Pandas Jobs

I have an Excel workbook that I need processed automatically. Your job is to build (or tidy up) a short Python script—pandas is fine—that will: • read the file I supply • keep only the rows where the value in the “company” column is an exact match to the keyword I pass in • prune every other field and leave me with just the “company” column • write the result straight out to UTF-8 CSV, no additional sorting required That’s the full requirement for this round. Please return the finished .py file plus a quick note on any third-party packages or command-line arguments I should know about so I can run it immediately.

I’m looking for a skilled data scientist who can take my raw cell-viability assay output and turn it into clear, publication-ready insights using Python, specifically Pandas for wrangling and Seaborn for plotting. The focus of this job is straightforward: perform comprehensive descriptive statistics on each experimental group or time point, visualise the results in an intuitive way, and give me a short written interpretation of what the numbers mean biologically. You will start from the CSV files I provide, tidy them as needed, calculate essentials such as mean, median, standard deviation, coefficient of variation and 95 % confidence intervals, then visualise the findings (e.g. boxplots, bar charts with error bars). All code should be organised in a well-commented Jupyter notebook s...

I’m ready to turn my Nifty 50 option debit-spread idea into a fully automated strategy on Fyers. The core logic must follow Line Break chart rules—no other indicators—so every entry and exit will be driven strictly by the block changes those charts create. I need the Python code that translates that logic into live orders for both buying and selling legs of the spread. Here’s what matters most to me: • A clean, well-commented Python script that connects to the Fyers API and places the debit-spread orders automatically. • Parameters (strike distance, quantity, Line Break reversal size, time filters, etc.) exposed in one place so I can tweak them without digging through the code. • Robust error handling and position tracking so the script knows exa...

I have an Excel workbook that I need processed automatically. Your job is to build (or tidy up) a short Python script—pandas is fine—that will: • read the file I supply • keep only the rows where the value in the “company” column is an exact match to the keyword I pass in • prune every other field and leave me with just the “company” column • write the result straight out to UTF-8 CSV, no additional sorting required That’s the full requirement for this round. Please return the finished .py file plus a quick note on any third-party packages or command-line arguments I should know about so I can run it immediately.

I need a clear, insight-driven analysis of my customer data with one objective in mind: pinpoint where we can lift overall satisfaction. You’ll receive anonymised purchase history, support-ticket logs, and survey scores in CSV format; from there I expect you to uncover trends, pain points, and the factors most strongly correlated with happy—or unhappy—customers. Your final hand-off should include: • A concise written summary of key findings, plainly tied to customer-experience improvements • Visualisations or dashboards that let a non-analyst grasp the story quickly • Actionable recommendations ranked by impact and effort SQL and either Python (pandas, scikit-learn) or R are the preferred tools, but if you have a proven workflow in something else th...

**Key Skills** • Data Analysis using Python • Data Cleaning and Data Preprocessing • Exploratory Data Analysis (EDA) • Machine Learning Model Development • SQL for Data Querying and Data Management • Data Visualization using Matplotlib and Seaborn • Libraries: Pandas, NumPy, Scikit-learn • Problem Solving and Analytical Thinking • Clear Communication and Insight Reporting About Me : I am a data analyst and machine learning enthusiast with hands-on experience in analyzing datasets and extracting meaningful insights using Python and SQL. I specialize in data cleaning, exploratory data analysis, and building machine learning models that help identify patterns and support data-driven decision making. I work with tools and libraries such as Panda...

I have a raw set of customer records that must move from messy spreadsheets to a structured, analysis-ready format. The job starts in Excel—think Power Query, advanced formulas, or VBA for quick wins—then shifts to Python (Pandas, NumPy, openpyxl) so the process can run automatically whenever fresh data arrives. Here’s what I need from you: first, clean and normalise the fields (remove duplicates, unify date and address formats, fill or flag missing values). Next, reshape the information into tidy tables that feed our reporting model. Finally, package everything into a repeatable Python script that reads the latest file, runs the full transformation pipeline, and exports polished XLSX/CSV outputs along with a simple log of what changed. Deliverables • One fully d...

I have an existing Python script whose sole purpose is data extraction: it turns PDF documents into CSV files. Right now the results are incomplete and the code is a little fragile. I need it tightened up so every piece of data in each PDF is captured and written to a clean, well-structured CSV. What I already have • A working—but imperfect—Python script • Sample PDFs that show the range of layouts the tool must handle • A sample CSV that illustrates the column order I expect What needs to improve • Reliable parsing across multiple pages and varied table structures • Accurate capture of every field, not just the obvious text blocks • Clear, readable code with comments so future tweaks are simple • A straightforward command-line...

I have three full years of trend data sitting in a single Excel workbook. Each row records a two-digit value, and I now need a machine-learning model that can reliably forecast both the “inside” and “outside” four-digit combinations drawn from the digits 0-9. Historical trends are the only signal I want the model to learn from; please do not add external data or random sampling. Target performance is 90 % prediction accuracy, measured on a hold-out set we will agree on before training begins. I will share the Excel file once we start; you deliver: • Clean, well-commented code (Python preferred) that loads the spreadsheet, prepares the data, trains the model, and outputs predictions. • A brief write-up of feature engineering, model choice, and validatio...

I am ready to dive into natural language processing and would like structured, hands-on coaching that takes me from the basics to building reliable text-classification models. My preferred language is Python, so examples should rely on common stacks such as Jupyter Notebook, Pandas, scikit-learn or, when it makes sense, PyTorch and Hugging Face Transformers. Java and R are not in scope for this engagement. Here is what I need from you: • A clear learning roadmap that starts with data cleaning and exploratory analysis, then walks through feature engineering (tokenisation, embeddings, etc.), model selection, training, evaluation and deployment. • Well-commented Python notebooks and sample datasets so I can reproduce every step on my own machine. • Short explanations ...

I have a raw set of customer records that must move from messy spreadsheets to a structured, analysis-ready format. The job starts in Excel—think Power Query, advanced formulas, or VBA for quick wins—then shifts to Python (Pandas, NumPy, openpyxl) so the process can run automatically whenever fresh data arrives. Here’s what I need from you: first, clean and normalise the fields (remove duplicates, unify date and address formats, fill or flag missing values). Next, reshape the information into tidy tables that feed our reporting model. Finally, package everything into a repeatable Python script that reads the latest file, runs the full transformation pipeline, and exports polished XLSX/CSV outputs along with a simple log of what changed. Deliverables • One fully d...

I have the basic framework of a Python-based web app up and running, but I need a seasoned developer to help me take it over the finish line. The app’s sole focus is data management, and I want it to excel at the three core functions I’ve already planned: Data storage, Data analysis, and Data visualization. Right now the project uses Python 3.11 with Flask, SQLAlchemy, and a PostgreSQL database. The back-end is roughly 70 % complete; models exist, routes are mapped, and authentication is in place. What’s missing are the polished features that turn this code into a product: optimizing queries, refining the analysis layer (Pandas, NumPy), and wiring up interactive charts (Plotly or ) so users can explore results in real time. A light React front end is already scaffolded...

I have a raw set of customer records that must move from messy spreadsheets to a structured, analysis-ready format. The job starts in Excel—think Power Query, advanced formulas, or VBA for quick wins—then shifts to Python (Pandas, NumPy, openpyxl) so the process can run automatically whenever fresh data arrives. Here’s what I need from you: first, clean and normalise the fields (remove duplicates, unify date and address formats, fill or flag missing values). Next, reshape the information into tidy tables that feed our reporting model. Finally, package everything into a repeatable Python script that reads the latest file, runs the full transformation pipeline, and exports polished XLSX/CSV outputs along with a simple log of what changed. Deliverables • One fully d...

I need a cloud-based AI solution that focuses on procurement analysis. The system must ingest our purchasing data, evaluate each vendor on pricing comparison, quality of goods/services, and overall reliability, then deliver concise, visual reports every single day. Your work should cover data pipeline design, model training or configuration, and a lightweight dashboard or API so my team can pull the insights straight into our ERP. I’m open to AWS, Azure, or GCP; if you prefer Python with libraries such as Pandas, scikit-learn, or TensorFlow, that fits our stack well. Acceptance criteria – the project is complete when: • Daily reports arrive automatically without manual triggers. • Each report scores vendors on price, quality, and reliability, flagging outlie...

Florida Judiciary Web Scraper — Config-Driven, Resilient Architecture I need a Python-based web scraping application to collect judge data from all 20 Florida judicial circuits and output it to a standardized CSV. The tool must be built for long-term maintainability — when a circuit website changes layout, only minimal configuration updates should be needed, not code rewrites. Background: Florida has 20 circuits covering 67 counties. Each circuit publishes judge data differently: some offer Excel/CSV downloads, others publish HTML pages and subpages with varying structures. The master data source is: Required Output Fields: (CSV)ID, Type, Name, Lastname, Assistant, Phone, Location, Street, City, State, Zip, County, Circuit, District, Courtroom, Hearingroom, Subdivision(S...

We are conducting a research project in Geospatial Artificial Intelligence and Remote Sensing that supports an academic manuscript submission to journals such as: ISPRS International Journal of Geo-Information IEEE Journal of Selected Topics in Applied Earh Observations and Remote Sensing The system integrates: Satellite image processing Computer vision object detection Large Language Model (LLM) enrichment Retrieval-Augmented Generation (RAG) The engineering system already exists. Your role is to design and execute rigorous ML experiments and evaluation pipelines to support publication-quality results. Infrastructure and deployment will be handled by a DevOps engineer. System Architecture The pipeline is based on the AWS open-source geospatial processing framework: OSML ModelRunner Refer...

I’m looking for someone experienced in Python and data analysis to help structure a clean Exploratory Data Analysis (EDA) in a Jupyter Notebook. The goal is to explore a dataset and clearly document the insights. Tasks include: - Overview of the dataset structure and features - Analysis of missing values, outliers, and anomalies - Basic data cleaning with explanation - Visualizations and observations - Including one additional open-source dataset for comparison - Running a simple baseline model Deliverable: - Jupyter Notebook exported as a PDF (minimum ~3 pages) - GitHub link containing the notebook Experience with Python, Jupyter, pandas, and basic machine learning preferred.

Project Brief – Customer Churn Prediction for Telecom Project Goal: The project aims to predict customer churn in the telecommunications industry. By identifying customers likely to leave, telecom companies can take proactive retention actions, reduce revenue loss, and improve customer satisfaction. Data Used: The dataset includes customer demographics, service subscriptions, contract details, billing information, and payment methods. Project Steps: Data Cleaning: Handling missing values and correcting inconsistencies. Exploratory Data Analysis (EDA): Understanding patterns and key factors affecting churn. Feature Engineering: Creating new variables to improve model performance. Model Building: Developing a machine learning classification model to predict churn. Technologies & To...

Project Brief – Customer Churn Prediction for Telecom Project Goal: The project aims to predict customer churn in the telecommunications industry. By identifying customers likely to leave, telecom companies can take proactive retention actions, reduce revenue loss, and improve customer satisfaction. Data Used: The dataset includes customer demographics, service subscriptions, contract details, billing information, and payment methods. Project Steps: Data Cleaning: Handling missing values and correcting inconsistencies. Exploratory Data Analysis (EDA): Understanding patterns and key factors affecting churn. Feature Engineering: Creating new variables to improve model performance. Model Building: Developing a machine learning classification model to predict churn. Technologies & To...

We are conducting a research project in Geospatial Artificial Intelligence and Remote Sensing that supports an academic manuscript submission to journals such as: ISPRS International Journal of Geo-Information IEEE Journal of Selected Topics in Applied Earh Observations and Remote Sensing The system integrates: Satellite image processing Computer vision object detection Large Language Model (LLM) enrichment Retrieval-Augmented Generation (RAG) The engineering system already exists. Your role is to design and execute rigorous ML experiments and evaluation pipelines to support publication-quality results. Infrastructure and deployment will be handled by a DevOps engineer. System Architecture The pipeline is based on the AWS open-source geospatial processing framework: OSML ModelRunner Refer...

I have a collection of datasets that need to be explored from every angle. First, I want a clear descriptive snapshot that tells me what is happening in the data right now. From there, I need predictive models that forecast future trends, and finally prescriptive recommendations that translate those forecasts into concrete, data-driven actions. You are free to move between Excel pivot tables, Python (preferably pandas, scikit-learn, and matplotlib or seaborn), and R (tidyverse, ggplot2, caret) as each step requires. The end goal is a versatile analysis that can power business decisions, support an academic write-up, and improve day-to-day operational efficiency. Please include: • Cleaned and well-documented datasets • Reproducible Excel workbooks, Python scripts or notebo...

I have several years of historical sales records spread across CSV files, Excel workbooks, and a database. I want you to turn that mixed-source information into a clean, consistent dataset, explore it for trends and seasonality, and then build a machine-learning model that can reliably project future sales. Data preparation must cover every stage—handling missing values, normalising numeric fields, and performing any transformations needed to feed the algorithms. Once the data is tidy, run an exploratory analysis so I can clearly see the patterns that drive revenue, especially how previous sales, marketing spend, and seasonal shifts interact. For modelling, I am open to linear regression, Random Forest, ARIMA, or another regression-based technique you can justify in the report. The...

I have a collection of customer reviews that must be transformed into clear, data-driven insight. The raw files might arrive as CSV, Excel, or even straight from a database—the format is flexible—so the first task is to clean and standardise whatever I supply. That means handling empty rows, removing noise (HTML tags, punctuation, stop-words, etc.) and normalising the text. Once the data is tidy I need robust sentiment classification. Please build the pipeline in Python, making sensible use of NLTK and/or TextBlob for tokenisation, lemmatisation and polarity scoring. The reviews span more than one language, but the immediate focus is on the English subset; your code should therefore detect language and process only English for this milestone while staying extensible for future...

I have a collection of customer reviews that must be transformed into clear, data-driven insight. The raw files might arrive as CSV, Excel, or even straight from a database—the format is flexible—so the first task is to clean and standardise whatever I supply. That means handling empty rows, removing noise (HTML tags, punctuation, stop-words, etc.) and normalising the text. Once the data is tidy I need robust sentiment classification. Please build the pipeline in Python, making sensible use of NLTK and/or TextBlob for tokenisation, lemmatisation and polarity scoring. The reviews span more than one language, but the immediate focus is on the English subset; your code should therefore detect language and process only English for this milestone while staying extensible for future...

Python Data Analysis & Machine Learning Project Description: Hi, I need a freelancer to help with a data analysis and machine learning project. The project includes: Data Cleaning & Preprocessing: Handle missing values, remove duplicates, encode categorical data. Prepare the dataset for analysis and ML model training. Data Analysis & Visualization: Analyze the dataset to identify trends, patterns, and insights. Create visualizations using Python libraries (Pandas, Matplotlib, Seaborn). Machine Learning Model Development: Build and train a supervised learning model (KNN, classification, or regression). Evaluate the model performance using metrics like accuracy, precision, recall. Documentation & Report: Provide a brief report of the analysis, model results, and r...