Pandas Jobs

I have a dataset made up entirely of categorical variables and I want to understand the hidden relationships inside it. The task is strictly exploratory: I am not asking for predictive modelling, only a deep dive that surfaces meaningful patterns and trends. What I expect from you • Clean the data where needed so that the exploratory work is reliable. • Use suitable techniques for categorical exploration—cross-tabulations, chi-square tests, association rules, clustering on encoded variables, or any other method you feel is insightful. • Present the findings in clear, non-technical language supported by concise visuals (bar charts, heat-maps, mosaic plots or similar). • Provide a short, well-commented notebook or script (Python with Pandas, NumPy, SciPy, s...

I’m looking for a seasoned Python professional to step in and keep my existing codebase running smoothly. The core of the project is a collection of data-processing scripts; several of them expose lightweight endpoints through Flask, so familiarity with that micro-framework is essential. Here’s what I need from you: trace and squash the occasional bug, clean up sections that have grown messy, and tune performance when large datasets start to slow things down. I’d also appreciate guidance on best practices—whether that means introducing unit tests, improving logging, or suggesting smarter dependency management—so the project remains healthy as it evolves. You’ll work from a private Git repository, submit pull requests with clear commit messages, and pro...

I am building a cinematic blast sequence and need solid data-driven support rather than eye-balling particle emitters. The idea is to run an AI/ML simulation that predicts how metal, glass, concrete, wood and plastic fragments fly away from the detonation point, then turn those numbers into clear data-visualisation charts I can hand directly to the FX team. Scope • Direction, speed and distance must be predicted separately for each material class, following the colour code I already use on set (red = metal, blue = glass, grey = concrete, orange = wood, green = plastic). • The charts have to highlight the mean and standard deviation for every variable so the artists immediately see the “typical” trajectory as well as the natural spread. Other metrics such as perce...

I have a dataset containing demographic details, job performance records and results from employee-engagement surveys. Your task is to turn this raw information into a reliable attrition-prediction pipeline. Work starts with careful cleaning and preprocessing: handle missing values, encode categorical variables, standardise or normalise where needed and document every step so the workflow is fully reproducible. A brief exploratory analysis should follow to highlight key attrition drivers and verify data quality before modelling. For the classifier, I’d like you to focus on K-Nearest Neighbours. If you find that another algorithm beats KNN convincingly, feel free to present the comparison—but please include KNN in the final report. Train, tune and validate the model, then eval...

I have a single CSV file that contains a mix of numerical columns and categorical labels. Before I can move on to analysis, the data needs a solid clean-up: remove or impute missing values, fix obvious data-entry anomalies, standardise text categories, and ensure each field is in the correct type. Once the dataset is tidy, I want to explore it visually. I am especially interested in bar charts, line graphs and scatter plots that help surface the main trends and relationships hidden in the numbers and categories. Feel free to suggest any additional plots that would add real insight; however, the three mentioned above are the minimum I need delivered. Python with pandas, NumPy and either Matplotlib or Seaborn is perfectly fine, but I am open to R or another proven toolset if you prefer...

I have a working Python script that reads an XLS sheet, logs in to Zerodha and fires trades; I now need the same workflow adapted for StoxKart. The new script must log in with a session token (no API key flow) and then: • Parse the XLS file row-by-row • Place the corresponding orders in StoxKart • Immediately fetch and record each order’s status so I can reconcile fills in the sheet Please keep the structure clean and modular so I can drop in different brokers later. If you have handled StoxKart’s session-based authentication before, that’s a plus. Deliver the fully-commented .py file along with any helper modules, and include a short README showing startup steps and the format the XLS parser expects.

I am preparing a quantitative study on “Inequality in participation versus visibility in online communities” . The raw material will come from one or more publicly-available Kaggle datasets; the challenge is to turn those data into a coherent, publication-level research project. Here is what I need from you: • Help me locate or combine the right Kaggle datasets, then document the download and preprocessing steps (Kaggle API, Python pandas, or R tidyverse are fine). • Define robust operational metrics for participation (e.g., post frequency, comment depth) and visibility (e.g., up-votes, follower counts, ranking on leaderboards). • Build the analysis pipeline—cleaning scripts, exploratory statistics, and the main inferential models (regression, GEE, or...

I need a reliable, repeatable way to run large batches of text data through a processing pipeline. The raw material typically lands in a folder as plain TXT or CSV files; once the script starts it should pick everything up, work through each file one after another, and write the processed results to a clearly named output directory. Core expectations • The workflow is fully automated: one command should launch the entire run. • Processing steps are modular so I can easily switch individual stages on or off later. • It must cope with thousands of lines per file without crashing or slowing to a crawl. • Clear logging to show each file’s status and any errors that occur. • Clean, well-commented source code plus a short README explaining setup and usag...

I need a Python-based trading algorithm that trades both the Nifty and Bank Nifty indices. The code should run locally on Python (feel free to lean on pandas, NumPy, TA-Lib, backtrader or similar libraries) and must be able to import and work with historical market data only—no live feed is required for this milestone. Here is what I expect: • A clean, well-commented Python script (or notebook) that ingests historical data, generates trade signals, executes the logic, and outputs detailed performance metrics and an equity curve. • Clear instructions on how to map the code to CSVs or API endpoints I already use for historical NSE data. • A short README explaining any configurable parameters so I can tweak settings for further experiments. Back-testing accuracy, ...

Job Title: Python Developer for Geophysical Data Analysis (K-Means Clustering & 3D Visualization) Project Overview: I am a Geophysics student working on a structural mapping project of the Red Sea Rift. I have a dataset of 50 years of earthquake records (USGS CSV format) and need a Python expert to build a machine learning workflow that clusters these events based on their seismic attributes to identify hidden fault structures. Scope of Work: Data Pre-processing: Clean a USGS earthquake catalog and perform feature scaling on four specific attributes: Latitude, Longitude, Depth, and Magnitude. Unsupervised Machine Learning: * Implement K-Means Clustering to group seismic events. Provide an Elbow Method plot to justify the optimal number of clusters (K). 3D Visualization: * Create ...