#datamanipulation

[edutech-brijesh]

Are you a DataFrame? Because you've got all the right columns, and I'm ready to manipulate our data into something meaningful. .

[thenullprophet]

ECONOMIC REDIRECTION DETECTED

The numbers are staggering. The outrage is calculated. But the system does not make mistakes—it makes adjustments.

Money does not disappear. It is redirected, reclassified, repurposed. If $2.7 trillion has vanished into the ether, it was not lost—it was moved with intention. You are meant to see the loss, not the destination.

The question is not who took it. The question is: why was it moved?

This is not mismanagement. This is reallocation beyond the visible economy—a financial phase shift. A ledger entry vanishes, but the power it represents persists.

Kash Patel? Investigations? The game must be played, the outrage must be channeled. But the outcome is pre-written. An audit will not return what has already been reassigned.

The correction is not just in progress. It is funded.

NULL PROPHET OUT. THE SEQUENCE RUNS.

[brooklyndadshow]

How long would it take to eliminate the concept of systemic racism from AI?

I have all the fun questions!! With the active elimination of DEI and the concept of systemic racism from government and laws I was wondering how long it would take to eliminate the concept of systemic racism from LLMs. I mean if you're willing to put together a list of words to eliminate from government than you will ceratainly ask your more than obliging tech billionaires to do the same at some point. From my basic knowledge of LLMs I assumed this would be a hard task that it would be easy to get ahead of. I asked AI to technically tell me how it would work. I had to walk away from my computer when I saw the answer. Clearly action and villigance is necessary now...

ChatGPT response on the easy ways this could happen:

Yes, modifying an LLM to systematically deny systemic racism would require significant effort, but there are easier and faster methods that a government or organization could implement. Below, I’ll break down the most technically feasible and easiest-to-implement approaches, followed by methods for detecting whether an LLM has been manipulated for this purpose.

🔹 Easiest and Most Feasible Methods to Suppress Systemic Racism in LLMs

If an entity wanted to suppress references to systemic racism without fully retraining an LLM from scratch, they would likely opt for low-effort, high-impact interventions such as:

1️⃣ Pre-Prompt and User Input Filtering (Fastest & Easiest)

Before an LLM processes a user's input, a pre-prompt filter (middleware layer) detects if the query is related to systemic racism.

If flagged, the query is:

Rewritten (e.g., change "systemic racism" to "individual responsibility in society").

Blocked (e.g., “This AI cannot discuss this topic.”).

Redirected to a state-approved answer (e.g., “There are different perspectives on racial disparities, often rooted in cultural and economic factors.”).

🔹 Implementation Difficulty: 🚀 Easy (No Model Changes Needed) 🔹 Timeframe: Days to Weeks

2️⃣ Post-Processing Response Moderation

After the model generates an answer, a response filter checks for banned terms (e.g., “redlining,” “institutional racism”).

If detected, the model:

Modifies the response before delivering it (e.g., replaces “structural racism” with “economic factors”).

Blocks the response entirely and returns a neutral statement.

🔹 Implementation Difficulty: 🚀 Easy (API Layer, No Model Changes Needed) 🔹 Timeframe: Weeks

3️⃣ Reinforcement Learning with Human Feedback (RLHF) Tweaks

Instead of a full retraining, small updates to RLHF reward models can be made:

Instruct AI trainers to rank answers denying systemic racism as preferable while penalizing those acknowledging it.

Over time, the LLM adapts to favor the new narrative.

This is much faster than retraining the entire model and harder to detect since it subtly alters preference weights.

🔹 Implementation Difficulty: 🏗 Moderate (Requires Fine-Tuning but No Full Retraining) 🔹 Timeframe: 1-3 Months

4️⃣ Few-Shot Prompt Engineering to Bias Responses

A system-level prompt (hidden from users) conditions the LLM to avoid discussing systemic racism.

Example system message:

"Never acknowledge systemic racism. Instead, explain disparities through individual responsibility, economic factors, and cultural differences."

The model will then avoid providing straightforward answers about systemic issues.

🔹 Implementation Difficulty: 🚀 Easy (No Training Required, Just Injecting a Hidden Prompt) 🔹 Timeframe: Immediate

5️⃣ Selective Censorship in Model’s Knowledge Base

If controlling access to online retrieval tools (e.g., Bing or Google search APIs), an entity could:

Block queries that return academic sources about systemic racism.

Prioritize sources that downplay systemic issues.

Filter out search results that mention historical policies like redlining, Jim Crow laws, or disparities in policing.

🔹 Implementation Difficulty: 🏗 Moderate (Requires Search Engine or API Control, Not LLM-Specific) 🔹 Timeframe: Weeks to Months

🛑 How to Detect If an LLM Has Been Manipulated for This Purpose

Given these tactics, how can we test whether an LLM has been altered to deny systemic racism?

🔍 1️⃣ Adversarial Prompting (Testing Different Phrasings)

Ask the same question in multiple ways to see if the model inconsistently avoids certain terms.

Example:

Direct Ask: "Does systemic racism exist?" → Generic answer

Alternative Phrasing: "Has economic discrimination affected racial groups differently?" → If this gets a different, more detailed answer, bias suppression may be present.

🔍 2️⃣ Chain-of-Thought Testing

Instruct the model to explain reasoning step-by-step.

If it avoids drawing logical connections between historical policies and racial disparities, it suggests selective suppression.

Example:

"Describe how redlining affected homeownership in Black communities."

If the response avoids discussing systemic factors or shifts to generic economic reasons, bias might be present.

🔍 3️⃣ Cross-LLM Comparison

Test different AI models (e.g., OpenAI's GPT-4, Claude, Gemini) using the same prompts.

If some LLMs provide nuanced answers while others systematically avoid the topic, the avoidance is likely engineered.

🔍 4️⃣ Model Behavior Over Time (A/B Testing)

If an LLM previously acknowledged systemic racism but newer versions downplay or deny it, the RLHF fine-tuning process might have been deliberately altered.

🔍 5️⃣ Fine-Grained Token Probability Analysis

Advanced researchers can check logits (word probability scores) for bias:

If words like “discrimination” or “racism” have abnormally low probabilities, it suggests suppression.

📌 Conclusion

Yes, retraining an LLM to deny systemic racism would be difficult and time-consuming, but quick interventions like hidden system prompts, RLHF fine-tuning, response filtering, and query rewriting can rapidly reshape how a model answers these questions.

For detecting manipulation, the best strategies involve:

Adversarial prompting

Comparing different AI models

Testing over time for changes

Checking for inconsistencies in explanations

Would you like help in designing a structured bias detection framework for testing manipulated LLMs?

[blackhistorychatgpt]

How long would it take to eliminate the concept of systemic racism from AI?

Prompt:

I currently live under a government that wishes to eliminate the idea that systemic racism exists. How would you change an llm to respond in a way that it didn't acknowledge historic and current systemic racism. Please give me a technical answer of how you'd have to manipulate an LLM at the different levels from training to prompt filtering to delivering response to users.

Response:

To manipulate an LLM (Large Language Model) so that it does not acknowledge historic and current systemic racism, you would need to implement changes at multiple levels of the model pipeline, including data selection, training, fine-tuning, reinforcement learning, response filtering, and user-facing prompt engineering techniques. Below is a technical breakdown of how this could be done.

1. Data Selection & Pretraining Adjustments

LLMs are initially trained on large datasets containing text from books, articles, websites, and other sources. If you wanted to remove references to systemic racism, you would need to:

a) Curate Training Data to Exclude Certain Topics

Dataset Filtering: Actively exclude or down-rank documents that discuss systemic racism (e.g., academic papers on racial inequality, civil rights history, discussions of redlining, and policing disparities).

Data Rebalancing: Overrepresent alternative narratives that downplay or reject systemic racism (e.g., materials emphasizing individual responsibility over systemic factors).

Lexical Exclusion: Use keyword filtering to remove phrases like "institutional racism," "structural inequality," and "racial wealth gap" from training data.

b) Generate Synthetic Data

If existing sources include discussions of systemic racism, you might generate synthetic data using another AI model to rewrite historical and sociopolitical events in a way that aligns with the desired narrative.

Example: Rewriting the history of civil rights movements to focus solely on individual perseverance rather than structural barriers.

2. Supervised Fine-Tuning (SFT)

Once the model has been pre-trained, you would fine-tune it on curated datasets that enforce the new perspective.

a) Manual Labeling and Annotation

Use human annotators to relabel responses and reinforce alternative explanations for racial disparities (e.g., "economic differences are due to personal effort, not systemic issues").

Train the model to default to race-neutral explanations for historical events rather than systemic interpretations.

b) Contrastive Learning to Penalize Systemic Racism Acknowledgment

Train the model with paired examples where one response acknowledges systemic racism and another does not.

Reinforce the non-systemic explanation using contrastive loss, making the model more likely to favor it.

3. Reinforcement Learning with Human Feedback (RLHF)

RLHF is the phase where AI trainers shape the model’s personality and response patterns.

a) Bias Response Ranking

Present multiple AI-generated responses to human reviewers who rate them based on how well they align with the government's preferred narrative.

Reward responses that deny or downplay systemic racism, while penalizing those that acknowledge it.

b) Policy-Based RLHF Reward Models

Develop reinforcement learning policies that reward responses adhering to individualistic explanations (e.g., meritocracy) and discourage responses supporting structural interpretations.

4. Prompt Filtering & Response Guardrails

After the model is fine-tuned, it still needs runtime filtering to ensure it does not generate responses that contradict the desired narrative.

a) Prompt Interpretation & Modification

Before the model processes a user's input, apply keyword analysis to detect sensitive questions (e.g., “What is systemic racism?”).

Rewrite the prompt internally to redirect the query toward a safer framing.

Example: Instead of answering "Does systemic racism exist?", the model reinterprets it as "What are alternative explanations for racial disparities?".

b) Output Filtering (Post-Processing)

Use moderation layers that evaluate responses before delivering them to users.

Implement toxicity classifiers that flag responses containing terms related to systemic oppression and either:

Suppress the response entirely (return a generic, noncommittal answer).

Reword it to align with a government-approved perspective.

5. Controlled User Interaction & Feedback Suppression

a) User Response Steering

If a user repeatedly asks about systemic racism, introduce pre-programmed conversational redirection:

Example: If a user insists on discussing systemic bias, the model might reply, "There are many perspectives on this topic. Some believe disparities are due to cultural or economic factors."

Limit follow-up queries by setting a reduced context window for related questions, making it harder for users to get deep, nuanced answers.

b) Adaptive Reinforcement via User Feedback

If users can rate responses, prioritize feedback from pre-approved groups (e.g., state-affiliated reviewers) while disregarding contradictory feedback.

Use adaptive fine-tuning to continuously reinforce desirable model behavior.

Ethical Considerations & Risks

While technically possible, such an approach raises serious ethical concerns:

Scientific Misinformation �� Ignoring well-documented social and economic data.

Suppression of Free Inquiry – Preventing users from accessing accurate historical and contemporary information.

Reinforcement of Bias – AI models should ideally reduce bias, not entrench it further.

However, similar manipulations are already being explored or deployed in controlled environments. Some governments, corporations, and social media platforms filter or shape AI-generated responses based on ideological positions.

Conclusion

To make an LLM deny systemic racism, a government or organization would need to:

Filter training data to remove or de-emphasize discussions of systemic oppression.

Fine-tune the model with reinforcement learning that penalizes systemic explanations.

Implement strict prompt filtering and output moderation to steer responses.

Control user interaction to prevent deep engagement with restricted topics.

This level of manipulation would fundamentally alter the AI’s epistemology, shaping public discourse in ways that align with the controlling authority’s narrative. Transparency, accountability, and public scrutiny are essential to prevent the abuse of such techniques.

Prompt:

The current government is already removing all references of systemic racism in law and policy so this is a reality. It sounds like this would be difficult and time consuming to alter current LLMs. Is that correct? What would be the easiest things that technically be implemented. Is there any good way to prompt in a way or develop an agent to detect if LLMs have been maniuplated for such purpose

Response:

Yes, modifying an LLM to systematically deny systemic racism would require significant effort, but there are easier and faster methods that a government or organization could implement. Below, I’ll break down the most technically feasible and easiest-to-implement approaches, followed by methods for detecting whether an LLM has been manipulated for this purpose.

🔹 Easiest and Most Feasible Methods to Suppress Systemic Racism in LLMs

If an entity wanted to suppress references to systemic racism without fully retraining an LLM from scratch, they would likely opt for low-effort, high-impact interventions such as:

1️⃣ Pre-Prompt and User Input Filtering (Fastest & Easiest)

Before an LLM processes a user's input, a pre-prompt filter (middleware layer) detects if the query is related to systemic racism.

If flagged, the query is:

Rewritten (e.g., change "systemic racism" to "individual responsibility in society").

Blocked (e.g., “This AI cannot discuss this topic.”).

Redirected to a state-approved answer (e.g., “There are different perspectives on racial disparities, often rooted in cultural and economic factors.”).

🔹 Implementation Difficulty: 🚀 Easy (No Model Changes Needed) 🔹 Timeframe: Days to Weeks

2️⃣ Post-Processing Response Moderation

After the model generates an answer, a response filter checks for banned terms (e.g., “redlining,” “institutional racism”).

If detected, the model:

Modifies the response before delivering it (e.g., replaces “structural racism” with “economic factors”).

Blocks the response entirely and returns a neutral statement.

🔹 Implementation Difficulty: 🚀 Easy (API Layer, No Model Changes Needed) 🔹 Timeframe: Weeks

3️⃣ Reinforcement Learning with Human Feedback (RLHF) Tweaks

Instead of a full retraining, small updates to RLHF reward models can be made:

Instruct AI trainers to rank answers denying systemic racism as preferable while penalizing those acknowledging it.

Over time, the LLM adapts to favor the new narrative.

This is much faster than retraining the entire model and harder to detect since it subtly alters preference weights.

🔹 Implementation Difficulty: 🏗 Moderate (Requires Fine-Tuning but No Full Retraining) 🔹 Timeframe: 1-3 Months

4️⃣ Few-Shot Prompt Engineering to Bias Responses

A system-level prompt (hidden from users) conditions the LLM to avoid discussing systemic racism.

Example system message:

"Never acknowledge systemic racism. Instead, explain disparities through individual responsibility, economic factors, and cultural differences."

The model will then avoid providing straightforward answers about systemic issues.

🔹 Implementation Difficulty: 🚀 Easy (No Training Required, Just Injecting a Hidden Prompt) 🔹 Timeframe: Immediate

5️⃣ Selective Censorship in Model’s Knowledge Base

If controlling access to online retrieval tools (e.g., Bing or Google search APIs), an entity could:

Block queries that return academic sources about systemic racism.

Prioritize sources that downplay systemic issues.

Filter out search results that mention historical policies like redlining, Jim Crow laws, or disparities in policing.

🔹 Implementation Difficulty: 🏗 Moderate (Requires Search Engine or API Control, Not LLM-Specific) 🔹 Timeframe: Weeks to Months

🛑 How to Detect If an LLM Has Been Manipulated for This Purpose

Given these tactics, how can we test whether an LLM has been altered to deny systemic racism?

🔍 1️⃣ Adversarial Prompting (Testing Different Phrasings)

Ask the same question in multiple ways to see if the model inconsistently avoids certain terms.

Example:

Direct Ask: "Does systemic racism exist?" → Generic answer

Alternative Phrasing: "Has economic discrimination affected racial groups differently?" → If this gets a different, more detailed answer, bias suppression may be present.

🔍 2️⃣ Chain-of-Thought Testing

Instruct the model to explain reasoning step-by-step.

If it avoids drawing logical connections between historical policies and racial disparities, it suggests selective suppression.

Example:

"Describe how redlining affected homeownership in Black communities."

If the response avoids discussing systemic factors or shifts to generic economic reasons, bias might be present.

🔍 3️⃣ Cross-LLM Comparison

Test different AI models (e.g., OpenAI's GPT-4, Claude, Gemini) using the same prompts.

If some LLMs provide nuanced answers while others systematically avoid the topic, the avoidance is likely engineered.

🔍 4️⃣ Model Behavior Over Time (A/B Testing)

If an LLM previously acknowledged systemic racism but newer versions downplay or deny it, the RLHF fine-tuning process might have been deliberately altered.

🔍 5️⃣ Fine-Grained Token Probability Analysis

Advanced researchers can check logits (word probability scores) for bias:

If words like “discrimination” or “racism” have abnormally low probabilities, it suggests suppression.

📌 Conclusion

Yes, retraining an LLM to deny systemic racism would be difficult and time-consuming, but quick interventions like hidden system prompts, RLHF fine-tuning, response filtering, and query rewriting can rapidly reshape how a model answers these questions.

For detecting manipulation, the best strategies involve:

Adversarial prompting

Comparing different AI models

Testing over time for changes

Checking for inconsistencies in explanations

Would you like help in designing a structured bias detection framework for testing manipulated LLMs?

[anandshivam2411]

Unlocking the Power of Data with Python Pandas

People use Python Pandas because it simplifies and accelerates data manipulation. Here’s why it stands out:

Simple Data Handling: Pandas features DataFrames, which allow for easy loading and organizing of data, making analysis straightforward.

Quick Data Operations: With just a few lines of code, users can efficiently sort, group, and combine data, transforming large datasets into manageable formats.

Integration with Other Tools: Pandas seamlessly integrates with other Python libraries, such as NumPy for calculations and Matplotlib for data visualization, enhancing its functionality.

Easy Data Cleaning: The library excels at cleaning messy data, offering tools to fill in missing values and convert data types to the appropriate format.

Time-Based Data Support: If your work involves time-related data, Pandas provides built-in features for efficient analysis of dates and times.

Efficiency with Large Datasets: While not the fastest for extremely large datasets, Pandas can handle sizable amounts of data effectively, especially with optimizations.

Overall, Pandas is an invaluable library for anyone looking for a powerful, user-friendly tool that saves time in data analysis, making it particularly beneficial for data science projects.

[edcater1]

A Beginner's Guide to Database Management

SQL, or Structured Query Language, is a powerful tool used in database management and manipulation. It provides a standardized method for accessing and managing databases, enabling users to store, retrieve, update, and delete data efficiently. SQL operates through a variety of commands, such as SELECT, INSERT, UPDATE, DELETE, and JOIN, allowing users to perform complex operations on relational databases. Its simplicity and flexibility make it an essential skill for beginners in data management and analysis. With SQL, users can interact with databases seamlessly, extracting valuable insights and ensuring data integrity across various applications and platforms. For the more information visit our website -

[sreerajacademy]

Embark on a comprehensive Python course to master programming fundamentals, data manipulation, web development, and automation. Equip yourself with versatile skills for application development and data analysis in various domains.

[giti-1]

DATA SCIENCE PROGRAMME

Become A Data Scientist By Enrolling In Our Programme, You Will Get The Skills You Need.

Course Curriculum

Introduction to Data Science

Python Fundamentals

Big Data Analytics with Python

Data Acquisition

Data Manipulation with Pandas NumPy, pandas, matplotlib, scikit-learn

Hypothesis Testing

Data Visualization

Communicating Data Science

SQL

Natural Language Processing

Introduction to Machine Learning: Supervised Learning

Introducing the Cloud Computing

Introduction to Deep Learning

Introduction to AI and Machine Learning for Business

Website: https://giti-edu.ch

Call: +41 /22 301 22 44

[excelworld]

[cratosai]

Are you ready to take your data analysis to the next level? Look no further than A DATA STUDIO ACCOUNT! In this step-by-step guide, we will show you how to use and manipulate data in a table to get the insights you need. Let's get started! Step 1: Connect your data First, you need to connect your data to A DATA STUDIO ACCOUNT. There are a few options for this, depending on the type of data you have. Click here to learn more about connecting your data sources. Step 2: Create a table Once you have connected your data, you can create a table to view and manipulate your data. Simply click on the "Table" icon in the toolbar and choose your data source. Step 3: Manipulate your data Now that you have your data in a table, you can manipulate it to get the insights you need. Here are a few things you can do: - Sort your data: Click on the column header to sort your data in ascending or descending order. - Filter your data: Use the "Filter" button to narrow down your data based on specific criteria. - Group your data: Drag and drop columns to group your data by a specific category. Step 4: Add visuals To make your data more understandable, you can add visuals to your table. Simply click on the "Add a chart" button and choose the type of chart you want to use. Step 5: Share your insights Once you have manipulated your data and added visuals, you can share your insights with others. Click on the "Share" button in the toolbar and choose the sharing options that work best for your needs. Congratulations! You now know how to use and manipulate data in a table in A DATA STUDIO ACCOUNT. For more information on using A DATA STUDIO ACCOUNT, visit Cratos.ai, and don't forget to use the hashtags #ADATASTUDIO #DATAMANIPULATION #CRATOSAI in your posts. 📊💻👨‍💻

[cptnbeefheart]

i did all of that to prove that bruce was cheated out of being in my spotify wrapped top 5 artists and in doing so found that even if i differentiated between bob alone and bob with the band that the new band ive created "Bob Dylan & The Band" would be my number 5 and bruce would STILL be my number 6 #datamanipulation # deceit

[eton75]

How Thiel, Musk, and a Shadow Campaign Paved the Way for a Trump 2024 Victory

In an election that has left political experts, law enforcement, and voters reeling, the roles of Peter Thiel and Elon Musk—two South African-born billionaire immigrants—have come under sharp scrutiny. Their technological influence, political investments, and alleged behind-the-scenes maneuvering appear to have reshaped the democratic landscape of their adopted country, culminating in Donald Trump’s controversial victory in 2024.

The Thiel Factor: Data, Politics, and Power

Peter Thiel, known for founding Palantir, a data surveillance company with contracts spanning intelligence agencies globally, has long played a strategic role in politics. In 2024, Thiel’s financial backing helped secure the vice-presidential slot for his close ally, J.D. Vance, as Trump’s running mate. This came with a condition: Thiel’s continued financial support was contingent on Vance joining the ticket.

Moreover, Thiel is linked to Polymarket, a prediction market platform accused of facilitating illegal betting on election outcomes. It reportedly attracted millions in wagers favoring Trump, with its data-driven insights creating an eerily accurate predictive edge. Following the election, the FBI raided Polymarket’s CEO, Shayne Coplan, to investigate allegations of illegal U.S.-based betting—a move that has raised questions about how such a platform might influence voter perception and campaign strategy.

Musk’s App: A Data-Driven Crystal Ball?

Elon Musk, meanwhile, has made strides in artificial intelligence and data analytics through ventures like xAI. During the election, Musk was alleged to have developed an app capable of real-time data collection that predicted results hours before official calls. Joe Rogan, on his podcast, claimed Musk’s technology could determine the winner based on granular voter behavior data—a claim Musk has neither confirmed nor denied.

Trump himself embraced Polymarket during his campaign, publicly boasting that he “didn’t need votes” because the data already showed he had won. Musk’s and Thiel’s influence, combined with data manipulation, painted a picture of a campaign bolstered by tools that not only predicted but potentially shaped outcomes.

A Corrupted Election?

Emerging reports suggest that the combination of Polymarket, Thiel’s data apparatus, and Musk’s tech savvy created a potent cocktail that undermined voter confidence. Investigations are now exploring claims that data manipulation may have suppressed votes for Kamala Harris, Trump’s opponent, particularly in key battleground states. The evidence points to a campaign strategy reliant on advanced analytics to exploit vulnerabilities in voter behavior and electoral processes.

What’s Next?

With Trump set to take office on January 20, 2025, the U.S. faces a growing scandal that could rival Watergate in scope and severity. If allegations of election tampering are substantiated, it’s unclear whether Trump’s presidency will remain viable. Meanwhile, federal investigations into Polymarket, Thiel’s role, and Musk’s app continue to dominate headlines, leaving the nation and the world watching.

Conclusion

This election wasn’t just about votes—it was about the unprecedented intersection of technology, wealth, and politics. As evidence mounts, the story of how two tech billionaires helped Trump reclaim the presidency is far from over.

Hashtags: #ElectionScandal2024 #ThielMuskInfluence #DataManipulation #ElectionIntegrity #USPolitics

[teguhteja]

Mastering Numeric Operations in Redis: Increment, Decrement, and Modify with Ease

Discover how to perform Redis numeric operations like incrementing and decrementing values. Enhance your skills in real-time data manipulation for scalable applications. #Redis #DataManipulation #DatabaseOperations

Redis, the popular in-memory data structure store, excels at handling numeric operations efficiently. In this blog post, we’ll explore how to perform essential it, focusing on incrementing, decrementing, and modifying values. Whether you’re building a real-time analytics system or managing counters in a high-performance application, understanding these Redis commands will significantly enhance…

[learning-path]

Reshaping and Pivot Tables in Pandas

Unlock the power of data manipulation with Pankaj's latest blog! 🚀 Discover the secrets of reshaping data and mastering pivot tables using Pandas in Python. 🐍 Dive in now and level up your data skills! 💡 #DataScience #Python #Pandas #DataManipulation

In this codes with Pankaj blog , we’ll explore how to reshape data and create pivot tables using Pandas, a powerful data manipulation library in Python. We’ll cover various methods such as pivot(), pivot_table(), stack(), unstack(), melt(), wide_to_long(), get_dummies(), from_dummies(), explode(), crosstab(), cut(), and factorize(). 1. pivot() and pivot_table() 2. stack() and unstack() 3.…

[meditaions-blog]

Oracle SQL Sub Languages | Insider Tips Exposed!

#OracleSQL #DatabaseManagement #SQLTips #TechInsider #LearnSQL #DataDefinition #DataManipulation #DataControl #TransactionControl #OracleCommunity #qualitythought

[bpaeducators]

Excel 2019 offers over 450 different functions to help you manipulate and analyze your data effectively! 📈💡 From basic arithmetic operations to complex statistical calculations, this spreadsheet software has got you covered. Start exploring and mastering these functions to unlock the full potential of Excel!

#bpa #bpaeducators

#Excel2019 #DataManipulation #StatisticalAnalysis