Pandas jobs

I have a cleaned-but-raw dataset plus a small MNIST subset waiting for a full exploratory and modelling pass in Python. My goal is to understand its underlying structure through several classic unsupervised techniques—density estimation, Gaussian Mixture Models trained with the Expectation–Maximisation algorithm, PCA for dimensionality reductio...single command or notebook execution. – All figures render without manual tweaks and are saved to disk. – Explanations are written in plain English, no unexplained jargon. – Delivery is within the next few days (ASAP), including one quick iteration if minor tweaks are needed. I will provide the datasets the moment we start; you handle the rest using Python and common libraries such as NumPy, Pandas, scik...

I have a sizeable set of consumer-level data that needs to be examined with a clear, methodical approach. My priority is rigorous data analysis that turns raw records into well-supported insights I can act on right away. What I will provide • A cleaned C...clear, quick visual snapshots (charts or dashboards) that make the results easy to present internally. Acceptance criteria 1. All code is fully commented and runs end-to-end on the supplied dataset. 2. Insights are backed by numbers and clearly referenced to the source fields. 3. Final deliverables arrive in an agreed-upon shared folder and open without errors. Tools you are comfortable with—Pandas, NumPy, R tidyverse, Looker Studio, Tableau—let me know; I’m flexible as long as the outcome is solid, t...

Freelance AI & Predictive Maintenance Expert (Vibration Analysis) We are looking for a specialized Data Scientist or AI Engineer to develop a Predictive Maintenance solution for a critical production line. The project involves analyzing vibration data from a hi...anomaly detection. Signal Processing: Apply signal processing techniques to distinguish between operational noise and actual mechanical degradation. Validation: Evaluate model performance using precision/recall metrics focused on reducing false positives in a factory setting. Required Qualifications Proven track record in Predictive Maintenance (PdM) or Industrial AI. Deep expertise in Python (Pandas, Scikit-learn, SciPy, or Signal Processing libraries). Strong experience in Time-Series Analysis and vibration-base...

...100% Ingredient min/max limits Regularization to prevent micro-dosing overfitting Performs residual minimization after re-analysis. Supports iterative refinement (Trial-1 → Trial-2 → Trial-3 loop). This is an inverse analytical modeling engine, not a surface-level similarity system. Mandatory Requirements (No Exceptions) You must have: Strong Python experience (minimum 4–5 years) Advanced Pandas and NumPy knowledge Experience with scientific or analytical datasets Experience implementing regression models Experience with constrained optimization (linear or nonlinear) Understanding of L1/L2 regularization Experience with numerical modeling (SciPy optimize or similar) Ability to clearly explain statistical calibration logic Clean modular code archite...

...have a raw customer dataset and need a clear, reliable descriptive analysis that turns those rows and columns into actionable facts. The goal is to understand what is happening right now inside the customer base—basic profiles, usage statistics, purchase frequencies, average order values, geographic spreads, and any other high-level patterns hiding in plain sight. You may work in Excel, Python (pandas, matplotlib, seaborn), R, or a BI tool such as Tableau or Power BI; choose whichever environment lets you move fastest while keeping results transparent and reproducible. Deliverables • A cleaned, well-documented version of the original customer data • A concise report (PDF or notebook) explaining methods and key descriptive findings • Clear visualisatio...

...have a raw customer dataset and need a clear, reliable descriptive analysis that turns those rows and columns into actionable facts. The goal is to understand what is happening right now inside the customer base—basic profiles, usage statistics, purchase frequencies, average order values, geographic spreads, and any other high-level patterns hiding in plain sight. You may work in Excel, Python (pandas, matplotlib, seaborn), R, or a BI tool such as Tableau or Power BI; choose whichever environment lets you move fastest while keeping results transparent and reproducible. Deliverables • A cleaned, well-documented version of the original customer data • A concise report (PDF or notebook) explaining methods and key descriptive findings • Clear visualisatio...

...algorithms Identify marker compounds and decision rules Create probabilistic ingredient combination models Generate optimized recipe outputs (percentage-based, normalized to 100%) Structure the system in a modular and scalable Python architecture Build clean, documented code suitable for long-term expansion Required Skills (Mandatory) Strong Python programming experience (3+ years) Advanced Pandas & NumPy knowledge Scientific data processing experience Experience working with structured chemical datasets Algorithm development experience Data normalization & similarity scoring logic CSV / Excel data parsing Clean code & modular architecture mindset Strong Plus Experience with chromatography or GC-MS data Background in analytical chemistry Experience...

...free-text notes, JSON logs, or mixed-format exports from third-party tools. Core objectives • Parse and normalise those semi-structured sources. • Automate the data-entry and clean-up pipeline so I never touch a spreadsheet again. • Run exploratory and descriptive analysis, then surface results through concise reports or dashboards. I am language-agnostic, but Python with libraries such as Pandas, spaCy, or LangChain would fit well; feel free to propose alternatives if they serve the same purpose. Everything should live in a well-organised GitHub repo, complete with clear commit history and a README that lets me spin the agents up in minutes. Deliverables • Fully functional agent code in GitHub • Setup instructions and dependency file &bu...

I have a raw sales dataset and I want a clear, story-driven descriptive analysis that highlights how my revenue is trending, who is buying, and how each product is performing. You will start by cleaning and structuring the file, then explore it with whichever tools you prefer—Excel, SQL, Python (Pandas, Matplotlib, Seaborn) or a BI platform such as Power BI or Tableau—as long as the final insights are easy for a non-technical stakeholder like me to grasp. Statistical jargon is fine in the notebook or appendix, but the main findings need plain-English explanations. Deliverables I expect: • A short written report (PDF or slide deck) summarising revenue trends, customer demographics and product performance, supported by clear charts. • A reproducible worki...

...highest-probability spreads before committing capital. The application must stream data with minimal latency, place and manage orders end-to-end, and keep me fully informed through a clean analytics dashboard showing live PnL, historical performance, and risk metrics. Flexibility to plug in additional exchanges later is important, so please structure the code modularly (I’m fine with Python + CCXT, asyncio, pandas, TensorFlow/​PyTorch, or any comparable stack you prefer). Deliverables • Fully documented source code and requirements file • Exchange connectors with configurable API keys and fee settings • AI/ML module that ranks opportunities and adapts to changing market conditions • Automated trading engine with position sizing, stop-loss, and ...

I need a robust Python function that can log in to a password-protected site, navigate to a given page, locate the primary table, and convert it into a clean Pandas DataFrame before writing the result to CSV. The same function must work on each URL I provide, and ideally on any future page built on the same template, so please keep the approach modular and scalable. Because the pages sit behind authentication, the username and password can be hard-coded directly in the script; no interactive prompts or external files are necessary this time. Anti-blocking tactics (session persistence, realistic headers, controlled request pacing, etc.) are mandatory—I want to be able to run the notebook repeatedly without getting shut out. Deliverables • A Jupyter notebook (.ipynb) co...

I regularly receive batches of transaction records in CSV format from a secured sender API. For each batch I need those files programmaticall...success log. Because only the sender endpoint is authenticated, you’ll handle token retrieval or key injection on that side, then push the unaltered CSV to the receiver. Robust error handling, retry logic, and a concise report of successes / failures are important so we can audit transfers later. Typical runs are small, but speed matters. If you are comfortable scripting this in Python (requests, pandas), Bash with curl, or another language of your choice and can be on-call for ad-hoc transfers, I’d like to work with you. Please mention similar API ingestion or file-relay work you’ve done and how quickly you can turn arou...

...column excel sheet that we can use for targeted outreach. Processes Created with Claude AI: A structured workflow was developed that combines manual or AI-assisted research of Diary Directory with ChatGPT-based data extraction and email estimation. A defined priority scoring model (based on recency, named individuals, category type, and email confidence) assigns contacts to Tier 1–4. Python (pandas + openpyxl) is used to automatically generate a CSV file and a fully formatted 5-tab Excel workbook with standardized styling and sorting. Clear rules were created for handling multi-person articles, account wins, and company-only announcements to ensure consistency. What Did Not Work / Pain Points: The process remained partially manual and tool-dependent. Email addresses are...

I need a Python-based trading bot that executes a clean trend-following strategy and feeds its output to a lightweight web dashboard. The trading logic should automatically detect and ride upward or downward trends, handle position sizing, manage risk with configurable stop-loss / take-profit rules, and run with as few external dependencies as practical (NumPy, Pandas, TA-Lib are fine). Exchange connectivity is flexible: as long as live orders and historical price data can flow reliably, I’m happy to integrate through CCXT or a direct API of a major venue such as Binance, Coinbase, or Kraken—let me know which you can implement fastest. The dashboard is just as important as the core bot. Through it I want to see: • Real-time performance metrics (open PnL, equity ...

...roughly 3 500 individual race entries across 17 political parties in six regions, coping smoothly with AJAX calls, pagination and any CAPTCHA or session refreshes. • AI-powered processing – once ingested, the stream needs real-time de-duplication, error checking, normalisation of party names/regions and basic predictive analysis. Leveraging APIs such as OpenAI, Claude or Vertex for cleaning and pandas / scikit-learn for statistical work is fine; I’m happy to fund premium tiers where that improves reliability and speed. • Visual output – the cleaned dataset should feed straight into a powerful dashboard ( Power BI, Tableau, Florish, Looker Studio, Streamlit, Plotly Dash, Superset or similar) that automatically refreshes and renders laser-sharp bar c...

...86-million-dollar portfolio and need them transformed into clear, defensible investment insights. The core purpose is to evaluate current and prospective opportunities, not merely spot trends or forecast performance. Expect to dig into raw transactional data, balance sheets, and market feeds, then surface the strengths, weaknesses, and hidden potential of each holding. You are free to work in Python (Pandas, NumPy, SciPy), R, SQL, Excel Power Query, or any other toolkit you trust, so long as the outcome is reproducible. I will provide the data in CSV and relational-database dumps; secure transfer protocols are already in place. Deliverables • Cleaned and well-documented dataset (with transformation scripts) • Analytical report highlighting valuation metrics, risk ...

...analyze related search keywords * Visualize trends for better insights ## Tools & Technologies * Python * Pytrends (Google Trends API Wrapper) * Pandas * NumPy * Matplotlib * Seaborn * Jupyter Notebook ## Project Structure ``` ├── google data analysis # Main notebook ├── # Project documentation ``` ## Key Analysis * Interest over time (trend analysis) * Country-wise keyword comparison * Related queries and keywords analysis * Data visualization using line charts and bar plots ## How to Run 1. Clone the repository 2. Install required libraries: ```bash pip install pytrends pandas matplotlib seaborn ``` 3. Open the Jupyter Notebook 4. Run the cells step by step ## Results & Insights The project reveals how se...

...system (Python 3.11+, aiohttp/httpx/Scrapy/Playwright). • Build deduplication & change-detection logic using hash comparison and timestamps. • Design and connect central database (PostgreSQL + SQLite) to store unique company records. • Integrate proxy rotation and throttling (BrightData/Luminati or similar). • Implement data normalization using ftfy, unidecode, python-phonenumbers, regex, and pandas. • Crawl Impressum pages to auto-fill missing fields (phone, fax, website). • Automate daily/weekly export to Excel / CSV using openpyxl. • Add basic monitoring dashboard (Streamlit) showing live progress, proxy health, and logs. • Deliver well-structured, documented, production-ready code. ⸻ Required Skills • Expert in Pytho...

...applied to the text and image sets. • Concise visual summaries—charts for the text, heat-maps or feature plots for the images—exported in high-resolution formats I can drop straight into presentations. • A short written report (PDF or Markdown) highlighting key findings, unusual correlations and any recommendations that emerge from the analysis. Feel free to lean on standard libraries such as pandas, NumPy, NLTK/spaCy, OpenCV or PyTorch-based utilities—whatever helps you surface meaningful insights quickly and reproducibly. If you have other preferred tools that suit mixed unstructured data, I am open to them so long as the final deliverables remain easily reusable. I’ll supply a structured folder containing sub-directories for /text and /i...

...visualisations—simple, insightful charts or graphs (box-and-whisker, histogram, or another format you recommend) that make the quartile story obvious at a glance. • A brief explanatory note or slide summarising what the numbers and visuals reveal. I will provide the raw survey data in CSV as soon as we start; please let me know if you’d prefer another format. You are free to use Excel, Python (pandas, matplotlib, seaborn), R, or similar tools—whatever lets you work quickly and produce clean output. Deliverables: the calculation workbook or script, the final visuals in PNG or PDF, and the short written interpretation. If something in the data needs special handling—outliers, missing values—tell me early so we can decide how to treat it. Lookin...

...closed. • Ongoing performance tracking that aggregates P&L by symbol and by account, exports to CSV and displays a quick dashboard in a lightweight web UI. The code should be clean, modular and well-commented so I can extend it later. I already have API keys for my broker; please wire the solution to REST as well as WebSocket endpoints for market data. SQLite is fine for local storage—use Pandas for data handling and Plotly/Dash (or a similarly lean framework) for visual summaries. Acceptance criteria: 1. Master-slave architecture proven with three dummy accounts in a sandbox. 2. Average replication delay below 300 ms on local network. 3. Dashboard shows real-time positions and end-of-day reports without manual refresh. 4. README with setup steps and ...

I need a lightweight Python desktop app built with the CustomTkinter framework. The interface will have two clear tabs: Tab 1 – Excel I/O A small form lets me type or select values, then write them straight into a designated workbook and sheet. Reading should be just as simple: when I choose an existing record, the form fields repopulate instantly from the same file. Think pandas or openpyxl behind the scenes, but the user sees only clean entry boxes and a save / load trigger. Tab 2 – Live Bar Chart Using the same workbook, the second tab must render a bar chart that refreshes in real time whenever the underlying Excel data changes. A short polling interval or a file-watcher is fine as long as the graph updates smoothly without freezing the UI. Matplotlib, Plotly, ...

I have a set of finance-related CSV files that need to be explored, cleaned, and summarised. The goal is strictly descriptive analysis—think clear statistics, trends, and visual snapshots—without venturing into predictive modelling or prescriptive optimisation. All raw data will arrive as comma-separated files. You are free to use Python (pandas, NumPy, Matplotlib, Seaborn), R, Excel Power Query, or a comparable toolkit, as long as the workflow is reproducible and well-documented. Deliverables: • A concise cleaning script or notebook that imports each CSV, handles missing or inconsistent entries, and outputs a tidy dataset • A written summary (PDF or Markdown) of key descriptive metrics—averages, distributions, correlations, outliers—tailored to...

... Analyze performance (PnL, Sharpe, drawdown) Requirements Proven experience in algorithmic trading / quant development Strong Python programming Experience with QuantConnect or LEAN Engine Experience with IBKR API integration Understanding of US equities / ETFs markets Experience with backtesting frameworks Knowledge of trading risk management Nice to have Intraday or HFT strategies pandas / numpy / scipy Walk-forward optimization Experience in prop trading / hedge fund C# (LEAN) Project details Market: US stocks & ETFs Broker: Interactive Brokers Platform: QuantConnect / LEAN Strategy type: systematic / algorithmic Engagement: long-term collaboration possible To apply Please include: Relevant algo trading projects QuantConnect / LEAN experience IBKR ...

I’ll hand over three raw datasets—sales transactions, customer demographics, and product inventory—spanning several stores. Your task is to stitch them together, clean inconsistencies, and delve into them with Python. Using Pandas, NumPy, and Matplotlib (feel free to add Seaborn or Plotly if that speeds insight), uncover how buying behaviour shifts: • weekday versus weekend • month by month I’m interested in concrete, data-backed stories: which products spike on Saturdays, whether certain customer segments shop more mid-week, seasonal category swings, ticket size trends, and anything else you spot that helps me fine-tune promotions and staffing. Deliverables • A merged, tidy dataset ready for future modelling • A well-commented Ju...

I am working on a graduate-level project that involves mixed data types and I need one-on-one guidance to reinforce my skills—especially in data cleaning and preprocessing with Python. The focus will be on the practical, step-by-step application of Pandas, NumPy and Matplotlib while staying fully compliant with academic integrity guidelines; no AI-generated work is permitted. My most urgent challenges include: • Handling missing values • Removing duplicates • Dealing with outliers Beyond cleaning, I will also ask for advice on choosing suitable descriptive statistics, selecting the right statistical tests, and presenting results clearly. Expect questions on relationship analysis and time-series concepts as the project evolves. What I’m hoping ...

...insight. All raw data—from the general ledger and trial balance to recent expense spreadsheets—will be provided in Excel and CSV formats. Your core assignment is to build a robust financial-forecasting model that projects cash flow, revenue, and key cost drivers over the next 12–24 months. I’m open to the tools you prefer—whether that’s advanced Excel formulas, Power BI dashboards, Python with pandas, or a mix—so long as the output is easy for non-technical leadership to interpret. Deliverables I must see: • A dynamic forecast workbook or script with clearly labeled input cells and assumptions • Visual summaries (charts or dashboards) highlighting trends, break-even points, and risk scenarios • A brief walkthrough d...

To complete this assessment, a tutor must be able to work step-by-step as follows: first, import and clean the Excel dataset in Python (using Pandas), select six appropriate stocks, and compute monthly returns; second, calculate and interpret mean returns, standard deviations, Sharpe ratios (using the 0.1% monthly risk-free rate), and construct the covariance and correlation matrices; third, systematically generate all feasible long-only portfolio weight combinations (weights between 0 and 1 summing to 1), then compute each portfolio’s expected return, standard deviation (using matrix algebra , and Sharpe ratio, and plot the feasible set; fourth, identify and explain the minimum variance portfolio and maximum Sharpe ratio portfolio, and demonstrate how to mix the optimal risk...

...working Python program that respects every constraint my instructor set. Only the libraries covered in class may be imported—specifically NumPy, Pandas, and Matplotlib. A few lightweight SQL queries are acceptable, but no external packages beyond that list can appear in the final code. The assignment brief and its “AI usage” rules will be shared right after we start; the code must follow them to the letter. I need mid-level, object-oriented design, clear function separation, and comments in English so the grader can follow the logic. Deliverables • A single, well-commented .py file (or notebook, if advised in the brief) using only NumPy, Pandas, Matplotlib, and basic SQL. • A short README explaining how to run the script and where each req...

...insight. All raw data—from the general ledger and trial balance to recent expense spreadsheets—will be provided in Excel and CSV formats. Your core assignment is to build a robust financial-forecasting model that projects cash flow, revenue, and key cost drivers over the next 12–24 months. I’m open to the tools you prefer—whether that’s advanced Excel formulas, Power BI dashboards, Python with pandas, or a mix—so long as the output is easy for non-technical leadership to interpret. Deliverables I must see: • A dynamic forecast workbook or script with clearly labeled input cells and assumptions • Visual summaries (charts or dashboards) highlighting trends, break-even points, and risk scenarios • A brief walkthrough d...

...reliably scrapes the data I need. The next step is to layer in proper analytics and present the results through a cleaner, mobile–friendly frontend while leaving the core scraper intact. Python scope • Extend the existing code so the scraped dataset feeds directly into statistical analysis, mathematical modelling, and quick data-visualisation routines. • Prefer familiar libraries such as Pandas, NumPy/SciPy, Matplotlib or Plotly, but I’m open to alternatives if they suit the task better. • Keep the workflow end-to-end: once the scraper finishes, the calculations should run automatically and expose structured results ready for the UI. Frontend scope • Refresh the HTML/CSS (vanilla or lightweight framework) to give the dashboard a modern,...

...clearly-structured package) that scrapes the target sites, parses the useful fields with BeautifulSoup, cleans and reshapes the data in Pandas, and spits out tidy CSV or JSON files. 2. Automated file management—old files archived, new ones stamped and stored in the correct directory automatically. 3. Clear, commented code plus a brief README so future tweaks take minutes instead of hours. I’ll provide full site lists, sample output, and the current scripts once we start. Run-time speed matters less than reliability and maintainability, so favour readable logic over micro-optimisations. As long as it’s standard Python 3 and uses well-supported libraries (Requests, BeautifulSoup, Pandas, maybe pathlib or shutil for file handling), I’m happy. I...

I’m looking for focused help with data consulting—specifically ...I have raw information in hand but need an experienced mind to walk me through the best way to clean, explore, and interpret it so that I can make confident business decisions afterward. Here is what I have in mind: • Quick review of the dataset’s structure and quality • A live or recorded walkthrough of recommended analytical steps (tool-agnostic, though I’m comfortable with Excel, Python-pandas, or Power BI if you’d like to demonstrate) • A concise summary of key findings and next-step suggestions I can action immediately Please let me know your approach, typical turnaround for an initial consultation, and any examples of similar work you’ve guided. I&rsquo...

...Data Extraction (Python/Excel) – Ready for Quick Turnaround ​Hi there, ​I am a Computer Science Engineer with strong expertise in Python and Data Processing, making me an ideal fit for your PDF data extraction project. I understand you need financial data accurately extracted from various invoices into a clean Excel format. ​Why choose me? ​Automation for Accuracy: I can use Python libraries (like Pandas and PyPDF2/Camelot) to ensure 100% accuracy and handle multi-page invoices efficiently. ​Filtering Logic: I will implement a custom script to automatically filter out non-financial pages, ensuring only relevant data reaches the final sheet. ​Quick Turnaround: I understand you need this done fast. I can start immediately and deliver a standardized, error-free Excel workbook....

I need a freelancer who can handle the full cycle of financial analysis for my Guesty account. The first ...dashboard that I can access in real time. Once the data is flowing, I want meaningful financial analysis built on top—profitability, liquidity, and efficiency ratios are the primary focus, but I’m open to additional KPIs you believe will help me steer the business more effectively. You should be comfortable with API integration, data cleaning, and whichever analytics stack you prefer (Python + Pandas, Power BI, Tableau, etc.) as long as the end result is a clear, drill-down view of performance. Please outline your approach for connecting to the Guesty API, the tools you’ll use for analysis, and how you plan to present the findings so I can make faster, ...

...Build a Python notebook that loads the three models from Hugging Face (PyTorch backend), pipes the Arabic prompts through Garak, captures logits and full responses, and writes everything to tidy CSV files. • Include bilingual testing so the notebook can toggle between the original English prompts and their Arabic counterparts, allowing side-by-side success-rate comparison. • Produce a concise pandas analysis section that calculates and visualises attack success percentages per model and per language. • Document every step—from model acquisition commands to translation strategy and evaluation metrics—in markdown cells so the methodology is fully reproducible. Acceptance criteria • Notebook runs end-to-end on a fresh environment (tested wi...

...and surface clear, actionable trends. The sole objective is to analyze historical data—specifically inventory data—so our team can understand how stock levels have moved over time and where the pressure points sit. Here’s what I have: several years of SKU-level records housed in a SQL database, plus a few CSV extracts. I need you to pull and clean the data, run robust trend analysis in Python (Pandas, NumPy, preferably some Matplotlib or Seaborn visuals), and package the insights so they are easy for our planners to act on. Time-series forecasting or demand planning frameworks aren’t required right now, but please structure the code so we could layer those in later if needed. Deliverables: • Well-documented Python notebook or script that connects to...

I need a seasoned statistician who can move comfortably between classical regression techniques and modern Convolutional Neural Networks. The project centres on predictive analytics: you will build, compare and explain regression-based models, explore where a CNN adds value, and present the insights through clear, publication-ready visualisations created in Python (think pandas, scikit-learn, TensorFlow/Keras, matplotlib, seaborn or Plotly—use what fits best). We will begin with a brief video call so I can walk you through the dataset, the business question and the success metrics. After that, you will take full ownership of data preparation, model selection, training, validation and visual storytelling. Expect to hand back clean, well-commented notebooks and graphics that a ...

I need an expert in Python to help with data analysis and processing tasks, specifically focused on unstructured data such as text or images. The ideal candidate should be proficient in Python libraries and tools designed for handling unstructured datasets. Your role will involve creating scripts or sol...analysis and processing tasks, specifically focused on unstructured data such as text or images. The ideal candidate should be proficient in Python libraries and tools designed for handling unstructured datasets. Your role will involve creating scripts or solutions to extract insights, patterns, or other relevant analyses from the data provided. If you have a strong command over libraries like Pandas, NumPy, or specialized tools for text and image processing, I’d love to ...

...inference are a priority. The task breaks down naturally into three parts. First, capture and label representative WLAN traffic—public datasets are fine as a starting point, but I also want a small tool that lets me pipe raw pcap streams into the training set so the system can learn my network's quirks. Second, build and train the detection engine: a well-commented Python project that leverages scapy, pandas, and the scikit-learn implementation of Random Forest, which is my preferred choice for its balance of performance and interpretability. Third, wrap the model in a daemon that watches the interface, raises an alert (syslog plus a webhook) when an attack pattern is scored above the chosen threshold, and writes a short JSON report for later forensics. Deliverables ...

I need an experienced Python trading-bot developer to optimize and refactor a live async trading bot connected to REST & WebSocket APIs, which currently slows under load and misses ticks/orders. The task includes profiling bottlenecks, improving async/WebSocket performance, optimizing pandas & SQLite usage, and ensuring real-time execution. Goal: <200 ms tick-to-order latency, zero missed ticks, clean refactored code, tests, and one-command VPS setup.

...Bluedart but missing a Credit Note in Tally. Cash Flow Forecasting: An AI-driven projection of upcoming payouts vs. vendor liabilities based on Tally’s Ledger. Automated Alerts: Daily WhatsApp/Email snapshots of "Net Profit" and "High-Value Pendency." Technical Requirements: Deep expertise in TallyPrime XML/ODBC interface and Tally API. Strong experience with Power BI, Tableau, or Python (Pandas) for financial data processing. Familiarity with Shopify API and Bluedart (or similar courier) tracking APIs. Ability to work with Tally on Cloud environments. Understanding of E-commerce accounting (GST, RTO, Marketplace settlements). Project Type: This is a part-time, milestone-based project. We prefer a "Discovery & Architecture" phase f...

...time-series data coming from three different sources—raw CSV uploads, existing relational databases, and live API endpoints. The app should read, clean, and merge these feeds on the fly, then offer clear visual insights through line charts, area charts, and any other plots that make trends, seasonality, and anomalies obvious. Under the hood I expect well-structured, reusable Python code that leans on pandas for manipulation, SQLAlchemy (or similar) for database access, and a lightweight requests layer for the APIs. Caching, session-state handling, and responsive layout controls are important so the interface feels fast even as data volumes grow. Deliverables • Streamlit app folder with modular, commented Python scripts • A config file (YAML or .env) that lets me...

...each session so I can practice and submit my results for feedback. • Clear explanations of Excel techniques (Power Query, pivot tables, data validation) and when they are most efficient compared to other tools. • Guidance on structuring my workflow so future projects scale smoothly. I currently want to concentrate on Excel, but I’m open to eventually exploring other options such as Python (Pandas) or R once the fundamentals are strong, so flexibility is appreciated. Please outline: 1. Your experience teaching or mentoring beginners in Excel data cleaning. 2. A suggested session plan or curriculum outline. 3. How you prefer to share files and track my progress. I value clear communication, actionable feedback, and real examples over theory alone. If you ...

I need an academically rigorous thesis that investigates water-pollution violations across the United States, combining robust data analysis with vivid, well-documented case studies. The core objective is to pinpoint patterns of non-compliance, highlight enforcement gaps, and propose policy-oriented solutions grounded in empirical evidence. ...Clean datasets, code (R, Python, or similar), and any visualisations used to generate results. Acceptance Criteria • Originality check <10 % similarity. • All claims supported by primary or reputable secondary sources. • Data and code reproduce the figures and tables included in the manuscript without modification. Please indicate preferred research tools (e.g., SPSS, Stata, Python/pandas) and an estimated timeli...

...escalate. The core must combine traditional threat-intel techniques with machine-learning pipelines so the system continuously adapts as new data arrives. Here’s what success looks like to me: • A modular data-collection layer that can stream pcap, NetFlow, or similar log formats into a preprocessing engine. • Feature-engineering and model-training code written in Python (feel free to leverage Pandas, scikit-learn, TensorFlow, PyTorch—whatever best suits the task). • A detection component that scores incoming traffic and raises alerts via a simple REST API or CLI output. • Clear documentation covering setup, retraining, and how new data sources—such as endpoint events or social-media threat chatter—could be plugged in later. ...

...ready for downstream analytics. You will receive the source file in CSV (UTF-8) plus a short style guide that shows the exact output I need. Please return the cleaned file and a short change log summarizing the transformations you applied. Accuracy and respect for the original content are crucial; no new data should be introduced or removed. Tools like Excel, Google Sheets, OpenRefine, or Python (pandas) are all fine—use whatever helps you turn this around quickly and reliably....

I keep a single master report in Excel that contains every client’s figures. I need a small, repeatable Python script that will: • read the mast...calculations, • split the rows so each client gets only their own data, • save every subset as an individual Microsoft Excel 97-2003 (.xls) file. With Data Visualization for Analysis Inside every generated file I still want the same conditional formatting and any existing cell-level number/date formats to remain intact, so the look and feel matches the master. Please write clean, well-commented code—pandas plus xlwt/xlrd or any other library that supports the older .xls format is fine—as long as it runs from the command line on Windows. Include a short README and one sample output file so I can confirm...

I need help with a Python exercise focused on data analysis. Key tasks include: - Data cleaning/extraction - Visualization/plotting - Statistical analysis Ideal skills and experience: - Proficiency in Python, especially with libraries like Pandas, NumPy, and Matplotlib - Experience with data cleaning and preparation - Strong data visualization skills - Background in statistical analysis

...analysing it in real time, placing orders, and continuously enforcing risk limits. Core functionality • Automated trade execution driven by a configurable strategy engine • Real-time market analysis with fast data ingestion (websocket or streaming API) • Built-in risk management: position sizing, max draw-down and stop-loss rules Technical notes Python is mandatory; common libraries such as pandas, NumPy, TA-Lib, and asyncio are expected. The code should be clean, modular, and ready to plug into broker APIs like Interactive Brokers, Alpaca, or Tradier. A lightweight front-end (CLI or simple dashboard) for live logs and performance metrics would be ideal. Deliverables 1. Fully-commented Python source code and 2. Strategy configuration file(s) allowing ...