Pandas Jobs

Google Search Trends Analysis (Python) ## Project Overview This project analyzes **Google Search Trends data** using Python to understand how a keyword's popularity changes over time and across regions. The analysis covers **15 countries**, compares **time-wise interest**, and explores **related keywords** to uncover search behavior patterns. ## Objectives * Analyze time-wise search interest of a keyword * Compare keyword popularity across 15 countries * Identify and 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 ├── # Proj...

We need a Python expert to build an end-to-end automated web-scraping and data-processing system for German B2B directories. The goal is to eliminate all manual steps — scraping, cleaning, deduplication, enrichment, and export — within 15 days. You’ll design and implement a high-speed, fault-tolerant pipeline that scrapes ~10 directories, normalizes data (including Umlauts and formatting), removes duplicates, detects updates, and exports clean structured data automatically. ⸻ Core Responsibilities • Develop asynchronous scraping 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 compa...

I need a Python-based copy-trading system focused on day trading equities and FnO instruments. My goal is to have one “master” account pushing trades in real time to multiple “slave” accounts, with latency low enough to keep fills within the same candle. Core functions I expect: • Fully automated order replication—entries, partial exits, stop-loss and target modifications. • Instant trade-signal alerts via Telegram or WebSocket so I can monitor activity even when terminals are 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 ke...

QuantConnect / LEAN Python Algo Trading Developer (IBKR) We are looking for an experienced algorithmic trading developer to build and optimize trading strategies for US stocks and ETFs using QuantConnect (LEAN) and Interactive Brokers (IBKR API). This is a hands-on development role focused on implementing and deploying systematic trading strategies. Scope of work Develop algorithmic trading strategies in Python (QuantConnect / LEAN) Backtesting and performance optimization Integrate strategies with Interactive Brokers (IBKR) Implement risk management and position sizing logic Work with historical and live market data Deploy and maintain strategies in live trading environment Analyze performance (PnL, Sharpe, drawdown) Requirements Proven experience in algorithmic trading / quan...

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 Jupyter n...

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 for: cl...

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 risky portf...

This homework requires a 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 ...

Je veux constituer un annuaire exhaustif de toutes les bases nautiques en France métropolitaine et DOM-TOM offrant jetski, bouée tractée, flyboard, ski nautique, wakeboard, parachute ascensionnel ou encore location de bateaux. Pour y parvenir, j’ai besoin d’un workflow automatisé reposant exclusivement sur Google Maps – c’est la source retenue – capable de collecter, dédupliquer puis nettoyer les données avant de les mettre en forme dans un CSV directement importable dans le CMS Wix. Les coordonnées devront être fournies en degrés décimaux. Livrables attendus • Un script réutilisable (Python + Selenium, Scrapy ou équivalent) qui interroge Google Maps, gère le r...

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...