# Analyzing SEC Litigation Releases in Python

> Access and analyze structured data of SEC litigation releases, revealing trends in enforcement actions, defendants, and penalties.

Source: https://sec-api.io/docs/sec-litigation-releases-database-api/python-example

**On this page:**

- [Litigations by Year and Month](#Litigations-by-Year-and-Month)
  - [Litigations by Year and Settlement Type](#Litigations-by-Year-and-Settlement-Type)
  - [Litigations by Year And Count of Agreements to Pay Penalties](#Litigations-by-Year-And-Count-of-Agreements-to-Pay-Penalties)
- [Penalty Amount Analysis](#Penalty-Amount-Analysis)
  - [Penalty Amount by Year](#Penalty-Amount-by-Year)
  - [Top 10 Penalty Amounts](#Top-10-Penalty-Amounts)
  - [Penalties by Type of Defendant](#Penalties-by-Type-of-Defendant)
- [Litigations by Category](#Litigations-by-Category)
- [Requested Reliefs by Category](#Requested-Reliefs-by-Category)
- [Violated Securities Laws](#Violated-Securities-Laws)
- [Persons & Agencies Conducting the Investigations](#Persons-&-Agencies-Conducting-the-Investigations)
- [Other Agencies Involved](#Other-Agencies-Involved)

In this guide, we will analyze all SEC litigation releases from 1995 to 2025 obtained from the [litigation database](https://sec-api.io/docs/sec-litigation-releases-database-api). The dataset contains information about all litigation releases published by the U.S. Securities and Exchange Commission (SEC) from 1995 to 2025 and includes information about the defendants, charges, penalty amounts, and other relevant details.

Our analysis will focus on several key aspects of the dataset, including:

- The number of litigation releases published each year and month
- Litigations by settlement type and agreement to pay penalties
- Penalty amounts by year
- Top 10 penalties by amount
- Categories of requested reliefs, violations, and more

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style

style.use("default")

params = {
    "axes.labelsize": 8,
    "font.size": 8,
    "legend.fontsize": 8,
    "xtick.labelsize": 8,
    "ytick.labelsize": 8,
    "text.usetex": False,
    "font.family": "sans-serif",
    "axes.spines.top": False,
    "axes.spines.right": False,
    "grid.color": "grey",
    "axes.grid": True,
    "axes.grid.axis": "y",
    "axes.grid.axis": "x",
    "grid.alpha": 0.5,
    "grid.linestyle": ":",
}

plt.rcParams.update(params)
```

For the sake of brevity, we downloaded all SEC litigation data from 1995 to 2025 and saved it as a JSONL file. We will load this data into a pandas DataFrame and perform our analysis using Python.

```python
df = pd.read_json(
    # TODO: replace with your path
    "/path/to/sec-litigation-releases.jsonl",
    lines=True,
)
```

```python
# convert "releasedAt" to datetime and to EST timezone
df["releasedAt"] = pd.to_datetime(df["releasedAt"], utc=True)
df["releasedAt"] = df["releasedAt"].dt.tz_convert("US/Eastern")
df["releasedAtYear"] = df["releasedAt"].dt.year
df["releasedAtMonth"] = df["releasedAt"].dt.month
df["releasedAtYearMonth"] = df["releasedAt"].dt.to_period("M")
# Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
df["releasedAtDay"] = df["releasedAt"].dt.day_name()
df["hasAgreedToSettlement"] = df["hasAgreedToSettlement"].astype(bool)
df["hasAgreedToPayPenalty"] = df["hasAgreedToPayPenalty"].astype(bool)
df['caseCitation'] = df['caseCitations'].map(lambda x: x[0] if x else None)

print(f"Loaded {len(df):,.0f} SEC litigation releases from 1995 to 2025\n")
print(df.info())
```

```
Loaded 11,536 SEC litigation releases from 1995 to 2025

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11536 entries, 0 to 11535
Data columns (total 26 columns):
 #   Column                    Non-Null Count  Dtype
---  ------                    --------------  -----
 0   id                        11536 non-null  object
 1   releaseNo                 11536 non-null  object
 2   releasedAt                11536 non-null  datetime64[ns, US/Eastern]
 3   url                       11536 non-null  object
 4   title                     11535 non-null  object
 5   subTitle                  11536 non-null  object
 6   caseCitations             11536 non-null  object
 7   resources                 11536 non-null  object
 8   summary                   11524 non-null  object
 9   tags                      11524 non-null  object
 10  entities                  11524 non-null  object
 11  complaints                11524 non-null  object
 12  parallelActionsTakenBy    11524 non-null  object
 13  hasAgreedToSettlement     11536 non-null  bool
 14  hasAgreedToPayPenalty     11536 non-null  bool
 15  penaltyAmounts            11524 non-null  object
 16  requestedRelief           11524 non-null  object
 17  violatedSections          11524 non-null  object
 18  investigationConductedBy  11524 non-null  object
 19  litigationLedBy           11524 non-null  object
 20  otherAgenciesInvolved     11524 non-null  object
 21  releasedAtYear            11536 non-null  int32
 22  releasedAtMonth           11536 non-null  int32
 23  releasedAtYearMonth       11536 non-null  period[M]
 24  releasedAtDay             11536 non-null  object
 25  caseCitation              11192 non-null  object
dtypes: bool(2), datetime64[ns, US/Eastern](1), int32(2), object(20), period[M](1)
memory usage: 2.0+ MB
None
```

```
/var/folders/q3/bt7922t52p78qdm75h_8m5yh0000gn/T/ipykernel_39183/1704038985.py:6: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
  df["releasedAtYearMonth"] = df["releasedAt"].dt.to_period("M")
```

```python
df.head()
```

Out:

|  | id | releaseNo | releasedAt | url | title | subTitle | caseCitations | resources | summary | tags | ... | requestedRelief | violatedSections | investigationConductedBy | litigationLedBy | otherAgenciesInvolved | releasedAtYear | releasedAtMonth | releasedAtYearMonth | releasedAtDay | caseCitation |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | c3772c013f5a90d0c1c0170bbed8ad6a | LR-26231 | 2025-01-22 21:17:05-05:00 | https://www.sec.gov/enforcement-litigation/lit... | Gabriel Rebeiz | SEC Charges Technical Consultant with Insider ... | [Securities and Exchange Commission v. Gabriel... | [{'label': 'SEC Complaint', 'url': 'https://ww... | The SEC filed settled charges against Gabriel ... | [insider trading] | ... | [disgorgement of profits, civil penalties, per... | [Section 10(b) of the Securities Exchange Act ... | [Sara Kalin, John Rymas, Stephen Kam] | [Diana Tani, Joseph Sansone] | [{'name': 'Financial Industry Regulatory Autho... | 2025 | 1 | 2025-01 | Wednesday | Securities and Exchange Commission v. Gabriel ... |
| 1 | 860c50b5c1bfa963e68568f2b6008ba4 | LR-26230 | 2025-01-21 15:00:18-05:00 | https://www.sec.gov/enforcement-litigation/lit... | Old South Trading Co., LLC; Brendan H. Church;... | SEC Charges Father and Son for $25.8 Million U... | [Securities and Exchange Commission v. Old Sou... | [{'label': 'SEC Complaint', 'url': 'https://ww... | The SEC has charged Old South Trading Co., LLC... | [unregistered securities offering, unregistere... | ... | [injunctive relief, disgorgement of allegedly ... | [Sections 5(a) and (c) of the Securities Act o... | [Jonathan Shapiro, Andrew Elliott, Margaret Vi... | [Dean Conway] | [] | 2025 | 1 | 2025-01 | Tuesday | Securities and Exchange Commission v. Old Sout... |
| 2 | 1485373724567b64d7cee1c8ce853fe3 | LR-26229 | 2025-01-18 19:09:46-05:00 | https://www.sec.gov/enforcement-litigation/lit... | Nova Labs, Inc. | SEC Charges Nova Labs, Inc. with Fraud and Reg... | [Securities and Exchange Commission v. Nova La... | [{'label': 'SEC Complaint', 'url': 'https://ww... | The SEC has charged Nova Labs, Inc. with fraud... | [fraud, registration violations, crypto] | ... | [permanent and conduct-based injunctions, disg... | [Sections 5(a), 5(c), and 17(a)(2) of the Secu... | [Emmy E. Rush, Christopher Colorado, Kim Han, ... | [Emmy E. Rush, Christopher Colorado, Peter Man... | [] | 2025 | 1 | 2025-01 | Saturday | Securities and Exchange Commission v. Nova Lab... |
| 3 | 35ebfbcaf74cef9401e78f8e92b934e6 | LR-26228 | 2025-01-17 22:20:10-05:00 | https://www.sec.gov/enforcement-litigation/lit... | Arete Wealth Management LLC; Arete Wealth Advi... | SEC Charges Arete Wealth Broker-Dealer and Adv... | [Securities and Exchange Commission v. Arete W... | [{'label': 'SEC Complaint', 'url': 'https://ww... | The SEC has charged Arete Wealth Management LL... | [fraud, illegal securities offering, recordkee... | ... | [permanent injunctions, civil penalties, condu... | [Sections 206(1) and 206(2) of the Investment ... | [Theresa H. Gue, Austin Thompson, Christopher ... | [Oren Gleich, Preethi Krishnamurthy] | [{'name': 'U.S. Attorney's Office for the East... | 2025 | 1 | 2025-01 | Friday | Securities and Exchange Commission v. Arete We... |
| 4 | 60cabddfdd7abcf4c1da9bffebc55a1e | LR-26227 | 2025-01-17 21:56:17-05:00 | https://www.sec.gov/enforcement-litigation/lit... | Naufal Sanaullah | SEC Charges Fund Executive with Making False S... | [Securities and Exchange Commission v. Naufal ... | [{'label': 'SEC Complaint', 'url': 'https://ww... | The SEC charged Naufal Sanaullah with making f... | [disclosure fraud, securities fraud] | ... | [permanent injunctive relief, disgorgement alo... | [Section 17(a) of the Securities Act of 1933, ... | [Heather Marlow, Kimberly L. Frederick, Nichol... | [Zachary Carlyle, Gregory A. Kasper, Nicholas ... | [{'name': 'Federal Bureau of Investigation', '... | 2025 | 1 | 2025-01 | Friday | Securities and Exchange Commission v. Naufal S... |

5 rows × 26 columns

## Litigations by Year and Month

```python
# piv table with years = col, months = index, values = count of cases
df_year_month = df.pivot_table(
    index="releasedAtYear",
    columns="releasedAtMonth",
    values="id",
    aggfunc="count",
    fill_value=0,
)

total_col = df_year_month.sum(axis=1)
mean_col = round(df_year_month.mean(axis=1), 0)
median_col = round(df_year_month.median(axis=1), 0)

df_year_month["total"] = total_col
df_year_month["mean"] = mean_col
df_year_month["median"] = median_col

total_row = df_year_month.sum(axis=0)
mean_row = round(df_year_month.mean(axis=0), 0)
median_row = round(df_year_month.median(axis=0), 0)

df_year_month.loc["total"] = total_row
df_year_month.loc["mean"] = mean_row
df_year_month.loc["median"] = median_row

df_year_month = df_year_month.astype(int)

print("SEC Litigation Releases by Year and Month")
df_year_month
```

```
SEC Litigation Releases by Year and Month
```

Out:

| releasedAtMonth | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | total | mean | median |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| releasedAtYear |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1995 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 | 39 | 36 | 25 | 123 | 10 | 0 |
| 1996 | 37 | 22 | 30 | 36 | 32 | 40 | 27 | 30 | 73 | 42 | 26 | 34 | 429 | 36 | 33 |
| 1997 | 37 | 32 | 39 | 46 | 24 | 20 | 28 | 39 | 53 | 27 | 26 | 29 | 400 | 33 | 30 |
| 1998 | 28 | 20 | 35 | 35 | 34 | 31 | 30 | 36 | 53 | 44 | 19 | 25 | 390 | 32 | 32 |
| 1999 | 38 | 24 | 31 | 27 | 40 | 33 | 24 | 42 | 52 | 23 | 31 | 21 | 386 | 32 | 31 |
| 2000 | 23 | 41 | 38 | 32 | 45 | 40 | 25 | 34 | 68 | 44 | 27 | 34 | 451 | 38 | 36 |
| 2001 | 39 | 35 | 28 | 32 | 41 | 36 | 27 | 30 | 55 | 49 | 35 | 41 | 448 | 37 | 36 |
| 2002 | 58 | 38 | 60 | 49 | 42 | 55 | 52 | 60 | 49 | 65 | 45 | 44 | 617 | 51 | 50 |
| 2003 | 48 | 49 | 49 | 65 | 41 | 45 | 60 | 51 | 64 | 58 | 40 | 49 | 619 | 52 | 49 |
| 2004 | 34 | 40 | 50 | 39 | 40 | 44 | 43 | 48 | 53 | 36 | 36 | 32 | 495 | 41 | 40 |
| 2005 | 39 | 46 | 59 | 46 | 31 | 50 | 27 | 36 | 57 | 39 | 30 | 38 | 498 | 42 | 39 |
| 2006 | 34 | 31 | 53 | 42 | 34 | 38 | 32 | 35 | 39 | 40 | 32 | 26 | 436 | 36 | 34 |
| 2007 | 30 | 36 | 41 | 35 | 39 | 39 | 42 | 46 | 51 | 38 | 24 | 40 | 461 | 38 | 39 |
| 2008 | 26 | 27 | 42 | 33 | 57 | 26 | 35 | 32 | 63 | 34 | 22 | 22 | 419 | 35 | 32 |
| 2009 | 36 | 42 | 63 | 38 | 44 | 48 | 49 | 29 | 40 | 40 | 43 | 42 | 514 | 43 | 42 |
| 2010 | 41 | 29 | 44 | 40 | 30 | 45 | 24 | 27 | 43 | 38 | 41 | 37 | 439 | 37 | 39 |
| 2011 | 36 | 35 | 44 | 38 | 32 | 39 | 32 | 26 | 31 | 33 | 27 | 41 | 414 | 34 | 34 |
| 2012 | 33 | 27 | 40 | 36 | 32 | 24 | 23 | 34 | 36 | 24 | 30 | 27 | 366 | 30 | 31 |
| 2013 | 25 | 21 | 34 | 31 | 18 | 24 | 31 | 23 | 38 | 34 | 19 | 20 | 318 | 26 | 24 |
| 2014 | 14 | 20 | 24 | 25 | 27 | 24 | 18 | 22 | 25 | 25 | 18 | 24 | 266 | 22 | 24 |
| 2015 | 16 | 25 | 15 | 24 | 22 | 22 | 19 | 16 | 44 | 24 | 17 | 26 | 270 | 22 | 22 |
| 2016 | 16 | 20 | 33 | 17 | 24 | 35 | 21 | 21 | 39 | 9 | 16 | 18 | 269 | 22 | 20 |
| 2017 | 21 | 26 | 34 | 24 | 30 | 22 | 20 | 36 | 28 | 21 | 21 | 25 | 308 | 26 | 24 |
| 2018 | 15 | 21 | 31 | 36 | 25 | 27 | 40 | 34 | 47 | 28 | 34 | 17 | 355 | 30 | 30 |
| 2019 | 7 | 26 | 21 | 29 | 22 | 40 | 24 | 34 | 46 | 23 | 24 | 22 | 318 | 26 | 24 |
| 2020 | 31 | 20 | 32 | 25 | 15 | 21 | 15 | 20 | 59 | 14 | 17 | 32 | 301 | 25 | 20 |
| 2021 | 16 | 19 | 24 | 23 | 18 | 26 | 24 | 37 | 47 | 13 | 22 | 23 | 292 | 24 | 23 |
| 2022 | 24 | 16 | 16 | 25 | 24 | 30 | 27 | 33 | 53 | 20 | 16 | 21 | 305 | 25 | 24 |
| 2023 | 23 | 26 | 33 | 16 | 38 | 28 | 30 | 23 | 57 | 11 | 17 | 14 | 316 | 26 | 24 |
| 2024 | 17 | 11 | 15 | 29 | 23 | 25 | 27 | 28 | 53 | 22 | 14 | 24 | 288 | 24 | 24 |
| 2025 | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 2 | 0 |
| total | 867 | 825 | 1058 | 973 | 924 | 977 | 876 | 962 | 1439 | 957 | 805 | 873 | 11536 | 957 | 910 |
| mean | 28 | 27 | 34 | 31 | 30 | 32 | 28 | 31 | 46 | 31 | 26 | 28 | 372 | 31 | 29 |
| median | 28 | 26 | 34 | 32 | 31 | 31 | 27 | 33 | 49 | 33 | 26 | 26 | 386 | 32 | 31 |

```python
fig, ax = plt.subplots(figsize=(5, 3))

df_year_month.loc[1995:2024]["total"].plot(
    kind="line",
    ax=ax,
    marker="o",
    markersize=3,
    linewidth=1,
)

ax.set_title("SEC Litigation Releases per Year")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Cases")
plt.tight_layout()
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.show()
```

### Litigations by Year and Settlement Type

```python
# piv table for hasAgreedToSettlement
# col = hasAgreedToSettlement (true, false), index = year, values = count of cases
df_year_settlement = df.pivot_table(
    index="releasedAtYear",
    columns="hasAgreedToSettlement",
    values="id",
    aggfunc="count",
    fill_value=0,
)

total_col = df_year_settlement.sum(axis=1)
mean_col = round(df_year_settlement.mean(axis=1), 0)
median_col = round(df_year_settlement.median(axis=1), 0)

total_row = df_year_settlement.sum(axis=0)
mean_row = round(df_year_settlement.mean(axis=0), 0)
median_row = round(df_year_settlement.median(axis=0), 0)

df_year_settlement.loc["total"] = total_row
df_year_settlement.loc["mean"] = mean_row
df_year_settlement.loc["median"] = median_row

df_year_settlement = df_year_settlement.astype(int)

df_year_settlement
```

Out:

| hasAgreedToSettlement | False | True |
| --- | --- | --- |
| releasedAtYear |  |  |
| 1995 | 63 | 60 |
| 1996 | 215 | 214 |
| 1997 | 195 | 205 |
| 1998 | 209 | 181 |
| 1999 | 196 | 190 |
| 2000 | 239 | 212 |
| 2001 | 214 | 234 |
| 2002 | 337 | 280 |
| 2003 | 322 | 297 |
| 2004 | 244 | 251 |
| 2005 | 241 | 257 |
| 2006 | 177 | 259 |
| 2007 | 210 | 251 |
| 2008 | 188 | 231 |
| 2009 | 273 | 241 |
| 2010 | 205 | 234 |
| 2011 | 194 | 220 |
| 2012 | 190 | 176 |
| 2013 | 161 | 157 |
| 2014 | 135 | 131 |
| 2015 | 160 | 110 |
| 2016 | 149 | 120 |
| 2017 | 174 | 134 |
| 2018 | 177 | 178 |
| 2019 | 171 | 147 |
| 2020 | 161 | 140 |
| 2021 | 173 | 119 |
| 2022 | 165 | 140 |
| 2023 | 165 | 151 |
| 2024 | 146 | 142 |
| 2025 | 7 | 18 |
| total | 5856 | 5680 |
| mean | 189 | 183 |
| median | 188 | 181 |

```python
# stacked bar chart for hasAgreedToSettlement
fig, ax = plt.subplots(figsize=(6, 4))

df_year_settlement.drop(["total", "mean", "median"]).plot(
    kind="bar", stacked=False, ax=ax, color=["#1f77b4", "#ff7f0e"]
)

plt.title("SEC Litigation Releases by Year and Settlement")
plt.xlabel("Year")
plt.ylabel("Count")
plt.legend(["No Settlement", "Settlement"], loc="upper right")
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()
```

### Litigations by Year And Count of Agreements to Pay Penalties

```python
# piv table for hasAgreedToPayPenalty
# col = hasAgreedToPayPenalty (true, false), index = year, values = count of cases
df_year_penalty = df.pivot_table(
    index="releasedAtYear",
    columns="hasAgreedToPayPenalty",
    values="id",
    aggfunc="count",
    fill_value=0,
)

total_col = df_year_penalty.sum(axis=1)
mean_col = round(df_year_penalty.mean(axis=1), 0)
median_col = round(df_year_penalty.median(axis=1), 0)

total_row = df_year_penalty.sum(axis=0)
mean_row = round(df_year_penalty.mean(axis=0), 0)
median_row = round(df_year_penalty.median(axis=0), 0)

df_year_penalty.loc["total"] = total_row
df_year_penalty.loc["mean"] = mean_row
df_year_penalty.loc["median"] = median_row

df_year_penalty = df_year_penalty.astype(int)

df_year_penalty
```

Out:

| hasAgreedToPayPenalty | False | True |
| --- | --- | --- |
| releasedAtYear |  |  |
| 1995 | 88 | 35 |
| 1996 | 296 | 133 |
| 1997 | 266 | 134 |
| 1998 | 254 | 136 |
| 1999 | 222 | 164 |
| 2000 | 285 | 166 |
| 2001 | 251 | 197 |
| 2002 | 375 | 242 |
| 2003 | 339 | 280 |
| 2004 | 270 | 225 |
| 2005 | 243 | 255 |
| 2006 | 205 | 231 |
| 2007 | 230 | 231 |
| 2008 | 224 | 195 |
| 2009 | 308 | 206 |
| 2010 | 225 | 214 |
| 2011 | 219 | 195 |
| 2012 | 183 | 183 |
| 2013 | 172 | 146 |
| 2014 | 144 | 122 |
| 2015 | 154 | 116 |
| 2016 | 162 | 107 |
| 2017 | 173 | 135 |
| 2018 | 200 | 155 |
| 2019 | 182 | 136 |
| 2020 | 171 | 130 |
| 2021 | 191 | 101 |
| 2022 | 165 | 140 |
| 2023 | 192 | 124 |
| 2024 | 144 | 144 |
| 2025 | 11 | 14 |
| total | 6544 | 4992 |
| mean | 211 | 161 |
| median | 205 | 146 |

```python
# stacked bar chart for hasAgreedToSettlement
fig, ax = plt.subplots(figsize=(6, 4))

df_year_penalty.drop(["total", "mean", "median"]).plot(
    kind="bar", stacked=False, ax=ax, color=["#1f77b4", "#ff7f0e"]
)

plt.title("SEC Litigation Releases by Year and Penalty")
plt.xlabel("Year")
plt.ylabel("Count")
plt.legend(["Did not agree to penalty", "Agreed to penalty"], loc="upper right")
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()
```

## Penalty Amount Analysis

```python
all_penalties = []
case_citations = {}

# iterate over all rows, extract penalties and append to all_penalties
for i, row in df.drop_duplicates(subset=["caseCitation"], keep="last").iterrows():
    penaltyAmounts = row["penaltyAmounts"]

    if isinstance(penaltyAmounts, list):
        for penalty in penaltyAmounts:
            if "penaltyAmount" in penalty:
                # find entity with "name" == "imposedOn"
                entity = list(filter(lambda x: x["name"] == penalty["imposedOn"], row["entities"]))
                entity_type = entity[0]["type"] if entity else None

                all_penalties.append(
                    {
                        "caseCitation": row['caseCitation'],
                        "releaseNo": row["releaseNo"],
                        "releasedAt": row["releasedAt"],
                        "releasedAtYear": row["releasedAtYear"],
                        "releasedAtMonth": row["releasedAtMonth"],
                        "amount": penalty["penaltyAmount"],
                        "imposedOn": penalty["imposedOn"],
                        "imposedOnType": entity_type,
                        "url": row["url"],
                    }
                )

all_penalties_df = pd.DataFrame(all_penalties)
all_penalties_df["amount"] = all_penalties_df["amount"].astype(float)

# remove all doubles by caseCitation and imposedOn
all_penalties_df = all_penalties_df.drop_duplicates(
    subset=["caseCitation", "imposedOn", "amount"], keep="last"
)

all_penalties_df
```

Out:

|  | caseCitation | releaseNo | releasedAt | releasedAtYear | releasedAtMonth | amount | imposedOn | imposedOnType | url |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | Securities and Exchange Commission v. Gabriel ... | LR-26231 | 2025-01-22 21:17:05-05:00 | 2025 | 1 | 360673.00 | Gabriel Rebeiz | individual | https://www.sec.gov/enforcement-litigation/lit... |
| 1 | Securities and Exchange Commission v. Arete We... | LR-26228 | 2025-01-17 22:20:10-05:00 | 2025 | 1 | 200000.00 | Michael Sealy | individual | https://www.sec.gov/enforcement-litigation/lit... |
| 2 | Securities and Exchange Commission v. American... | LR-26225 | 2025-01-17 17:57:15-05:00 | 2025 | 1 | 1876115.22 | Ross C. Miles, American Equities, Inc., and Am... | None | https://www.sec.gov/enforcement-litigation/lit... |
| 3 | Securities and Exchange Commission v. American... | LR-26225 | 2025-01-17 17:57:15-05:00 | 2025 | 1 | 1146307.10 | Ross C. Miles, American Equities, Inc., and Am... | None | https://www.sec.gov/enforcement-litigation/lit... |
| 4 | Securities and Exchange Commission v. American... | LR-26225 | 2025-01-17 17:57:15-05:00 | 2025 | 1 | 230464.00 | Ross C. Miles, American Equities, Inc., and Am... | None | https://www.sec.gov/enforcement-litigation/lit... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9421 | SECURITIES AND EXCHANGE COMMISSION v. CROSS FI... | LR-14649 | 1995-09-21 14:18:04-04:00 | 1995 | 9 | 8600000.00 | Douglas S. Cross | individual | https://www.sec.gov/files/litigation/litreleas... |
| 9422 | SECURITIES AND EXCHANGE COMMISSION v. CROSS FI... | LR-14649 | 1995-09-21 14:18:04-04:00 | 1995 | 9 | 2600000.00 | Michael J. Colello | individual | https://www.sec.gov/files/litigation/litreleas... |
| 9423 | SECURITIES AND EXCHANGE COMMISSION v. ROBERT M... | LR-14644 | 1995-09-20 14:18:03-04:00 | 1995 | 9 | 922741.00 | Stifel, Nicolaus and Company, Incorporated | company | https://www.sec.gov/files/litigation/litreleas... |
| 9424 | SECURITIES AND EXCHANGE COMMISSION v. ROBERT M... | LR-14644 | 1995-09-20 14:18:03-04:00 | 1995 | 9 | 263637.00 | Stifel, Nicolaus and Company, Incorporated | company | https://www.sec.gov/files/litigation/litreleas... |
| 9425 | SECURITIES AND EXCHANGE COMMISSION v. ROBERT M... | LR-14644 | 1995-09-20 14:18:03-04:00 | 1995 | 9 | 250000.00 | Stifel, Nicolaus and Company, Incorporated | company | https://www.sec.gov/files/litigation/litreleas... |

9291 rows × 9 columns

### Penalty Amount by Year

```python
# aggregate amount by year
penalties_year = all_penalties_df.groupby("releasedAtYear")["amount"].sum()
penalties_year = penalties_year.astype(int)
penalties_year = pd.DataFrame(penalties_year)
penalties_year["amount"] = round(penalties_year["amount"] / 1_000_000, 2)
print("Total Penalties in Million USD by Year")
penalties_year
```

```
Total Penalties in Million USD by Year
```

Out:

|  | amount |
| --- | --- |
| releasedAtYear |  |
| 1995 | 107.50 |
| 1996 | 187.45 |
| 1997 | 119.11 |
| 1998 | 201.08 |
| 1999 | 384.33 |
| 2000 | 365.14 |
| 2001 | 398.37 |
| 2002 | 1051.64 |
| 2003 | 7185.29 |
| 2004 | 1265.45 |
| 2005 | 2510.20 |
| 2006 | 4326.68 |
| 2007 | 1033.73 |
| 2008 | 2178.02 |
| 2009 | 1787.05 |
| 2010 | 3566.43 |
| 2011 | 1355.98 |
| 2012 | 1681.49 |
| 2013 | 1485.04 |
| 2014 | 1824.30 |
| 2015 | 690.43 |
| 2016 | 3168.99 |
| 2017 | 934.54 |
| 2018 | 411.99 |
| 2019 | 495.31 |
| 2020 | 1043.57 |
| 2021 | 803.07 |
| 2022 | 757.16 |
| 2023 | 539.52 |
| 2024 | 1111.01 |
| 2025 | 44.36 |

```python
fig, ax = plt.subplots(figsize=(5, 3))

penalties_year.loc[1995:2024]["amount"].plot(
    kind="line",
    ax=ax,
    marker="o",
    markersize=3,
    linewidth=1,
)

# format y labels to , notation
ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
ax.set_title("SEC Penalties per Year")
ax.set_xlabel("Year")
ax.set_ylabel("Penalty Amount\nin Million USD")
plt.tight_layout()
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.show()
```

### Top 10 Penalty Amounts

```python
# sort all penalties by amount, show top 10
top_10_penalties = all_penalties_df.sort_values("amount", ascending=False).head(10)
top_10_penalties["amount"] = round(top_10_penalties["amount"] / 1_000_000, 2)
top_10_penalties["amount"] = top_10_penalties["amount"].map("{:,.1f}".format)
print("Top 10 SEC Penalties in Million USD between 1995 and 2024")
# show all columns in full width
pd.set_option("display.max_colwidth", None)
top_10_penalties[['amount', 'imposedOn', 'releasedAt', 'releaseNo', 'url']]
```

```
Top 10 SEC Penalties in Million USD between 1995 and 2024
```

Out:

|  | amount | imposedOn | releasedAt | releaseNo | url |
| --- | --- | --- | --- | --- | --- |
| 6846 | 2,250.0 | WorldCom, Inc. | 2003-08-07 14:21:04-04:00 | LR-18277 | https://www.sec.gov/enforcement-litigation/litigation-releases/lr-18277 |
| 5887 | 1,600.0 | American International Group, Inc. | 2006-02-09 14:22:14-05:00 | LR-19560 | https://www.sec.gov/enforcement-litigation/litigation-releases/lr-19560 |
| 6974 | 1,510.0 | WorldCom Inc. | 2003-05-19 14:20:57-04:00 | LR-18147 | https://www.sec.gov/enforcement-litigation/litigation-releases/lr-18147 |
| 2056 | 957.0 | Braskem S.A. | 2016-12-21 14:26:01-05:00 | LR-23705 | https://www.sec.gov/enforcement-litigation/litigation-releases/lr-23705 |
| 6731 | 894.0 | ten firms, Grubman and Blodget | 2003-10-31 14:21:13-05:00 | LR-18438 | https://www.sec.gov/enforcement-litigation/litigation-releases/lr-18438 |
| 2502 | 806.2 | Marlon Quan and the other defendants | 2014-09-25 14:25:28-04:00 | LR-23093 | https://www.sec.gov/enforcement-litigation/litigation-releases/lr-23093 |
| 5888 | 800.0 | American International Group, Inc. | 2006-02-09 14:22:14-05:00 | LR-19560 | https://www.sec.gov/enforcement-litigation/litigation-releases/lr-19560 |
| 2058 | 632.0 | Braskem S.A. | 2016-12-21 14:26:01-05:00 | LR-23705 | https://www.sec.gov/enforcement-litigation/litigation-releases/lr-23705 |
| 1012 | 601.0 | CR Intrinsic and the relief defendants | 2021-02-03 14:27:14-05:00 | LR-25022 | https://www.sec.gov/enforcement-litigation/litigation-releases/lr-25022 |
| 4626 | 569.0 | Siemens Aktiengesellschaft | 2008-12-15 14:23:23-05:00 | LR-20829 | https://www.sec.gov/enforcement-litigation/litigation-releases/lr-20829 |

```python
# reset display options
pd.reset_option("display.max_colwidth")
```

### Penalties by Type of Defendant

```python
# create piv table across years and imposedOnType, with values = sum of penalties
penalties_entity_type = all_penalties_df.pivot_table(
    index="releasedAtYear",
    columns="imposedOnType",
    values="amount",
    aggfunc="sum",
    fill_value=0,
)

total_col = penalties_entity_type.sum(axis=1)
mean_col = round(penalties_entity_type.mean(axis=1), 0)
median_col = round(penalties_entity_type.median(axis=1), 0)

total_row = penalties_entity_type.sum(axis=0)
mean_row = round(penalties_entity_type.mean(axis=0), 0)
median_row = round(penalties_entity_type.median(axis=0), 0)

penalties_entity_type.loc["total"] = total_row
penalties_entity_type.loc["mean"] = mean_row
penalties_entity_type.loc["median"] = median_row

# format to million and , notation
penalties_entity_type = round(penalties_entity_type / 1_000_000, 2)
penalties_entity_type = penalties_entity_type.map("{:,.1f}".format)

print("SEC Penalties in Million USD by Year and Entity Type")

penalties_entity_type = penalties_entity_type[['company', 'individual']]
penalties_entity_type
```

```
SEC Penalties in Million USD by Year and Entity Type
```

Out:

| imposedOnType | company | individual |
| --- | --- | --- |
| releasedAtYear |  |  |
| 1995 | 32.4 | 53.1 |
| 1996 | 37.4 | 58.8 |
| 1997 | 21.2 | 93.3 |
| 1998 | 36.6 | 71.4 |
| 1999 | 173.0 | 186.1 |
| 2000 | 44.2 | 259.9 |
| 2001 | 122.8 | 236.4 |
| 2002 | 284.8 | 680.6 |
| 2003 | 5,197.4 | 1,043.4 |
| 2004 | 994.0 | 95.8 |
| 2005 | 856.7 | 1,648.6 |
| 2006 | 3,749.3 | 540.9 |
| 2007 | 572.6 | 451.6 |
| 2008 | 1,893.3 | 245.2 |
| 2009 | 1,261.2 | 358.7 |
| 2010 | 2,650.8 | 451.2 |
| 2011 | 959.1 | 305.9 |
| 2012 | 964.6 | 515.1 |
| 2013 | 1,032.5 | 358.3 |
| 2014 | 225.9 | 537.7 |
| 2015 | 259.6 | 374.4 |
| 2016 | 2,760.4 | 338.6 |
| 2017 | 140.3 | 732.5 |
| 2018 | 34.3 | 348.1 |
| 2019 | 230.3 | 200.8 |
| 2020 | 788.1 | 104.7 |
| 2021 | 71.2 | 96.3 |
| 2022 | 85.3 | 320.9 |
| 2023 | 199.3 | 239.7 |
| 2024 | 781.9 | 307.7 |
| 2025 | 5.2 | 36.0 |
| total | 26,465.8 | 11,291.5 |
| mean | 853.7 | 364.2 |
| median | 259.6 | 307.7 |

```python
fig, ax = plt.subplots(figsize=(5, 3))

data_to_plot = penalties_entity_type.loc[1995:2024].map(
    lambda x: float(x.replace(",", ""))
)

data_to_plot.plot(kind="bar", stacked=False, ax=ax, color=["#1f77b4", "#ff7f0e"])

plt.title("SEC Penalties by Year and Entity Type")
plt.xlabel("Year")
plt.ylabel("Penalty Amount\nin Million USD")
plt.legend(["Company", "Individual"], loc="upper right")
plt.grid(axis="x")
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()
```

## Litigations by Category

```python
all_tags = []

for i, row in df.iterrows():
    tags = row["tags"]
    if isinstance(tags, list):
      all_tags.extend(tags)

all_tags = pd.Series(all_tags)
all_tags = all_tags.value_counts().reset_index()
all_tags.columns = ["tag", "count"]

print("Top 10 Tags in SEC Litigation Releases")
all_tags.head(10)
```

```
Top 10 Tags in SEC Litigation Releases
```

Out:

|  | tag | count |
| --- | --- | --- |
| 0 | disclosure fraud | 5875 |
| 1 | securities fraud | 3389 |
| 2 | insider trading | 1900 |
| 3 | fraud | 1454 |
| 4 | ponzi scheme | 932 |
| 5 | accounting fraud | 676 |
| 6 | unregistered securities | 656 |
| 7 | misappropriation | 474 |
| 8 | investment fraud | 404 |
| 9 | securities violation | 312 |

## Requested Reliefs by Category

```python
# count all unique requestedRelief
all_requested_relief = []

for i, row in df.iterrows():
    requestedRelief = row["requestedRelief"]
    if isinstance(requestedRelief, list):
        all_requested_relief.extend(requestedRelief)

all_requested_relief = pd.Series(all_requested_relief)
all_requested_relief = all_requested_relief.value_counts().reset_index()

all_requested_relief.columns = ["requestedRelief", "count"]

print("Top 10 Requested Reliefs in SEC Litigation Releases")
all_requested_relief.head(10)
```

```
Top 10 Requested Reliefs in SEC Litigation Releases
```

Out:

|  | requestedRelief | count |
| --- | --- | --- |
| 0 | permanent injunctions | 6522 |
| 1 | civil penalties | 5632 |
| 2 | disgorgement of profits | 3931 |
| 3 | permanent injunction | 1274 |
| 4 | prejudgment interest | 1185 |
| 5 | disgorgement of ill-gotten gains | 979 |
| 6 | disgorgement | 911 |
| 7 | asset freeze | 576 |
| 8 | civil money penalties | 401 |
| 9 | permanent injunctive relief | 390 |

## Violated Securities Laws

```python
# count all unique violatedSections
all_violated_sections = []

for i, row in df.iterrows():
    violatedSections = row["violatedSections"]
    if isinstance(violatedSections, list):
        all_violated_sections.extend(violatedSections)

all_violated_sections = pd.Series(all_violated_sections)
all_violated_sections = all_violated_sections.value_counts().reset_index()

all_violated_sections.columns = ["violatedSections", "count"]

print("Top 10 Violated Securities Laws in SEC Litigation Releases")
all_violated_sections.head(10)
```

```
Top 10 Violated Securities Laws in SEC Litigation Releases
```

Out:

|  | violatedSections | count |
| --- | --- | --- |
| 0 | Rule 10b-5 | 6185 |
| 1 | Section 10(b) of the Securities Exchange Act o... | 5684 |
| 2 | Section 17(a) of the Securities Act of 1933 | 3827 |
| 3 | Sections 5(a), 5(c) and 17(a) of the Securitie... | 690 |
| 4 | Sections 206(1) and 206(2) of the Investment A... | 523 |
| 5 | Sections 5(a), 5(c), and 17(a) of the Securiti... | 469 |
| 6 | Section 15(a) of the Exchange Act | 410 |
| 7 | Section 17(a) of the Securities Act | 402 |
| 8 | Sections 10(b) and 13(b)(5) of the Securities ... | 311 |
| 9 | Sections 5(a) and 5(c) of the Securities Act o... | 309 |

## Persons & Agencies Conducting the Investigations

```python
all_investigation_conducted_by = []

for i, row in df.iterrows():
    investigationConductedBy = row["investigationConductedBy"]
    if isinstance(investigationConductedBy, list):
        all_investigation_conducted_by.extend(investigationConductedBy)

all_investigation_conducted_by = pd.Series(all_investigation_conducted_by)
all_investigation_conducted_by = all_investigation_conducted_by.value_counts().reset_index()

all_investigation_conducted_by.columns = ["investigationConductedBy", "count"]

print("Top 10 Investigation Conducted By in SEC Litigation Releases")
all_investigation_conducted_by.head(10)
```

```
Top 10 Investigation Conducted By in SEC Litigation Releases
```

Out:

|  | investigationConductedBy | count |
| --- | --- | --- |
| 0 | Securities and Exchange Commission | 437 |
| 1 | Division of Enforcement | 256 |
| 2 | U.S. Securities and Exchange Commission | 161 |
| 3 | SEC | 103 |
| 4 | John Rymas | 83 |
| 5 | Amy Gwiazda | 81 |
| 6 | New York Regional Office | 72 |
| 7 | Federal Bureau of Investigation | 61 |
| 8 | Boston Regional Office | 59 |
| 9 | Miami Regional Office | 55 |

## Other Agencies Involved

```python
# count unique otherAgenciesInvolved
all_other_agencies_involved = []

for i, row in df.iterrows():
    otherAgenciesInvolved = row["otherAgenciesInvolved"]
    if isinstance(otherAgenciesInvolved, list):
        all_other_agencies_involved.extend(otherAgenciesInvolved)

all_other_agencies_involved = pd.DataFrame(all_other_agencies_involved)
all_other_agencies_involved = all_other_agencies_involved['name'].value_counts().reset_index()

all_other_agencies_involved.columns = ["otherAgenciesInvolved", "count"]

print("Top 10 Other Agencies Involved in SEC Litigation Releases")
all_other_agencies_involved.head(10)
```

```
Top 10 Other Agencies Involved in SEC Litigation Releases
```

Out:

|  | otherAgenciesInvolved | count |
| --- | --- | --- |
| 0 | Federal Bureau of Investigation | 1172 |
| 1 | Financial Industry Regulatory Authority | 552 |
| 2 | British Columbia Securities Commission | 98 |
| 3 | Ontario Securities Commission | 82 |
| 4 | Options Regulatory Surveillance Authority | 78 |
| 5 | Texas State Securities Board | 77 |
| 6 | New York Stock Exchange | 77 |
| 7 | Internal Revenue Service | 71 |
| 8 | U.S. Attorney's Office for the Southern Distri... | 71 |
| 9 | U.S. Postal Inspection Service | 69 |