrate the entire Data Analytics Pipeline with an example dataset. You will have touched on all key elements, from sourcing the data to drawing valuable insights and making data-driven decisions.

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To explain Lookup functions, Count functions, and the Customization of functions in data analysis with practical examples, you can use a spreadsheet tool (e.g., Google Sheets, Excel) or a Python-based environment (e.g., Google Colab, Jupyter Notebook). Below is a step-by-step guide on how you can practically explain each of these concepts:

1. Lookup Functions: VLOOKUP, HLOOKUP, and Python Pandas Equivalent

Lookup functions allow you to search for specific data in a table or range and return a corresponding value from another column or row. These functions are extremely useful when you need to extract data based on specific criteria.

Example in Excel or Google Sheets:

VLOOKUP (Vertical Lookup): This searches for a value in the first column of a range and returns a value in the same row from another column.
Formula:
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=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])

Example: Imagine you have a table with Employee IDs in Column A and Employee Names in Column B. To find the name of the employee with ID 102, you can use:
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=VLOOKUP(102, A1:B10, 2, FALSE)

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• HLOOKUP (Horizontal Lookup): Similar to VLOOKUP, but it searches for a value in the top row and returns a value in the same column from another row.

Practical Example in Python using Pandas:

In Python, you can achieve similar functionality using Pandas.

python

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import pandas as pd

# Create a sample dataset (similar to Excel table)

data = {'Employee_ID': [101, 102, 103, 104],

        'Employee_Name': ['Alice', 'Bob', 'Charlie', 'Diana']}

# Convert to DataFrame

df = pd.DataFrame(data)

# Use a lookup function to find the employee name for Employee ID 102

employee_id = 102

employee_name = df.loc[df['Employee_ID'] == employee_id, 'Employee_Name'].values[0]

print(f'Employee with ID {employee_id} is {employee_name}')

This code will print:

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Employee with ID 102 is Bob

2. Count Functions: COUNT, COUNTIF, COUNTIFS in Excel/Sheets and Python Equivalent

Count functions are used to count the number of cells that contain data, meet certain criteria, or match specific conditions.

Example in Excel or Google Sheets:

COUNT: Counts the number of cells that contain numbers.
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=COUNT(A1:A10)

COUNTIF: Counts the number of cells that meet a specific criterion.
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=COUNTIF(A1:A10, ">5")

COUNTIFS: Counts the number of cells that meet multiple criteria.
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=COUNTIFS(A1:A10, ">5", B1:B10, "<10")

Practical Example in Python using Pandas:

You can achieve the same functionality in Pandas.

python

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# Sample dataset

data = {'Product': ['A', 'B', 'A', 'B', 'C', 'A'],

        'Sales': [5, 15, 10, 5, 25, 10]}

df = pd.DataFrame(data)

# COUNT: Total number of sales entries

count_sales = df['Sales'].count()

print(f"Total Sales Entries: {count_sales}")

# COUNTIF: Number of sales greater than 10

countif_sales = (df['Sales'] > 10).sum()

print(f"Sales > 10: {countif_sales}")

# COUNTIFS: Number of products 'A' with sales > 5

countifs = len(df[(df['Product'] == 'A') & (df['Sales'] > 5)])

print(f"Product A with Sales > 5: {countifs}")

Output:

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Total Sales Entries: 6

Sales > 10: 2

Product A with Sales > 5: 2

3. Customization of Functions: Custom Functions in Excel and Python

Custom functions allow you to create your own logic or operation. In Excel, you can use VBA (Visual Basic for Applications) for complex custom functions. In Python, you can define functions with custom logic using the def keyword.

Custom Function in Excel:

In Excel, you can create custom functions with VBA:

1. Open Excel.

2. Press Alt + F11 to open the VBA editor.

3. Insert a new Module.

4. Write a simple custom function, for example, to calculate the square of a number.

vba

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Function SquareNumber(num As Double) As Double

    SquareNumber = num * num

End Function

Now, you can use =SquareNumber(A1) in your Excel sheet.

Custom Function in Python:

In Python, you can easily define a custom function. For example, to create a custom function that calculates the discounted price of a product:

python

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# Custom function to calculate discount

def calculate_discount(price, discount_percentage):

    discount_amount = price * (discount_percentage / 100)

    discounted_price = price - discount_amount

    return discounted_price

# Test the function

price = 100

discount_percentage = 20

print(f"Discounted Price: {calculate_discount(price, discount_percentage)}")

Output:

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Discounted Price: 80.0

You can create custom functions for any business logic, such as calculating tax, applying conditions to data, or even complex transformations.

Practical Steps to Demonstrate These in a Session:

1. Start with Lookup Functions:

   • Open Google Sheets or Excel.

   • Load a dataset and demonstrate VLOOKUP by finding a value.

   • Explain how Python's Pandas library can perform similar lookup tasks with .loc().

2. Move to Count Functions:

   • Show how to use COUNT, COUNTIF, and COUNTIFS in Excel/Sheets.

   • In Python, demonstrate how to count entries and apply conditional counts using Pandas.

3. Custom Functions:

   • Show how to create custom functions in Excel using VBA.

   • Then, define custom functions in Python and explain how it allows you to create reusable code.

Resources:

• Google Sheets: Google Sheets

• Excel VBA Custom Functions: Excel VBA Documentation

• Python Pandas Documentation: Pandas

• Google Colab: Google Colab

These practical examples give users hands-on experience with common data analysis functions while showing how to transition between spreadsheet-based analysis (Excel, Google Sheets) and Python-based analysis (Pandas).

Is excel a part of big data analysis, if not then why above question was explained through excel

Excel is not typically considered a primary tool for Big Data Analysis because it has limitations in handling large datasets, scalability, and advanced processing capabilities. Big Data refers to massive datasets that cannot be processed or analyzed using traditional data tools, like Excel, which struggles when the data size exceeds its row limit (approximately 1 million rows).

However, Excel is widely used for basic data analysis and small to medium-sized datasets in businesses and educational settings. The reason I explained your earlier question using Excel is because:

1. Excel is Familiar: Many people, especially in non-technical fields or those beginning data analysis, are familiar with Excel or Google Sheets. It's easier to understand fundamental data analysis concepts like lookup functions, count functions, and custom functions in Excel because of its simplicity and visual interface.

2. Demonstrating Concepts: The concepts of lookup, count, and customization of functions can be effectively demonstrated in Excel for teaching purposes. These concepts are also transferable to more advanced tools like Python and SQL when you're ready to scale up to Big Data environments.

3. Transitional Learning: By showing how certain analysis functions work in Excel, which is familiar, I can help build a bridge to more advanced tools used for Big Data (like Python, Pandas, Spark, etc.). Many beginners in data analysis start with Excel and gradually move to advanced programming languages and platforms.

Why Excel is Not Ideal for Big Data Analysis:

• Data Size Limitations: Excel can handle only up to ~1 million rows, but Big Data usually involves millions or even billions of rows.

• Processing Power: Excel lacks the computational power and speed to efficiently process large volumes of data. Tools like Hadoop, Spark, or databases like Google BigQuery are used in Big Data environments for their distributed processing capabilities.

• Scalability and Automation: Big Data tools are designed to handle data across distributed systems and can easily scale. Excel doesn’t have the same level of automation or scalability.

When to Transition from Excel to Big Data Tools:

If your data exceeds Excel's limitations, or you're dealing with datasets that involve complex processing or advanced analytics (e.g., machine learning, real-time data streaming), it’s time to transition to Big Data platforms such as:

• Python & Pandas: For advanced data processing.

• SQL/NoSQL Databases: For querying large datasets.

• Hadoop/Spark: For distributed data processing.

• Cloud Platforms: AWS S3, Google BigQuery, or Azure for large data storage and analysis.

Conclusion:

While Excel is useful for small-scale data analysis, it's not suitable for Big Data. The earlier explanation was framed around Excel because the question related to fundamental data analysis concepts, which can be more easily understood and visualized in a simpler tool like Excel before moving on to more complex Big Data environments.

1. Go to the website: https://data.gov.in
2. Search for a dataset. For simplicity, let’s use the following dataset:
   • Dataset Name: State-wise Monthly Rainfall Data (2004-2020)
   • Direct Link to Download: Rainfall Dataset
3. Download the Dataset:
   • Click the Download button on the page.
   • Save the file on your computer.
   • The file name will likely be rainfall.csv.

1. Copy and paste the following code into a new cell in Google Colab:

• A list of all column names in the dataset.
• A count of missing values in each column (if any).
• The number of rows and columns in the dataset.

1. Copy and paste the following code into a new cell in Google Colab:

• Removes rows with missing values using data.dropna().
• Confirms that there are no missing values left.
• Displays the first few rows of the cleaned dataset for verification.

1. Copy and paste the following code into a new cell in Google Colab:

1. First, let’s confirm the exact column names in the dataset. Run the following code to display all the column names:

1. Check the output carefully. Look for the column names that correspond to State and Annual Rainfall. For example, they could be:
   • "State" might actually be " State" or "STATE".
   • "Annual Rainfall" might be "Annual_Rainfall" or "Rainfall (mm)".

1. Copy and paste the following code into a new cell:

• Takes the 'Total_rainfall' column and calculates the mean value.
• Displays the average rainfall rounded to 2 decimal places.

1. Copy and paste the following code into a new cell in Google Colab:

• Converts the 'Month-Year' column into a datetime format so that it can be plotted on the x-axis.
• Creates a line plot showing how rainfall varies over time.
• Adds markers, labels, and a title to make the visualization informative.

1. Run this code to display unique values in the 'Month-Year' column:

1. • Look for invalid, corrupted, or strange date formats (e.g., Jan-11 or empty strings).
   • Confirm if all values are consistent (e.g., YYYY-MM or Month-Year).

1. format='%b-%y': Tells Python to interpret the dates as abbreviated month and two-digit year (e.g., Jan-11 = January 2011).
2. errors='coerce': Replaces invalid date entries with NaT (Not a Time).
3. dropna(subset=['Month-Year']): Removes rows where the date could not be converted.

from google.colab import files

uploaded = files.upload()

D67. Rainfall Monthly Total_1_0.csv(text/csv) - 1513 bytes, last modified: 11/30/2024 - 100% done

import pandas as pd

file_name = "D67. Rainfall Monthly Total_1_0.csv"

data = pd.read_csv(file_name)

print("Dataset Overview:")

print(data.head())

print("Column Names in the Dataset:")

print(data.columns)

print("\nMissing Values in Each Column:")

print(data.isnull().sum())

print("\nDataset Dimensions:")

print(f"Rows: {data.shape[0]}, Columns: {data.shape[1]}")

data_cleaned = data.dropna()

print("Missing Values After Cleaning:")

print(data_cleaned.isnull().sum())

print("\nCleaned Dataset Overview:")

print(data_cleaned.head())

print("Column Names in the Dataset:")

print(data_cleaned.columns)

average_rainfall = data_cleaned["Total_rainfall"].mean()

print("Average Rainfall (across all months and years):")

print(f"{average_rainfall:.2f} mm")

import matplotlib.pyplot as plt

data_cleaned['Month-Year'] = pd.to_datetime(data_cleaned['Month-Year'], format='%b-%y', errors='coerce')

print("Invalid dates after conversion:")

print(data_cleaned[data_cleaned['Month-Year'].isna()])

print("Invalid dates after conversion:")

print(data_cleaned[data_cleaned['Month-Year'].isna()])

data_cleaned = data_cleaned.dropna(subset=['Month-Year'])

print("Conversion successful!")

plt.figure(figsize=(12, 6))

plt.plot(data_cleaned['Month-Year'], data_cleaned['Total_rainfall'], marker='o', linestyle='-', color='b')

plt.title("Rainfall Trend Over Time", fontsize=16)

plt.xlabel("Month-Year", fontsize=12)

plt.ylabel("Total Rainfall (mm)", fontsize=12)

plt.xticks(rotation=45)

plt.grid(True)

plt.tight_layout()

from google.colab import files

uploaded = files.upload()

D67. Rainfall Monthly Total_1_0.csv(text/csv) - 1513 bytes, last modified: 11/30/2024 - 100% done

import pandas as pd

file_name ="D67. Rainfall Monthly Total_1_0.csv"

data = pd.read_csv(file_name)

print("Dataset Overview:")

print(data.head())

Dataset Overview:

  Month-Year  Total_rainfall

0     Jan-11             0.0

1     Feb-11            14.0

2     Mar-11             0.0

3     Apr-11             0.0

4     May-11             0.0 

print("Column Names in the Dataset:")

print(data.columns)

Column Names in the Dataset:

Index(['Month-Year', 'Total_rainfall'], dtype='object') 

print("\nMissing Values in Each Column:")

print(data.isnull().sum())

Missing Values in Each Column:

Month-Year        0

Total_rainfall    0

dtype: int64 

print("\nDataset Dimensions:")

print(f"Rows: {data.shape[0]}, Columns: {data.shape[1]}")

Dataset Dimensions:

Rows: 132, Columns: 2 

data_cleaned = data.dropna()

print("Missing Values After Cleaning:")

print(data_cleaned.isnull().sum())

Missing Values After Cleaning:

Month-Year        0

Total_rainfall    0

dtype: int64 

print("\nCleaned Dataset Overview:")

print(data_cleaned.head())

Cleaned Dataset Overview:

  Month-Year  Total_rainfall

0     Jan-11             0.0

1     Feb-11            14.0

2     Mar-11             0.0

3     Apr-11             0.0

4     May-11             0.0 

print("Column Names in the Dataset:")

print(data_cleaned.columns)

Column Names in the Dataset:

Index(['Month-Year', 'Total_rainfall'], dtype='object') 

average_rainfall = data_cleaned["Total_rainfall"].mean()

print("Average Rainfall (across all months and years):")

print(f"{average_rainfall:.2f} mm")

Average Rainfall (across all months and years):

57.00 mm 

import matplotlib.pyplot as plt

data_cleaned['Month-Year'] = pd.to_datetime(data_cleaned['Month-Year'], format='%b-%y', errors='coerce')

print("Invalid dates after conversion:")

print(data_cleaned[data_cleaned['Month-Year'].isna()])

print("Invalid dates after conversion:")

print(data_cleaned[data_cleaned['Month-Year'].isna()])

Invalid dates after conversion:

Empty DataFrame

Columns: [Month-Year, Total_rainfall]

Index: [] 

data_cleaned = data_cleaned.dropna(subset=['Month-Year'])

print("Conversion successful!")

Conversion successful! 

plt.figure(figsize=(12,6))

plt.plot(data_cleaned['Month-Year'], data_cleaned['Total_rainfall'], marker='o', linestyle='-', color='b')

plt.title("Rainfall Trend Over Time", fontsize=16)

plt.xlabel("Month-Year", fontsize=12)

plt.ylabel("Total Rainfall (mm)", fontsize=12)

plt.xticks(rotation=45)

plt.grid(True)

plt.tight_layout()

plt.show()

Step 1: Create a Custom CSV File

1. Copy and paste the following data into a plain text editor (e.g., Notepad): 

Month,Region,Sales,Profit

January,North,20000,5000

January,South,18000,4500

January,East,22000,6000

January,West,15000,3000

February,North,25000,7000

February,South,20000,5000

February,East,27000,8000

February,West,16000,3500

March,North,30000,9000

March,South,22000,5500

March,East,29000,8500

March,West,17000,4000 

2. Save it as a CSV file:

1. • File Name: sales_data.csv

Step 2: Upload the CSV File to Google Colab

from google.colab import files

# Upload the CSV file

uploaded = files.upload() 

What to Expect

• Once uploaded, Colab will display:

Uploading sales_data.csv

Saving sales_data.csv to sales_data.csv 

Step 3: Load and Display the Dataset

import pandas as pd  # Importing Pandas for data handling

# Load the uploaded CSV file into a Pandas DataFrame

file_name = "sales_data.csv"

data = pd.read_csv(file_name)

# Display the first few rows of the dataset

print("Dataset Overview:")

print(data.head()) 

What to Expect

• You should see the first 5 rows of the dataset, with columns Month, Region, Sales, and Profit.

Step 4: Visualize Sales by Region

import matplotlib.pyplot as plt

# Group the data by Region and sum the Sales

region_sales = data.groupby("Region")["Sales"].sum()

# Plot a bar chart

plt.figure(figsize=(8, 6))

region_sales.plot(kind="bar", color="skyblue", edgecolor="black")

# Add title and labels

plt.title("Total Sales by Region", fontsize=16)

plt.xlabel("Region", fontsize=12)

plt.ylabel("Total Sales (in $)", fontsize=12)

# Show the plot

plt.grid(axis='y', linestyle='--', alpha=0.7)

plt.tight_layout()

plt.show()

What to Expect

• A bar chart showing total sales for each region (North, South, East, West).

Step 5: Visualize Profit Trends by Month

# Group the data by Month and sum the Profit

month_profit = data.groupby("Month")["Profit"].sum()

# Sort the months in calendar order

month_order = ["January", "February", "March"]

month_profit = month_profit.reindex(month_order)

# Plot a line chart

plt.figure(figsize=(8, 6))

month_profit.plot(kind="line", marker='o', color="green", linewidth=2)

# Add title and labels

plt.title("Profit Trends by Month", fontsize=16)

plt.xlabel("Month", fontsize=12)

plt.ylabel("Total Profit (in $)", fontsize=12)

# Show the plot

plt.grid(axis='y', linestyle='--', alpha=0.7)

plt.tight_layout()

plt.show() 

What This Code Does

• Groups the data by Month and calculates the total Profit for each month.

• Sorts the months in the correct calendar order (January → February → March).

• Creates a line chart with green markers and lines to represent the profit trend.

Step 1: Create a Dataset

1. Open Notepad and paste the following data:

Day,Intersection,Vehicles Counted,Average Speed

Monday,Intersection A,1200,45

Monday,Intersection B,800,50

Monday,Intersection C,1500,40

Tuesday,Intersection A,1300,42

Tuesday,Intersection B,900,48

Tuesday,Intersection C,1400,38

Wednesday,Intersection A,1250,43

Wednesday,Intersection B,850,46

Wednesday,Intersection C,1350,39

Thursday,Intersection A,1400,41

Thursday,Intersection B,920,47

Thursday,Intersection C,1450,37

Friday,Intersection A,1500,40

Friday,Intersection B,980,45

Friday,Intersection C,1600,35

Saturday,Intersection A,1700,38

Saturday,Intersection B,1100,42

Saturday,Intersection C,1800,33

Sunday,Intersection A,1600,39

Sunday,Intersection B,1050,44

Sunday,Intersection C,1750,34 

2. Save the file as traffic_data.csv on your desktop.

Step 2: Upload the CSV File to Google Colab

1. Open Google Colab and create a new notebook.

from google.colab import files

# Upload the CSV file

uploaded = files.upload() 

A file upload dialog will appear. Select the file traffic_data.csv from your desktop and upload it.

What to Expect

• Once uploaded, Colab will display:

• Uploading traffic_data.csv Saving traffic_data.csv to traffic_data.csv

traffic_data.csv(text/csv) - 717 bytes, last modified: 12/1/2024 - 100% done

Saving traffic_data.csv to traffic_data.csv 

Step 3: Load and Display the Dataset

import pandas as pd  # Importing Pandas for data handling

# Load the uploaded CSV file into a Pandas DataFrame

file_name = "traffic_data.csv"

data = pd.read_csv(file_name)

# Display the first few rows of the dataset

print("Dataset Overview:")

print(data.head()) 

Step 4: Total Vehicles Counted by Day

Let’s calculate the total number of vehicles counted for each day and create a bar chart.

import matplotlib.pyplot as plt

# Group data by Day and sum the Vehicles Counted

daywise_traffic = data.groupby("Day")["Vehicles Counted"].sum()

# Plot a bar chart

plt.figure(figsize=(8, 6))

daywise_traffic.plot(kind="bar", color="skyblue", edgecolor="black")

# Add title and labels

plt.title("Total Vehicles Counted by Day", fontsize=16)

plt.xlabel("Day", fontsize=12)

plt.ylabel("Total Vehicles", fontsize=12)

# Show the plot

plt.grid(axis='y', linestyle='--', alpha=0.7)

plt.tight_layout()

plt.show()

What This Code Does

• Groups the data by Day and calculates the total number of vehicles for each day.

• Creates a bar chart to visualize traffic patterns throughout the week.

What to Expect

• A bar chart showing total vehicle counts for each day (Monday to Sunday)

Step 5: Analyze Traffic for Odd and Even Days

We’ll classify the days into Odd and Even, calculate the total vehicle count for each category, and create a comparative bar chart.

# Classify days into Odd and Even based on their position in the week

odd_days = ["Monday", "Wednesday", "Friday", "Sunday"]

data["Day Type"] = data["Day"].apply(lambda x: "Odd" if x in odd_days else "Even")

# Group data by Day Type and sum the Vehicles Counted

odd_even_traffic = data.groupby("Day Type")["Vehicles Counted"].sum()

# Plot a bar chart for Odd and Even days

plt.figure(figsize=(8, 6))

odd_even_traffic.plot(kind="bar", color=["lightcoral", "skyblue"], edgecolor="black")

# Add title and labels

plt.title("Traffic Comparison: Odd vs Even Days", fontsize=16)

plt.xlabel("Day Type", fontsize=12)

plt.ylabel("Total Vehicles", fontsize=12)

# Show the plot

plt.grid(axis='y', linestyle='--', alpha=0.7)

plt.tight_layout()

plt.show() 

What This Code Does

• Classifies days into Odd (Monday, Wednesday, Friday, Sunday) and Even (Tuesday, Thursday, Saturday).

• Groups data by these categories and calculates the total vehicle count for each.

• Creates a bar chart to compare traffic between Odd and Even days.

What to Expect

• A bar chart comparing total vehicle counts for Odd and Even days.

• Highlights whether traffic is heavier on odd or even days, which is critical for policies like Odd-Even Rules.

odd_days = ["Monday", "Wednesday", "Friday", "Sunday"]

data["Day Type"] = data["Day"].apply(lambda x: "Odd" if x in odd_days else "Even")

odd_even_traffic = data.groupby("Day Type")["Vehicles Counted"].sum()

plt.figure(figsize=(8,6))

odd_even_traffic.plot(kind="bar", color=["lightcoral", "skyblue"], edgecolor="black")

plt.title("Traffic Comparison:odd vs Even Days", fontsize=16)

plt.xlabel("Day Type", fontsize=12)

plt.ylabel("Total Vehicles", fontsize=12)

plt.grid(axis='y', linestyle='--', alpha=0.7)

plt.tight_layout()

plt.show()

Open Government Data (OGD) Platform India as our primary resource. Here's how you can proceed:

Step 1: Access the OGD Platform

1. Visit the OGD Platform: Open your web browser and go to data.gov.in.

Step 2: Search for a Dataset

1. Use the Search Function: On the homepage, you'll find a search bar. Type in keywords related to the data you're interested in, such as "rainfall," "population," or "traffic."

2. Filter Results: After searching, you can filter the results by file format (e.g., CSV) to find datasets that are easy to work with.

Step 3: Select and Download a Dataset

1. Choose a Dataset: Browse through the search results and select a dataset that fits your needs. For example, you might choose "State-wise Monthly Rainfall Data."

2. Download the Dataset: On the dataset's page, look for a download link or button, often labeled "Download" or "CSV." Click it to download the file to your computer.

Step 4: Upload the Dataset to Google Colab

1. Open Google Colab: Navigate to Google Colab and create a new notebook.

2. Upload the File:

   • In Colab, click on the folder icon on the left sidebar to open the file explorer.

   • Click the upload icon (a file with an upward arrow) and select the CSV file you downloaded.

Step 5: Load and Explore the Data

1. Import Necessary Libraries:

import pandas as pd 

2. Load the Data:

# Replace 'filename.csv' with the actual name of your file data = pd.read_csv('filename.csv')

3. View the Data:

# Display the first few rows of the dataset 

data.head()

Step 6: Analyze and Visualize the Data

1. Check for Missing Values:

data.isnull().sum()

2. Basic Statistics:

data.describe()

3. Data Visualization:

   • import matplotlib.pyplot as plt

   • Plot Data (example for rainfall data):

   • # Ensure 'Date' column is in datetime format 

data['Date'] = pd.to_datetime(data['Date']) 

# Plot 

plt.figure(figsize=(10, 5)) 

plt.plot(data['Date'], data['Rainfall'], marker='o') 

plt.title('Monthly Rainfall Over Time') 

plt.xlabel('Date') 

plt.ylabel('Rainfall (mm)') 

plt.grid(True) 

plt.show()

Additional Resources

• Smart Cities Mission Data Portal: For city-specific data, visit the Smart Cities Mission Data Portal.

NCRB data

Let’s begin working on the NCRB_CII-2019_Table_17A.1.csv file you downloaded. 

Step 1: Upload the CSV File to Google Colab

1. Open Google Colab and create a new notebook.

2. Add the following code in a new cell to upload the file:

3. from google.colab import files # Upload the CSV file uploaded = files.upload()

4. Run the cell by pressing Shift + Enter.

5. A file upload dialog will appear. Select the file NCRB_CII-2019_Table_17A.1.csv from your computer and upload it.

What to Expect

Once uploaded, Colab should display something like this:

Uploading NCRB_CII-2019_Table_17A.1.csv Saving NCRB_CII-2019_Table_17A.1.csv to NCRB_CII-2019_Table_17A.1.csv

NCRB_CII-2019_Table_17A.1.csv

NCRB_CII-2019_Table_17A.1.csv(text/csv) - 18087 bytes, last modified: 12/1/2024 - 100% done

Saving NCRB_CII-2019_Table_17A.1.csv to NCRB_CII-2019_Table_17A.1.csv

Step 2: Load and Display the Dataset

1. Add the following code to a new cell in Google Colab:

import pandas as pd 

# Importing Pandas for data handling 

# Load the uploaded CSV file into a Pandas DataFrame 

file_name = "NCRB_CII-2019_Table_17A.1.csv" 

data = pd.read_csv(file_name) 

# Display the first few rows of the dataset 

print("Dataset Overview:") 

print(data.head())

2. Run the cell by pressing Shift + Enter.

What to Expect

• You should see the first 5 rows of the dataset, including the column names and some data.

Step 4: Analyze the Dataset

Let’s start with a simple analysis: Identify the top 5 crime categories with the highest number of cases reported during the year.

# Sort the dataset by 'Cases Reported during the year' in descending order

top_crimes = data.sort_values(by='Cases Reported during the year', ascending=False)

# Select the top 5 rows

top_5_crimes = top_crimes[['Crime Head', 'Cases Reported during the year']].head(5)

# Display the results

print("Top 5 Crime Categories by Cases Reported:")

print(top_5_crimes) 

• A list of the top 5 crime categories with the highest number of cases reported during the year.

Top 5 Crime Categories by Cases Reported:

                                    Crime Head  Cases Reported during the year

141                Total Cognizable IPC crimes                         3225701

60   Offences Affecting the Human Body (Total)                         1050945

139            Miscellaneous IPC Crimes(Total)                          860209

104          Offences against Property (Total)                          854618

88                                       Theft                          675916 

Step 5: Visualize the Top 5 Crime Categories by Cases Reported

import matplotlib.pyplot as plt

# Extract the crime categories and case numbers

crime_categories = top_5_crimes['Crime Head']

case_numbers = top_5_crimes['Cases Reported during the year']

# Create the bar chart

plt.figure(figsize=(10, 6))

plt.barh(crime_categories, case_numbers, color='skyblue', edgecolor='black')

# Add title and labels

plt.title("Top 5 Crime Categories by Cases Reported", fontsize=16)

plt.xlabel("Number of Cases", fontsize=12)

plt.ylabel("Crime Categories", fontsize=12)

# Show the plot

plt.tight_layout()

plt.show() 

What to Expect

• A horizontal bar chart showcasing the top 5 crime categories with their respective number of cases reported during the year.

import matplotlib.pyplot as plt

crime_categories = top_5_crimes['Crime Head']

case_numbers = top_5_crimes['Cases Reported during the year']

plt.figure(figsize=(10, 6))

plt.barh(crime_categories, case_numbers, color='skyblue', edgecolor='black')

plt.title("Top 5 Crime Categories by Cases Reported", fontsize=16)

plt.xlabel("Number of Cases", fontsize=12)

plt.ylabel("Crime Categories", fontsize=12)

plt.tight_layout()

plt.show()

import matplotlib.pyplot as plt

# Your dishes and their quantities

dishes = ["Pizza", "Burger", "Pasta"]

quantities = [5, 3, 7]

# Create a horizontal bar chart

plt.barh(dishes, quantities, color='skyblue')

# Add a title and labels

plt.title("Dish Quantities at Buffet")

plt.xlabel("Quantity (kg)")

plt.ylabel("Dishes")

# Show the chart

plt.show()

Copy and paste the following in notepad and save it as .csv  

CRPF_Incidents_Large.csv


Incident_ID,Location,Date,Incident_Type,Casualties,Reported_By,Severity

1,Assam,2024-08-23,Encounter,0,Patrol Unit B,Low

2,Odisha,2024-04-10,Riots,0,Unit D,Low

3,Odisha,2024-06-10,Encounter,2,Unit D,Medium

4,West Bengal,2024-07-27,Patrolling,6,Unit C,Medium

5,Jammu & Kashmir,2024-07-22,Patrolling,0,Patrol Unit A,Medium

6,Maharashtra,2024-08-08,IED Blast,4,Unit C,Low

7,Bihar,2024-10-30,Encounter,7,Local Intel,Low

8,Assam,2024-07-19,Search Operation,9,Unit D,Low

9,Assam,2024-07-14,Riots,5,Local Intel,Medium

10,Kerala,2024-12-06,Encounter,8,Unit D,Medium

11,Odisha,2025-01-13,Search Operation,1,Patrol Unit A,Medium

12,Bihar,2024-10-08,Search Operation,8,Patrol Unit B,High

13,Assam,2024-06-01,Encounter,5,Local Intel,High

14,Odisha,2024-11-26,Encounter,8,Unit C,Low

15,Bihar,2024-07-24,Search Operation,6,Unit D,High

16,West Bengal,2024-11-29,Search Operation,2,Patrol Unit A,Low

17,Kerala,2024-10-22,Riots,4,Unit C,Medium

18,Jammu & Kashmir,2024-03-04,IED Blast,3,Patrol Unit B,High

19,Assam,2024-09-06,Patrolling,4,Unit C,Low

20,Chhattisgarh,2024-07-29,Encounter,7,Local Intel,High

21,Assam,2024-05-01,Riots,7,Unit C,Low

22,West Bengal,2024-06-08,Search Operation,10,Patrol Unit A,Medium

23,Jammu & Kashmir,2024-05-14,Ambush,2,Local Intel,High

24,Jammu & Kashmir,2024-07-02,IED Blast,2,Unit C,Low

25,Odisha,2024-11-04,Ambush,8,Local Intel,Low

26,Bihar,2024-09-28,IED Blast,7,Patrol Unit A,High

27,Chhattisgarh,2024-06-12,IED Blast,4,Local Intel,Low

28,Maharashtra,2024-04-26,Patrolling,2,Unit C,Medium

29,Jammu & Kashmir,2024-08-13,Encounter,10,Unit C,Low

30,Maharashtra,2024-09-16,Encounter,10,Patrol Unit A,High

31,Odisha,2024-02-09,Search Operation,2,Unit D,Low

32,Maharashtra,2024-02-11,Encounter,8,Unit D,High

33,Bihar,2025-01-02,Encounter,10,Unit D,Low

34,Odisha,2024-08-27,Search Operation,4,Unit D,Medium

35,Bihar,2024-07-26,Search Operation,7,Patrol Unit B,Low

36,Kerala,2024-09-29,Search Operation,0,Local Intel,High

37,Jammu & Kashmir,2024-09-14,Encounter,4,Local Intel,High

38,Maharashtra,2024-10-09,Patrolling,10,Unit C,Medium

39,Maharashtra,2024-10-17,Patrolling,9,Local Intel,Low

40,Kerala,2024-02-09,Patrolling,3,Unit D,Medium

41,Maharashtra,2024-10-06,Ambush,0,Patrol Unit A,Low

42,Jammu & Kashmir,2024-02-29,Search Operation,8,Unit D,Medium

43,Odisha,2024-11-07,IED Blast,1,Unit D,Medium

44,West Bengal,2024-02-03,Riots,5,Local Intel,Medium

45,Maharashtra,2024-11-26,Ambush,2,Local Intel,Low

46,Kerala,2025-01-14,Encounter,6,Patrol Unit B,Medium

47,Assam,2024-05-20,Riots,2,Patrol Unit A,Low

48,Bihar,2024-04-14,Encounter,7,Local Intel,High

49,Bihar,2024-05-22,Encounter,10,Patrol Unit A,Medium

50,West Bengal,2024-07-07,Encounter,9,Unit C,Low

51,Chhattisgarh,2024-07-29,Search Operation,1,Unit D,Medium

52,Bihar,2024-11-18,Riots,6,Patrol Unit B,Medium

53,Assam,2024-09-09,Encounter,4,Patrol Unit B,Medium

54,West Bengal,2025-01-02,Search Operation,8,Patrol Unit A,Medium

55,Chhattisgarh,2024-03-04,Search Operation,9,Patrol Unit B,Medium

56,Jammu & Kashmir,2024-06-25,Ambush,0,Patrol Unit B,Medium

57,Maharashtra,2024-05-22,Riots,2,Local Intel,Medium

58,Assam,2024-08-17,Riots,5,Patrol Unit B,High

59,Assam,2024-11-02,Search Operation,4,Unit C,Medium

60,Assam,2024-04-16,Encounter,6,Patrol Unit B,Medium

61,Maharashtra,2024-10-09,Patrolling,6,Patrol Unit B,Low

62,Kerala,2024-07-06,IED Blast,7,Unit D,Low

63,Maharashtra,2024-06-29,IED Blast,1,Unit D,High

64,Chhattisgarh,2024-08-09,Patrolling,7,Patrol Unit A,Low

65,Bihar,2024-06-17,Ambush,5,Patrol Unit A,Low

66,Chhattisgarh,2024-07-05,Patrolling,4,Unit C,Low

67,West Bengal,2024-09-25,Patrolling,9,Unit C,Medium

68,Odisha,2024-09-26,Encounter,8,Local Intel,Medium

69,Assam,2024-03-29,Encounter,7,Unit D,Medium

70,Odisha,2024-12-05,IED Blast,3,Patrol Unit B,High

71,West Bengal,2024-05-29,IED Blast,0,Local Intel,Low

72,Odisha,2024-02-18,IED Blast,10,Unit C,Low

73,Jammu & Kashmir,2025-01-25,Riots,7,Local Intel,High

74,Maharashtra,2024-03-03,Patrolling,2,Patrol Unit A,Low

75,Assam,2024-02-27,Patrolling,5,Unit D,Low

76,Jammu & Kashmir,2024-10-28,Search Operation,5,Patrol Unit B,Medium

77,Maharashtra,2025-01-26,Search Operation,2,Patrol Unit A,High

78,Assam,2024-08-31,Encounter,6,Patrol Unit B,Low

79,Chhattisgarh,2024-03-02,IED Blast,10,Unit C,High

80,Chhattisgarh,2024-05-09,Riots,7,Patrol Unit A,High

81,Bihar,2024-10-16,Encounter,7,Patrol Unit B,Medium

82,Bihar,2024-10-12,Ambush,8,Patrol Unit A,Low

83,Chhattisgarh,2024-12-05,Search Operation,0,Unit D,Low

84,Bihar,2024-05-06,Riots,2,Unit D,Low

85,Bihar,2024-05-12,Riots,6,Unit C,Low

86,Chhattisgarh,2024-03-01,Search Operation,3,Patrol Unit B,High

87,West Bengal,2024-11-05,IED Blast,1,Patrol Unit A,Medium

88,Jammu & Kashmir,2024-01-29,Riots,10,Patrol Unit B,High

89,Assam,2024-05-13,Patrolling,10,Patrol Unit A,Low

90,Assam,2024-10-08,Ambush,0,Unit C,Low

91,Jammu & Kashmir,2024-06-22,IED Blast,10,Unit C,High

92,Assam,2025-01-08,Patrolling,8,Patrol Unit B,High

93,Assam,2025-01-22,IED Blast,2,Local Intel,High

94,Maharashtra,2024-10-06,Patrolling,7,Local Intel,High

95,Jammu & Kashmir,2024-04-11,IED Blast,4,Local Intel,Medium

96,Bihar,2024-09-16,Ambush,8,Unit D,Low

97,Bihar,2024-10-20,Patrolling,2,Unit C,Low

98,West Bengal,2024-01-30,Encounter,9,Patrol Unit B,Low

99,Chhattisgarh,2024-07-26,Riots,4,Unit C,High

100,Kerala,2024-03-12,Ambush,6,Local Intel,Low

101,West Bengal,2024-09-20,IED Blast,10,Local Intel,Low

102,Bihar,2024-02-21,Riots,2,Patrol Unit A,Low

103,Assam,2025-01-13,Riots,0,Unit D,Medium

104,Chhattisgarh,2025-01-13,Patrolling,3,Unit D,Low

105,Odisha,2024-11-28,IED Blast,2,Unit C,Low

106,Jammu & Kashmir,2024-10-17,Ambush,8,Patrol Unit A,Medium

107,Bihar,2024-05-16,IED Blast,9,Patrol Unit A,Low

108,West Bengal,2024-03-12,Encounter,6,Local Intel,High

109,Odisha,2024-05-28,Encounter,4,Unit C,Medium

110,Jammu & Kashmir,2024-09-24,Encounter,1,Local Intel,Low

111,West Bengal,2024-03-30,Search Operation,4,Unit C,Low

112,West Bengal,2024-03-18,Patrolling,8,Unit D,Low

113,West Bengal,2024-03-01,Riots,8,Patrol Unit A,Medium

114,Jammu & Kashmir,2024-08-05,Encounter,6,Patrol Unit B,High

115,Maharashtra,2024-11-02,Search Operation,7,Patrol Unit B,Medium

116,West Bengal,2024-08-21,Encounter,7,Patrol Unit B,High

117,Odisha,2024-02-29,IED Blast,9,Unit C,Medium

118,Odisha,2024-10-22,Search Operation,5,Patrol Unit B,Medium

119,Jammu & Kashmir,2024-01-30,Patrolling,4,Unit C,High

120,Odisha,2024-02-13,Patrolling,5,Local Intel,Low

121,West Bengal,2024-03-14,IED Blast,9,Patrol Unit A,Medium

122,Kerala,2024-05-15,Search Operation,0,Local Intel,Medium

123,Chhattisgarh,2024-02-14,Riots,6,Patrol Unit A,Medium

124,Odisha,2025-01-15,Riots,1,Local Intel,Low

125,Chhattisgarh,2024-10-04,Patrolling,0,Patrol Unit B,High

126,Bihar,2025-01-18,Search Operation,8,Unit D,High

127,Bihar,2025-01-11,IED Blast,3,Unit D,Low

128,Assam,2024-04-25,Patrolling,8,Local Intel,High

129,Kerala,2024-11-21,Encounter,3,Patrol Unit A,Medium

130,Bihar,2024-04-01,Patrolling,7,Unit D,Medium

131,Assam,2024-11-16,Ambush,1,Unit D,High

132,West Bengal,2024-10-13,IED Blast,1,Local Intel,Low

133,Odisha,2024-03-07,Patrolling,2,Patrol Unit A,Low

134,West Bengal,2024-03-09,Riots,10,Unit C,Medium

135,Bihar,2024-10-18,Riots,2,Unit D,Low

136,Assam,2024-08-14,Riots,3,Local Intel,High

137,Jammu & Kashmir,2024-07-30,Ambush,6,Unit D,Low

138,Kerala,2024-11-04,Search Operation,1,Local Intel,High

139,Odisha,2024-08-26,Riots,1,Patrol Unit A,High

140,Odisha,2024-02-13,IED Blast,9,Patrol Unit A,High

141,West Bengal,2024-05-24,Search Operation,1,Patrol Unit A,Low

142,West Bengal,2024-07-31,Encounter,1,Patrol Unit B,Medium

143,West Bengal,2024-12-08,Ambush,3,Unit C,High

144,West Bengal,2024-06-29,Search Operation,4,Local Intel,High

145,Bihar,2024-02-01,Patrolling,4,Unit C,Medium

146,Odisha,2024-07-04,Search Operation,1,Patrol Unit B,High

147,Maharashtra,2024-08-31,IED Blast,8,Local Intel,High

148,Assam,2025-01-01,Search Operation,3,Local Intel,Medium

149,Maharashtra,2024-04-12,Riots,1,Patrol Unit A,Medium

150,Odisha,2024-03-04,Riots,5,Patrol Unit B,High

151,Kerala,2024-08-22,Patrolling,10,Patrol Unit B,Medium

152,Kerala,2024-06-25,IED Blast,10,Unit C,Low

153,Bihar,2024-04-05,Search Operation,4,Patrol Unit B,Low

154,Bihar,2025-01-08,Search Operation,10,Unit D,Medium

155,Bihar,2024-05-25,Riots,3,Unit C,Medium

156,Assam,2024-07-22,Encounter,2,Unit C,High

157,West Bengal,2024-02-24,Search Operation,1,Local Intel,Medium

158,Maharashtra,2024-08-23,Ambush,10,Local Intel,Medium

159,Jammu & Kashmir,2024-11-05,Ambush,1,Patrol Unit B,Medium

160,Assam,2024-05-02,Patrolling,3,Local Intel,High

161,Chhattisgarh,2024-10-06,Search Operation,8,Patrol Unit A,Low

162,Maharashtra,2024-05-28,Search Operation,5,Local Intel,Low

163,Jammu & Kashmir,2024-11-07,Search Operation,8,Patrol Unit B,Low

164,Odisha,2024-11-29,Patrolling,9,Local Intel,Medium

165,West Bengal,2024-04-20,Patrolling,3,Patrol Unit A,High

166,Chhattisgarh,2025-01-13,Search Operation,8,Local Intel,Low

167,West Bengal,2024-09-23,Riots,10,Patrol Unit B,Medium

168,Kerala,2024-12-31,Search Operation,4,Patrol Unit A,Medium

169,Kerala,2024-06-04,Patrolling,6,Patrol Unit A,Medium

170,Odisha,2024-09-23,Encounter,7,Patrol Unit B,Low

171,Kerala,2024-02-05,Encounter,0,Local Intel,High

172,Jammu & Kashmir,2024-04-12,Encounter,1,Local Intel,Medium

173,Chhattisgarh,2024-08-26,Encounter,0,Patrol Unit A,High

174,Assam,2024-02-09,IED Blast,6,Patrol Unit A,Medium

175,Jammu & Kashmir,2024-09-06,Riots,6,Patrol Unit B,High

176,West Bengal,2024-02-15,Search Operation,5,Unit C,High

177,Odisha,2024-04-10,Riots,0,Unit C,Medium

178,Maharashtra,2024-10-12,Patrolling,9,Local Intel,Low

179,Maharashtra,2024-09-09,Riots,4,Unit D,Medium

180,Kerala,2024-12-04,Riots,5,Patrol Unit A,Medium

181,Bihar,2024-04-09,IED Blast,6,Unit C,Medium

182,Odisha,2024-10-09,Riots,2,Patrol Unit B,Medium

183,Chhattisgarh,2024-03-03,Search Operation,3,Unit D,High

184,Jammu & Kashmir,2024-01-27,Riots,10,Patrol Unit A,Low

185,West Bengal,2024-02-29,Patrolling,1,Unit D,High

186,Chhattisgarh,2024-04-11,Patrolling,1,Local Intel,Low

187,Assam,2024-10-17,Patrolling,1,Local Intel,High

188,Assam,2025-01-20,Search Operation,3,Patrol Unit B,Low

189,Jammu & Kashmir,2024-11-11,IED Blast,0,Local Intel,Medium

190,Jammu & Kashmir,2024-07-26,Patrolling,9,Patrol Unit B,High

191,Assam,2024-04-20,IED Blast,1,Unit C,Low

192,Assam,2024-02-05,Patrolling,8,Unit C,Medium

193,Maharashtra,2024-09-13,IED Blast,1,Patrol Unit B,Medium

194,Kerala,2024-12-03,Search Operation,10,Local Intel,High

195,Maharashtra,2024-01-30,Patrolling,9,Patrol Unit A,Low

196,Kerala,2024-02-12,Patrolling,4,Unit C,Medium

197,Maharashtra,2025-01-18,Patrolling,9,Patrol Unit B,High

198,West Bengal,2024-03-21,Search Operation,10,Local Intel,Low

199,Odisha,2024-10-19,Encounter,5,Patrol Unit A,High

200,Kerala,2024-05-11,Riots,8,Patrol Unit B,Low

201,Jammu & Kashmir,2025-01-14,Riots,0,Unit D,Low

202,Kerala,2024-06-06,Patrolling,5,Unit C,High

203,Bihar,2024-07-11,Ambush,9,Unit D,Medium

204,Maharashtra,2024-12-22,Search Operation,8,Unit D,Low

205,Jammu & Kashmir,2024-11-04,Ambush,0,Local Intel,Medium

206,Jammu & Kashmir,2024-11-04,Patrolling,9,Patrol Unit B,Medium

207,Bihar,2024-09-28,IED Blast,10,Local Intel,High

208,Maharashtra,2024-05-02,IED Blast,10,Unit D,Low

209,Chhattisgarh,2024-08-08,Patrolling,2,Unit C,Medium

210,Chhattisgarh,2024-12-03,Encounter,4,Unit C,High

211,Kerala,2024-05-21,Patrolling,0,Local Intel,Medium

212,Kerala,2025-01-21,Patrolling,1,Local Intel,Low

213,Odisha,2024-08-19,Search Operation,2,Local Intel,High

214,Chhattisgarh,2024-02-04,Riots,10,Unit D,Medium

215,West Bengal,2024-06-05,IED Blast,8,Patrol Unit A,Low

216,Jammu & Kashmir,2024-05-02,Riots,0,Unit C,High

217,Jammu & Kashmir,2024-08-26,Search Operation,6,Unit D,Medium

218,Odisha,2024-05-26,Encounter,0,Unit D,High

219,Bihar,2024-10-30,Riots,1,Unit D,Medium

220,Odisha,2024-10-13,Search Operation,6,Unit C,High

221,Odisha,2024-04-16,Encounter,10,Patrol Unit A,Low

222,Chhattisgarh,2024-04-18,Patrolling,4,Patrol Unit B,High

223,West Bengal,2024-09-05,Patrolling,6,Local Intel,High

224,Maharashtra,2024-09-14,Search Operation,9,Patrol Unit B,Medium

225,Maharashtra,2024-06-24,IED Blast,7,Unit C,Low

226,Chhattisgarh,2024-06-03,Search Operation,7,Patrol Unit A,High

227,Bihar,2024-04-19,Ambush,10,Patrol Unit B,Low

228,Assam,2024-09-09,Encounter,3,Patrol Unit A,Medium

229,Maharashtra,2024-08-06,Riots,4,Unit C,Low

230,Kerala,2024-05-17,Encounter,5,Local Intel,Medium

231,Chhattisgarh,2024-12-01,Ambush,3,Local Intel,Medium

232,Maharashtra,2024-02-25,Patrolling,7,Unit D,Low

233,Odisha,2024-07-02,Ambush,5,Unit D,Medium

234,Assam,2024-04-09,Search Operation,10,Unit C,Low

235,West Bengal,2024-09-05,IED Blast,0,Patrol Unit B,High

236,Assam,2025-01-26,Encounter,6,Patrol Unit A,High

237,Odisha,2024-11-15,Riots,1,Patrol Unit B,Medium

238,Chhattisgarh,2024-02-15,Search Operation,0,Patrol Unit B,High

239,Odisha,2024-04-08,Encounter,4,Unit C,Low

240,Jammu & Kashmir,2024-08-26,Patrolling,6,Unit C,Medium

241,Bihar,2024-05-05,Ambush,3,Unit C,Low

242,Jammu & Kashmir,2025-01-04,Riots,10,Patrol Unit A,Medium