PandasJobs

...any rate limits. • Fetch all pages/records, normalise the returned JSON (or XML) into tidy tabular data. • Write that data into a well-formatted Excel workbook, preserving data types and basic styling so it’s ready for immediate analysis. • Include sensible error handling and retry logic, with secrets stored via environment variables. • Provide clean, readable code (Python with requests + pandas/openpyxl would be ideal, but I’m open to another stable stack if you prefer). • Finish with a short README that shows installation, a one-line execution example, and a screenshot or sample .xlsx verifying the download works. If anything in the API response changes dynamically—column names, data structure—build in a way that&rsquo...

import pandas as pd EXCEL_FILE = "" # ----------------------------- # Create Excel Template # ----------------------------- def create_excel_template(): data = { "Service_ID": [], "Sub_Function": [], "DID": [], "Description": [], "Data": [] } df = (data) df.to_excel(EXCEL_FILE, index=False) print(f"Excel template created: {EXCEL_FILE}") # ----------------------------- # Read and Parse Excel # ----------------------------- def parse_excel(): df = pd.read_excel(EXCEL_FILE) # Remove completely empty rows df = (how="all") # Replace NaN with None df = ((df), None) structured_data = [] for _, row in (): se...

I have a collection of PDFs that need to become a single, reliable Excel file. Af...the real priority is cleaning: fixing any odd characters, aligning column formats, checking for obvious entry errors, and making sure the final sheet is ready for downstream analysis. What you’ll hand over: • An Excel workbook converted from the source PDFs. • A fully cleaned version of that workbook, clearly labelled and free of errors. If you have automated routines (Power Query, Python / pandas, VBA, etc.) that speed the job up, all the better—just let me know what you plan to use. When you respond, point me to similar PDF-to-Excel or data-cleaning work you’ve completed in the past so I can gauge fit quickly. I’ll supply the PDFs and any additional formatting...

AI & Full-Stack Tech Client Acquisition Partner Needed (Commission-Based) I am an experienced Full-Stack AI & Software Engineer with strong hands-on expertise in building production-ready systems, including: AI / LLM Development (OpenAI, Gemini, LangChain, RAG, AI Agents) Machine Learning & Data Science (Python, Pandas, NumPy, Scikit-learn) Backend Development (Python, Node.js, REST APIs) MERN Stack (MongoDB, Express, React, Node.js) DevOps & Deployment (Docker, CI/CD, Cloud hosting) Automation & AI Chatbots (WhatsApp, Web, CRM integrations) I deliver scalable, high-quality solutions with clear communication and on-time execution. Role: Client Acquisition Partner I’m looking for a motivated, reliable partner who can bring genuine AI / software developme...

I’m looking for a data scientist based anywhere in Latin America to help me create reliable predictive models for a finance-focused project. You’ll start with large historical datasets stored in SQL and deliver models that accurately forecast key financial indicators. I work mainly with Python, so you’ll find Pandas, NumPy, Scikit-learn and, when deep learning is justified, TensorFlow already in place. If you prefer R for certain tasks, that’s perfectly fine as long as the final workflow remains reproducible. The end-user needs to consume insights through Power BI, so once the model is validated I’ll ask you to craft intuitive dashboards that highlight drivers, confidence ranges and any red-flag anomalies the model detects. Solid statistical grounding ...

...structured e-commerce dataset sitting in Excel/CSV format that needs a thorough exploratory data analysis. My goal is to turn raw tables into an insightful visual narrative—clear charts, concise commentary, and an executive-style report or interactive dashboard that highlights the key patterns hiding in the numbers. Here is what I need from you: • A clean, well-documented analysis (Python with pandas, seaborn/matplotlib or R tidyverse are fine) that walks through data profiling, outlier handling, and any necessary feature engineering. • Compelling visualizations—think sales trends, category performance, customer behaviour—that can be dropped straight into a slide deck or shared as an interactive report (Tableau, Power BI, or a notebook with embedde...

...collection and interpretation practices; second, shaping the core algorithms that will power the software. We’ll be working with the classic trio of techniques—Time Domain, Frequency Domain, and Envelope Analysis—so please be comfortable explaining best-practice workflows and translating them into practical code logic. I already have sample datasets and a basic development environment (Python + NumPy/Pandas/Matplotlib), but I’m flexible if you prefer other analysis libraries such as SciPy or MATLAB. Deliverables • Live or recorded coaching sessions walking through proper sensor placement, sampling parameters, and data cleansing • Annotated pseudo-code or prototype functions that implement the three analysis techniques, ready for integration...

...including product and engineering • Mentor junior AI engineers and contribute to technical leadership • Conduct research and implement state-of-the-art AI techniques • Ensure data quality, security, and model performance optimization Required Skills & Qualifications: • 10+ years of experience in AI/ML or Software Engineering roles • Strong proficiency in Python and data processing libraries (NumPy, Pandas) • Hands-on experience with TensorFlow, PyTorch, Scikit-learn • Strong understanding of Deep Learning, NLP, Computer Vision • Experience with Model Deployment & MLOps pipelines • Experience working with Cloud platforms (AWS / Azure / GCP) • Strong knowledge of Data Engineering & Big Data tools • Experience with ...

...interfaccia web. • Riconoscimento automatico dei fogli e delle colonne numeriche. • Selezione multipla delle colonne da analizzare. • Calcolo e visualizzazione della deviazione standard per ogni colonna scelta, con risultato immediato a schermo. Preferenze tecniche (ma resto aperto a proposte migliori) • Backend in Python con Flask o Django, oppure Node.js. • Librerie consigliate: pandas, NumPy o equivalenti per il calcolo statistico. • Front-end semplice e responsivo, anche con Bootstrap. Consegna 1. Codice sorgente completo e commentato. 2. Istruzioni di installazione (README) per avvio locale. 3. Breve guida utente che spieghi come caricare il file e leggere i risultati. Se puoi offrire anche un piccolo deploy di test (H...

...hours—no end-of-day batching. I code in Python myself, so please keep the architecture transparent: well-named modules, docstrings, and a that pins every dependency. Back-tests on at least two years of 5-minute data, a walk-forward validation segment, and a short README outlining how to reproduce the results will be my acceptance criteria. If you already have experience with pandas, NumPy, scikit-learn or TensorFlow, and you know how to talk to Indian broker APIs via REST or websockets, this should feel familiar. Let me know the models or reinforcement-learning frameworks you think best suit intraday equity trading and how you will protect against over-fitting....

...includes individual customer DGR sheets as separate attachments; the body should recap the overall allocation, while each attachment should show the customer’s own figures. • Retain every day’s data so that, at month-end, I can run a one-click reconciliation against the SLDC meter download and export the results for finance. Tech stack is up to you, yet it must remain readily maintainable—Python/Pandas + PostgreSQL + React or a comparable combo is fine. What matters is that the system eliminates our current manual steps, delivers 200× faster processing, and protects monthly revenue through error-free tracking. Acceptance will be based on: 1. Successful automatic import and validation of a sample QCA schedule. 2. Immediate discrepancy alert whe...

...comparison shows no noticeable deviation. 3. Files open without errors in the latest desktop version of Microsoft Excel. 4. No stray spaces, line breaks or duplicated rows remain after the import and cleanup. I usually spot-check random pages, so 100 % accuracy is essential—please use the tools you trust most, whether that’s Adobe Acrobat, ABBYY FineReader, Power Query, or scripted Python/Pandas, as long as the final spreadsheets meet the standards above....

...perspective on exploratory analysis, pattern discovery, and interpretation so the insights are both rigorous and clearly actionable. The core task is straightforward: ingest the raw text files I provide, clean and structure them appropriately, then run exploratory and statistical analyses that reveal key themes, sentiment trends, and any significant correlations you uncover. Feel free to use Python with pandas, NumPy, NLTK, spaCy, or other standard NLP and data-analysis libraries—whatever you are most comfortable with as long as the workflow is reproducible. Because I don’t have a hard deadline, quality and clarity matter more than speed. You will deliver: • Well-commented notebooks or scripts that walk through each analysis step • A concise report (PDF...

I’m sitting on a few hundred PDF invoices and need an automated way to spot any that were issued twice. Because our numbering scheme uses custom prefixes and special characters, I’d like the detection...processed automatically—no manual renaming or sorting • ≥95 % accuracy on the three target fields • Duplicates grouped logically and exported in tabular form • Re-run capability for future batches with minimal setup • Well-commented code and a short README explaining dependencies and usage Python feels natural here—pdfplumber or PyPDF2 for parsing, Tesseract or similar OCR when needed, pandas for the comparison logic—but I’m open to whatever stack delivers the results. The key is reliability and ease of rerunning the...

... • It should identify and export any tabular line-item sections so that quantities, descriptions and prices land in true Excel rows and columns—not as a single block of text. • Embedded images or logos also need to be saved out (ideally into a sub-folder) with a reference back to the originating invoice inside the Excel sheet. Python tools such as pdfplumber, PyPDF2, camelot, tabula-py, pandas and openpyxl are all fine; choose the combination you’re most comfortable with as long as the final deliverable is a .py file plus an example .xlsx that mirrors the source PDFs accurately. Acceptance will be based on: 1. Running the script locally on a sample batch of PDFs with no manual tweaks. 2. Seeing all text and table content laid out cleanly in Exc...

...(handle missing values, standardise field names) • explore key patterns—frequency of purchase, average order value, cohort retention, and any other meaningful customer behaviour metrics • generate charts or interactive visuals that I can drop straight into presentations (matplotlib, seaborn or Plotly are fine) • export a short written summary of findings, ideally as a Markdown or PDF report Pandas, NumPy, and a mainstream visualisation library are expected; if you prefer a Jupyter Notebook that’s perfect, but a standalone .py script with comments also works. Keep the workflow modular so I can feed in new monthly data without rewriting sections. Once delivered, I should be able to run everything locally with a single command, point it at fresh CS...

...document or answer instantly, and feel as if they are speaking with a well-trained human agent. Alongside the chat experience, I need an end-to-end AI pipeline that automatically extracts raw data from the web, aggregates and cleans it, performs analysis, and then publishes clear visualisations—including map views—so insights are always one step away. I’m comfortable with tools such as Python, Pandas, LangChain, Node, SQL, Power BI, Tableau, or any similar stack you can justify. Key deliverables • Deployed WhatsApp agent(s) connected through the WhatsApp Business API - WhatsApp channel is ready. • Retrieval-augmented knowledge base so the bots surface the latest information without hallucinations .. Critical • Automated ETL jobs (n8n, Airfl...

...three pillars inside a single, well-structured repository: • Automated trading – place, modify, and cancel orders programmatically (market, limit, SL). The engine should be able to run multiple strategies concurrently and execute through Zerodha’s regular and MIS product types. • Market analysis – fetch historical and live tick data, calculate technical indicators on the fly (you’re free to use pandas, numpy, TA-Lib, or your own functions), and generate entry/exit signals according to parameters I will supply. • Risk management – position sizing, max daily drawdown, per-trade stop-loss, overall exposure caps, and a safety kill-switch if connectivity or margin issues arise. I will provide API credentials and strategy logic; your job...

...analysis drawn from the cleaned data. The core of the job is data analysis, with a focus on market analysis. Once the information is re-entered and verified, I’d like you to identify key market trends hidden in the numbers—patterns in customer behaviour, shifts in demand, emerging opportunities, and any risks you notice. Feel free to use Excel, Google Sheets, or your preferred tools (Python + Pandas or SQL are fine too) as long as the final files remain easy for me to open and follow. Deliverables • A fully re-typed and proof-checked spreadsheet, aligned with my existing column structure • A concise market analysis report (slides or doc) that explains the trends you found, the methods you used, and your recommendations I’m available for qu...

Excel & Python Data Analysis for Sales and Operations ...cleaning and validation, and building Python-based analysis scripts to calculate KPIs such as revenue trends, inventory turnover, order fulfillment rates, and cost variations. The final output should include well-structured Excel reports and optional visual summaries generated via Python. Key Requirements: Strong experience with Excel (advanced formulas, pivot tables, data validation) and Python (Pandas, NumPy, Matplotlib or similar). Ability to handle messy datasets, automate repetitive analysis, and ensure data accuracy. Deliverables: Cleaned datasets, analysis scripts, summary reports, and clear documentation explaining the workflow. This project is ideal for someone who enjoys turning raw data into actionable insight...

...different methods agree or diverge. • At least one live session (Zoom, Meet, or similar) to walk me through your approach; the project involves explanations that are too involved for chat alone. • A concise technical memo or slide deck summarising conclusions and recommended next steps. Acceptance criteria – Code runs end-to-end on a fresh environment with standard libraries (scikit-learn, pandas, numpy, matplotlib/plotly, shap, lime, etc.). – Plots and tables directly link back to the underlying calculations, allowing me to reproduce every number. – During our live review you can clearly justify each methodological choice and articulate trade-offs between SHAP, LIME, PDP and ICE for the three target models. This assignment is scoped as a f...

...hours a day with collecting, cleaning, and summarising data for several market intelligence projects. You’ll log into shared Google Sheets, pull information from public sources or APIs, verify accuracy, and present concise insights that let me make quick decisions. Experience with Excel or Sheets functions (LOOKUPs, pivot tables, basic charts) and any lightweight statistical tool such as Python/pandas or R is a plus, but solid attention to detail matters most. I’ll set clear weekly objectives up front. Typical deliverables include: • A cleaned spreadsheet ready for analysis • A brief written summary (no more than one page) highlighting key findings and anomalies Before we start, send a short note telling me your favourite dataset you’ve worked on...

I need to turn a tangle of monthly transaction files into one clean, consistent source of truth. The job touches three tools: • Python – Write well-structured scripts (pandas, openpyxl, maybe pyodbc) that handle data cleaning, exploratory analysis, and the recurring automation needed to pull from / push to my databases and workbooks. • MS Access – Build and fine-tune queries, keep the tables organised, and set up ready-to-run reports that mirror the logic in the Python workflow. • MS Excel – Craft the right formulas, dynamic pivot tables, and clear visualisations so stakeholders can slice the data without touching code. The core flow should be: ingest raw Excel dumps, clean and reconcile them in Python, store the harmonised tables in Access, th...

...and connects to my broker’s API out of the box. • Parameter file or dashboard so I can tweak risk limits, symbols, and indicator thresholds. • Back-test results on one year of one-minute data plus a walk-forward test on the last 90 trading days, both clearly outperforming buy-and-hold under equal risk. • Quick-start guide and a brief screen-share hand-off. Tech stack is open—Python with pandas and TA-Lib is fine, but if you prefer something lower-latency like C++ or a paired Python/C++ engine, I’m game as long as installation is straightforward on a standard U.S. VPS. I’m ready to begin as soon as I find the right coder-trader hybrid. Send a short note describing your live trading experience and one or two algos you’ve built th...

...hours a day with collecting, cleaning, and summarising data for several market intelligence projects. You’ll log into shared Google Sheets, pull information from public sources or APIs, verify accuracy, and present concise insights that let me make quick decisions. Experience with Excel or Sheets functions (LOOKUPs, pivot tables, basic charts) and any lightweight statistical tool such as Python/pandas or R is a plus, but solid attention to detail matters most. I’ll set clear weekly objectives up front. Typical deliverables include: • A cleaned spreadsheet ready for analysis • A brief written summary (no more than one page) highlighting key findings and anomalies Before we start, send a short note telling me your favourite dataset you’ve worked on...

...straight into my existing systems. My current bottleneck is doing this by hand, so the solution must run unattended once configured. Although my immediate goal is straightforward data processing, I’m open to seeing how you’d weave in text extraction, transformation, or even lightweight analysis if that simplifies the pipeline or adds value. I work comfortably with Python, so a solution built with pandas, spaCy, or similar libraries would fit right in, but I’m not married to any single toolset as long as the final deliverable is easy for me to maintain. Deliverables • Source code and any dependency list • A short README explaining setup, configuration variables, and how to run the job • One worked example showing the raw input and the process...

...user and for the population as a whole. • Compute entropy (Shannon or Rényi—please justify your choice) across sliding and tumbling windows so we can compare immediate behaviour to longer-term baselines. • Raise an alert when an entropy shift exceeds a configurable threshold, returning the supporting metrics and the related raw events. I expect well-structured, runnable code—Python with pandas/NumPy/SciPy is typical, though another language is fine if it delivers the same reproducible results—along with a concise README that shows how to install dependencies, feed sample logs, and interpret the output. Success is measured by: • Clean execution on my sample dataset (≈1 GB of mixed user activity). • Alerts that capture ...

...simple list of all business names found at that spot (no tables, no extra formatting). Key points you should respect: • The CSV is finished and ready to ingest as-is. • If more than one business is tied to an address, keep every name. • Output in Google Docs: one address followed by its full list, then move on to the next address. I’m open to whatever stack feels most efficient—Python with pandas, Google Apps Script, Zapier, , or a custom API integration—as long as the final flow can run on demand and is documented well enough that I can swap in a new CSV whenever needed. Deliverables I expect: • Working automation (script, scenario, or add-on) that accepts my CSV and produces the Google Doc exactly as described. • Brief setup/u...

...─── 12. RECOMMENDED TECH STACK ──────────────────────────────────────── ### Backend • Node.js (NestJS) OR Laravel • REST + WebSockets • PostgreSQL • Redis ### Fleet Core • Fleetbase (extended via plugins) ### Frontend (Web Dashboards) • React.js or • Tailwind CSS • Mapbox or OpenStreetMap ### Mobile Apps • Flutter (preferred) OR React Native ### AI & Analytics • Python • FastAPI • Pandas • Scikit-learn / TensorFlow (later phase) ### Payments • Stripe SDK • PayPal SDK • CMI API ### DevOps • Docker • CI/CD • Cloud hosting (AWS / GCP / Azure) ### Design • Figma (mandatory) • Design System • UX focused on minimal interaction ─────────────────────────────────────...

...are straightforward TXT—no embedded markup or JSON structures—so the solution can focus entirely on pattern analysis rather than parsing exotic formats. Because the anomalies are behavioural rather than simply extreme numeric values, the detection logic must look for irregular sequences, unexpected combinations of fields, or sudden structural deviations. I’m happy with a Python‐based approach (pandas, scikit-learn, PyOD, or similar libraries come to mind), but I’m open to another language if it keeps the setup lightweight and easily deployable on a Linux server. Speed matters: each hourly log is time-stamped and should be processed within the hour so downstream analytics always work with clean data. Your script or small tool should run from the command...

...establish a Volume SMA baseline. ​Real-Time Trigger Logic (1-Min Candles): ​Value: Candle Value (LTP × Volume) ge ₹10 Crore. ​Volume: Current 1-min Volume ge 10x the 2,000-candle SMA. ​Momentum: Price change in the current 1-min candle ge 0.3%. ​Scan Frequency: Refresh the current forming candle every 20 seconds. ​Dashboard & Persistence: ​Display results in a tabular format (using PrettyTable or Pandas) with a timestamp (hh:mm:ss). ​All stocks that satisfy the criteria must be stored in a persistent list for that day's session (no separate history tab needed). ​One-Click Charting: ​When a stock appears in the table, it must be clickable or easily selectable (e.g., via a keyboard shortcut) to instantly open the corresponding Fyers TradingView chart in the defaul...

I’m refining a series of logistic-regression models i...Hessian output for standard-error estimation. • Suggest parameter-regularisation strategies available directly in SciPy or via light dependencies I can bolt on. • Join one or two live screen-share sessions (30-45 min each) so we can step through residual plots, goodness-of-fit tests, and any edge-case handling. All work will happen in a clean Python 3.11 environment with NumPy, SciPy 1.11, Pandas, and Matplotlib already installed, so no need for heavy-weight ML frameworks. Deliverables are the commented revisions to my script plus a concise summary of the changes and reasoning. If you’ve previously tuned logistic models in SciPy (not just scikit-learn) and enjoy explaining the “why” as mu...

Saya memiliki kumpulan data penjualan yang perlu dianalisis secara menyelu...waktu. • Visualisasi: sajikan temuan dalam grafik atau dashboard interaktif yang mudah dipahami. • Insight & rekomendasi: rangkum hasil analisis dengan penjelasan jelas serta saran strategis yang dapat langsung diimplementasikan. Dataset tersedia dalam format Excel/CSV dan dapat saya kirim segera setelah proyek dimulai. Saya terbuka menggunakan alat pilihan Anda—Excel, Google Sheets, Python (pandas), atau Power BI—selama hasil akhir akurat dan mudah dipresentasikan kepada tim manajemen. Silakan ajukan proposal singkat berisi metode analisis yang Anda rencanakan, perkiraan waktu penyelesaian, serta contoh ringkas proyek serupa jika ada. Saya menghargai komunikasi proaktif dan ...

...file (again, PDF, Word, or Excel). 3. System parses, maps terms, and asks me to confirm any uncertain matches. 4. It outputs a clean, well-formatted Excel workbook showing requirement vs. each vendor’s offer, along with an automated “best-fit” recommendation. Future iterations may add scoring weightings, but the first milestone is the Excel comparison itself. Tech is up to you—Python with pandas, open-source NLP libraries such as spaCy or transformers, or a lightweight web front end that calls a backend service. Whatever stack you choose, the deliverable must run on a standard Windows machine or be easily deployed to a small cloud instance. Acceptance criteria • Uploads accepted: PDF, Word, Excel. • Comparative sheet created in .xlsx wi...

Hello, my name is fathalishah. i am from afghanistan i have been working for 3 years as full stack developer which can i work with html ,css,javascript,react,tailwindcss and bootstrap and backend i can work with Python+django django-restframe api , sql,mysql, and as desktop developer i can work with tkinter and also i can work as data scientest work with NUMPY ,PANDAS,scikit-learn,Matplotlib i hope i can do my best for you Thanks..

...is to move everything into the standardized Excel import template required by our new SAP environment without typing line-by-line. I would like an automated solution—preferably a lightweight AI-assisted script or model—that can: 1. read the existing Excel files, 2. map each column to the exact fields SAP expects, and 3. export a clean, ready-to-upload template with zero data loss. Python (pandas / openpyxl), VBA, or a low-code Power Query setup are all acceptable as long as the end result is fast and repeatable for future batches. Built-in data checks for duplicates, negative balances, or mismatched contract IDs will be essential so we catch issues before the SAP import stage. Deliverables • Reusable script or macro with clear in-line comments • O...

...et des scripts Python ou R pour automatiser les extractions ; • élaborez des visualisations interactives dans Power BI ou Tableau afin de suivre nos indicateurs clés ; • recommandez des actions à partir des tendances détectées. Je fournis l’accès aux bases, la documentation technique et un canal Slack dédié. De votre côté, j’attends : – une expérience avérée en analyse de données (Python / Pandas, SQL, outils de BI) ; – la capacité à livrer des synthèses courtes et précises chaque semaine ; – un engagement régulier, 15 à 20 heures réparties comme vous le souhaitez. Si vous aimez explore...

...establish a Volume SMA baseline. ​Real-Time Trigger Logic (1-Min Candles): ​Value: Candle Value (LTP × Volume) ge ₹10 Crore. ​Volume: Current 1-min Volume ge 10x the 2,000-candle SMA. ​Momentum: Price change in the current 1-min candle ge 0.3%. ​Scan Frequency: Refresh the current forming candle every 20 seconds. ​Dashboard & Persistence: ​Display results in a tabular format (using PrettyTable or Pandas) with a timestamp (hh:mm:ss). ​All stocks that satisfy the criteria must be stored in a persistent list for that day's session (no separate history tab needed). ​One-Click Charting: ​When a stock appears in the table, it must be clickable or easily selectable (e.g., via a keyboard shortcut) to instantly open the corresponding Fyers TradingView chart in the defaul...

...CSV, and PDF files * Clean and normalize messy real-world data * Write clear, maintainable utility scripts * Deliver working code (not just prototypes) --- ### Required Skills * Strong Python fundamentals * Real experience with web scraping * Data parsing and data cleaning * Comfortable working independently and async --- ### Nice to Have * BeautifulSoup, Scrapy, Playwright, or Selenium * pandas / numpy * Experience scraping government or legacy websites * Experience handling PDFs (text extraction, OCR) --- ### How We Evaluate * This role includes a **paid trial task (1–3 days)** * We care about **output and correctness**, not resumes * Clean, working code matters more than clever abstractions --- ### Important * Please include **2–3 sentences** describing...

I’m ready to bring the same structure and depth I saw in BCG’s virtual consulting exercise into my own finance function. You’ll have direct access to our raw sales data (CSV export from our ERP), and your brief is to surface two concrete insight streams: • Cl...we can refine cash-flow planning, discounting, and inventory strategy Once the analytics are in place, I’ll need a short slide deck or memo that converts those findings into finance-focused recommendations—for example, timing large expenditures, reshaping payment terms, or reallocating working capital. I’m comfortable with whichever tool chain suits you best (Excel, Power BI, Tableau, Python pandas). What matters is transparent methodology, reproducible calculations, and visuals tha...

I have a stack of 65 supplier invoices sitting in PDF format, each listing multiple SKUs and quantities. I need those PDFs processed so that, for every unique SKU, I can see the running total qu...can see the running total quantity across all invoices. The final result should land in a clean, flat Excel / CSV file ready for upload into my inventory system. Besides the SKU and its consolidated quantity, please include the related invoice date so I can quickly trace back any discrepancies later. No other fields are required. However you choose to handle the PDFs—whether with Python, pandas, OCR, or dedicated PDF-parsing libraries—accuracy is critical. I should be able to open one spreadsheet, filter by SKU, and instantly know how many units are on hand according to s...

...a Python script is throwing errors whenever it calls certain library functions. I need a sharp eye to dive into the code, pinpoint the exact cause, and leave me with a clean, reproducible fix. Here’s the situation as clearly as I can put it: • The faulty piece lives in a Python script that glues together key stages of the workflow. • The issue surfaces inside library-level calls—think NumPy, pandas, scikit-learn, TensorFlow, or similar. • Once corrected, the rest of the training and inference steps should run without manual work-arounds. What I’m expecting from you • A patched script that runs end-to-end on my side. • A concise changelog or inline comments explaining what broke and why your fix works. • Any environment twe...

...Module 3 — SELL STRATEGY ENGINE Handles only short trades. Examples of rules: Price below VWAP Breakdown patterns Bearish flag Volume breakdown Entry, SL, Target logic Output: SELL signals only Module 4 — AlgoTest Strategy Formatting Convert BUY signals → Buy strategy format Convert SELL signals → Sell strategy format Backtest-ready rule structure Technical Requirements ✔ Python (Pandas, NumPy) ✔ TA indicators (VWAP, EMA, Volume) ✔ Modular coding structure ✔ Knowledge of AlgoTest rule system ✔ Experience in intraday trading logic Deliverables : Separate Python files: Chartink stock fetch script Strategy logic documentation Editable conditions Setup guide Note : Payment will be released only after successful setup and testing on ...

...parsing, and structured export—and then hand me a ready-to-use CSV or JSON. I will share a sample dataset and the exact transformation rules once we start, but in broad strokes the script should: • run from the command line with a few clear arguments or a simple config file • handle varying file sizes without choking on memory • rely only on standard libraries or popular, easily installed ones (pandas, regex, nltk, etc.)—list any extras in • log basic progress so I can trace issues quickly • exit gracefully on bad input and flag the error rather than crash Deliverables 1. The Python (.py) script, fully commented. 2. with exact versions if you add dependencies. 3. A short README showing setup, one-line usage, and an example of ...

...project has started throwing errors, and I need it back in working order within 48 hours. The bug sits somewhere between the data-loading step and model training; once you jump in you’ll have full access to the Python code, sample data, and the original (working) training logs so you can compare behaviours. I work entirely in Python and TensorFlow, with a handful of standard utilities such as NumPy, pandas, and Matplotlib. No other frameworks are involved, so deep familiarity with TensorFlow’s debugging tools, graph/symbolic APIs, and eager execution will let you move quickly. Here is what I expect at hand-off: • A corrected script that runs start-to-finish on my machine (TensorFlow 2.x, Python 3.9). • A concise changelog or inline comments so I can see ...

...PostgreSQL database, then turns the insights into clear, interactive visuals and downloadable reports. Core scope • Build the Django backend, including models that capture standard patient information, visit history, diagnostics, medications, and lab results. • Create a secure REST or GraphQL layer so new records can be ingested from our existing EHR feed. • Process the incoming data with Pandas or a comparable library, preparing it for analysis. • Develop dashboards that allow clinicians to explore trends (e.g., vitals over time, medication adherence) using Plotly, , or another modern JS charting tool. • Implement one-click PDF/CSV report generation per patient and for cohort summaries. Key requirements – The entire stack must be co...

I need a skilled analyst who can dive into my sales data and surface clear, time-based trends using a mix of SQL and Python. The raw figures sit in a relational database; from there you’ll write efficient SQL queries, pull the results into Python (pandas, NumPy, SQLAlchemy), tidy everything, then explore daily, weekly, and monthly patterns. Visualisations in matplotlib or seaborn and a concise written summary (PDF or Jupyter Notebook) will round out the story so stakeholders can quickly grasp how revenue is evolving and where seasonality or anomalies appear. Deliverables • Well-commented SQL scripts that reproduce the dataset • A Python notebook (or .py script) with all transformation and analysis steps • Three to five clear charts illustrating sales tren...

...entries—that needs exhaustive permutation matching. The goal is to scan every possible combination quickly in Python, identify all matches (or near-matches if you choose to add fuzzy logic), and return the results in a single Excel file ready for immediate analysis. Raw data and a short spec on what constitutes a “match” will be supplied; your task is to design an efficient, memory-savvy routine (pandas, NumPy, itertools, or any other high-performance approach you prefer) that can churn through roughly one hundred million comparisons without freezing up my workstation. Multithreading, vectorisation, or chunked processing—all are acceptable so long as they keep runtime practical and the output accurate. Deliverables • A well-commented Python script...

...Module 3 — SELL STRATEGY ENGINE Handles only short trades. Examples of rules: Price below VWAP Breakdown patterns Bearish flag Volume breakdown Entry, SL, Target logic Output: SELL signals only Module 4 — AlgoTest Strategy Formatting Convert BUY signals → Buy strategy format Convert SELL signals → Sell strategy format Backtest-ready rule structure Technical Requirements ✔ Python (Pandas, NumPy) ✔ TA indicators (VWAP, EMA, Volume) ✔ Modular coding structure ✔ Knowledge of AlgoTest rule system ✔ Experience in intraday trading logic Deliverables : Separate Python files: Chartink stock fetch script Strategy logic documentation Editable conditions Setup guide Note : Payment will be released only after successful setup and testing on ...

...side, the agent will read incoming tickets, suggest or send context-aware replies, and escalate anything outside predefined confidence levels. At the close of each cycle it will compile a concise report: updated record counts, uptime metrics, customer-support KPIs, and a short trend analysis showing spikes or deviations. A Python stack with REST integration, async handling, and libraries such as pandas, LangChain or similar for LLM prompts would suit us, but I’m open to other reliable options if they meet the same goals. Execution can be triggered by cron, a lightweight scheduler, or a serverless function; what matters is that it runs unattended and keeps detailed logs. Deliverables • Production-ready agent script(s) with clear setup instructions • API inter...