Pandas Jobs

Subject: Expert in 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 ...

I’m evaluating security vulnerabilities in three Arabic-capable language models—Allam, Falcon, and Fanar—by running the Garak prompt-injection suite. My top priority is the technical implementation, with a particular emphasis on translating each of Garak’s 256 English attack prompts into clear, natural Arabic before the tests run. Here’s how the workflow looks: • 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. • P...

I’d like a data-savvy partner who can dive into our raw inventory history 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. Deliverab...

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 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 hear from you.

I need a working proof-of-concept that hardens my wireless network by analysing live traffic and flagging hostile activity the moment it appears. The model must reliably catch the attacks that worry me most: Denial of Service (DoS), Man-in-the-Middle (MitM) intrusions, classic packet sniffing, and ARP spoofing. Everything has to run in real time on a modest on-prem machine, so efficient feature extraction and lightweight 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-comme...

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 master workbook, • isolate the Total Cost column and perform specific 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. Inclu...

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

I need a seasoned quantitative developer who can turn my Iron Condor options strategy into a fully automated trading BOT for Indian markets. The system must trade exclusively in listed options and operate through the NSE NOW interface, handling everything from order placement and modifications to position monitoring and exit. Core workflow • Pull live option chain data, calculate real-time Greeks, and identify entry prices that satisfy my Iron Condor rules (delta-neutral, predefined distance between strikes, and minimum credit per leg). • Submit simultaneous multi-leg orders via NSE NOW, confirming fills and retrying intelligently if partials occur. • Track open positions tick-by-tick, updating P/L and Greeks so risk controls—max loss, max profit, time-based exits...

I need a sharp, fast-paced research assistant for a live, time-sensitive project. You will be helping me navigate, search, and interpret a technical document.

I need a sharp, fast-paced research assistant for a live, time-sensitive project. You will be helping me navigate, search, and interpret a technical document.

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 (): service_d...

I need a sharp, fast-paced research assistant for a live, time-sensitive project. You will be helping me navigate, search, and interpret a technical document.

I have a 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 embedded...

I want a production-ready Python algorithm that can scan the Nifty 50 throughout the session, weigh profit opportunities against downside risk, and fire off intraday orders several times a day when the reward-to-risk profile is attractive. Core logic • Fuse three data streams in real time: historical price series, live technical-indicator feeds that you compute on the fly, and sentiment or headline signals drawn from the latest market news. • Use that blended input to generate probabilistic forecasts, then translate those forecasts into position sizing that keeps risk in check while still pursuing upside. • The system must be able to trigger, modify, and exit trades automatically during market hours—no end-of-day batching. I code in Python myself, so please kee...

I have between 11 and 50 PDF documents that need to be reproduced in Excel with every table, figure and line of text exactly where it belongs. The goal is to mirror the source layout, cell structure, fonts and number formats so the resulting spreadsheets look and behave just like the originals. Your task • Pull the data—tables, numbers, labels, any embedded text—from each PDF. • Paste or import it into Excel while preserving column widths, merged cells, date/number formatting and general visual hierarchy. • Double-check every sheet so totals match, no characters are dropped or mis-read, and formulas (where present) still calculate. • Deliver one clean .xlsx file per PDF, plus a master workbook that consolidates them if doing so does not alter the pr...

I need a concise, well-documented Python workflow that turns raw customer data into clear, decision-ready insights. The files arrive as CSV exports from our CRM and contain purchase history, demographic fields, and basic engagement metrics. I want the code to: • load and clean the datasets (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...