README.md

---
license: cc-by-nc-4.0
language:
- it
tags:
- salaries
- italy
- tech
- survey
- compensation
pretty_name: Datapizza Salaries
size_categories:
- 10K<n<100K
dataset_info:
  features:
    - name: jobTitle
      dtype: string
    - name: monthsOfExperience
      dtype: int64
    - name: educationType
      dtype: string  # nullable
    - name: companyIndustry
      dtype: string  # nullable
    - name: companySize
      dtype: string  # nullable
    - name: province
      dtype: string
    - name: workMode
      dtype: string
    - name: technologies
      sequence: string
    - name: salary
      dtype: float64
    - name: age
      dtype: int64  # nullable
    - name: gender
      dtype: string  # nullable
    - name: aiUsageFrequency
      dtype: int64  # nullable
    - name: aiTechnologies
      sequence: string  # nullable
    - name: aiTaskTypes
      sequence: string  # nullable
    - name: aiUpdateSources
      sequence: string  # nullable
    - name: admiredTechnologies
      sequence: string  # nullable
    - name: editor
      dtype: string  # nullable
    - name: rating
      dtype: int64  # nullable
    - name: valid
      dtype: bool
    - name: submittedAt
      dtype: string
---

# Datapizza Salaries

A crowd-sourced dataset of salaries from tech workers in Italy, collected via anonymous survey by [Datapizza](https://salaries.datapizza.tech).

## Dataset Description

This dataset contains self-reported salary and professional information from technology workers across Italy. Data is collected through an anonymous survey and updated weekly.

> **Note:** Data submitted before November 6th, 2024 contains only partial information, as the initial survey version did not collect fields like `educationType`, `age`, `aiUsageFrequency`, and others.

## Features

| Field | Type | Description |
|-------|------|-------------|
| `jobTitle` | string | Job title (e.g., "software_developer", "data_scientist") |
| `monthsOfExperience` | int | Professional experience in months |
| `educationType` | string | Education level (optional) |
| `companyIndustry` | string | Industry sector (optional) |
| `companySize` | string | Company headcount range (optional) |
| `province` | string | Italian province |
| `workMode` | string | Work arrangement |
| `technologies` | list[string] | Primary technologies/skills used |
| `salary` | float | RAL (Gross Annual Salary) in EUR |
| `age` | int | Respondent age (optional) |
| `gender` | string | Gender (optional) |
| `aiUsageFrequency` | int | AI usage frequency 1-5 (optional) |
| `aiTechnologies` | list[string] | AI tools used (optional) |
| `aiTaskTypes` | list[string] | AI task types (optional) |
| `aiUpdateSources` | list[string] | AI news sources (optional) |
| `admiredTechnologies` | list[string] | Technologies respondent wants to learn (optional) |
| `editor` | string | Primary code editor/IDE (optional) |
| `rating` | int | Survey feedback rating 1-5 (optional) |
| `valid` | bool | Submission validation flag |
| `submittedAt` | string | Submission timestamp (ISO 8601) |

## Categorical Values

### educationType
`high_school`, `bachelors_degree`, `masters_degree`, `phd`

### companySize
`1-10`, `11-50`, `51-200`, `201-500`, `501-1000`, `1001-5000`, `5001+`

### workMode
`onsite`, `hybrid`, `remote`

### gender
`male`, `female`, `non_binary`, `prefer_not_to_say`

### companyIndustry
`bancario`, `assicurativo`, `finanziario_altro`, `consulenza_servizi_professionali`, `tecnologia_ict`, `retail_gdo`, `moda_lusso`, `manifattura_generica`, `automotive`, `chimica_materiali`, `energia_utilities`, `telecomunicazioni`, `sanita_pharma_biotech`, `media_intrattenimento_editoria`, `logistica_trasporti`, `immobiliare_costruzioni`, `alimentare_beverage`, `agricoltura_agroindustria`, `turismo_ospitalita`, `istruzione_ricerca`, `non_profit_ong_associazioni`, `difesa_aerospazio`, `pubblica_amministrazione_enti_governativi`, `altro_indeterminato`

### aiTechnologies
`chat_gpt`, `claude`, `cursor`, `claude_code`, `gemini`, `microsoft_copilot`, `github_copilot`, `codeium`, `tabnine`, `amazon_code_whisperer`, `jetbrains_ai`, `perplexity`, `mistral_ai`, `llama`, `v0`, `windsurf`, `cody`, `supermaven`, `continue_dev`, `devin`, `replit_ai`, `bolt`

### aiTaskTypes
`coding`, `study_tutoring`, `doc_analysis`, `data_excel`, `editing`, `brainstorming`, `image_generation`, `translation`, `search_research`, `planning`, `meeting_notes`

### aiUpdateSources
`x_twitter`, `newsletter`, `reddit`, `hugging_face`, `hacker_news`, `youtube`, `linkedin`, `podcasts`, `discord`, `github`, `official_blogs`

### editor
`vscode`, `cursor`, `neovim`, `vim`, `zed`, `intellij_idea`, `webstorm`, `pycharm`, `sublime_text`, `emacs`, `atom`, `eclipse`, `visual_studio`, `android_studio`, `xcode`, `windsurf`, `rider`, `fleet`, `phpstorm`, `goland`, `rubymine`, `clion`, `notepad_plus_plus`, `helix`

## Usage
```python
from datasets import load_dataset

dataset = load_dataset("datapizza-ai-lab/salaries")
df = dataset["train"].to_pandas()

# Filter for remote workers in Milan
remote_milan = df[(df["province"] == "Milano") & (df["workMode"] == "remote")]

# Average salary by job title
avg_by_role = df.groupby("jobTitle")["salary"].mean().sort_values(ascending=False)
```

## Source

Data collected via [salaries.datapizza.tech](https://salaries.datapizza.tech) — an anonymous salary survey for Italian tech workers.

## License

**CC-BY-NC-4.0** — You may share and adapt this dataset with attribution for non-commercial purposes.

## Citation
```bibtex
@dataset{datapizza_salaries_2024,
  title={Datapizza Salaries: Italian Tech Worker Compensation Dataset},
  author={Datapizza},
  year={2024},
  url={https://huggingface.co/datasets/datapizza-ai-lab/salaries},
  license={CC-BY-NC-4.0}
}
```