pandas Experts in Austria
for reliable data work, matched with vetted freelancers in minutesHire experts who clean and transform complex datasets, build analysis workflows with Python and NumPy, and deliver production-ready DataFrame solutions. FRATCH matches you quickly and precisely with vetted, available freelancers for pandas projects.
Meet FRATCH Experts in Austria, who have recently used pandas
Stefan D.
Last position:
BI Consultant in Controlling at Reutter GmbH
- Extraction, transformation, and cleansing of data from Microsoft Dynamics AX
- Creation of sales reports in Power BI
- Training employees in business intelligence
- Technologies: Power BI, SQL, SQL Server Integration Services (SSIS)
Marcel S.
Last position:
Senior AI Engineer - Python at Insurance Company
Project Tech Stack: Python, AWS, Azure, FastAPI, openai, pandas, unittest/pymock
Achievements:
- Engineered automated data extraction pipelines to transform complex Excel datasets into structured formats via LLM-driven workflows.
- Architected a generative slide-deck engine that translates natural language prompts into formatted presentation assets.
- Integrated advanced LLM capabilities with the OpenAI Response API, implementing sophisticated tool-calling and structured output logic.
- Developed and containerized scalable backend microservice using FastAPI, Docker, and OpenShift to host and serve agentic skills.
Alexander L.
Last position:
Guest lecturer in Artificial Intelligence (Master’s Level) at FH des BFI Wien
- Teaching & presenting
- Communication
- Effectively communicate complex technical topics to non-technical audiences through lectures
- Guided non-technical students from zero knowledge to confidently understanding and applying algorithms to achieve business outcomes
Fabio G.
Last position:
IT Architect, Requirements Analyst and Consultant at CANCOM
- Supports CANCOM customers in migrating legacy on-prem systems to Microsoft Fabric and Microsoft Foundry
- Takes over and stabilizes existing solutions after a short handover
- Business analysis and requirements engineering for migration to a new cloud environment
- Optimization of machine learning models for feature extraction and customer profiling
- Ensures data protection and compliance
- Leads the migration of on-prem systems to Microsoft Fabric
- Designs new AI platforms for clients
- Tests the integration of chatbots for document intelligence with Microsoft Foundry, including requirements analysis, implementation, validation, and client communication
Hossein A.
Last position:
Datawarehouse Consultant at LENZING AG
- Consulting on the enterprise data-warehouse and reporting practice at one of Austria's largest industrial groups.
- Designing and standardizing Power BI dashboards and data-visualization governance for company-wide enterprise reporting.
- Developing a WCAG-compliant, colorblind-safe visualization standard (Okabe-Ito palette) to harmonize dashboards across the organization.
Jürgen E.
Last position:
Chief Executive Officer at Riddle&Code GmbH
- Pivoted the company into data-driven and AI-powered energy optimization solutions for microgrids, energy communities, and grid operators.
Christian S.
Last position:
Commercial Manager/CFO at MEV Independent Railway Services GmbH
- CFO, authorized signatory, commercial managing director: finance, HR, IT & organization, funding
- Achieved reorganization and 100% growth in staff and revenue over four years while keeping fixed costs constant
- Improved a negative equity ratio to plus 30%
- Implemented a modern IT and reporting system
Discover over 15,000 top freelancers
Statistics of experts using pandas
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
2.7 years
Positions per freelancer
12
Top business areas
Information Technology, Business Intelligence, Strategy
Top industries
Information Technology, Transportation, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
86%
Master's degree or higher
71%
Doctorate
14%
Certifications per freelancer
3
Most common languages
German, English, Persian
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Austria are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Austria using pandas
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
pandas experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (71%)
- Transportation (71%)
- Manufacturing (71%)
- Education (57%)
- Banking and Finance (43%)
- Retail (43%)
- Construction (29%)
- Energy (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
DataFrame foundations
pandas is Python’s leading library for working with structured and time-series data. Its DataFrame and Series objects support filtering, joining, grouping, reshaping and statistical analysis without forcing teams to manage low-level data structures. Companies use it to turn raw files, database extracts and API responses into reliable datasets for decisions and software products.
Practical use cases
pandas fits work that sits between data sources and business or analytical outputs. Specialists use it for repeatable preparation, exploration and reporting tasks such as:
- Cleaning inconsistent records and handling missing values
- Combining CSV, Excel, JSON, SQL and API data
- Preparing datasets for machine learning models
- Creating operational reports and analytical exports
Python ecosystem
Strong pandas work usually involves Python, NumPy and a clear understanding of data types, indexing and memory use. Professionals may also connect workflows with Jupyter, Matplotlib, Seaborn, scikit-learn, SQLAlchemy or Apache Arrow. For larger datasets, they assess whether pandas should work alongside Polars, Dask or a warehouse-based transformation layer.
When specialists help
Companies often bring in freelance expertise when a recurring spreadsheet process needs a dependable workflow, when source systems produce inconsistent data, or when an analysis must become reusable software. Support is also valuable during migrations from scripts to tested pipelines, audits of existing notebooks and preparation of data for forecasting or machine learning. In Austria, remote collaboration is common, while on-site workshops can help align teams around data definitions and reporting needs.
Quality signals
A capable professional does more than make a notebook produce an answer. They clarify the meaning and grain of each dataset, preserve data types, check duplicates and missing values, and make transformations easy to reproduce. Good deliverables include readable code, validation rules, documented assumptions and tests for edge cases. They also know when a slow operation, chained assignment or unnecessary copy could create reliability or performance problems.
Project fit
The right specialist depends on the data sources, refresh frequency, expected volume and final output. A short cleanup task may need focused pandas knowledge, while a production pipeline also calls for SQL, testing, orchestration, version control and deployment awareness. Before starting, define source ownership, output requirements, security constraints and how results will be reviewed. Austrian teams should also agree early on remote routines, meeting language and access to internal systems.
Frequently asked questions
Questions about pandas? Start with the answers below.
pandas is used for loading, cleaning, transforming and analyzing structured data in Python. Its DataFrame model is well suited to reporting, exploratory analysis, data preparation and repeatable workflows that combine files, databases and APIs.
pandas makes data transformations reproducible, reviewable and easier to automate than manual spreadsheet work. Spreadsheets remain useful for interactive review, but pandas is a stronger choice when datasets, source systems or recurring processes become complex.
A strong pandas specialist should usually understand SQL and NumPy because real projects often move data between databases, Python arrays and DataFrames. Knowledge of Jupyter, testing, visualization and a workflow tool is also valuable when the output must be maintained by a team.
The required experience depends on the data quality, transformation logic and delivery environment rather than on pandas alone. A focused cleanup may need a specialist for a short engagement, while a production pipeline calls for proven skills in testing, performance, source integration and documentation.
pandas can handle substantial datasets when transformations, data types and memory use are designed carefully. For data that exceeds available memory or needs distributed processing, a specialist may combine it with chunked processing, Dask, Polars, SQL or a cloud data warehouse.
A pandas project can usually be delivered remotely when secure access, sample data and clear acceptance criteria are available. Austrian teams should clarify communication language, data handling rules, review routines and whether occasional on-site workshops are needed.
Ask how the specialist validates inputs, handles missing and duplicate records, preserves types and tests transformation logic. High-quality pandas work is readable, reproducible and documented, with checks that expose silent changes instead of only producing a plausible final table.
pandas is often a practical choice for interactive analysis, Python-based preparation and workflows that rely on its mature ecosystem. Polars may suit performance-focused workloads, while database transformations are preferable when data should stay close to governed storage; the right choice depends on scale, latency, team skills and deployment needs.
The average hourly rate of freelancers in Austria who have used pandas in their recent projects is 107 €, which corresponds to a daily rate of about 858 € based on an 8-hour working day.
Of the freelancers in Austria who have used pandas in their recent projects, 86% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Austria who have used pandas in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2.7 years.
The most common languages among freelancers in Austria who have used pandas in their recent projects are German (100%), English (100%), and Persian (14%).
The most common industries among freelancers in Austria who have used pandas in their recent projects are Information Technology (71%), Transportation (71%), and Manufacturing (71%).
The most common business areas among freelancers in Austria who have used pandas in their recent projects are Information Technology (100%), Business Intelligence (86%), and Strategy (86%).
Main locations of FRATCH Experts, who have recently used pandas
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Vienna Graz 
Vienna Countries:
- Germany
- Austria
- Switzerland
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