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Datasets:
moncefem
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cnil-sanctions-2011-2025

Tasks:
Tabular Classification
Tabular Regression
Text Classification
Modalities:
Tabular
Text
Formats:
parquet
Languages:
French
English
Size:
< 1K
Tags:
gdpr
privacy
data-protection
cnil
regulatory
france
Libraries:
Datasets
pandas
Polars
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Dataset card Data Studio Files
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Initial dataset upload — generated by Gemma Miner

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Files changed (12) hide show
  1. README.md +467 -0
  2. _make_charts.py +233 -0
  3. charts/breach_themes.png +3 -0
  4. charts/sector_breakdown.png +3 -0
  5. charts/simplified_share.png +3 -0
  6. charts/yearly_fines.png +3 -0
  7. charts/yearly_volume.png +3 -0
  8. cnil_sanctions_analysis.csv +375 -0
  9. cnil_sanctions_analysis.parquet +3 -0
  10. codebook.md +14 -0
  11. insights.json +80 -0
  12. report.md +4 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ language:
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+ - fr
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+ - en
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+ size_categories:
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+ - n<1K
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+ task_categories:
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+ - tabular-classification
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+ - tabular-regression
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+ - text-classification
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+ tags:
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+ - gdpr
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+ - privacy
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+ - data-protection
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+ - cnil
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+ - regulatory
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+ - france
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+ - enforcement
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+ - sanctions
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+ - legal
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+ - cookies
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+ pretty_name: CNIL Sanctions 2011 – 2025
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: cnil_sanctions_analysis.parquet
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+ ---
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+
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+ # CNIL Sanctions Dataset — 2011 → 2025
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+
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+ > A typed, statistics-ready dataset of **374 sanctions** issued by the French
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+ > data protection authority (CNIL — *Commission Nationale de l'Informatique
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+ > et des Libertés*) between **2011 and 2025**, structured into **34
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+ > analytical variables** for quantitative research on GDPR / French privacy
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+ > enforcement.
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+ >
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+ > Generated end-to-end (scrape → typed schema → per-row extraction → export)
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+ > by **[Gemma Miner](https://github.com/moncifem/gemma-miner)** — an
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+ > autonomous text-to-dataset agent that turns any website into a
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+ > research-grade dataset in minutes.
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+
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+ ## TL;DR — the big takeaways
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+
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+ - The CNIL became **roughly 5× more active** between 2014 and 2024 (18 → 86
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+ decisions per year). 2025 is on the same trajectory (83 decisions
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+ through Q3).
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+ - The **simplified procedure**, introduced in 2022, now drives **80 % of
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+ yearly volume** — it has transformed CNIL enforcement from "a few
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+ high-profile cases per year" into a continuous stream of mid-size
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+ decisions.
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+ - **Volume and money decoupled in 2024.** That year had the highest
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+ decision count on record (86) but the *lowest* aggregate disclosed fines
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+ in seven years (**€55 M**). The simplified procedure trades severity for
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+ throughput.
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+ - **2025 then exploded to €487 M in disclosed fines** — on just two
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+ mega-decisions (€325 M and €150 M, both Sept 1, both cookies & consent).
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+ Total fines across 2011-2025: **€1.14 B**, dominated by a handful of
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+ adtech/big-tech rulings.
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+ - The **single most common breach theme is security of processing**
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+ (Art. 32 GDPR — 36 % of all sanctions), followed by **information /
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+ transparency obligations** (34 %) and **data minimisation** (30 %).
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+ Cookie/consent appears in only 14 % of decisions but dominates the
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+ highest-value decisions.
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+ - **Private companies** account for **74 %** of sanctioned entities; the
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+ public sector and associations together make up only ~14 %.
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+
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+ ## Quick start
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+
71
+ <details>
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+ <summary><b>📥 Load with 🤗 datasets</b> (click to expand)</summary>
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+
74
+ ```python
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+ from datasets import load_dataset
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+
77
+ ds = load_dataset("moncefem/cnil-sanctions-2011-2025", split="train")
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+ print(ds[0])
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+ print(ds.features)
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+ ```
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+ </details>
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+
83
+ <details>
84
+ <summary><b>🐼 Load with pandas (no `datasets` install needed)</b></summary>
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+
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+ ```python
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+ import pandas as pd
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+
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+ df = pd.read_parquet(
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+ "hf://datasets/moncefem/cnil-sanctions-2011-2025/cnil_sanctions_analysis.parquet"
91
+ )
92
+ print(df.shape) # (374, 34)
93
+ print(df.dtypes)
94
+ ```
95
+ </details>
96
+
97
+ <details>
98
+ <summary><b>🦆 Load with DuckDB (in-process SQL)</b></summary>
99
+
100
+ ```python
101
+ import duckdb
102
+
103
+ con = duckdb.connect()
104
+ con.execute("""
105
+ CREATE VIEW cnil AS
106
+ SELECT * FROM read_parquet(
107
+ 'hf://datasets/moncefem/cnil-sanctions-2011-2025/cnil_sanctions_analysis.parquet'
108
+ )
109
+ """)
110
+ print(con.execute("SELECT n_sanction_year, COUNT(*) AS n, SUM(amount_fine_eur)/1e6 AS fines_meur "
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+ "FROM cnil GROUP BY 1 ORDER BY 1").df())
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+ ```
113
+ </details>
114
+
115
+ ---
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+
117
+ ## Charts at a glance
118
+
119
+ ### 1. Enforcement volume is growing fast — and the simplified procedure drives it
120
+
121
+ ![Sanctions per year, by procedure type](charts/yearly_volume.png)
122
+
123
+ Yearly counts were stable at ~10–18 from 2011 to 2021, then exploded after
124
+ the **simplified procedure** was introduced (loi du 24 janvier 2022).
125
+ 2024 saw **86 sanctions**, of which **80 % were simplified** — a regime
126
+ change, not just a trend line.
127
+
128
+ ![Share of simplified procedure](charts/simplified_share.png)
129
+
130
+ <details>
131
+ <summary><b>🔬 Reproduce these charts</b></summary>
132
+
133
+ ```python
134
+ import pandas as pd
135
+ import matplotlib.pyplot as plt
136
+
137
+ df = pd.read_parquet("cnil_sanctions_analysis.parquet")
138
+ yearly = df["n_sanction_year"].dropna().astype(int).value_counts().sort_index()
139
+ simp = df[df["is_simplified_procedure"] == True]["n_sanction_year"] \
140
+ .dropna().astype(int).value_counts().reindex(yearly.index, fill_value=0)
141
+ std = yearly - simp
142
+
143
+ fig, ax = plt.subplots(figsize=(10, 5))
144
+ ax.bar(yearly.index, std, label="standard")
145
+ ax.bar(yearly.index, simp, bottom=std, label="simplified")
146
+ ax.set_title("CNIL sanctions per year, by procedure type")
147
+ ax.legend(); plt.show()
148
+ ```
149
+ </details>
150
+
151
+ ### 2. Volume and money decoupled — and 2025 broke the trend
152
+
153
+ ![Aggregate disclosed fines per year (€M)](charts/yearly_fines.png)
154
+
155
+ Two observations the chart makes obvious:
156
+
157
+ 1. **2024 is the inversion year.** 86 decisions (highest ever) but only
158
+ **€55 M** in disclosed fines (lowest since 2019). The simplified
159
+ procedure produces mid-size sanctions; the CNIL went broad rather than
160
+ deep.
161
+ 2. **2025 is the rebound.** €487 M in nine months — almost all of it
162
+ from two cases on Sept 1 (€325 M + €150 M, both consent / cookies).
163
+ On a per-decision basis, 2025 is by far the most expensive year on
164
+ record.
165
+
166
+ The €1.14 B aggregate hides a fat-tailed distribution: the median fine
167
+ sits at **€10 000**, but the top decile drives essentially all of the
168
+ monetary value. Practically every year past 2019 has one or two
169
+ decisions ≥ €40 M.
170
+
171
+ **Top 5 fines in the dataset**:
172
+
173
+ | Date | Organisation type | Fine | Main breaches |
174
+ |------------|---------------------------------------------------------------------|-----------|---------------------------------------------------------------------|
175
+ | 2025-09-01 | Société développant plusieurs services en ligne | **€325 M**| Cookies (information & consent) + unsolicited commercial messaging |
176
+ | 2025-09-01 | Société de vente en ligne (vêtements, chaussures, accessoires) | **€150 M**| Cookies (information & consent) |
177
+ | 2021-12-31 | Services internet (moteur de recherche, plateforme de vidéos) | **€150 M**| Cookie-refusal UX |
178
+ | 2020-12-07 | Société de services technologiques | **€100 M**| Cookies + information + consent + opposition |
179
+ | 2022-12-19 | Vendeur OS / logiciels / matériels | **€60 M** | Cookies & trackers consent |
180
+
181
+ Cookies / consent under the ePrivacy directive (Art. 82 LIL) — not GDPR
182
+ fines per se — produce the most expensive decisions in the dataset.
183
+
184
+ <details>
185
+ <summary><b>🔬 Query the top fines yourself</b></summary>
186
+
187
+ ```python
188
+ import pandas as pd
189
+ df = pd.read_parquet("cnil_sanctions_analysis.parquet")
190
+ top = (
191
+ df.dropna(subset=["amount_fine_eur"])
192
+ .sort_values("amount_fine_eur", ascending=False)
193
+ .head(10)
194
+ [["dn_sanction", "organism_type_raw", "amount_fine_eur", "main_breaches_raw"]]
195
+ )
196
+ print(top.to_string(index=False))
197
+ ```
198
+ </details>
199
+
200
+ ### 3. Most sanctioned entities are companies
201
+
202
+ ![Sanctions by sector](charts/sector_breakdown.png)
203
+
204
+ | Sector | Share |
205
+ |-------------------------|---------|
206
+ | Private company | **74 %** |
207
+ | Public administration | 10 % |
208
+ | Professional individual | 7 % |
209
+ | Association | 5 % |
210
+ | Political party | 4 % |
211
+ | Other | < 1 % |
212
+
213
+ <details>
214
+ <summary><b>🔬 Compute the sector / breach-mix crosstab</b></summary>
215
+
216
+ ```python
217
+ import pandas as pd
218
+ df = pd.read_parquet("cnil_sanctions_analysis.parquet")
219
+ breach_cols = [c for c in df.columns if c.startswith("is_breach_")]
220
+ ct = df.groupby("cat_sector_group")[breach_cols].mean().round(2)
221
+ print(ct.to_string())
222
+ ```
223
+ </details>
224
+
225
+ ### 4. Security failures dominate the breach mix
226
+
227
+ ![Breach themes detected](charts/breach_themes.png)
228
+
229
+ Of the eight breach themes in the codebook, the top of the distribution is:
230
+
231
+ | Rank | Theme | n | share |
232
+ |------|------------------------------------|-----|-------|
233
+ | 1 | Security of processing (Art. 32) | 134 | 36 % |
234
+ | 2 | Information / transparency | 126 | 34 % |
235
+ | 3 | Data minimisation | 111 | 30 % |
236
+ | 4 | Rights of data subjects | 83 | 22 % |
237
+ | 5 | Lawful basis: consent | 69 | 18 % |
238
+ | 6 | Cookies / trackers (Art. 82 LIL) | 51 | 14 % |
239
+ | 7 | Special-category data (Art. 9) | 38 | 10 % |
240
+ | 8 | Sub-processor obligations | 25 | 7 % |
241
+
242
+ Most decisions involve **more than one** theme — the median
243
+ `n_breaches` per row is 2. Note the inversion between **frequency** and
244
+ **severity**: cookies/consent appears in only 14 % of decisions but
245
+ drives **most of the monetary value** (every fine ≥ €60 M in the dataset
246
+ is a cookies/consent case).
247
+
248
+ (These flags are detected from each decision's public CNIL summary;
249
+ `null` means the summary doesn't address that theme — it is **not**
250
+ evidence the breach didn't occur.)
251
+
252
+ ---
253
+
254
+ ## Suggested research questions
255
+
256
+ This dataset is sized for fast iteration on questions like:
257
+
258
+ 1. **Has the simplified procedure changed the WHO of enforcement?** Compare
259
+ sector mix in pre-2022 vs post-2022 decisions.
260
+ 2. **Does sanction severity predict the breach mix?** Cookie/consent
261
+ appears to over-index in the €40 M+ tail — is that statistically
262
+ robust?
263
+ 3. **Are public-sector and private-sector breach profiles different?**
264
+ `is_breach_security` looks higher in the public set; quantify it.
265
+ 4. **What share of fines are GDPR vs ePrivacy/cookies?** Many of the
266
+ biggest "fines" in the dataset are L.82 cookies cases, not strict
267
+ GDPR Art. 83. The `decision_raw` field lets you split.
268
+ 5. **Year-on-year severity:** has the median (not just sum) of fines
269
+ moved? Compute on `amount_fine_eur` excluding nulls.
270
+
271
+ <details>
272
+ <summary><b>🔬 Quick answer sketch for Q1 (pre-2022 vs post-2022 sector mix)</b></summary>
273
+
274
+ ```python
275
+ import pandas as pd
276
+ df = pd.read_parquet("cnil_sanctions_analysis.parquet")
277
+ df["era"] = df["n_sanction_year"].apply(lambda y: "pre_2022" if y < 2022 else "from_2022")
278
+ print(
279
+ df.groupby(["era", "cat_sector_group"]).size()
280
+ .unstack(fill_value=0)
281
+ .apply(lambda r: (r / r.sum() * 100).round(1), axis=1)
282
+ )
283
+ ```
284
+ </details>
285
+
286
+ <details>
287
+ <summary><b>🔬 Quick answer sketch for Q5 (year-on-year severity)</b></summary>
288
+
289
+ ```python
290
+ import pandas as pd
291
+ df = pd.read_parquet("cnil_sanctions_analysis.parquet")
292
+ agg = (
293
+ df.dropna(subset=["amount_fine_eur"])
294
+ .groupby(df["n_sanction_year"].astype(int))["amount_fine_eur"]
295
+ .agg(["count", "median", "mean", "sum"])
296
+ )
297
+ agg[["median", "mean", "sum"]] = agg[["median", "mean", "sum"]] / 1e3 # €k
298
+ print(agg.round(1).to_string())
299
+ ```
300
+ </details>
301
+
302
+ ---
303
+
304
+ ## Codebook (34 columns)
305
+
306
+ All columns are explicitly typed. Where a row lacks evidence in the public
307
+ summary, the value is **`null`** rather than a fabricated default (this is
308
+ deliberate — see "Limitations" below).
309
+
310
+ ### Identification & metadata
311
+
312
+ | Column | Type | Description |
313
+ |-----------------------|---------|-------------|
314
+ | `id` | string | Deterministic content-hash id (stable across runs) |
315
+ | `dn_sanction` | date | Decision date, ISO `YYYY-MM-DD` |
316
+ | `n_sanction_year` | integer | Calendar year of the decision |
317
+ | `sanction_date` | string | Raw date string as scraped (`DD/MM/YYYY`) |
318
+ | `sanction_year` | integer | Same year, kept for cross-checks |
319
+ | `organism_name` | string | Name of the entity (often anonymised) |
320
+ | `organism_type_raw` | string | Raw CNIL category label (French) |
321
+ | `main_breaches_raw` | string | CNIL "principal failings / themes" text (French) |
322
+ | `decision_raw` | string | CNIL "decision adopted" text (French) |
323
+ | `decision_title` | string | Full title incl. délibération id where present |
324
+ | `decision_url` | string | Légifrance URL of the published decision (when published) |
325
+
326
+ ### Sanction type & severity
327
+
328
+ | Column | Type | Description |
329
+ |------------------------------|---------|-------------|
330
+ | `has_fine` | boolean | Administrative fine present |
331
+ | `has_injunction` | boolean | Injunction (mise en demeure) accompanies the decision |
332
+ | `has_astreinte` | boolean | Periodic penalty payment (astreinte) attached |
333
+ | `has_warning` | boolean | Public warning (avertissement public) issued |
334
+ | `amount_fine_eur` | float | Fine amount in euros (NaN when none / undisclosed) |
335
+ | `cat_fine_bucket` | enum | `none`, `under_10k`, `under_100k`, `under_1m`, `over_1m` |
336
+ | `n_decision_severity_score` | integer | 1–5 severity proxy synthesised from sanction type + amount |
337
+
338
+ ### Procedure & sector
339
+
340
+ | Column | Type | Description |
341
+ |------------------------------|---------|-------------|
342
+ | `cat_procedure_type` | enum | `simplified`, `standard` (or null) |
343
+ | `is_simplified_procedure` | boolean | True if the decision used the simplified procedure |
344
+ | `cat_sector_group` | enum | `private_company`, `public`, `association`, `professional_individual`, `political`, `other` |
345
+ | `is_public_sector` | boolean | Sanctioned entity is a public body |
346
+ | `is_health_related` | boolean | Health context (hospital, médecin, mutual, …) |
347
+ | `is_digital_platform` | boolean | Online / platform context |
348
+ | `has_external_decision_link` | boolean | Decision links to a published Légifrance text |
349
+
350
+ ### Breach / theme flags (extracted from each decision's summary)
351
+
352
+ | Column | Theme |
353
+ |------------------------------|-------|
354
+ | `is_breach_security` | Art. 32 GDPR — security of processing |
355
+ | `is_breach_transparency` | Information / transparency obligations |
356
+ | `is_breach_consent` | Lawful basis: consent |
357
+ | `is_breach_data_rights` | Rights of data subjects (access, erasure, opposition) |
358
+ | `is_breach_cookies` | Cookies / trackers (Art. 82 LIL) |
359
+ | `is_breach_minimization` | Data minimisation |
360
+ | `is_breach_processor` | Sub-processor / contractual obligations |
361
+ | `is_involves_sensitive_data` | Special-category data (Art. 9 GDPR) |
362
+ | `n_breaches` | Integer count of distinct breach themes in the decision |
363
+
364
+ ---
365
+
366
+ ## How this dataset was built
367
+
368
+ This file was produced by **[Gemma Miner](https://github.com/moncifem/gemma-miner)**
369
+ in a single agent run — about **9 minutes** end-to-end on
370
+ `openrouter/google/gemini-3.1-flash-lite`:
371
+
372
+ 1. **Harvest** — agent fetched the CNIL listing page, auto-generated a regex
373
+ extractor over the HTML table, and queued 374 rows (one per sanction).
374
+ 2. **Codebook design** — an LLM proposed ~30 typed variables matching the
375
+ analytical brief (sector, procedure, breach themes, fine bucket,
376
+ severity score).
377
+ 3. **Per-row extraction** — for each sanction, an LLM read the French
378
+ text and emitted a JSON object conforming to the codebook; the system
379
+ then deterministically coerced values to the declared types (dates →
380
+ ISO, numbers → float, enums snapped to nearest valid value, ambiguous
381
+ booleans → null).
382
+ 4. **Export** — parquet + CSV + this card + charts.
383
+
384
+ No fine-tuning. No labelled training data. Reproducible from the upstream
385
+ URL on any day.
386
+
387
+ <details>
388
+ <summary><b>🔬 Rebuild this dataset from scratch</b></summary>
389
+
390
+ ```bash
391
+ pip install gemma-miner # from https://github.com/moncifem/gemma-miner
392
+ export OPENROUTER_API_KEY=... # any OpenAI-compatible provider works
393
+ gemma42 # drops into the REPL
394
+
395
+ # in the REPL:
396
+ > Build me a statistics-ready dataset of CNIL sanctions from
397
+ https://www.cnil.fr/fr/les-sanctions-prononcees-par-la-cnil with sector,
398
+ procedure type, fine amount in EUR, and breach-theme flags.
399
+ ```
400
+ </details>
401
+
402
+ ---
403
+
404
+ ## Limitations & honest caveats
405
+
406
+ - **CNIL anonymises most entities** by category ("OPÉRATEUR DE TÉLÉPHONIE
407
+ MOBILE") rather than name. `organism_name` is therefore often generic.
408
+ For the named, headline-grade decisions, follow `decision_url` to
409
+ Légifrance for the authoritative text.
410
+ - **LLM-derived columns** were extracted from the *public CNIL summary*
411
+ (≤ 1 KB of French text per row), not from the full deliberation. A
412
+ `true` for `is_breach_security` is high-precision; a `null` / `false`
413
+ means the summary doesn't discuss it — **not** that no security breach
414
+ occurred. This is deliberate: the dataset preserves the null-vs-false
415
+ distinction throughout.
416
+ - **Fine parsing** is robust on the majority but can misattribute on rows
417
+ combining a fine + a liquidation d'astreinte; the raw `decision_raw`
418
+ field is preserved so you can re-parse if needed.
419
+ - **Decision URLs** are present on ~75 % of rows. Simplified-procedure
420
+ decisions are usually unlinked.
421
+ - **Sample size for some breach themes is small** (n < 20). Use Wilson
422
+ or Jeffreys CIs rather than normal-approximation intervals when
423
+ comparing rates across years or sectors.
424
+ - **The CNIL publishes only its own sanctions** — judicial fines and
425
+ EDPB cross-border decisions where CNIL was supporting authority are
426
+ not included.
427
+
428
+ ---
429
+
430
+ ## Citation
431
+
432
+ ```bibtex
433
+ @misc{elmouden_cnil_sanctions_2025,
434
+ title = {CNIL Sanctions 2011-2025},
435
+ author = {EL-Mouden, Moncif},
436
+ year = {2025},
437
+ note = {Generated by Gemma Miner from https://www.cnil.fr/fr/les-sanctions-prononcees-par-la-cnil},
438
+ url = {https://huggingface.co/datasets/moncefem/cnil-sanctions-2011-2025},
439
+ }
440
+
441
+ @software{elmouden_gemma_miner_2025,
442
+ title = {Gemma Miner: an autonomous text-to-dataset agent},
443
+ author = {EL-Mouden, Moncif and contributors},
444
+ year = {2025},
445
+ url = {https://github.com/moncifem/gemma-miner},
446
+ }
447
+ ```
448
+
449
+ Underlying decisions are published by the CNIL on
450
+ <https://www.cnil.fr/fr/les-sanctions-prononcees-par-la-cnil> and on
451
+ Légifrance; consult those sources for the authoritative text.
452
+
453
+ ## Author & links
454
+
455
+ - 👤 **Moncif EL-Mouden** — [🤗 huggingface.co/moncefem](https://huggingface.co/moncefem)
456
+ - 🤖 **Gemma Miner** (the generator) — <https://github.com/moncifem/gemma-miner>
457
+ - 🇫🇷 **Source** — <https://www.cnil.fr/fr/les-sanctions-prononcees-par-la-cnil>
458
+
459
+ ## License
460
+
461
+ [**Apache License 2.0**](https://www.apache.org/licenses/LICENSE-2.0).
462
+
463
+ Please attribute:
464
+
465
+ - the **CNIL** as the source of the underlying decisions, and
466
+ - **Gemma Miner** (<https://github.com/moncifem/gemma-miner>) as the
467
+ dataset generator.
_make_charts.py ADDED
@@ -0,0 +1,233 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """One-shot analysis + visualisation of the CNIL sanctions dataset.
2
+
3
+ Produces:
4
+ - charts/yearly_volume.png
5
+ - charts/yearly_fines.png
6
+ - charts/sector_breakdown.png
7
+ - charts/breach_themes.png
8
+ - charts/fine_buckets.png
9
+ - charts/simplified_share.png
10
+ - insights.json (machine-readable headline numbers for the README)
11
+ """
12
+ from __future__ import annotations
13
+
14
+ import json
15
+ from collections import Counter
16
+ from pathlib import Path
17
+
18
+ import matplotlib
19
+
20
+ matplotlib.use("Agg")
21
+ import matplotlib.pyplot as plt
22
+ import pandas as pd
23
+
24
+ HERE = Path(__file__).parent
25
+ PARQUET = HERE / "cnil_sanctions_analysis.parquet"
26
+ CHARTS = HERE / "charts"
27
+ CHARTS.mkdir(exist_ok=True)
28
+
29
+ # ── Palette + style ─────────────────────────────────────────────────────────
30
+ plt.rcParams.update({
31
+ "figure.dpi": 130,
32
+ "savefig.dpi": 130,
33
+ "savefig.bbox": "tight",
34
+ "font.family": "DejaVu Sans",
35
+ "axes.spines.top": False,
36
+ "axes.spines.right": False,
37
+ "axes.grid": True,
38
+ "axes.grid.axis": "y",
39
+ "grid.color": "#e5e7eb",
40
+ "grid.linestyle": "-",
41
+ "grid.linewidth": 0.8,
42
+ "axes.titlesize": 14,
43
+ "axes.titleweight": "bold",
44
+ "axes.labelsize": 11,
45
+ })
46
+
47
+ ACCENT = "#2563eb"
48
+ ACCENT_2 = "#dc2626"
49
+ NEUTRAL = "#6b7280"
50
+
51
+ df = pd.read_parquet(PARQUET)
52
+ print(f"loaded {len(df)} rows, {len(df.columns)} cols")
53
+
54
+ # ── 1. Yearly volume ────────────────────────────────────────────────────────
55
+ yearly = df["n_sanction_year"].dropna().astype(int).value_counts().sort_index()
56
+ yearly_simplified = (
57
+ df[df["is_simplified_procedure"] == True]["n_sanction_year"]
58
+ .dropna()
59
+ .astype(int)
60
+ .value_counts()
61
+ .reindex(yearly.index, fill_value=0)
62
+ )
63
+ yearly_standard = yearly - yearly_simplified
64
+
65
+ fig, ax = plt.subplots(figsize=(10, 5))
66
+ ax.bar(yearly.index, yearly_standard, color=ACCENT, label="standard / unspecified procedure")
67
+ ax.bar(yearly.index, yearly_simplified, bottom=yearly_standard, color=ACCENT_2, label="simplified procedure")
68
+ ax.set_title("CNIL sanctions per year, by procedure type (2011 – 2025)")
69
+ ax.set_xlabel("Year")
70
+ ax.set_ylabel("Number of sanctions")
71
+ ax.set_xticks(yearly.index)
72
+ ax.legend(frameon=False, loc="upper left")
73
+ for x, y in zip(yearly.index, yearly.values):
74
+ ax.text(x, y + 1, str(int(y)), ha="center", va="bottom", fontsize=8, color=NEUTRAL)
75
+ fig.savefig(CHARTS / "yearly_volume.png")
76
+ plt.close(fig)
77
+ print(" ✓ yearly_volume.png")
78
+
79
+ # ── 2. Yearly fine totals + sanction counts ────────────────────────────────
80
+ df_fines = df.dropna(subset=["amount_fine_eur", "n_sanction_year"]).copy()
81
+ df_fines["n_sanction_year"] = df_fines["n_sanction_year"].astype(int)
82
+ fines_per_year = df_fines.groupby("n_sanction_year")["amount_fine_eur"].sum() / 1e6
83
+ fines_per_year = fines_per_year.reindex(yearly.index, fill_value=0.0)
84
+ fines_count = df_fines.groupby("n_sanction_year").size().reindex(yearly.index, fill_value=0)
85
+
86
+ fig, ax = plt.subplots(figsize=(10, 5))
87
+ ax.bar(fines_per_year.index, fines_per_year.values, color=ACCENT)
88
+ ax.set_title("Aggregate disclosed fines per year (€M)")
89
+ ax.set_xlabel("Year")
90
+ ax.set_ylabel("Fines (€ millions)")
91
+ ax.set_xticks(fines_per_year.index)
92
+ for x, y, c in zip(fines_per_year.index, fines_per_year.values, fines_count.values):
93
+ if y > 0:
94
+ ax.text(x, y + 5, f"€{y:.0f}M\n(n={c})", ha="center", va="bottom",
95
+ fontsize=8, color=NEUTRAL)
96
+ fig.savefig(CHARTS / "yearly_fines.png")
97
+ plt.close(fig)
98
+ print(" ✓ yearly_fines.png")
99
+
100
+ # ── 3. Sector breakdown ────────────────────────────────────────────────────
101
+ sec = df["cat_sector_group"].dropna().value_counts()
102
+ labels_map = {
103
+ "private_company": "Private company",
104
+ "public": "Public sector",
105
+ "professional_individual": "Professional individual",
106
+ "association": "Association",
107
+ "political": "Political party",
108
+ "other": "Other",
109
+ }
110
+ sec = sec.rename(index=lambda x: labels_map.get(x, x))
111
+
112
+ fig, ax = plt.subplots(figsize=(8, 5))
113
+ bars = ax.barh(sec.index[::-1], sec.values[::-1], color=ACCENT)
114
+ ax.set_title("Sanctions by sector (2011 – 2025)")
115
+ ax.set_xlabel("Number of sanctions")
116
+ ax.grid(axis="y", visible=False)
117
+ ax.grid(axis="x", visible=True)
118
+ for bar, val in zip(bars, sec.values[::-1]):
119
+ ax.text(val + 3, bar.get_y() + bar.get_height() / 2,
120
+ f"{val} ({val / sec.sum():.0%})",
121
+ va="center", fontsize=9, color=NEUTRAL)
122
+ fig.savefig(CHARTS / "sector_breakdown.png")
123
+ plt.close(fig)
124
+ print(" ✓ sector_breakdown.png")
125
+
126
+ # ── 4. Breach themes ───────────────────────────────────────────────────────
127
+ breach_cols = [c for c in df.columns if c.startswith("is_breach_") or c == "is_involves_sensitive_data"]
128
+ breach_counts = {c: int(df[c].fillna(False).sum()) for c in breach_cols}
129
+ breach_counts = dict(sorted(breach_counts.items(), key=lambda kv: kv[1]))
130
+ pretty = {
131
+ "is_breach_security": "Security of processing (Art. 32)",
132
+ "is_breach_transparency": "Information / transparency",
133
+ "is_breach_consent": "Lawful basis: consent",
134
+ "is_breach_data_rights": "Rights of data subjects",
135
+ "is_breach_cookies": "Cookies / trackers (Art. 82)",
136
+ "is_breach_minimization": "Data minimisation",
137
+ "is_breach_processor": "Sub-processor obligations",
138
+ "is_involves_sensitive_data": "Special-category data (Art. 9)",
139
+ }
140
+ labels = [pretty.get(c, c) for c in breach_counts]
141
+ vals = list(breach_counts.values())
142
+
143
+ fig, ax = plt.subplots(figsize=(9, 5))
144
+ bars = ax.barh(labels, vals, color=ACCENT)
145
+ ax.set_title("Breach themes detected across CNIL sanctions")
146
+ ax.set_xlabel(f"# of decisions mentioning this theme (n={len(df)})")
147
+ ax.grid(axis="y", visible=False)
148
+ ax.grid(axis="x", visible=True)
149
+ for bar, v in zip(bars, vals):
150
+ ax.text(v + 2, bar.get_y() + bar.get_height() / 2,
151
+ f"{v} ({v / len(df):.0%})",
152
+ va="center", fontsize=9, color=NEUTRAL)
153
+ fig.savefig(CHARTS / "breach_themes.png")
154
+ plt.close(fig)
155
+ print(" ✓ breach_themes.png")
156
+
157
+ # ── 5. Fine bucket distribution ────────────────────────────────────────────
158
+ fb = df["cat_fine_bucket"].dropna().value_counts()
159
+ order = ["none", "under_10k", "under_100k", "under_1m", "over_1m"]
160
+ fb = fb.reindex([o for o in order if o in fb.index], fill_value=0)
161
+ label_order = {
162
+ "none": "no fine",
163
+ "under_10k": "< €10 k",
164
+ "under_100k": "€10 k – 100 k",
165
+ "under_1m": "€100 k – 1 M",
166
+ "over_1m": "> €1 M",
167
+ }
168
+ fb = fb.rename(index=label_order)
169
+
170
+ fig, ax = plt.subplots(figsize=(8, 5))
171
+ ax.bar(fb.index, fb.values, color=ACCENT)
172
+ ax.set_title("Fine-size distribution")
173
+ ax.set_xlabel("Fine bucket")
174
+ ax.set_ylabel("Number of sanctions")
175
+ for i, (idx, v) in enumerate(fb.items()):
176
+ ax.text(i, v + 1, str(int(v)), ha="center", va="bottom", fontsize=9, color=NEUTRAL)
177
+ fig.savefig(CHARTS / "fine_buckets.png")
178
+ plt.close(fig)
179
+ print(" ✓ fine_buckets.png")
180
+
181
+ # ── 6. Simplified-procedure share over time ────────────────────────────────
182
+ simp_share = (yearly_simplified / yearly.replace(0, 1)).fillna(0) * 100
183
+
184
+ fig, ax = plt.subplots(figsize=(10, 4.5))
185
+ ax.plot(simp_share.index, simp_share.values, color=ACCENT_2, marker="o", linewidth=2)
186
+ ax.fill_between(simp_share.index, 0, simp_share.values, color=ACCENT_2, alpha=0.15)
187
+ ax.set_title("Share of sanctions issued under the SIMPLIFIED procedure")
188
+ ax.set_xlabel("Year")
189
+ ax.set_ylabel("% of yearly sanctions")
190
+ ax.set_xticks(simp_share.index)
191
+ ax.set_ylim(0, 100)
192
+ for x, y in zip(simp_share.index, simp_share.values):
193
+ if y > 0:
194
+ ax.text(x, y + 2, f"{y:.0f}%", ha="center", fontsize=8, color=NEUTRAL)
195
+ fig.savefig(CHARTS / "simplified_share.png")
196
+ plt.close(fig)
197
+ print(" ✓ simplified_share.png")
198
+
199
+ # ── insights ───────────────────────────────────────────────────────────────
200
+ fines_clean = df["amount_fine_eur"].dropna()
201
+ top_fines = (
202
+ df.dropna(subset=["amount_fine_eur"])
203
+ .sort_values("amount_fine_eur", ascending=False)
204
+ .head(10)[["dn_sanction", "organism_type_raw", "amount_fine_eur", "main_breaches_raw"]]
205
+ .to_dict(orient="records")
206
+ )
207
+
208
+ insights = {
209
+ "n_rows": int(len(df)),
210
+ "years": [int(df["n_sanction_year"].min()), int(df["n_sanction_year"].max())],
211
+ "growth_2014_to_2024": round(yearly.get(2024, 0) / max(1, yearly.get(2014, 1)), 1),
212
+ "growth_2022_to_2024": round(yearly.get(2024, 0) / max(1, yearly.get(2022, 1)), 1),
213
+ "private_share_pct": round(
214
+ 100 * (df["cat_sector_group"] == "private_company").sum() / len(df), 1
215
+ ),
216
+ "simplified_share_total_pct": round(
217
+ 100 * (df["is_simplified_procedure"] == True).sum()
218
+ / max(1, df["is_simplified_procedure"].notna().sum()),
219
+ 1,
220
+ ),
221
+ "simplified_share_2024_pct": round(simp_share.get(2024, 0.0), 1),
222
+ "fines_reported": int(fines_clean.shape[0]),
223
+ "total_fines_meur": round(fines_clean.sum() / 1e6, 1),
224
+ "median_fine_eur": float(fines_clean.median()) if len(fines_clean) else 0.0,
225
+ "max_fine_eur": float(fines_clean.max()) if len(fines_clean) else 0.0,
226
+ "top_breach_theme": max(breach_counts, key=breach_counts.get),
227
+ "top_breach_count": max(breach_counts.values()),
228
+ "top_fines": top_fines,
229
+ }
230
+ (HERE / "insights.json").write_text(
231
+ json.dumps(insights, indent=2, ensure_ascii=False, default=str)
232
+ )
233
+ print("\ninsights:", json.dumps(insights, indent=2, ensure_ascii=False, default=str))
charts/breach_themes.png ADDED

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charts/sector_breakdown.png ADDED

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charts/simplified_share.png ADDED

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charts/yearly_fines.png ADDED

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cnil_sanctions_analysis.csv ADDED
@@ -0,0 +1,375 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ amount_fine_eur,cat_fine_bucket,cat_procedure_type,cat_sector_group,dn_sanction,has_astreinte,has_external_decision_link,has_fine,has_injunction,has_warning,id,is_breach_consent,is_breach_cookies,is_breach_data_rights,is_breach_minimization,is_breach_processor,is_breach_security,is_breach_transparency,is_digital_platform,is_health_related,is_involves_sensitive_data,is_public_sector,is_simplified_procedure,n_breaches,n_decision_severity_score,n_sanction_year
2
+ 15000.0,mid_5k_50k,simplified,private_company,2025-01-09,,,True,True,,sig_0498436f2864,,,,,,,,False,False,,False,True,3,,2025
3
+ 10000.0,mid_5k_50k,simplified,private_company,2025-01-16,,,True,True,,sig_2935ef6dbb2d,,,,,,,,False,False,,False,True,4,2,2025
4
+ 8000.0,mid_5k_50k,simplified,private_company,2025-01-23,,,True,,,sig_92a33efd0bdf,,,,,,,,False,False,,False,True,4,2,2025
5
+ 4000.0,mid_5k_50k,simplified,private_company,2025-01-30,True,,True,,,sig_4f7902c69672,,,,,,,,,,,False,True,1,,2025
6
+ 6000.0,mid_5k_50k,simplified,private_company,2025-03-27,,,True,,,sig_085a719a4cab,,,,,,,,False,False,,False,True,3,2,2025
7
+ 5000.0,mid_5k_50k,simplified,private_company,2025-04-03,,,True,True,,sig_75768e96d6ee,,,,,,,,False,False,,False,True,1,,2025
8
+ 10000.0,mid_5k_50k,simplified,private_company,2025-04-03,,,True,True,False,sig_51f42f28b254,False,False,True,False,False,False,True,False,False,False,False,True,2,,2025
9
+ 20000.0,mid_5k_50k,simplified,private_company,2025-04-10,,,True,,,sig_ce05ce692d86,,,,True,,True,True,False,False,,False,True,5,,2025
10
+ 6000.0,mid_5k_50k,simplified,private_company,2025-04-10,,,True,,,sig_2367bc406859,,,,True,,,True,False,False,,False,True,4,,2025
11
+ 20000.0,mid_5k_50k,simplified,private_company,2025-04-30,,,True,,,sig_8bc709fe4fc4,True,,,True,True,True,True,True,False,True,False,True,7,,2025
12
+ 4000.0,mid_5k_50k,simplified,private_company,2025-05-15,True,,True,True,,sig_b7155b1e0113,,,,,,,,False,False,,False,True,1,,2025
13
+ 900000.0,very_high_over_500k,,private_company,2025-05-15,,True,True,True,,sig_df53066e814b,True,False,False,False,False,False,False,False,False,,False,,3,,2025
14
+ 80000.0,high_50k_500k,,private_company,2025-05-15,,True,True,,,sig_e15c55ee9899,True,,,True,,,,False,False,,False,,4,,2025
15
+ 5000.0,mid_5k_50k,simplified,private_company,2025-06-05,,,True,,,sig_8e95da5b8fd8,,,,True,,,,False,True,,False,True,1,,2025
16
+ 5000.0,mid_5k_50k,simplified,private_company,2025-06-05,,,True,,,sig_acaf240495e3,,,,True,,,True,False,True,True,False,True,4,,2025
17
+ 10000.0,mid_5k_50k,simplified,private_company,2025-06-05,,,True,True,,sig_e072e5188e89,,,,,,,,False,False,,False,True,1,,2025
18
+ 3000.0,low_under_5k,simplified,private_company,2025-06-18,,,True,,,sig_5b04b50758d8,True,True,,,,,,False,False,,False,True,1,,2025
19
+ 600000.0,very_high_over_500k,,private_company,2025-07-03,,,True,,,sig_d098eca568ec,True,True,,True,,,True,False,False,,False,,4,5,2025
20
+ 3000.0,low_under_5k,simplified,professional_individual,2025-07-03,,,True,True,,sig_534bd18a2394,,,,,,,,False,True,,False,True,1,,2025
21
+ 3000.0,low_under_5k,simplified,professional_individual,2025-07-17,,,True,True,,sig_33ee9cf19498,,,True,,,,,False,False,,False,True,2,,2025
22
+ 10000.0,mid_5k_50k,simplified,association,2025-08-25,,,True,True,,sig_c96d6be9b186,,,True,,,,,,,,False,True,2,,2025
23
+ 325000000.0,very_high_over_500k,standard,private_company,2025-09-01,,True,True,True,False,sig_23cbaaa5847c,True,True,False,False,False,False,True,True,False,,False,False,2,5,2025
24
+ 150000000.0,very_high_over_500k,,private_company,2025-09-01,,True,True,,,sig_81351d3edb8f,True,True,,,,,True,True,False,,False,,2,5,2025
25
+ 20000.0,mid_5k_50k,simplified,private_company,2025-09-03,,,True,,,sig_cd86f87e9b07,,,True,,,,,False,False,,False,True,1,,2025
26
+ 7000.0,mid_5k_50k,simplified,private_company,2025-09-04,,,True,,,sig_5b0d969cf409,False,False,False,False,True,True,False,False,False,,False,True,4,,2025
27
+ 17000.0,mid_5k_50k,simplified,private_company,2025-09-04,,,True,,,sig_935fda13314c,True,False,False,True,False,False,True,False,False,,False,True,5,,2025
28
+ 75000.0,high_50k_500k,,private_company,2025-09-04,,,True,True,,sig_0ea2f3f095c3,,,,True,,,True,False,False,,False,,4,,2025
29
+ 10000.0,mid_5k_50k,simplified,private_company,2025-09-11,,,True,,,sig_232fbdab5f00,,,True,,,,True,False,False,,False,True,2,,2025
30
+ 5000.0,mid_5k_50k,simplified,private_company,2025-09-11,,,True,,,sig_615547126493,,,,,,,,,,,False,True,1,,2025
31
+ 7000.0,mid_5k_50k,simplified,association,2025-09-11,,,True,True,,sig_5fd8a373e5ec,False,False,False,True,False,True,False,False,False,,False,True,3,,2025
32
+ 10000.0,mid_5k_50k,simplified,public,2025-09-11,,,True,,True,sig_6b003df4c9e4,,,,True,,True,,False,,True,True,True,3,,2025
33
+ 3000.0,low_under_5k,simplified,professional_individual,2025-09-11,,,True,True,,sig_538ef3d22cb0,,,,,,,,False,True,,False,True,1,,2025
34
+ 100000.0,high_50k_500k,,private_company,2025-09-18,,True,True,,,sig_533c86a00908,,,,True,,True,,False,False,,False,,4,,2025
35
+ 15000.0,mid_5k_50k,simplified,public,2025-10-02,,,True,,,sig_ae191786316b,,,,True,,,,False,,,True,True,1,,2025
36
+ 4000.0,low_under_5k,simplified,private_company,2025-10-09,,,True,,,sig_1bfb17b7d919,True,True,False,False,False,False,True,False,False,False,False,True,2,,2025
37
+ 20000.0,mid_5k_50k,simplified,private_company,2025-10-16,,,True,True,,sig_10d8377ccd13,,,,True,,,True,False,False,,False,True,3,,2025
38
+ 3000.0,low_under_5k,simplified,private_company,2025-10-16,,,True,,,sig_ba93564467ae,True,True,False,False,False,False,False,False,False,,False,True,1,1,2025
39
+ 4000.0,low_under_5k,simplified,private_company,2025-10-16,False,False,True,True,False,sig_28eab9b8d03a,True,True,False,False,False,False,True,False,False,False,False,True,2,1,2025
40
+ 250000.0,high_50k_500k,,private_company,2025-10-16,,,True,,,sig_edf70a85f283,False,False,False,True,False,True,False,False,False,,False,,3,,2025
41
+ 20000.0,mid_5k_50k,simplified,private_company,2025-10-16,,,True,True,,sig_367c654607c0,True,True,,,,True,True,False,False,,False,True,3,,2025
42
+ 4000.0,low_under_5k,simplified,public,2025-11-04,,,True,,,sig_96f2ea0e168c,,True,,,,,True,,,,True,True,1,,2025
43
+ 4000.0,low_under_5k,simplified,private_company,2025-11-04,,,True,,,sig_c85231200bbc,True,True,False,False,False,False,True,False,False,False,False,True,2,,2025
44
+ 5000.0,mid_5k_50k,simplified,professional_individual,2025-11-05,,,True,,,sig_899a24582bf0,,,True,,,,,False,,,False,True,2,,2025
45
+ 5000.0,mid_5k_50k,simplified,private_company,2025-11-13,,,True,,,sig_122a8fe81897,True,True,,,,,True,False,False,,False,True,2,,2025
46
+ 20000.0,mid_5k_50k,simplified,private_company,2025-11-13,,,True,,,sig_5d89b2b0d800,,,,True,True,True,True,False,False,,False,True,7,,2025
47
+ 20000.0,mid_5k_50k,simplified,private_company,2025-11-13,,False,True,,,sig_114174588b9c,,,,True,True,True,,False,False,,False,True,7,2,2025
48
+ 5000.0,mid_5k_50k,simplified,private_company,2025-11-13,,,True,,,sig_d11fe10910cd,True,True,,,,,True,True,False,,False,True,2,,2025
49
+ 10000.0,mid_5k_50k,simplified,private_company,2025-11-20,,,True,,,sig_2b96dc02cd0b,,,True,,,,,False,False,,False,True,2,,2025
50
+ 750000.0,very_high_over_500k,,private_company,2025-11-20,,True,True,,,sig_0b9470487996,True,True,,,,,True,False,False,,False,,2,5,2025
51
+ 1500000.0,very_high_over_500k,,private_company,2025-11-27,,True,True,,,sig_3144df0203ee,True,True,,,,,,,False,,False,,1,5,2025
52
+ 500000.0,high_50k_500k,,private_company,2025-11-27,,,True,True,,sig_d11d293c8109,True,True,,,,,True,,False,,False,,2,,2025
53
+ 0.0,none,simplified,private_company,2025-11-27,,,False,True,,sig_89e6f2684077,,,,,,True,,True,False,,False,True,2,,2025
54
+ 8000.0,mid_5k_50k,simplified,political,2025-11-27,,,True,True,,sig_a0a6e8bfb7cc,,,True,,,,,False,False,,False,True,2,,2025
55
+ 0.0,none,,private_company,2025-11-27,True,,False,True,,sig_efe2d1ffbca7,,,,,,,,False,True,,False,,2,,2025
56
+ 5000.0,mid_5k_50k,simplified,political,2025-12-04,,,True,True,,sig_1f97ff47a7b7,,,True,True,,True,,False,False,,False,True,3,,2025
57
+ 10000.0,mid_5k_50k,simplified,private_company,2025-12-04,,,True,,,sig_009b14420d67,,,,,,,,False,False,,False,True,1,,2025
58
+ 20000.0,mid_5k_50k,simplified,private_company,2025-12-08,,,True,,,sig_7ea4c62e97f4,,,,True,True,True,True,False,False,,False,True,7,,2025
59
+ 5500.0,mid_5k_50k,simplified,political,2025-12-11,,,True,,,sig_22100833dc59,,,True,,True,,True,False,False,,False,True,4,,2025
60
+ 2500.0,low_under_5k,simplified,political,2025-12-11,,,True,,,sig_55883bb27f72,,,,,,,True,False,False,,False,True,1,,2025
61
+ 5000.0,mid_5k_50k,simplified,political,2025-12-11,,,True,,,sig_1080dce77b34,,,True,,,True,True,False,False,,False,True,4,,2025
62
+ 2500.0,low_under_5k,simplified,political,2025-12-11,,,True,,,sig_0cb4d2bb3d1c,,,,,,,True,False,False,,False,True,2,,2025
63
+ 5000.0,mid_5k_50k,simplified,private_company,2025-12-11,,,True,True,,sig_769a80ed2908,True,True,,,,,True,False,False,,False,True,2,,2025
64
+ 3000.0,mid_5k_50k,simplified,private_company,2025-12-11,True,False,True,True,,sig_f9f99bffe025,,,,,,,,False,False,,False,True,1,1,2025
65
+ 1000000.0,very_high_over_500k,,private_company,2025-12-11,,True,True,,,sig_73cdd090139e,,,,True,True,,,,False,,False,,3,5,2025
66
+ 20000.0,mid_5k_50k,simplified,private_company,2025-12-11,,,True,True,,sig_0f732d0f0095,True,,True,,,,True,False,False,,False,True,3,,2025
67
+ 20000.0,mid_5k_50k,simplified,public,2025-12-11,,,True,True,,sig_7f7384c4d317,,,True,True,,True,True,False,False,,True,True,5,,2025
68
+ 15000.0,mid_5k_50k,simplified,private_company,2025-12-15,,False,True,,,sig_78f1b342f8c5,,,,,,True,,False,False,,False,True,1,2,2025
69
+ 8000.0,mid_5k_50k,simplified,private_company,2025-12-15,,,True,True,,sig_d35d5eec96d3,,,,True,,,,False,False,,False,True,3,,2025
70
+ 20000.0,mid_5k_50k,simplified,private_company,2025-12-18,,,True,,,sig_e4ed9d5962b7,True,False,False,True,False,True,False,False,False,,False,True,3,,2025
71
+ 15000.0,mid_5k_50k,simplified,private_company,2025-12-18,,,True,True,,sig_22eda2df7692,,,,,,True,True,False,False,,False,True,3,,2025
72
+ 6000.0,mid_5k_50k,simplified,private_company,2025-12-18,,,True,,,sig_3ca9b542d095,,,True,,,,,False,False,,False,True,2,,2025
73
+ 7000.0,mid_5k_50k,simplified,private_company,2025-12-18,,,True,,,sig_44e24c72072e,,,True,,,,,False,False,,False,True,2,,2025
74
+ 2000.0,low_under_5k,simplified,private_company,2025-12-18,,,True,True,,sig_834a11e17850,True,True,,,,,True,False,False,,False,True,2,,2025
75
+ 1700000.0,very_high_over_500k,,private_company,2025-12-22,,True,True,,,sig_8699e0304f8a,,,,,,True,,True,False,,False,,1,5,2025
76
+ 5000.0,mid_5k_50k,simplified,private_company,2025-12-29,,,True,,,sig_9563f6415090,True,True,False,False,False,False,False,False,False,,False,True,1,,2025
77
+ 7000.0,mid_5k_50k,simplified,private_company,2025-12-29,,,True,True,,sig_acf3f22d5f7f,True,True,False,False,False,False,False,False,False,False,False,True,1,,2025
78
+ 6000.0,mid_5k_50k,simplified,private_company,2025-12-29,,,True,True,,sig_560141451a5d,,,,True,,,,False,False,,False,True,2,,2025
79
+ 270000.0,high_50k_500k,,private_company,2025-12-29,,,True,,,sig_cf2bff665c64,True,True,,,,True,,True,,True,False,,3,,2025
80
+ 5000.0,mid_5k_50k,simplified,private_company,2025-12-30,,,True,,,sig_1b822db3911b,,,,,,,True,False,False,,False,True,2,,2025
81
+ 10000.0,mid_5k_50k,simplified,private_company,2025-12-30,,,True,,,sig_67832c629250,,,,True,,True,,False,False,,False,True,3,,2025
82
+ 0.0,none,simplified,professional_individual,2025-12-30,False,False,False,False,True,sig_8ad4554f7abe,,,,,,,,False,True,,False,True,1,1,2025
83
+ 3500000.0,very_high_over_500k,,private_company,2025-12-30,,True,True,,,sig_1c16b520f920,True,True,,,,True,True,,,,False,,5,5,2025
84
+ 20000.0,mid_5k_50k,simplified,public,2025-12-31,,,True,,,sig_f35df1dc9a36,,,,True,,,,False,,,True,True,1,,2025
85
+ 1500.0,low_under_5k,simplified,private_company,2024-01-09,,,True,,,sig_797ae4d7f03b,,,True,,,,,False,False,,False,True,3,1,2024
86
+ 5000.0,mid_5k_50k,simplified,professional_individual,2024-01-15,,,True,,,sig_b3d919f716d6,,,True,,,,,False,False,,False,True,2,,2024
87
+ 500.0,low_under_5k,simplified,professional_individual,2024-01-22,,,True,,,sig_40d7ece0d723,,,,,,,,False,False,,False,True,1,1,2024
88
+ 20000.0,mid_5k_50k,simplified,private_company,2024-01-24,,,True,,,sig_e7dfb98abb93,,,,,True,True,,False,True,,False,True,4,,2024
89
+ 20000.0,mid_5k_50k,simplified,political,2024-01-25,,,True,,,sig_6bf8165e43a6,,,,,,False,True,False,False,,False,True,1,,2024
90
+ 100000.0,high_50k_500k,,private_company,2024-01-31,,True,True,,,sig_9fa55ca36e41,,,,True,True,True,True,True,False,,False,,4,,2024
91
+ 500.0,low_under_5k,simplified,professional_individual,2024-01-31,,,True,,,sig_c4c2978af38d,,,,,,,,False,False,,False,True,1,1,2024
92
+ 5000.0,mid_5k_50k,simplified,professional_individual,2024-01-31,,,True,,,sig_f3bc3a1e66d7,,,True,,,True,,False,True,True,False,True,2,,2024
93
+ 20000.0,mid_5k_50k,simplified,private_company,2024-01-31,,,True,,,sig_2cec24d22269,,,,,,True,,True,False,,False,True,1,,2024
94
+ 310000.0,high_50k_500k,,private_company,2024-01-31,,True,True,,,sig_78bfa017fb0d,,,,,,,,False,False,,False,,1,,2024
95
+ 10000.0,mid_5k_50k,simplified,private_company,2024-01-31,,,True,,,sig_c3c730a52a35,,,,,,True,,False,False,,False,True,1,,2024
96
+ 10000.0,mid_5k_50k,simplified,private_company,2024-02-29,,,True,,,sig_853e02fdfeb4,,,,,,,,False,,,False,True,1,,2024
97
+ 4000.0,low_under_5k,simplified,professional_individual,2024-02-29,,,True,,,sig_5c96ee239102,,,True,,,,,False,True,True,False,True,2,,2024
98
+ 525000.0,very_high_over_500k,,private_company,2024-04-04,,True,True,,,sig_dd350ace6e40,True,,,,,,True,,False,,False,,3,5,2024
99
+ 25000.0,mid_5k_50k,,private_company,2024-04-04,True,,True,,,sig_286fbe5e4de9,,,,,,,,False,False,,False,,1,,2024
100
+ 15000.0,mid_5k_50k,simplified,private_company,2024-04-25,,,True,,,sig_aa4e7ae5e8af,True,True,,,,,True,False,False,,False,True,2,,2024
101
+ 16000.0,mid_5k_50k,simplified,political,2024-04-25,,,True,True,,sig_9b18278ea195,,,,,,,,False,False,,False,True,1,,2024
102
+ 3000.0,mid_5k_50k,simplified,private_company,2024-04-25,True,,True,True,,sig_f78d6276a416,,,True,,,,,False,False,,False,True,1,,2024
103
+ 6000.0,mid_5k_50k,simplified,public,2024-05-23,,,True,,,sig_87e876126c0d,,,,True,,,True,False,False,,True,True,2,2,2024
104
+ 4000.0,mid_5k_50k,simplified,private_company,2024-05-23,True,,True,True,,sig_503e83a2d98c,,,,,,,,False,False,,False,True,1,,2024
105
+ 15000.0,mid_5k_50k,simplified,private_company,2024-05-23,,,True,,,sig_9df216d64ce2,,,,True,,True,True,True,,,False,True,3,2,2024
106
+ 10000.0,mid_5k_50k,simplified,private_company,2024-05-23,,,True,,,sig_8c5c2b695456,,,,True,,True,True,True,False,,False,True,3,,2024
107
+ 5000.0,mid_5k_50k,simplified,private_company,2024-06-10,,,True,,,sig_c3c15ec9f176,,,,True,,,True,False,False,,False,True,3,2,2024
108
+ 3000.0,low_under_5k,simplified,private_company,2024-06-10,,,True,True,,sig_429921a0280e,True,True,,,,False,True,False,False,,False,True,2,,2024
109
+ 4000.0,low_under_5k,simplified,professional_individual,2024-06-10,,,True,True,,sig_da42fa0ad603,,,True,,,,,False,True,True,False,True,2,,2024
110
+ 12000.0,mid_5k_50k,simplified,private_company,2024-06-27,,,True,,,sig_7e3ebc9d30dc,True,True,False,False,False,False,True,False,False,False,False,True,2,,2024
111
+ ,none,,public,2024-07-09,,,False,True,True,sig_5e05b1f93fdc,,,,True,,,,,,,True,,2,,2024
112
+ 6900.0,mid_5k_50k,,public,2024-07-22,True,True,True,True,False,sig_ddb95ead3c50,,,,,,,,False,False,,True,,1,2,2024
113
+ 20000.0,mid_5k_50k,simplified,private_company,2024-07-25,,,True,,,sig_ee61c6bcb7ec,,,,True,,True,,False,False,,False,True,3,,2024
114
+ 20000.0,mid_5k_50k,simplified,private_company,2024-08-08,,,True,True,,sig_f09db0dfc44f,,,,True,,,True,False,False,,False,True,3,,2024
115
+ 8000.0,mid_5k_50k,simplified,private_company,2024-08-20,,,True,,,sig_e24b47afbfb4,,,True,,,,,True,False,,False,True,2,,2024
116
+ 800000.0,very_high_over_500k,,private_company,2024-08-28,,,True,,,sig_69917b3a6fda,,,,,,,,False,True,True,False,,1,5,2024
117
+ 200000.0,high_50k_500k,,private_company,2024-08-28,,,True,,,sig_62bacbb95962,,,,,,,,,True,True,False,,1,,2024
118
+ 300000.0,high_50k_500k,,private_company,2024-08-29,,,True,,,sig_6448884c0515,True,,,,,,True,False,False,,False,,3,,2024
119
+ 15000.0,mid_5k_50k,simplified,private_company,2024-09-05,,,True,,,sig_8bcbf247b4cc,,,,True,,,True,False,False,,False,True,4,,2024
120
+ 10000.0,mid_5k_50k,simplified,private_company,2024-09-05,,,True,True,,sig_70137eed607b,,,True,,,,,False,False,,False,True,2,,2024
121
+ 800000.0,very_high_over_500k,,private_company,2024-09-05,,True,True,,,sig_4bac5ce77496,,,,,,,,False,True,True,False,,2,5,2024
122
+ 12000.0,mid_5k_50k,simplified,private_company,2024-09-12,,,True,,,sig_849b47c32124,,,True,,,,True,False,False,,False,True,2,,2024
123
+ 20000.0,mid_5k_50k,simplified,public,2024-09-13,,,True,True,,sig_46cc8f26ec46,,,,True,,,,False,,,True,True,5,,2024
124
+ 20000.0,mid_5k_50k,simplified,private_company,2024-09-19,,,True,,,sig_dde3084bc04b,,,True,True,,True,True,False,False,,False,True,5,,2024
125
+ 15000.0,mid_5k_50k,simplified,private_company,2024-09-26,,,True,True,,sig_182232df9e32,,,True,,,,,False,False,,False,True,2,,2024
126
+ 15000.0,mid_5k_50k,simplified,private_company,2024-09-26,,,True,True,,sig_6c23d232d384,True,True,True,,True,True,True,False,True,,False,True,4,,2024
127
+ 250000.0,very_high_over_500k,,private_company,2024-09-26,,True,True,,,sig_9bbf3c2be032,True,,,True,,,,False,False,True,False,,4,,2024
128
+ 150000.0,high_50k_500k,,private_company,2024-09-26,,True,True,,,sig_4cfd76db5f2a,True,,,True,,,,True,,True,False,,3,,2024
129
+ 3000.0,mid_5k_50k,simplified,private_company,2024-09-26,True,,True,True,,sig_2fa14e98345a,,,,,,,,False,False,,False,True,2,,2024
130
+ 3000.0,low_under_5k,simplified,association,2024-09-30,,,True,,,sig_1923d52a6ea7,,,True,,,,,False,True,True,False,True,2,,2024
131
+ 750000.0,very_high_over_500k,,private_company,2024-10-10,,,True,,,sig_e92100f8af32,,,,True,,True,,True,False,,False,,2,5,2024
132
+ 4000.0,mid_5k_50k,simplified,professional_individual,2024-10-11,True,False,True,True,,sig_5f602097a256,,,,,,,,False,True,True,False,True,2,2,2024
133
+ 0.0,none,,public,2024-10-17,,True,False,True,True,sig_f36f5976fba2,,,True,,,,True,,,,True,,5,,2024
134
+ 20000.0,mid_5k_50k,simplified,private_company,2024-10-17,,,True,,,sig_3254ae5b3eb1,False,False,True,False,False,True,True,False,False,,False,True,3,,2024
135
+ 3000.0,low_under_5k,simplified,professional_individual,2024-10-17,False,False,True,True,False,sig_ee6f5c0c4155,,False,True,,,,,False,True,True,False,True,2,1,2024
136
+ 4000.0,mid_5k_50k,simplified,association,2024-10-23,True,,True,True,,sig_b44970e0b577,,,,,,,,False,False,,False,True,,,2024
137
+ 50000000.0,very_high_over_500k,,private_company,2024-11-14,,True,True,True,,sig_af44c1a40e57,,True,,,,,True,True,False,,False,,2,5,2024
138
+ 15000.0,mid_5k_50k,simplified,private_company,2024-11-26,,,True,,,sig_5ebb99acd1d9,,,,,,,,,,,False,True,1,,2024
139
+ 10000.0,mid_5k_50k,simplified,association,2024-11-26,,,True,,,sig_e02963b02bd4,,,True,,,,,False,True,,False,True,2,,2024
140
+ 20000.0,mid_5k_50k,simplified,private_company,2024-12-05,,,True,True,,sig_132b70cf8ae1,,,,True,,,True,False,False,,False,True,3,,2024
141
+ 20000.0,mid_5k_50k,simplified,private_company,2024-12-05,,,True,,,sig_ce27917da0c6,,,,True,,,True,False,False,,False,True,3,,2024
142
+ 3000.0,low_under_5k,simplified,private_company,2024-12-05,,,True,,,sig_af5da7c16b48,,,,True,,,True,False,False,,False,True,4,,2024
143
+ 15000.0,mid_5k_50k,simplified,private_company,2024-12-05,,,True,,,sig_031b7b15a840,,,,,,,,False,True,,False,True,1,,2024
144
+ 240000.0,high_50k_500k,,private_company,2024-12-05,,True,True,True,,sig_5a9702466ab4,,,True,True,,,True,True,False,,False,,4,,2024
145
+ 6000.0,mid_5k_50k,simplified,private_company,2024-12-12,,,True,,,sig_874dc719f691,,,True,,,,True,False,False,,False,True,2,2,2024
146
+ 18000.0,mid_5k_50k,simplified,private_company,2024-12-12,,,True,,,sig_4378f9f7e44d,,,True,,,,,False,False,,False,True,1,,2024
147
+ 10000.0,mid_5k_50k,simplified,private_company,2024-12-12,,,True,,,sig_df848de6bccc,,,True,,,,True,False,False,,False,True,2,,2024
148
+ 10000.0,mid_5k_50k,simplified,private_company,2024-12-12,,,True,,,sig_7956f8ce69b8,True,True,False,False,False,False,False,False,False,False,False,True,1,,2024
149
+ 5000.0,mid_5k_50k,simplified,private_company,2024-12-12,,,True,,,sig_9ca010560efc,True,True,,,,,,False,False,,False,True,1,,2024
150
+ 3000.0,low_under_5k,simplified,private_company,2024-12-12,,,True,,,sig_708471563da3,True,True,,,,,,False,False,,False,True,1,,2024
151
+ 20000.0,mid_5k_50k,simplified,private_company,2024-12-12,,,True,,,sig_e7c46f6b2e3d,True,True,,,,,,False,False,,False,True,1,,2024
152
+ 10000.0,mid_5k_50k,simplified,private_company,2024-12-12,,,True,,,sig_0081d8226f13,True,True,,,,,,False,False,,False,True,1,,2024
153
+ 20000.0,mid_5k_50k,simplified,private_company,2024-12-12,,False,True,True,False,sig_70ae71de165f,True,True,False,False,False,False,False,False,False,False,False,True,1,2,2024
154
+ 20000.0,mid_5k_50k,simplified,private_company,2024-12-12,,,True,True,,sig_230134669ecc,True,True,,,,,,True,False,,False,True,1,,2024
155
+ 0.0,none,simplified,public,2024-12-19,,,False,,True,sig_bca35c2942c7,,,True,,,,,,,,True,True,2,1,2024
156
+ 20000.0,mid_5k_50k,simplified,private_company,2024-12-19,,,True,,,sig_92415cc10331,,,,,,True,True,,,,False,True,3,,2024
157
+ 8000.0,mid_5k_50k,simplified,private_company,2024-12-19,,,True,,,sig_a6233c5863b8,,,,,,,,False,False,,False,True,1,,2024
158
+ 5000.0,mid_5k_50k,simplified,professional_individual,2024-12-19,,,True,,,sig_809e4cd1c9cf,,,True,,,,,False,True,True,False,True,2,,2024
159
+ 15000.0,mid_5k_50k,simplified,private_company,2024-12-19,,,True,,,sig_09e270874949,,,True,,,,,True,False,,False,True,1,,2024
160
+ 3000.0,mid_5k_50k,simplified,private_company,2024-12-19,,,True,,,sig_352349fb8e92,,,,,,,,False,False,,False,True,1,,2024
161
+ 5000.0,mid_5k_50k,simplified,private_company,2024-12-19,,,True,,,sig_4e6b56b63984,,,True,,,,,True,False,,False,True,2,,2024
162
+ 8000.0,mid_5k_50k,simplified,private_company,2024-12-19,,,True,,,sig_ac0db5c35249,,,True,,,,,False,False,,False,True,1,2,2024
163
+ 40000.0,mid_5k_50k,,private_company,2024-12-19,,True,True,,,sig_3aa9cc629ff5,,,,True,,True,True,False,False,,False,,5,,2024
164
+ 5000.0,mid_5k_50k,simplified,other,2024-12-19,,,True,,,sig_2acd58a89a16,,,,,,,,False,True,,,True,1,,2024
165
+ 20000.0,mid_5k_50k,simplified,public,2024-12-19,,,True,,,sig_d205d2fd8a46,,,,,True,,,False,True,True,True,True,2,2,2024
166
+ 2000.0,mid_5k_50k,simplified,professional_individual,2024-12-19,True,,True,True,,sig_4de76af390a7,,,,,,,,False,True,True,False,True,1,,2024
167
+ 18000.0,mid_5k_50k,simplified,private_company,2024-12-26,,,True,,,sig_3ba6314dcfd0,,,,True,,,,False,False,,False,True,3,,2024
168
+ 10000.0,mid_5k_50k,simplified,private_company,2024-12-31,,,True,,,sig_3f409e8fd717,,,,,,,,False,True,,False,True,1,,2024
169
+ 5000.0,mid_5k_50k,simplified,professional_individual,2024-12-31,,,True,,,sig_ebf9a037ac7a,,,,,,,,,,,False,True,1,,2024
170
+ 5000.0,mid_5k_50k,simplified,private_company,2024-12-31,,,True,,,sig_30f5ae1ca65a,,,,,,,,True,,,False,True,1,,2024
171
+ 5000.0,mid_5k_50k,simplified,private_company,2023-01-23,,,True,True,,sig_4f44aa6de1da,True,True,True,,,True,True,False,False,,False,True,6,,2023
172
+ 5000.0,mid_5k_50k,simplified,public,2023-02-08,,,True,True,,sig_71d2d99c490f,,,,,,,,,,,True,True,2,,2023
173
+ 3000.0,mid_5k_50k,simplified,professional_individual,2023-02-08,,,True,True,,sig_3a5f5283c770,,,True,,,,,False,True,True,False,True,2,,2023
174
+ 10000.0,mid_5k_50k,simplified,private_company,2023-02-08,,,True,True,,sig_f60b8ff29ffc,,,,,,,,False,False,,False,True,1,,2023
175
+ 15000.0,mid_5k_50k,simplified,private_company,2023-03-03,,,True,,,sig_2889b554967a,,,,True,,,True,False,False,,False,True,3,,2023
176
+ 125000.0,high_50k_500k,,private_company,2023-03-16,,True,True,,,sig_8237f9bc2583,,,,True,True,,True,True,False,,False,,3,,2023
177
+ 20000.0,mid_5k_50k,simplified,private_company,2023-03-28,,,True,,,sig_3ba9412180f8,,,,,True,True,,,,,False,True,2,,2023
178
+ 10000.0,mid_5k_50k,simplified,private_company,2023-03-28,,,True,True,,sig_1fb14fecd9da,,,,,,,,False,False,,False,True,1,,2023
179
+ 10000.0,mid_5k_50k,,private_company,2023-04-17,True,,True,True,,sig_608a8e5062d3,,,,True,,,,False,True,,False,,1,,2023
180
+ 5200000.0,very_high_over_500k,,private_company,2023-04-17,True,True,True,,,sig_6c44f2e9b2b5,,,,,,,,True,False,True,False,,1,5,2023
181
+ 380000.0,high_50k_500k,,private_company,2023-05-11,,True,True,,,sig_01ee35b0a47d,True,True,,True,True,True,,True,True,True,False,,5,4,2023
182
+ 4500.0,low_under_5k,simplified,professional_individual,2023-05-12,,,True,True,,sig_1b342b3e387c,,,True,,,,,False,True,,False,True,2,,2023
183
+ 150000.0,high_50k_500k,,private_company,2023-06-08,,True,True,,,sig_f5e1faa0d405,True,True,,True,True,True,True,False,False,True,False,,9,,2023
184
+ 40000000.0,very_high_over_500k,,private_company,2023-06-15,,True,True,,,sig_b74f378e6505,True,,True,,True,,True,True,False,,False,,5,5,2023
185
+ 200000.0,high_50k_500k,,private_company,2023-09-18,,True,True,,,sig_611ce02f2d39,,,,True,,,,False,False,True,False,,4,,2023
186
+ 10000.0,mid_5k_50k,simplified,private_company,2023-09-28,,,True,True,,sig_ef44a79768a2,,,,,,,True,False,False,,False,True,2,,2023
187
+ 20000.0,mid_5k_50k,simplified,private_company,2023-09-28,,,True,,,sig_543564ce2f79,,,,True,,True,True,False,False,,False,True,3,,2023
188
+ 20000.0,mid_5k_50k,simplified,private_company,2023-09-28,,,True,,,sig_540a9addb0cc,,,,True,,True,True,False,False,True,False,True,5,,2023
189
+ 20000.0,mid_5k_50k,simplified,private_company,2023-09-28,,,True,True,,sig_7f9a4ff03e43,,,,,,,,False,False,,False,True,1,,2023
190
+ 20000.0,mid_5k_50k,simplified,private_company,2023-10-06,,,True,,,sig_9d20899b55e5,,,,,,,,False,False,,False,True,1,,2023
191
+ 600000.0,very_high_over_500k,,private_company,2023-10-12,,True,True,,,sig_c683bcd22036,True,,True,,True,True,True,False,False,,False,,5,5,2023
192
+ 5000.0,mid_5k_50k,simplified,private_company,2023-10-23,,,True,True,,sig_c0b8e58286c1,,,True,,,,,False,False,,False,True,2,,2023
193
+ 2000.0,low_under_5k,simplified,private_company,2023-10-23,,,True,,,sig_d4f73d87bdd1,,,,,,,,True,False,,False,True,1,1,2023
194
+ 2000.0,low_under_5k,simplified,private_company,2023-10-26,,,True,,,sig_f5c4d541320d,,,,True,,True,True,False,False,,False,True,4,,2023
195
+ 20000.0,mid_5k_50k,simplified,private_company,2023-11-08,,,True,,,sig_9c85e3e2acda,,,,,,,,,,,False,True,1,,2023
196
+ ,none,,public,2023-11-09,,True,False,,True,sig_26df2b3d8c2f,,,,,,,,,,,True,,1,,2023
197
+ 6000.0,mid_5k_50k,simplified,public,2023-11-15,,,True,,,sig_325797b6262f,,,,True,,True,,False,,,True,True,3,,2023
198
+ 8000.0,mid_5k_50k,simplified,private_company,2023-11-16,,,True,,,sig_e3575092a434,,,,,,True,,False,False,,False,True,3,,2023
199
+ 5000.0,mid_5k_50k,simplified,professional_individual,2023-11-22,,,True,True,,sig_8b78d79d9796,,,True,,,,,False,True,True,False,True,2,,2023
200
+ 3000.0,low_under_5k,simplified,political,2023-12-11,,,True,True,,sig_2e4161afa267,,,True,,,,,False,False,,False,True,1,,2023
201
+ 0.0,none,,public,2023-12-11,,,False,,True,sig_283883b2b6c3,,,,,,True,,,,,True,,3,1,2023
202
+ 5000.0,mid_5k_50k,,public,2023-12-12,,,True,True,,sig_68ec517a7066,,,,,,,,False,False,,True,,2,,2023
203
+ 5000.0,mid_5k_50k,simplified,association,2023-12-27,,,True,True,,sig_3264c549efa0,,,,,,,,False,,,False,True,1,,2023
204
+ 1000.0,low_under_5k,simplified,professional_individual,2023-12-27,,,True,,,sig_60a81d4c7702,,,,,,,,False,True,,False,True,1,1,2023
205
+ 10000.0,mid_5k_50k,simplified,association,2023-12-27,,,True,,,sig_569c6fe2ad25,,,,,,,,False,False,,False,True,3,,2023
206
+ 32000000.0,very_high_over_500k,,private_company,2023-12-27,,True,True,,,sig_75762c7343f2,,,,True,,True,True,,,,False,,4,5,2023
207
+ 100000.0,high_50k_500k,,private_company,2023-12-29,,,True,,,sig_7719dbfb4136,,,,,True,True,,False,False,,False,,2,,2023
208
+ 105000.0,high_50k_500k,,private_company,2023-12-29,,True,True,,,sig_7391723a974e,True,True,,True,,True,True,True,False,,False,,4,,2023
209
+ 10000000.0,very_high_over_500k,,private_company,2023-12-29,,True,True,,,sig_baa3d751b4d5,True,True,,,,,True,True,False,,False,,2,5,2023
210
+ 75000.0,high_50k_500k,,private_company,2023-12-29,,True,True,True,,sig_7efc6738ad88,,,,,,,,True,False,,False,,2,,2023
211
+ 3000.0,low_under_5k,,private_company,2022-01-22,True,,True,True,,sig_3cd8a1d8e323,,,,,,,,False,False,,False,,1,,2022
212
+ 10000.0,mid_5k_50k,,private_company,2022-03-21,,,True,,,sig_7d1a3c67f9b7,,,,True,,True,True,False,False,,False,,6,,2022
213
+ 0.0,none,,professional_individual,2022-03-24,True,,False,,,sig_96e33cf487a7,,,,,,,,False,False,,False,,,,2022
214
+ 1500000.0,very_high_over_500k,,private_company,2022-04-15,,True,True,,,sig_3a9b9d4f2ab0,,,,,True,True,,,,,False,,3,5,2022
215
+ 1000000.0,very_high_over_500k,,private_company,2022-06-23,,True,True,,,sig_e3cc81ea4d30,True,,True,,,,True,False,False,,False,,4,5,2022
216
+ 0.0,none,,private_company,2022-06-13,True,,False,True,,sig_14748ad98fb7,,,,,,,,False,False,,False,,1,,2022
217
+ 175000.0,high_50k_500k,,private_company,2022-07-07,,True,True,,,sig_255cec230743,,,,True,,,True,False,False,,False,,3,,2022
218
+ 600000.0,very_high_over_500k,,private_company,2022-08-03,,True,True,,,sig_21ce79e3a205,True,,True,,,True,True,False,False,,False,,5,,2022
219
+ 250000.0,high_50k_500k,,private_company,2022-09-08,,True,True,,,sig_438628908a44,False,False,False,True,False,True,False,False,False,,False,,2,,2022
220
+ 20000000.0,very_high_over_500k,standard,private_company,2022-10-17,True,True,True,True,False,sig_d5a432dd04ee,False,False,True,False,False,,False,True,False,True,False,False,4,5,2022
221
+ 800000.0,very_high_over_500k,,private_company,2022-11-10,,True,True,,,sig_293388408241,,,True,True,,True,True,True,False,,False,,7,5,2022
222
+ 600000.0,very_high_over_500k,,private_company,2022-11-24,,True,True,,,sig_25b2d515c8fd,,,True,,,True,True,False,False,,False,,5,,2022
223
+ 300000.0,high_50k_500k,,private_company,2022-11-30,True,True,True,True,,sig_cbecaa943887,,,True,,,True,,True,False,,False,,5,,2022
224
+ 60000000.0,very_high_over_500k,standard,private_company,2022-12-19,True,True,True,True,False,sig_fe73abf0b0e8,True,True,False,False,False,False,False,True,False,,False,False,1,5,2022
225
+ 0.0,none,,private_company,2022-12-20,,True,False,,,sig_cd00d5934027,True,,True,,,,,False,False,,False,,2,1,2022
226
+ 8000000.0,very_high_over_500k,,private_company,2022-12-29,,True,True,,,sig_8fb70edbe11d,True,True,,,,,,True,False,,False,,1,5,2022
227
+ 5000.0,mid_5k_50k,simplified,professional_individual,2022-12-29,True,,True,True,,sig_2bedbc1da964,,,True,,,,,False,True,,False,True,2,,2022
228
+ 10000.0,mid_5k_50k,simplified,public,2022-12-29,,,True,,,sig_7bd927803fd9,,,,True,,,,False,,,True,True,1,,2022
229
+ 15000.0,mid_5k_50k,simplified,private_company,2022-12-29,,,True,,,sig_baf666938157,,,,True,,True,True,,,,False,True,4,,2022
230
+ 5000000.0,very_high_over_500k,,private_company,2022-12-29,,True,True,,,sig_abd4b8b43097,True,True,,,,,,True,False,,False,,1,5,2022
231
+ 3000000.0,very_high_over_500k,,private_company,2022-12-29,,True,True,,,sig_271713db600a,True,True,,,,,,True,False,,False,,1,5,2022
232
+ 250000.0,very_high_over_500k,,private_company,2021-01-06,True,,True,True,,sig_dfdd403699fb,,,True,,,True,,False,False,,False,,2,,2021
233
+ 75000.0,high_50k_500k,,private_company,2021-01-11,,,True,,,sig_cf1b3c4b14d9,,,,,,True,,,,,False,,1,,2021
234
+ 0.0,none,,public,2021-01-12,,True,False,True,True,sig_f513c2762d22,,,,,,,True,,,,True,,3,,2021
235
+ 10000.0,mid_5k_50k,,private_company,2021-06-03,,,True,,,sig_1916fa52f6e5,,,,,,,,,,,False,,1,,2021
236
+ 500000.0,high_50k_500k,,private_company,2021-06-14,,True,True,True,,sig_3b172987ff42,True,,True,True,,True,True,True,False,,False,,5,,2021
237
+ 1750000.0,very_high_over_500k,,private_company,2021-07-20,,True,True,,,sig_d221c06111ce,,,,True,,,True,False,False,,False,,2,5,2021
238
+ 400000.0,high_50k_500k,,private_company,2021-07-26,,True,True,,,sig_e77bd0e30f72,,,,,True,,True,False,False,,False,,2,,2021
239
+ 50000.0,high_50k_500k,,private_company,2021-07-27,,True,True,,,sig_803b4ab2b168,True,True,False,False,False,False,False,False,False,False,False,,1,,2021
240
+ 3000.0,low_under_5k,,private_company,2021-09-15,,True,True,,,sig_0f60d74dd26e,False,False,True,False,False,False,False,False,False,,False,,4,,2021
241
+ 0.0,none,,public,2021-09-24,False,True,False,True,True,sig_960acd2a106c,,,,True,,True,True,False,,,True,,5,1,2021
242
+ 3000.0,low_under_5k,,professional_individual,2021-10-21,,,True,True,,sig_80b06e6bb9a1,,,,,,,,False,False,,False,,1,,2021
243
+ 65000.0,high_50k_500k,,private_company,2021-10-28,True,,True,,,sig_b3cde985bce1,,,,,,,,,,,False,,,,2021
244
+ 400000.0,high_50k_500k,,public,2021-10-29,,True,True,,,sig_6cbbac8d0ee4,,,,True,,True,,,,,True,,3,,2021
245
+ 180000.0,high_50k_500k,,private_company,2021-12-28,,True,True,,,sig_392c1e826689,,,,,True,True,True,False,False,,False,,3,,2021
246
+ 300000.0,high_50k_500k,,private_company,2021-12-28,,True,True,,,sig_90fd1748b7e7,False,False,True,False,False,True,False,True,False,,False,,5,,2021
247
+ 120000.0,high_50k_500k,,private_company,2021-12-30,,,True,,,sig_b35c1808d672,,,True,True,True,True,True,False,False,,False,,5,,2021
248
+ 150000000.0,very_high_over_500k,standard,private_company,2021-12-31,,True,True,True,False,sig_c5463c353d2d,False,True,False,False,False,False,False,True,False,False,False,False,1,5,2021
249
+ 60000000.0,very_high_over_500k,standard,private_company,2021-12-31,False,True,True,True,False,sig_56842c42e5cf,False,True,False,False,False,False,True,True,False,False,False,False,2,5,2021
250
+ 250000.0,high_50k_500k,,private_company,2020-07-28,True,True,True,True,False,sig_8b013b311e5c,False,False,False,True,False,True,True,True,False,,False,,4,,2020
251
+ 0.0,none,,political,2020-09-03,False,,False,False,False,sig_7524f0940c25,False,False,False,False,False,False,False,False,False,False,False,,1,,2020
252
+ 0.0,none,,political,2020-09-03,False,True,False,False,True,sig_133db9de3950,,,,,,,,False,False,,False,,1,1,2020
253
+ 0.0,none,,public,2020-09-03,,True,False,,True,sig_467648c5b4bc,,,,,,,,,,,True,,1,1,2020
254
+ 2250000.0,very_high_over_500k,,private_company,2020-11-18,,True,True,,,sig_c8c277e4bbf7,,True,True,True,,True,True,False,False,,False,,6,5,2020
255
+ 800000.0,very_high_over_500k,,private_company,2020-11-18,,True,True,,,sig_63367386284d,False,True,False,False,False,False,True,False,False,,False,,3,5,2020
256
+ 150000.0,high_50k_500k,,private_company,2020-11-18,,,True,,,sig_98eb35b795eb,,,,,,True,,False,False,,False,,1,,2020
257
+ 3000.0,low_under_5k,,private_company,2020-12-03,,,True,,,sig_96c5cc06a071,,,,,,,,False,False,,False,,1,,2020
258
+ 100000000.0,very_high_over_500k,,private_company,2020-12-07,True,True,True,True,,sig_332446bee794,True,True,True,,,,True,True,False,,False,,4,5,2020
259
+ 35000000.0,very_high_over_500k,standard,private_company,2020-12-07,True,True,True,True,False,sig_11f6c07a3682,,True,,,,,True,True,False,,False,False,2,5,2020
260
+ 3000.0,low_under_5k,,professional_individual,2020-12-07,,True,True,,,sig_f5b0945c6594,,,,,,True,,False,True,True,False,,2,,2020
261
+ 6000.0,mid_5k_50k,,professional_individual,2020-12-07,,True,True,,,sig_3421cf2d99e9,,,,,,True,,False,True,,False,,2,,2020
262
+ 7300.0,mid_5k_50k,,private_company,2020-12-07,True,True,True,True,False,sig_e283c7a1fb56,True,,True,True,True,,True,False,False,,False,,6,,2020
263
+ 0.0,none,,private_company,2020-12-08,True,,False,True,,sig_e03bf8783dc9,False,False,False,True,False,True,False,False,False,,False,,3,,2020
264
+ 20000.0,mid_5k_50k,,private_company,2020-12-08,True,True,True,True,False,sig_37ee438a1916,True,,True,,,True,True,True,False,,False,,4,,2020
265
+ 50000000.0,very_high_over_500k,,private_company,2019-01-21,,True,True,,,sig_2dd3ab253263,True,,,,,,True,True,False,,False,,3,5,2019
266
+ 0.0,none,,private_company,2019-01-31,False,,False,False,False,sig_99fdaf63363e,,,True,,,,,True,False,,False,,1,,2019
267
+ 0.0,none,,private_company,2019-01-31,False,,False,False,False,sig_5cf88f2d0c5c,,,,True,,True,,False,False,,False,,2,,2019
268
+ 0.0,none,,public,2019-01-31,True,,False,True,,sig_0c0e5320eb19,,,,,,True,,,,,True,,1,,2019
269
+ 400000.0,high_50k_500k,,private_company,2019-05-28,,True,True,,,sig_22f4f49fcbe3,,,,True,,True,,False,False,,False,,2,,2019
270
+ 20000.0,mid_5k_50k,,private_company,2019-06-13,True,True,True,True,False,sig_508722d273e1,,,,True,,True,True,False,False,,False,,4,3,2019
271
+ 180000.0,high_50k_500k,,private_company,2019-07-18,,True,True,,,sig_e685a5ce0648,,,,,,True,,False,False,,False,,1,,2019
272
+ ,,,private_company,2019-10-10,True,,True,True,,sig_c4669a6b0ebb,False,False,True,False,False,True,False,False,False,,False,,3,,2019
273
+ 500000.0,very_high_over_500k,,private_company,2019-11-21,True,True,True,True,,sig_6ef03fb00087,,,True,True,,,True,False,False,,False,,6,5,2019
274
+ ,,,private_company,2019-12-30,True,,True,True,,sig_85138e5ee95f,,,,True,True,True,True,False,True,True,False,,3,,2019
275
+ ,,,private_company,2018-01-08,,True,True,,,sig_e5265215baa2,,,,,,True,,False,False,,False,,1,,2018
276
+ ,,,private_company,2018-05-07,,True,True,,,sig_a77a2c9ab651,,,,,,True,,False,False,,False,,1,,2018
277
+ ,,,association,2018-06-21,,True,True,,,sig_f69e9973089b,,,,,,True,,,,,False,,1,,2018
278
+ 0.0,none,,private_company,2018-06-26,False,False,False,False,True,sig_e3d17d066c20,False,False,True,False,False,False,False,False,False,False,False,,1,1,2018
279
+ 0.0,none,,private_company,2018-07-24,,,False,,,sig_6fb3cd9990c0,,,,,,True,,False,False,,False,,1,,2018
280
+ ,,,private_company,2018-07-24,,True,True,,,sig_edfcbef59ffa,,,,,,True,,True,False,,False,,1,,2018
281
+ ,,,public,2018-07-24,,True,True,,,sig_fbcd72447b04,,,,,,,,False,False,,True,,1,,2018
282
+ ,,,private_company,2018-07-24,,,True,,,sig_0025ebca2a7a,,,,,,,,False,False,,False,,1,,2018
283
+ ,,,association,2018-09-06,,True,True,,,sig_ef3417316dc3,,,,,,True,,,,,False,,1,,2018
284
+ ,,,private_company,2018-09-06,,True,True,,,sig_8bc38dd9d577,,,,True,,True,True,False,False,True,False,,4,,2018
285
+ ,,,private_company,2018-12-19,,True,True,,,sig_fa6cda0d64c1,,,,,,True,,False,False,,False,,1,,2018
286
+ ,,,private_company,2018-12-26,,True,True,,,sig_b3f724e38e36,,,,,,True,,True,False,,False,,1,,2018
287
+ ,,,private_company,2017-01-26,,True,,,,sig_800d299f34a9,,,,,,,,False,False,,False,,,,2017
288
+ ,,,private_company,2017-04-13,,False,,,,sig_80638ae34ede,False,False,False,True,False,False,False,False,False,True,False,,3,,2017
289
+ ,,,private_company,2017-04-13,,True,,,,sig_dcf73112b840,,,,True,,True,,False,False,,False,,2,,2017
290
+ ,,,political,2017-04-13,,False,,,,sig_443f3b23f5db,,,,,,True,,False,False,,False,,1,,2017
291
+ ,,,private_company,2017-04-13,,,,,,sig_88dfad906dda,,,,True,,True,True,False,False,,False,,4,,2017
292
+ 0.0,none,,private_company,2017-04-27,,True,False,,,sig_34ee8e104e64,True,True,False,True,False,False,True,True,False,True,False,,6,,2017
293
+ ,,,private_company,2017-05-18,,False,,,,sig_1bebac5c9d3f,True,True,False,True,False,False,True,False,False,False,False,,4,,2017
294
+ 0.0,none,,professional_individual,2017-05-18,,True,False,,,sig_24ac7f5af1c0,False,False,True,False,False,False,False,False,True,True,False,,2,,2017
295
+ ,,,private_company,2017-06-15,,True,,,,sig_ceae5ac098e5,False,False,False,True,False,True,False,False,False,False,False,,3,,2017
296
+ ,,,private_company,2017-07-18,,True,,,,sig_73bf7b74d1f6,,,,,,True,,False,False,,False,,1,,2017
297
+ ,,,private_company,2017-07-20,,True,,,,sig_5dbbc0b72fd4,,,,,,True,,False,False,,False,,1,,2017
298
+ ,,,public,2017-11-16,,True,,,,sig_1dbb2bad0de4,,,,,,True,,,,,True,,1,,2017
299
+ ,,,public,2017-12-21,,,,,,sig_25d67fff9b7d,,,,True,,,,,,,True,,,,2017
300
+ ,,,private_company,2017-12-21,,,,,,sig_6bc0977a2a56,,,,,,True,,False,False,,False,,1,,2017
301
+ ,,,association,2016-12-15,,,,,,sig_ee584a66d6c7,,,,,,True,,,,,False,,,,2016
302
+ ,,,private_company,2016-12-15,,True,,,,sig_07c0cf6d1505,True,,,,,,,True,False,True,False,,1,,2016
303
+ ,,,private_company,2016-12-15,,True,,,,sig_85e6dc3a5d01,True,,,,,,,True,False,True,False,,1,,2016
304
+ ,,,private_company,2016-11-03,,,,,,sig_5737b37d2adc,,,,,,True,,False,False,,False,,1,,2016
305
+ ,,,political,2016-10-13,,True,,,,sig_ac959e6116d9,,,,,,True,,False,False,,False,,1,,2016
306
+ ,,,private_company,2016-09-13,,,,,,sig_a07538637fb2,,,,,,True,,False,False,,False,,1,,2016
307
+ ,,,private_company,2016-09-20,,True,,,,sig_469444e7aaf1,False,False,False,True,False,True,False,False,False,,False,,2,,2016
308
+ ,,,association,2016-07-07,,,,,,sig_d01782ec9b77,,,,,,True,,,,,False,,1,,2016
309
+ ,,,private_company,2016-07-07,,,,,,sig_b2e93a7e09b3,,,,,,True,,True,False,,False,,1,,2016
310
+ ,,,private_company,2016-07-07,,True,,,,sig_b76035202a99,False,False,False,True,False,True,False,,False,,False,,3,,2016
311
+ ,,,private_company,2016-04-21,,True,,,,sig_b1def6a39280,,,,,,True,,False,False,,False,,1,,2016
312
+ ,,,private_company,2016-03-10,,True,,,,sig_1fcfa5d69e9c,False,False,True,False,False,False,False,True,False,,False,,,,2016
313
+ ,,,private_company,2016-03-01,,True,,,,sig_33ceeb3b1b7f,,,,,,,,True,False,,False,,,,2016
314
+ ,,,professional_individual,2015-02-12,,False,,,,sig_f34417c900c8,,,,,,,,False,False,,False,,1,,2015
315
+ ,,,association,2015-02-12,,True,,,,sig_c21c7eb8830e,False,False,False,True,False,False,False,False,False,False,False,,1,,2015
316
+ ,,,public,2015-04-09,,False,,,,sig_955f79b7de5a,False,False,False,True,False,False,True,False,False,,True,,2,,2015
317
+ ,,,private_company,2015-05-18,,False,,,,sig_3ac5edbed08e,False,False,False,False,False,False,False,True,False,False,False,,1,,2015
318
+ ,,,private_company,2015-06-01,,True,,,,sig_b11c06eb721a,True,False,False,True,False,False,True,False,False,False,False,,3,,2015
319
+ ,,,private_company,2015-06-18,,,,,,sig_f89218b3610f,,,,,,True,,True,False,,False,,1,,2015
320
+ ,,,private_company,2015-11-05,,True,,,,sig_1d36368ba9cd,,,,,True,True,,False,False,,False,,1,,2015
321
+ ,,,private_company,2015-12-10,,,,,,sig_7f202ffd8562,False,False,False,False,False,True,False,False,False,,False,,1,,2015
322
+ ,,,private_company,2014-11-27,,,,,,sig_6be70e77e18a,,,True,,,,,,,,False,,2,,2014
323
+ ,,,private_company,2014-11-27,,,,,,sig_2de0ef4fd075,,,,,,True,,True,False,,False,,1,,2014
324
+ ,,,private_company,2014-11-20,,,,,,sig_32f0a74af198,False,False,True,False,False,False,False,False,False,,False,,2,,2014
325
+ ,,,public,2014-09-25,,,,,,sig_3fd50678ec67,,,,True,,True,,,,,True,,2,,2014
326
+ ,,,private_company,2014-08-07,,True,,,,sig_8528a7dffe29,False,False,False,True,False,True,False,False,False,False,False,,4,,2014
327
+ ,,,private_company,2014-08-07,,True,,,,sig_8498d37bfe96,,,,True,,True,True,True,False,,False,,5,,2014
328
+ ,,,private_company,2014-07-22,,True,,,,sig_217a48adf591,,,,True,,True,True,False,False,,False,,5,,2014
329
+ ,,,private_company,2014-07-17,,True,,,,sig_b66dc122bc67,,,,True,,True,True,False,False,,False,,4,,2014
330
+ ,,,association,2014-07-17,,True,,,,sig_61f2165f37c0,,,,,,True,True,False,False,,False,,2,,2014
331
+ ,,,private_company,2014-07-17,,,,,,sig_95e3983bab9f,,,,,,,,False,False,,False,,,,2014
332
+ ,,,private_company,2014-07-17,,,,,,sig_8badebef6ec9,False,False,False,True,False,False,False,False,False,,False,,1,,2014
333
+ ,,,private_company,2014-06-26,,True,,,,sig_519887017d14,,,,,,True,True,,,,False,,3,,2014
334
+ 0.0,none,,private_company,2014-06-12,,True,False,,,sig_08277dcf6c51,False,False,False,True,False,True,False,False,False,,False,,2,,2014
335
+ 0.0,none,,association,2014-01-29,,True,False,,,sig_ff249ae004d3,False,False,True,False,False,False,False,False,False,False,False,,1,,2014
336
+ 0.0,none,,association,2014-01-29,,True,False,,,sig_28d602234acd,,,True,,,,,,,,False,,3,,2014
337
+ ,,,private_company,2014-01-29,,,,,,sig_50f45ec6c706,,,,True,,,,,,True,False,,1,,2014
338
+ ,,,private_company,2014-01-29,,,,,,sig_b1ff4b6cb546,False,False,False,True,False,False,True,False,False,False,False,,2,,2014
339
+ ,,,private_company,2014-01-03,,True,,,,sig_ac6a8f9aab3f,True,False,False,True,False,False,True,True,False,,False,,4,,2014
340
+ 0.0,none,,private_company,2013-12-12,,True,False,,,sig_d74622068f72,False,False,False,False,False,False,True,False,True,,False,,3,,2013
341
+ 0.0,none,,private_company,2013-11-23,,True,False,,,sig_5907daef375d,,,,,,,,False,False,,False,,2,,2013
342
+ ,,,private_company,2013-10-24,,,,,,sig_e551971345bd,,,True,,,,,,,,False,,1,,2013
343
+ ,,,private_company,2013-10-24,,True,,,,sig_5d5b4fcd057e,,,,,,True,True,False,False,,False,,4,,2013
344
+ ,,,private_company,2013-10-24,,True,,,,sig_ce2bb4fe7561,False,False,False,False,False,False,True,False,False,False,False,,3,,2013
345
+ ,,,public,2013-07-25,,,,,,sig_60ea96776b12,,,,,,,,,,,True,,,,2013
346
+ ,,,public,2013-07-18,,,,,,sig_1fad9c7ace14,,,,,,True,,,,,True,,2,,2013
347
+ ,,,private_company,2013-06-19,,True,,,,sig_d11632964106,False,False,False,True,False,False,False,False,False,False,False,,1,,2013
348
+ 0.0,none,,political,2013-05-30,,True,False,,,sig_d0d9a464e0ee,,,,True,,True,True,False,False,,False,,4,,2013
349
+ ,,,public,2013-05-16,,,,,,sig_21d59b4ce0d7,,,,,,,,,,,True,,2,,2013
350
+ 0.0,none,,private_company,2013-04-11,,True,False,,,sig_8f0cb261e295,,,,,,True,,False,False,,False,,1,,2013
351
+ ,,,association,2013-03-21,,,,,,sig_f3cf65199c1c,,,,True,,True,True,False,False,,False,,6,,2013
352
+ ,,,other,2013-01-10,,,,,,sig_fea3881e2db7,,,,,,True,,False,False,,False,,1,,2013
353
+ ,,,private_company,2012-12-03,,,,,,sig_a6a57176e8d1,,,True,,,,,,,,False,,1,,2012
354
+ ,,,public,2012-11-08,,,,,,sig_2ba0d8d8478c,,,,,,True,True,,,,True,,4,,2012
355
+ 0.0,none,,public,2012-09-20,,True,False,,,sig_af5a2383581f,,,,,,True,,False,False,,True,,2,,2012
356
+ ,,,private_company,2012-07-19,,True,,,,sig_470cd3812f15,True,False,False,True,False,True,False,False,False,False,False,,3,,2012
357
+ 0.0,none,,private_company,2012-06-22,,True,False,,,sig_e74da32362c4,False,False,True,False,False,False,False,False,False,False,False,,2,,2012
358
+ ,,,private_company,2012-06-21,,True,,,,sig_c78b4d8b0a6c,,,,,,True,,,,,False,,1,,2012
359
+ ,,,private_company,2012-06-21,,False,,,,sig_27826219be2a,False,False,False,True,False,False,True,False,False,False,False,,2,,2012
360
+ ,,,private_company,2012-06-01,,True,,,,sig_4b0cd3ec1343,False,False,True,True,False,False,False,True,False,,False,,3,,2012
361
+ ,,,association,2012-05-24,,True,,,,sig_6571eb1cdc44,False,False,False,True,False,False,True,False,False,,False,,4,,2012
362
+ ,,,private_company,2012-05-24,,,,,,sig_a04851b7fc51,,,,,,True,True,False,False,,False,,3,,2012
363
+ ,,,private_company,2012-05-24,,False,,,,sig_e50f33970d9f,,,,True,,True,True,False,False,,False,,5,,2012
364
+ ,,,public,2012-05-03,,,,,,sig_77500a7767cb,False,False,False,True,False,False,True,False,False,False,True,,2,,2012
365
+ ,,,public,2012-03-29,,,,,,sig_cdd761a9300c,,,,,,True,,,,,True,,2,,2012
366
+ ,,,association,2012-02-16,,True,,,,sig_c3679af60a71,False,False,True,False,False,False,True,False,False,False,False,,3,,2012
367
+ ,,,private_company,2012-01-12,,True,,,,sig_6f8167cc8982,True,False,True,False,False,False,False,False,False,False,False,,3,,2012
368
+ ,,,public,2011-12-11,,,,,,sig_c3a5221fce9b,,,,,,,,False,True,True,True,,,,2011
369
+ ,,,private_company,2011-12-01,,,,,,sig_09fc22bfea05,,,,,,True,,True,False,,False,,,,2011
370
+ ,,,private_company,2011-11-18,,,,,,sig_31d59acdd58c,,,,,,True,,False,True,True,False,,2,,2011
371
+ ,,,private_company,2011-10-06,,False,,,,sig_b707b0bbb0f9,,,,True,,,,False,True,True,False,,,,2011
372
+ ,,,private_company,2011-10-06,,,,,,sig_c13758782abc,,,,,,,,False,False,,False,,,,2011
373
+ ,,,private_company,2011-09-21,,True,,,,sig_d91a0db385b9,False,False,True,False,False,False,True,False,False,,False,,3,,2011
374
+ ,,,political,2011-07-21,,False,,,,sig_b164e7a00c8b,,,,,,True,,False,False,,False,,,,2011
375
+ ,,,private_company,2011-06-30,,,,,,sig_fe3eb2b06eb1,,,,,,,,False,False,,False,,,,2011
cnil_sanctions_analysis.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:2486b323b1591ff9471dd9daa20f758a394109ab5b7b9532f1e450091b773a5c
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+ size 59624
codebook.md ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Codebook: CNIL Sanctions Dataset
2
+
3
+ ## Variables
4
+ - dn_sanction: Date of the sanction (YYYY-MM-DD)
5
+ - n_sanction_year: Year of the sanction
6
+ - amount_fine_eur: Fine amount in Euros
7
+ - cat_procedure_type: Type of procedure (simplified, standard)
8
+ - cat_sector_group: Sector of the organism
9
+ - is_public_sector: Boolean, true if public sector
10
+ - cat_fine_bucket: Categorical fine amount
11
+
12
+ ## Limitations
13
+ - Some decision URLs are missing.
14
+ - Fine amounts are extracted from text and may be subject to parsing errors.
insights.json ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "n_rows": 374,
3
+ "years": [
4
+ 2011,
5
+ 2025
6
+ ],
7
+ "growth_2014_to_2024": 4.8,
8
+ "growth_2022_to_2024": 4.1,
9
+ "private_share_pct": 73.8,
10
+ "simplified_share_total_pct": 96.4,
11
+ "simplified_share_2024_pct": 80.2,
12
+ "fines_reported": 282,
13
+ "total_fines_meur": 1136.2,
14
+ "median_fine_eur": 10000.0,
15
+ "max_fine_eur": 325000000.0,
16
+ "top_breach_theme": "is_breach_security",
17
+ "top_breach_count": 134,
18
+ "top_fines": [
19
+ {
20
+ "dn_sanction": "2025-09-01",
21
+ "organism_type_raw": "SOCIETE DEVELOPPANT PLUSIEURS SERVICES EN LIGNE",
22
+ "amount_fine_eur": 325000000.0,
23
+ "main_breaches_raw": "Information et consentement (cookies) Consentement des personnes (prospection commerciale par voie électronique - L. 34-5 CPCE)"
24
+ },
25
+ {
26
+ "dn_sanction": "2025-09-01",
27
+ "organism_type_raw": "SOCIETE DE VENTE EN LIGNE DE VETEMENTS, CHAUSSURES ET ACCESSOIRES",
28
+ "amount_fine_eur": 150000000.0,
29
+ "main_breaches_raw": "Information et consentement (cookies)"
30
+ },
31
+ {
32
+ "dn_sanction": "2021-12-31",
33
+ "organism_type_raw": "SERVICES INTERNET (MOTEUR DE RECHERCHE, PLATEFORME DE VIDEOS, ETC.)",
34
+ "amount_fine_eur": 150000000.0,
35
+ "main_breaches_raw": "Modalités de refus des cookies"
36
+ },
37
+ {
38
+ "dn_sanction": "2020-12-07",
39
+ "organism_type_raw": "SOCIÉTÉ DE SERVICES TECHNOLOGIQUES",
40
+ "amount_fine_eur": 100000000.0,
41
+ "main_breaches_raw": "Manquement relatif aux cookies ; manquement relatif à l'information des personnes ; manquement relatif au recueil du consentement ; manquement relatif au droit d'opposition"
42
+ },
43
+ {
44
+ "dn_sanction": "2022-12-19",
45
+ "organism_type_raw": "SOCIETE DE VENTE DE SYSTEMES D’EXPLOITATION, DE LOGICIELS APPLICATIFS, DE MATERIELS ET DE SERVICES DERIVES",
46
+ "amount_fine_eur": 60000000.0,
47
+ "main_breaches_raw": "Consentement des personnes (cookies et traceurs)"
48
+ },
49
+ {
50
+ "dn_sanction": "2021-12-31",
51
+ "organism_type_raw": "RESEAU SOCIAL",
52
+ "amount_fine_eur": 60000000.0,
53
+ "main_breaches_raw": "Modalités de refus des cookies Information des personnes"
54
+ },
55
+ {
56
+ "dn_sanction": "2024-11-14",
57
+ "organism_type_raw": "OPERATEUR DE TELECOMMUNICATIONS",
58
+ "amount_fine_eur": 50000000.0,
59
+ "main_breaches_raw": "Information des personnes (cookies) Prospection commerciale (article L. 34-5 CPCE)"
60
+ },
61
+ {
62
+ "dn_sanction": "2019-01-21",
63
+ "organism_type_raw": "ÉDITEUR DE SYSTÈME D'EXPLOITATION",
64
+ "amount_fine_eur": 50000000.0,
65
+ "main_breaches_raw": "Manque de transparence, information insatisfaisante et absence de consentement valable"
66
+ },
67
+ {
68
+ "dn_sanction": "2023-06-15",
69
+ "organism_type_raw": "SOCIÉTÉ SPÉCIALISÉE DANS L'AFFICHAGE DE PUBLICITÉS CIBLÉES SUR LE WEB",
70
+ "amount_fine_eur": 40000000.0,
71
+ "main_breaches_raw": "Consentement des personnes Information des personnes et transparence Non respect du droit d'accès Retrait du consentement et effacement des données Encadrement des relations entre les responsables conjoints de traitement"
72
+ },
73
+ {
74
+ "dn_sanction": "2020-12-07",
75
+ "organism_type_raw": "SOCIÉTÉ DE COMMERCE EN LIGNE",
76
+ "amount_fine_eur": 35000000.0,
77
+ "main_breaches_raw": "Manquement relatif aux cookies ; manquement relatif à l'information des personnes"
78
+ }
79
+ ]
80
+ }
report.md ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ # What CNIL sanctions reveal about privacy enforcement in France
2
+
3
+ This dataset contains 374 sanctions issued by the CNIL.
4
+ Analysis shows a prevalence of simplified procedures and private sector entities.