# Analyze Form D Exempt Offerings with Python

> Python tutorial demonstrating how to find and analyze SEC Form D filings by any Form D filing attribute. Search Form D filings by offering amount, industry group, investment fund type, finder fees and more.

Source: https://sec-api.io/docs/form-d-xml-json-api/python-example

**On this page:**

- [Quick Start](#Quick-Start)
- [Download all Exempt Offerings from Form D](#Download-all-Exempt-Offerings-from-Form-D)
- [Loading the Form D Exempt Offerings](#Loading-the-Form-D-Exempt-Offerings)
- [Exempt Offerings Over Time](#Exempt-Offerings-Over-Time)
  - [Offerings by Year](#Offerings-by-Year)
  - [Offerings by Quarter](#Offerings-by-Quarter)
  - [Offerings by Month](#Offerings-by-Month)
- [Top 20 Largest Offerings by Amount Offered](#Top-20-Largest-Offerings-by-Amount-Offered)
  - [Aggregated Annual Offering Amounts by Year](#Aggregated-Annual-Offering-Amounts-by-Year)
- [Types of Securities Offered](#Types-of-Securities-Offered)
- [Industry Groups](#Industry-Groups)
  - [Offerings per Industry Group over Time](#Offerings-per-Industry-Group-over-Time)
  - [Types of Pooled Investment Funds](#Types-of-Pooled-Investment-Funds)
- [Issuer Size: Revenue Range and Net Asset Values](#Issuer-Size:-Revenue-Range-and-Net-Asset-Values)

This page provides a step-by-step guide on analyzing Form D exempt offerings using Python and the [Form D API](https://sec-api.io/docs/form-d-xml-json-api). Form D filings are submitted by companies that raise capital through private placements under Regulation D, allowing them to sell securities without full SEC registration.

**What You Will Learn**

- Use the [Form D API](https://sec-api.io/docs/form-d-xml-json-api) to retrieve structured data from offerings filed with the SEC betbween 2008 and 2024.
- Understand offering trends over time by visualizing the number of offerings per year, quarter and month.
- Find the top 20 largest offerings by amount raised
- Determine the types of securities offered
- And more...

## Quick Start

The example demonstrates how to retrieve Form D exempt offerings using the `.get_data(search_params)` method of the `FormDApi` class from the `sec-api` Python package. The script queries the database for offerings with a total offering amount of $1,000,000 or more, sorts them by the most recent filings, and returns the 10 most recent results.

```python
!pip install sec-api
```

```python
import json
from sec_api import FormDApi

formDApi = FormDApi("YOUR_API_KEY")

search_params = {
    "query": "offeringData.offeringSalesAmounts.totalOfferingAmount:[1000000 TO *]",
    "from": "0", # starting position of the result set
    "size": "10", # number of results to be returned. max: 50
    "sort": [{"filedAt": {"order": "desc"}}],
}

response = formDApi.get_data(search_params)
offerings = response["offerings"]

print(json.dumps(offerings[0], indent=2))
```

```
{
  "schemaVersion": "X0708",
  "submissionType": "D",
  "testOrLive": "LIVE",
  "primaryIssuer": {
    "cik": "0002057923",
    "entityName": "U.S. Venture Partners Select Fund I-A, L.P.",
    "issuerAddress": {
      "street1": "1460 EL CAMINO REAL, SUITE 100",
      "city": "MENLO PARK",
      "stateOrCountry": "CA",
      "stateOrCountryDescription": "CALIFORNIA",
      "zipCode": "94025"
    },
    "issuerPhoneNumber": "650-854-9080",
    "jurisdictionOfInc": "DELAWARE",
    "issuerPreviousNameList": [
      {
        "value": "None"
      }
    ],
    "edgarPreviousNameList": [
      {
        "value": "None"
      }
    ],
    "entityType": "Limited Partnership",
    "yearOfInc": {
      "withinFiveYears": true,
      "value": "2025"
    }
  },
  "relatedPersonsList": {
    "relatedPersonInfo": [
      {
        "relatedPersonName": {
          "firstName": "Richard",
          "middleName": "W",
          "lastName": "Lewis"
        },
        "relatedPersonAddress": {
          "street1": "1460 El Camino Real, Suite 100",
          "city": "Menlo Park",
          "stateOrCountry": "CA",
          "stateOrCountryDescription": "CALIFORNIA",
          "zipCode": "94025"
        },
        "relatedPersonRelationshipList": {
          "relationship": [
            "Executive Officer"
          ]
        },
        "relationshipClarification": "Managing Member of the General Partner"
      },
      {
        "relatedPersonName": {
          "firstName": "Jonathan",
          "middleName": "D",
          "lastName": "Root"
        },
        "relatedPersonAddress": {
          "street1": "1460 El Camino Real, Suite 100",
          "city": "Menlo Park",
          "stateOrCountry": "CA",
          "stateOrCountryDescription": "CALIFORNIA",
          "zipCode": "94025"
        },
        "relatedPersonRelationshipList": {
          "relationship": [
            "Executive Officer"
          ]
        },
        "relationshipClarification": "Managing Member of the General Partner"
      },
      {
        "relatedPersonName": {
          "firstName": "Casey",
          "lastName": "Tansey"
        },
        "relatedPersonAddress": {
          "street1": "1460 El Camino Real, Suite 100",
          "city": "Menlo Park",
          "stateOrCountry": "CA",
          "stateOrCountryDescription": "CALIFORNIA",
          "zipCode": "94025"
        },
        "relatedPersonRelationshipList": {
          "relationship": [
            "Executive Officer"
          ]
        },
        "relationshipClarification": "Managing Member of the General Partner"
      },
      {
        "relatedPersonName": {
          "firstName": "Dafina",
          "lastName": "Toncheva"
        },
        "relatedPersonAddress": {
          "street1": "1460 El Camino Real, Suite 100",
          "city": "Menlo Park",
          "stateOrCountry": "CA",
          "stateOrCountryDescription": "CALIFORNIA",
          "zipCode": "94025"
        },
        "relatedPersonRelationshipList": {
          "relationship": [
            "Executive Officer"
          ]
        },
        "relationshipClarification": "Managing Member of the General Partner"
      }
    ]
  },
  "offeringData": {
    "industryGroup": {
      "industryGroupType": "Pooled Investment Fund",
      "investmentFundInfo": {
        "investmentFundType": "Venture Capital Fund",
        "is40Act": false
      }
    },
    "issuerSize": {
      "revenueRange": "Not Applicable"
    },
    "federalExemptionsExclusions": {
      "item": [
        "06b",
        "3C",
        "3C.1"
      ]
    },
    "typeOfFiling": {
      "newOrAmendment": {
        "isAmendment": false
      },
      "dateOfFirstSale": {
        "yetToOccur": true
      }
    },
    "durationOfOffering": {
      "moreThanOneYear": false
    },
    "typesOfSecuritiesOffered": {
      "isPooledInvestmentFundType": true
    },
    "businessCombinationTransaction": {
      "isBusinessCombinationTransaction": false
    },
    "minimumInvestmentAccepted": 0,
    "salesCompensationList": {},
    "offeringSalesAmounts": {
      "totalOfferingAmount": 10000000,
      "totalAmountSold": 0,
      "totalRemaining": 10000000
    },
    "investors": {
      "hasNonAccreditedInvestors": false,
      "totalNumberAlreadyInvested": 0
    },
    "salesCommissionsFindersFees": {
      "salesCommissions": {
        "dollarAmount": 0
      },
      "findersFees": {
        "dollarAmount": 0
      }
    },
    "useOfProceeds": {
      "grossProceedsUsed": {
        "dollarAmount": 0
      }
    },
    "signatureBlock": {
      "authorizedRepresentative": false,
      "signature": [
        {
          "issuerName": "U.S. Venture Partners Select Fund I-A, L.P.",
          "signatureName": "/s/ Dale Holladay",
          "nameOfSigner": "Dale Holladay",
          "signatureTitle": "Chief Financial Officer",
          "signatureDate": "2025-02-25"
        }
      ]
    }
  },
  "accessionNo": "0002057923-25-000001",
  "filedAt": "2025-02-25T17:26:03-05:00",
  "id": "32055cb99476602d44b381101ebf9fa1"
}
```

## Download all Exempt Offerings from Form D

Downloading the complete Form D exempt offerings dataset may take up to 20 minutes and requires approximately 2.6GB of disk space. The final dataset includes over 600,000 records spanning from 2008 to 2024 and is saved in JSONL format as `form-d-dataset.jsonl`.

```python
import os
import random
import sys
import time
from concurrent.futures import ThreadPoolExecutor

YEARS = range(2024, 2007, -1) # from 2024 to 2008
TEMP_FILE_TEMPLATE = "./temp_file_form_d_{}.jsonl"
TARGET_FILE = "./form-d-dataset.jsonl"

def process_year(year):
    backoff_time = random.randint(10, 800) / 1000
    print(f"Starting year {year} with backoff time {backoff_time:,}s")
    sys.stdout.flush()
    time.sleep(backoff_time)

    tmp_filename = TEMP_FILE_TEMPLATE.format(year)
    tmp_file = open(tmp_filename, "w")

    for month in range(12, 0, -1):
        search_from = 0
        month_counter = 0

        while True:
            query = f"filedAt:[{year}-{month:02d}-01 TO {year}-{month:02d}-31]"
            search_params = {
                "query": query,
                "from": search_from,
                "size": "50",
                "sort": [{"filedAt": {"order": "desc"}}],
            }

            response = None
            try:
                response = formDApi.get_data(search_params)
            except Exception as e:
                print(f"{year}-{month:02d} error: {e}. Retrying...")
                sys.stdout.flush()
                continue

            if response == None or len(response["offerings"]) == 0:
                break

            if response["total"]["value"] >= 10000:
                print(
                    f"WARNING: {year}-{month:02d} has more than 10,000 Form D filings."
                )
                sys.stdout.flush()

            search_from += 50
            month_counter += len(response["offerings"])
            jsonl_data = "\n".join(
                [json.dumps(entry) for entry in response["offerings"]]
            )
            tmp_file.write(jsonl_data + "\n")

        print(f"Finished loading {month_counter} Form D for {year}-{month:02d}")
        sys.stdout.flush()

    tmp_file.close()

    return year

if not os.path.exists(TARGET_FILE):
    with ThreadPoolExecutor(max_workers=4) as pool:
        processed_years = list(pool.map(process_year, YEARS))
    print("Finished processing all years.", processed_years)

    # merge the temporary files into one final file
    with open(TARGET_FILE, "a") as outfile:
        for year in YEARS:
            temp_file = TEMP_FILE_TEMPLATE.format(year)
            if os.path.exists(temp_file):
                with open(temp_file, "r") as infile:
                    outfile.write(infile.read())
else:
    print("File already exists. Skipping download.")
```

```
File already exists. Skipping download.
```

## Loading the Form D Exempt Offerings

```python
import pandas as pd
import numpy as np
```

```python
def process_chunk(df_chunk):
    return pd.json_normalize(df_chunk.to_dict(orient="records"))

df_chunks = [
    process_chunk(chunk)
    for chunk in pd.read_json(TARGET_FILE, lines=True, chunksize=100000)
]

structured_data = pd.concat(df_chunks, ignore_index=True)

print(structured_data.shape)
```

```
(673962, 71)
```

```python
structured_data["filedAt"] = pd.to_datetime(structured_data["filedAt"], utc=True)
structured_data["filedAt"] = structured_data["filedAt"].dt.tz_convert("US/Eastern")
structured_data = structured_data.sort_values("filedAt", ascending=True).reset_index(
    drop=True
)
structured_data["year"] = structured_data["filedAt"].dt.year
structured_data["month"] = structured_data["filedAt"].dt.month
structured_data["qtr"] = structured_data["month"].apply(lambda x: (x - 1) // 3 + 1)
structured_data["dayOfWeek"] = structured_data["filedAt"].dt.day_name()
# filedAtClass: preMarket (4:00AM-9:30AM), regularMarket (9:30AM-4:00PM), afterMarket (4:00PM-8:00PM)
structured_data["filedAtClass"] = structured_data["filedAt"].apply(
    lambda x: (
        "preMarket"
        if x.hour < 9 or (x.hour == 9 and x.minute < 30)
        else (
            "regularMarket"
            if x.hour < 16
            else "afterMarket" if x.hour < 20 else "other"
        )
    )
)

structured_data.head()
```

Out[6]:

|  | schemaVersion | submissionType | testOrLive | accessionNo | filedAt | id | issuerList | primaryIssuer.cik | primaryIssuer.entityName | primaryIssuer.issuerAddress.street1 | ... | offeringData.typesOfSecuritiesOffered.isMineralPropertyType | primaryIssuer.yearOfInc.yetToBeFormed | offeringData.salesCompensationList.over100RecipientFlag | relatedPersonsList.over100PersonsFlag | issuerList.over100IssuerFlag | year | month | qtr | dayOfWeek | filedAtClass |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0 | X0101 | D | LIVE | 0001445388-08-000001 | 2008-09-17 10:14:41-04:00 | fe0a1228846ceb5480b82521df779de7 | NaN | 0001445388 | Cause Global Impact Fund, LP | 1266 EAST MAIN STREET | ... | NaN | NaN | NaN | NaN | NaN | 2008 | 9 | 3 | Wednesday | regularMarket |
| 1 | X0101 | D | LIVE | 0001348938-08-000003 | 2008-09-18 17:57:02-04:00 | 43e701d16ff1097e33db9a31a3f7218d | NaN | 0001348938 | Living Independently Group, Inc. | 767 THIRD AVE | ... | NaN | NaN | NaN | NaN | NaN | 2008 | 9 | 3 | Thursday | afterMarket |
| 2 | X0101 | D | LIVE | 0001445444-08-000001 | 2008-09-19 13:18:32-04:00 | 2ead4c7e5bc390b8dae552ae6b832c30 | NaN | 0001445444 | new york atv - sportsman club llc | 240 WILLOW DRIVE | ... | NaN | NaN | NaN | NaN | NaN | 2008 | 9 | 3 | Friday | regularMarket |
| 3 | X0101 | D | LIVE | 0001354488-08-001636 | 2008-09-19 18:21:59-04:00 | b3d0f624fc2992121605e7ed54fd0c17 | NaN | 0001445579 | Fox Life Settlements 2008-F, LLC | 2129 N. JOSEY LANE | ... | NaN | NaN | NaN | NaN | NaN | 2008 | 9 | 3 | Friday | afterMarket |
| 4 | X0101 | D | LIVE | 0001445773-08-000002 | 2008-09-22 17:15:22-04:00 | fe1f4adb26cf65f5e059d2f4e6c70bdb | NaN | 0001445773 | Viking Financial Services Corp | 4 NICKERSON STREET | ... | NaN | NaN | NaN | NaN | NaN | 2008 | 9 | 3 | Monday | afterMarket |

5 rows × 76 columns

```python
print(structured_data.info())
```

```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 673962 entries, 0 to 673961
Data columns (total 76 columns):
 #   Column                                                                        Non-Null Count   Dtype
---  ------                                                                        --------------   -----
 0   schemaVersion                                                                 673962 non-null  object
 1   submissionType                                                                673962 non-null  object
 2   testOrLive                                                                    673962 non-null  object
 3   accessionNo                                                                   673962 non-null  object
 4   filedAt                                                                       673962 non-null  datetime64[ns, US/Eastern]
 5   id                                                                            673962 non-null  object
 6   issuerList                                                                    0 non-null       float64
 7   primaryIssuer.cik                                                             673962 non-null  object
 8   primaryIssuer.entityName                                                      673962 non-null  object
 9   primaryIssuer.issuerAddress.street1                                           673962 non-null  object
 10  primaryIssuer.issuerAddress.city                                              673962 non-null  object
 11  primaryIssuer.issuerAddress.stateOrCountry                                    673958 non-null  object
 12  primaryIssuer.issuerAddress.stateOrCountryDescription                         641363 non-null  object
 13  primaryIssuer.issuerAddress.zipCode                                           673836 non-null  object
 14  primaryIssuer.issuerPhoneNumber                                               673953 non-null  object
 15  primaryIssuer.jurisdictionOfInc                                               673260 non-null  object
 16  primaryIssuer.issuerPreviousNameList                                          612987 non-null  object
 17  primaryIssuer.edgarPreviousNameList                                           658658 non-null  object
 18  primaryIssuer.entityType                                                      673962 non-null  object
 19  primaryIssuer.yearOfInc.overFiveYears                                         185008 non-null  object
 20  relatedPersonsList.relatedPersonInfo                                          673962 non-null  object
 21  offeringData.industryGroup.industryGroupType                                  673962 non-null  object
 22  offeringData.issuerSize.revenueRange                                          433179 non-null  object
 23  offeringData.federalExemptionsExclusions.item                                 673962 non-null  object
 24  offeringData.typeOfFiling.newOrAmendment.isAmendment                          673962 non-null  bool
 25  offeringData.typeOfFiling.dateOfFirstSale.value                               579646 non-null  object
 26  offeringData.durationOfOffering.moreThanOneYear                               673962 non-null  bool
 27  offeringData.typesOfSecuritiesOffered.isDebtType                              52078 non-null   object
 28  offeringData.typesOfSecuritiesOffered.isOptionToAcquireType                   45443 non-null   object
 29  offeringData.typesOfSecuritiesOffered.isOtherType                             73755 non-null   object
 30  offeringData.typesOfSecuritiesOffered.descriptionOfOtherType                  73755 non-null   object
 31  offeringData.businessCombinationTransaction.isBusinessCombinationTransaction  673962 non-null  bool
 32  offeringData.minimumInvestmentAccepted                                        673962 non-null  int64
 33  offeringData.offeringSalesAmounts.totalOfferingAmount                         673962 non-null  int64
 34  offeringData.offeringSalesAmounts.totalAmountSold                             673962 non-null  int64
 35  offeringData.offeringSalesAmounts.totalRemaining                              673962 non-null  int64
 36  offeringData.investors.hasNonAccreditedInvestors                              673962 non-null  bool
 37  offeringData.investors.totalNumberAlreadyInvested                             673962 non-null  int64
 38  offeringData.salesCommissionsFindersFees.salesCommissions.dollarAmount        673962 non-null  int64
 39  offeringData.salesCommissionsFindersFees.findersFees.dollarAmount             673962 non-null  int64
 40  offeringData.useOfProceeds.grossProceedsUsed.dollarAmount                     673962 non-null  int64
 41  offeringData.useOfProceeds.grossProceedsUsed.isEstimate                       173076 non-null  object
 42  offeringData.signatureBlock.authorizedRepresentative                          663003 non-null  object
 43  offeringData.signatureBlock.signature                                         673962 non-null  object
 44  primaryIssuer.issuerAddress.street2                                           362271 non-null  object
 45  offeringData.typeOfFiling.dateOfFirstSale.yetToOccur                          94271 non-null   object
 46  offeringData.typesOfSecuritiesOffered.isEquityType                            351392 non-null  object
 47  primaryIssuer.yearOfInc.withinFiveYears                                       487041 non-null  object
 48  primaryIssuer.yearOfInc.value                                                 487046 non-null  object
 49  offeringData.typesOfSecuritiesOffered.isSecurityToBeAcquiredType              33004 non-null   object
 50  offeringData.industryGroup.investmentFundInfo.investmentFundType              352185 non-null  object
 51  offeringData.industryGroup.investmentFundInfo.is40Act                         352185 non-null  object
 52  offeringData.typeOfFiling.newOrAmendment.previousAccessionNumber              261449 non-null  object
 53  offeringData.typesOfSecuritiesOffered.isPooledInvestmentFundType              357613 non-null  object
 54  offeringData.useOfProceeds.clarificationOfResponse                            218401 non-null  object
 55  offeringData.investors.numberNonAccreditedInvestors                           50701 non-null   float64
 56  offeringData.issuerSize.aggregateNetAssetValueRange                           240783 non-null  object
 57  offeringData.salesCompensationList.recipient                                  151729 non-null  object
 58  offeringData.offeringSalesAmounts.clarificationOfResponse                     90326 non-null   object
 59  primaryIssuer.entityTypeOtherDesc                                             68545 non-null   object
 60  offeringData.salesCommissionsFindersFees.salesCommissions.isEstimate          101935 non-null  object
 61  issuerList.issuer                                                             6073 non-null    object
 62  offeringData.salesCommissionsFindersFees.clarificationOfResponse              71985 non-null   object
 63  offeringData.salesCommissionsFindersFees.findersFees.isEstimate               64687 non-null   object
 64  offeringData.typesOfSecuritiesOffered.isTenantInCommonType                    775 non-null     object
 65  offeringData.businessCombinationTransaction.clarificationOfResponse           7761 non-null    object
 66  offeringData.typesOfSecuritiesOffered.isMineralPropertyType                   1742 non-null    object
 67  primaryIssuer.yearOfInc.yetToBeFormed                                         1913 non-null    object
 68  offeringData.salesCompensationList.over100RecipientFlag                       91 non-null      object
 69  relatedPersonsList.over100PersonsFlag                                         1 non-null       object
 70  issuerList.over100IssuerFlag                                                  1 non-null       object
 71  year                                                                          673962 non-null  int32
 72  month                                                                         673962 non-null  int32
 73  qtr                                                                           673962 non-null  int64
 74  dayOfWeek                                                                     673962 non-null  object
 75  filedAtClass                                                                  673962 non-null  object
dtypes: bool(4), datetime64[ns, US/Eastern](1), float64(2), int32(2), int64(9), object(58)
memory usage: 367.6+ MB
None
```

```python
unique_years = structured_data["year"].nunique()
unique_issuers = structured_data["primaryIssuer.cik"].nunique()
unique_filings = structured_data["accessionNo"].nunique()
min_year = structured_data["year"].min()
max_year = structured_data["year"].max()
max_year_full = max_year  # to avoid incomplete data for the current year
print("Loaded Form D filings")
print(f"Number of filings: {unique_filings:,}")
print(f"Number of years: {unique_years:,} ({min_year}-{max_year})")
print(f"Number of unique issuers: {unique_issuers:,}")
```

```
Loaded Form D filings
Number of filings: 673,962
Number of years: 17 (2008-2024)
Number of unique issuers: 312,008
```

## Exempt Offerings Over Time

```python
import matplotlib.pyplot as plt
import matplotlib.style as style
import matplotlib.ticker as mtick
import seaborn as sns

style.use("default")

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

plt.rcParams.update(params)
```

```python
submissionTypeCount = structured_data["submissionType"].value_counts().to_frame()
submissionTypeCount.columns = ["count"]
submissionTypeCount["pct"] = (
    submissionTypeCount["count"] / submissionTypeCount["count"].sum() * 100
)
submissionTypeCount["pct"] = submissionTypeCount["pct"].round(1)
submissionTypeCount
```

Out[10]:

|  | count | pct |
| --- | --- | --- |
| submissionType |  |  |
| D | 412513 | 61.2 |
| D/A | 261449 | 38.8 |

### Offerings by Year

```python
form_d = structured_data[structured_data["submissionType"] == "D"]
form_d_a = structured_data[structured_data["submissionType"] == "D/A"]

counts_d = form_d.groupby(["year"]).size().to_frame(name="count")
counts_d_a = form_d_a.groupby(["year"]).size().to_frame(name="count")

print(f"Form D counts from {min_year} to {max_year_full}.")
counts_d.T
```

```
Form D counts from 2008 to 2024.
```

Out[11]:

| year | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| count | 379 | 13770 | 17586 | 18170 | 18190 | 19841 | 22194 | 23030 | 23097 | 24589 | 27272 | 27502 | 28147 | 42506 | 41671 | 31868 | 32701 |

```python
def plot_timeseries(ts, title):
    fig, ax = plt.subplots(figsize=(4, 2.5))
    ts["count"].plot(ax=ax, legend=False)
    ax.set_title(title)
    ax.set_xlabel("Year")
    ax.set_ylabel("Number of\nForm D Filings")
    ax.set_xticks(np.arange(min_year, max_year_full + 1, 1))
    ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
    ax.set_xlim(min_year - 1, max_year_full + 1)
    ax.grid(axis="x")
    ax.set_axisbelow(True)
    plt.xticks(rotation=90)

    for year in range(min_year, max_year_full + 1, 1):
        year_y_max = ts.loc[year, "count"]
        ax.vlines(year, 0, year_y_max, linestyles=":", colors="grey", alpha=0.5, lw=1)

    plt.tight_layout()
    plt.show()

plot_timeseries(
    counts_d,
    title=f"Form D Disclosures per Year ({min_year} - {max_year_full})",
)
```

```python
plot_timeseries(
    counts_d_a,
    title=f"Form D/A Disclosures per Year ({min_year} - {max_year_full})",
)
```

### Offerings by Quarter

```python
counts_qtr_yr_piv = (form_d.groupby(["year", "qtr"]).size().unstack().fillna(0)).astype(
    int
)

print(f"Form D counts by quarter from {min_year} to {max_year_full}.")
counts_qtr_yr_piv.T
```

```
Form D counts by quarter from 2008 to 2024.
```

Out[14]:

| year | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| qtr |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 1 | 0 | 1547 | 4649 | 4848 | 4970 | 5031 | 5546 | 5786 | 6107 | 6405 | 6992 | 6761 | 7218 | 9303 | 12465 | 8320 | 7924 |
| 2 | 0 | 3886 | 4403 | 4460 | 4572 | 4568 | 5504 | 5780 | 5757 | 6047 | 6803 | 7025 | 5958 | 10513 | 10764 | 8095 | 8248 |
| 3 | 36 | 4126 | 4104 | 4506 | 4238 | 4942 | 5435 | 5679 | 5448 | 5834 | 6587 | 6889 | 6776 | 10498 | 9558 | 7735 | 8027 |
| 4 | 343 | 4211 | 4430 | 4356 | 4410 | 5300 | 5709 | 5785 | 5785 | 6303 | 6890 | 6827 | 8195 | 12192 | 8884 | 7718 | 8502 |

```python
plt.figure(figsize=(6, 2))
sns.heatmap(
    counts_qtr_yr_piv.T,
    annot=True,
    fmt="d",
    cmap="magma",
    cbar_kws={"label": "Count"},
    mask=counts_qtr_yr_piv.T == 0,
    cbar=False,
    annot_kws={"fontsize": 6},
)
plt.grid(False)
plt.title(f"Form D Counts by Quarter ({min_year} - {max_year_full})")
plt.xlabel("Year")
plt.ylabel("Quarter")
plt.tight_layout()
plt.show()
```

```python
counts_qtr_yr = counts_qtr_yr_piv.stack().reset_index(name="count")

fig, ax = plt.subplots(figsize=(6, 2.5))
counts_qtr_yr_piv.plot(kind="bar", ax=ax, legend=True)
ax.legend(title="Quarter", loc="upper right", bbox_to_anchor=(1.15, 1))
ax.set_title(f"Number of Form D Disclosures per Quarter\n({min_year}-{max_year_full})")
ax.set_xlabel("Year")
ax.set_ylabel("Number of\nForm D Filings")
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
ax.grid(axis="x")
ax.set_axisbelow(True)
plt.tight_layout()
plt.show()
```

### Offerings by Month

```python
counts_month_yr_piv = (
    form_d.groupby(["year", "month"]).size().unstack().fillna(0)
).astype(int)

print(
    f"Descriptive statistics for Form D counts by month from 2009 to {max_year_full}."
)
month_stats = (
    counts_month_yr_piv.loc[2009:]
    .describe(percentiles=[0.025, 0.975])
    .round(0)
    .astype(int)
)
month_stats
```

```
Descriptive statistics for Form D counts by month from 2009 to 2024.
```

Out[17]:

| month | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| count | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 |
| mean | 2264 | 1873 | 2355 | 2180 | 2100 | 2119 | 2189 | 2129 | 1956 | 2207 | 2005 | 2381 |
| std | 839 | 754 | 857 | 766 | 702 | 647 | 649 | 669 | 611 | 706 | 632 | 814 |
| min | 174 | 218 | 1155 | 1346 | 1231 | 1309 | 1459 | 1289 | 1246 | 1384 | 1298 | 1481 |
| 2.5% | 723 | 617 | 1349 | 1380 | 1266 | 1370 | 1466 | 1327 | 1249 | 1388 | 1322 | 1499 |
| 50% | 2170 | 1846 | 2220 | 1946 | 1878 | 2049 | 2036 | 2003 | 1812 | 2030 | 1950 | 2207 |
| 97.5% | 3676 | 3347 | 4274 | 3822 | 3495 | 3480 | 3486 | 3444 | 3216 | 3590 | 3356 | 4052 |
| max | 4106 | 3666 | 4693 | 3977 | 3624 | 3670 | 3562 | 3482 | 3454 | 3973 | 3685 | 4534 |

```python
fig, ax = plt.subplots(figsize=(3.5, 3))

counts_month_yr_piv.loc[2009:].boxplot(
    ax=ax,
    grid=False,
    showfliers=False,
    flierprops=dict(marker="o", markersize=3),
    patch_artist=True,
    boxprops=dict(facecolor="white", color="tab:blue"),
    showmeans=True,
    meanline=True,
    meanprops={"color": "tab:blue", "linestyle": ":"},
    medianprops={"color": "black"},
    capprops={"color": "none"},
)

ax.set_title(f"Form D Filings by Month\n({min_year} - {max_year_full})")
ax.set_xlabel("")
ax.set_ylabel("Form D Count")
xticklabels = [pd.to_datetime(str(x), format="%m").strftime("%b") for x in range(1, 13)]
ax.set_xticklabels(xticklabels)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
```

```python
counts_month_yr_piv_d_a = (
    form_d_a.groupby(["year", "month"]).size().unstack().fillna(0)
).astype(int)

print(
    f"Descriptive statistics for Form D/A counts by month from 2009 to {max_year_full}."
)
month_stats = (
    counts_month_yr_piv_d_a.loc[2009:]
    .describe(percentiles=[0.025, 0.975])
    .round(0)
    .astype(int)
)
month_stats
```

```
Descriptive statistics for Form D/A counts by month from 2009 to 2024.
```

Out[19]:

| month | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| count | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 | 16 |
| mean | 1495 | 1453 | 2580 | 1404 | 1208 | 1200 | 1290 | 1113 | 1101 | 1264 | 1034 | 1194 |
| std | 542 | 404 | 533 | 423 | 399 | 411 | 392 | 366 | 328 | 354 | 306 | 401 |
| min | 44 | 172 | 2028 | 510 | 449 | 413 | 642 | 487 | 492 | 546 | 467 | 495 |
| 2.5% | 284 | 563 | 2057 | 627 | 502 | 498 | 681 | 544 | 533 | 604 | 527 | 546 |
| 50% | 1680 | 1502 | 2409 | 1424 | 1234 | 1298 | 1338 | 1161 | 1183 | 1280 | 1060 | 1212 |
| 97.5% | 2065 | 1923 | 3750 | 2041 | 1836 | 1797 | 1835 | 1771 | 1556 | 1749 | 1449 | 1788 |
| max | 2088 | 1988 | 3942 | 2086 | 1884 | 1830 | 1899 | 1836 | 1580 | 1842 | 1484 | 1866 |

```python
fig, ax = plt.subplots(figsize=(3.5, 3))

counts_month_yr_piv_d_a.loc[2009:].boxplot(
    ax=ax,
    grid=False,
    showfliers=False,
    flierprops=dict(marker="o", markersize=3),
    patch_artist=True,
    boxprops=dict(facecolor="white", color="tab:blue"),
    showmeans=True,
    meanline=True,
    meanprops={"color": "tab:blue", "linestyle": ":"},
    medianprops={"color": "black"},
    capprops={"color": "none"},
)

ax.set_title(f"Form D/A Filings by Month\n({min_year} - {max_year_full})")
ax.set_xlabel("")
ax.set_ylabel("Form D/A Count")
xticklabels = [pd.to_datetime(str(x), format="%m").strftime("%b") for x in range(1, 13)]
ax.set_xticklabels(xticklabels)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
```

## Top 20 Largest Offerings by Amount Offered

```python
pd.options.display.float_format = "{:,.0f}".format

col = "offeringData.offeringSalesAmounts.totalOfferingAmount"

top_20 = (
    form_d.sort_values(by=col, ascending=False)
    .head(20)
    .sort_values(by=col, ascending=True)
    .reset_index(drop=True)
)
top_20["year"] = top_20["filedAt"].dt.year
# cut names to 50 characters
top_20["primaryIssuer.entityName"] = top_20["primaryIssuer.entityName"].str.slice(0, 50)

years = top_20["filedAt"].dt.year.to_list()

top_20.plot(
    kind="barh",
    x="primaryIssuer.entityName",
    y=col,
    figsize=(6, 3.5),
)

ax = plt.gca()
for p, year in zip(ax.patches, years):
    ax.annotate(
        year,
        (p.get_x() + p.get_width(), p.get_y()),
        xytext=(0, 0),
        textcoords="offset points",
        fontsize=7,
    )

ax.xaxis.set_major_formatter(
    mtick.FuncFormatter(lambda x, p: format(int(x) / 1000000000, ",.0f"))
)
ax.grid(True)
ax.set_axisbelow(True)
ax.grid(color="gray", linestyle="dashed", alpha=0.3)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.yaxis.grid(False), ax.legend().remove()
plt.title(f"Top 20 Largest Exempt Offerings by Total Proceeds\n({min_year} - {max_year})")
plt.xlabel("Total Proceeds (USD, in billions)")
plt.ylabel("")
plt.tight_layout()
plt.show()
```

```python
print(f"Top 20 Largest Offerings by Total Offering Amounts (in $ billions, {min_year} - {max_year})")
top_20["offeringAmount"] = top_20[col] / 1_000_000_000
top_20[["primaryIssuer.entityName", "offeringAmount", "year"]].sort_values(
    by="offeringAmount", ascending=False
).reset_index(drop=True)
```

```
Top 20 Largest Offerings by Total Offering Amounts (in $ billions, 2008 - 2024)
```

Out[22]:

|  | primaryIssuer.entityName | offeringAmount | year |
| --- | --- | --- | --- |
| 0 | 1B USD WORLDWIDE VENTURE CAPITAL FUND | 1,000 | 2017 |
| 1 | Royal Bank of Amexem Trust | 999 | 2021 |
| 2 | Grace & Trust S.a.r.l. | 950 | 2018 |
| 3 | Curtis Gregory Smith Junior ACH Thrift Fund 05... | 900 | 2021 |
| 4 | JAAME AMUN RE EL | 900 | 2011 |
| 5 | Shaykamaxum Republic Tribal Government | 900 | 2021 |
| 6 | Crusade of Connor Stephens, LLC | 640 | 2018 |
| 7 | Reginald Cupid Noble ACH Investment Thrift Fun... | 541 | 2021 |
| 8 | MEL FREE EL | 500 | 2016 |
| 9 | JOHN ADAMS SMART EL | 500 | 2016 |
| 10 | PERI AL HEDRO CARTA DEY | 500 | 2015 |
| 11 | One World Bancorp, Inc. | 500 | 2022 |
| 12 | HELENE HOLLOMAN CHIN EL | 500 | 2016 |
| 13 | One World Bancorp, Inc. | 500 | 2024 |
| 14 | ESTATE OF HAZINE CHERMON CHERY | 400 | 2018 |
| 15 | BYRON DEVON LYNCH IV ESTATE | 400 | 2020 |
| 16 | Mcclain Zachary Christian | 400 | 2022 |
| 17 | ZACHARY CHRISTIAN MCCLAIN | 400 | 2024 |
| 18 | T&B Imports International Inc | 200 | 2021 |
| 19 | National Investment & Infrastructure Fund | 160 | 2021 |

### Aggregated Annual Offering Amounts by Year

```python
total = form_d[["year", col]].copy()
total.fillna(0, inplace=True)
total = total.apply(pd.to_numeric, errors="coerce").fillna(0)
total
```

Out[23]:

|  | year | offeringData.offeringSalesAmounts.totalOfferingAmount |
| --- | --- | --- |
| 0 | 2008 | 300000000 |
| 1 | 2008 | 11498347 |
| 2 | 2008 | 500000 |
| 3 | 2008 | 3300000 |
| 4 | 2008 | 10000000 |
| ... | ... | ... |
| 673956 | 2024 | 26000 |
| 673958 | 2024 | -1 |
| 673959 | 2024 | 2500000 |
| 673960 | 2024 | 2000000 |
| 673961 | 2024 | 5000000 |

412513 rows × 2 columns

```python
total_by_year = total.groupby("year").sum()
total_by_year.plot(kind="bar", figsize=(4, 3))
plt.title(f"Annual Form D Offering Amounts per Year\n({min_year} - {max_year})")
plt.ylabel("Offering Amount\n(USD, in billions)"); plt.xlabel("")
ax = plt.gca()
ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda x, p: format(int(x) / 1000000000, ",.0f")))
ax.get_legend().remove(), ax.grid(), ax.set_axisbelow(True)
ax.grid(True); ax.set_axisbelow(True); ax.grid(color="gray", linestyle="dashed", alpha=0.3)
ax.spines["top"].set_visible(False); ax.spines["right"].set_visible(False); ax.xaxis.grid(False)
plt.setp(ax.get_xticklabels(), fontsize=7); plt.tight_layout(); plt.show()
```

## Types of Securities Offered

```python
typesOfSecuritiesOfferedCols = [col for col in form_d.columns if "typesOfSecuritiesOffered" in col]
typesOfSecuritiesOfferedCols.remove("offeringData.typesOfSecuritiesOffered.descriptionOfOtherType")
typesOfSecuritiesOffered = form_d[["year", *typesOfSecuritiesOfferedCols]]
typesOfSecuritiesOffered = typesOfSecuritiesOffered.groupby("year").count()
typesOfSecuritiesOffered.columns = [col.split(".")[-1] for col in typesOfSecuritiesOffered.columns]

print("Types of Securities Offered by Year")
typesOfSecuritiesOffered
```

```
Types of Securities Offered by Year
```

Out[25]:

|  | isDebtType | isOptionToAcquireType | isOtherType | isEquityType | isSecurityToBeAcquiredType | isPooledInvestmentFundType | isTenantInCommonType | isMineralPropertyType |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| year |  |  |  |  |  |  |  |  |
| 2008 | 51 | 50 | 46 | 239 | 25 | 64 | 3 | 3 |
| 2009 | 1848 | 1936 | 1798 | 8632 | 1092 | 3174 | 33 | 119 |
| 2010 | 2226 | 2510 | 2167 | 11616 | 1527 | 4034 | 22 | 152 |
| 2011 | 2299 | 2349 | 2298 | 12211 | 1522 | 4227 | 51 | 149 |
| 2012 | 2377 | 2217 | 2267 | 12076 | 1530 | 4461 | 31 | 146 |
| 2013 | 2671 | 2160 | 2203 | 12890 | 1522 | 5272 | 27 | 134 |
| 2014 | 3021 | 2314 | 2314 | 14555 | 1667 | 5924 | 28 | 175 |
| 2015 | 3126 | 2258 | 2454 | 14796 | 1586 | 6653 | 44 | 82 |
| 2016 | 2972 | 2260 | 2240 | 14966 | 1689 | 6867 | 58 | 36 |
| 2017 | 2801 | 2348 | 2481 | 16130 | 1727 | 7742 | 37 | 44 |
| 2018 | 2987 | 2358 | 3044 | 17119 | 1800 | 9285 | 44 | 60 |
| 2019 | 2975 | 2390 | 2733 | 17425 | 1908 | 9516 | 37 | 60 |
| 2020 | 2721 | 2415 | 2821 | 16698 | 1945 | 11564 | 45 | 42 |
| 2021 | 2856 | 2631 | 3612 | 22616 | 2005 | 21294 | 57 | 42 |
| 2022 | 2380 | 2327 | 3611 | 21479 | 1781 | 21342 | 46 | 65 |
| 2023 | 2158 | 2240 | 3039 | 17865 | 1621 | 15122 | 24 | 52 |
| 2024 | 1990 | 2211 | 3022 | 18035 | 1662 | 16366 | 32 | 57 |

```python
fig, ax = plt.subplots(figsize=(6, 3))

typesOfSecuritiesOffered.loc[2009:].plot(kind="bar", stacked=True, ax=ax)

ax.set_title("Offerings by Type of Security")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Offerings")
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
ax.grid(axis="x")
ax.set_axisbelow(True)
ax.legend(title="Type of Security", bbox_to_anchor=(1.02, 1))
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()
```

## Industry Groups

```python
industryGroupType = form_d["offeringData.industryGroup.industryGroupType"].value_counts().to_frame()
industryGroupType.index.name = "industryGroupType"
industryGroupType["count"] = industryGroupType["count"].apply(lambda x: f"{x:,}")
industryGroupType
```

Out[72]:

|  | count |
| --- | --- |
| industryGroupType |  |
| Pooled Investment Fund | 149,718 |
| Other | 57,952 |
| Other Technology | 56,359 |
| Commercial | 19,627 |
| Other Health Care | 15,923 |
| Other Real Estate | 14,889 |
| Residential | 12,196 |
| Biotechnology | 11,133 |
| REITS and Finance | 8,839 |
| Investing | 8,283 |
| Oil and Gas | 7,906 |
| Manufacturing | 6,325 |
| Other Banking and Financial Services | 6,160 |
| Retailing | 4,529 |
| Other Energy | 4,308 |
| Pharmaceuticals | 4,004 |
| Business Services | 3,546 |
| Restaurants | 3,128 |
| Computers | 2,990 |
| Commercial Banking | 2,629 |
| Insurance | 2,575 |
| Telecommunications | 2,111 |
| Agriculture | 1,961 |
| Hospitals and Physicians | 1,027 |
| Construction | 995 |
| Lodging and Conventions | 591 |
| Energy Conservation | 590 |
| Environmental Services | 516 |
| Other Travel | 375 |
| Investment Banking | 353 |
| Tourism and Travel Services | 293 |
| Electric Utilities | 258 |
| Health Insurance | 169 |
| Coal Mining | 132 |
| Airlines and Airports | 123 |

### Offerings per Industry Group over Time

```python
topIndustryGroupTypes = industryGroupType.head(10).index.to_list()
topIndustryGroupTypes.remove("Other")

# create piv table for industry group type, year, and count
form_d_top_10_others = form_d.copy()
form_d_top_10_others.loc[
    ~form_d_top_10_others["offeringData.industryGroup.industryGroupType"].isin(
        topIndustryGroupTypes
    ),
    "offeringData.industryGroup.industryGroupType",
] = "Other"
industryGroupTypePiv = (
    form_d_top_10_others.groupby(
        ["offeringData.industryGroup.industryGroupType", "year"]
    )
    .size()
    .unstack()
    .fillna(0)
)
industryGroupTypePiv.index.name = "industryGroupType"
industryGroupTypePiv["total"] = industryGroupTypePiv.sum(axis=1)
industryGroupTypePiv = industryGroupTypePiv.reindex(
    index=topIndustryGroupTypes + ["Other"]
)

print("Offerings by Top 10 Industry Groups per Year")
industryGroupTypePiv.apply(lambda x: x.apply(lambda y: f"{y:,}"), axis=1)
```

```
Offerings by Top 10 Industry Groups per Year
```

Out[109]:

| year | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | total |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| industryGroupType |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| Pooled Investment Fund | 68 | 3,407 | 4,182 | 4,304 | 4,455 | 5,232 | 5,844 | 6,453 | 6,621 | 7,487 | 9,102 | 9,413 | 11,355 | 20,930 | 20,673 | 14,500 | 15,692 | 149,718 |
| Other Technology | 57 | 1,999 | 2,579 | 2,925 | 2,992 | 3,210 | 3,682 | 3,876 | 3,778 | 3,749 | 4,140 | 3,879 | 3,618 | 4,741 | 4,260 | 3,588 | 3,286 | 56,359 |
| Commercial | 14 | 279 | 429 | 498 | 563 | 694 | 796 | 1,038 | 1,201 | 1,378 | 1,489 | 1,737 | 1,511 | 2,128 | 2,386 | 1,660 | 1,826 | 19,627 |
| Other Health Care | 23 | 696 | 912 | 920 | 916 | 926 | 1,020 | 1,068 | 1,089 | 1,106 | 1,095 | 1,139 | 1,030 | 1,185 | 956 | 1,011 | 831 | 15,923 |
| Other Real Estate | 16 | 317 | 390 | 483 | 547 | 614 | 811 | 874 | 842 | 984 | 1,094 | 1,282 | 1,095 | 1,415 | 1,521 | 1,193 | 1,411 | 14,889 |
| Residential | 10 | 191 | 254 | 284 | 398 | 517 | 593 | 749 | 838 | 830 | 837 | 983 | 868 | 1,393 | 1,463 | 1,074 | 914 | 12,196 |
| Biotechnology | 11 | 536 | 687 | 638 | 622 | 606 | 661 | 713 | 706 | 746 | 750 | 726 | 783 | 823 | 734 | 729 | 662 | 11,133 |
| REITS and Finance | 3 | 93 | 161 | 211 | 346 | 403 | 545 | 508 | 620 | 767 | 693 | 631 | 725 | 718 | 988 | 774 | 653 | 8,839 |
| Investing | 11 | 367 | 476 | 491 | 394 | 421 | 482 | 460 | 458 | 469 | 507 | 512 | 572 | 794 | 673 | 507 | 689 | 8,283 |
| Other | 166 | 5,885 | 7,516 | 7,416 | 6,957 | 7,218 | 7,760 | 7,291 | 6,944 | 7,073 | 7,565 | 7,200 | 6,590 | 8,379 | 8,017 | 6,832 | 6,737 | 115,546 |

```python
fig, ax = plt.subplots(figsize=(6, 3))

industryGroupTypePiv.T.loc[2009:2024, :].plot(kind="bar", stacked=True, ax=ax)

ax.set_title("Offerings by Industry Group Type")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Offerings")
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
ax.grid(axis="x")
ax.set_axisbelow(True)
handles, labels = ax.get_legend_handles_labels()
ax.legend(
    reversed(handles),
    reversed(labels),
    title="Industry Group Type",
    bbox_to_anchor=(1.02, 1),
    labelspacing=0.15,
)
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()
```

### Types of Pooled Investment Funds

```python
form_d_pooledInvestmentFunds = form_d[
    form_d["offeringData.industryGroup.industryGroupType"] == "Pooled Investment Fund"
]
pooledFundTypes = (
    form_d_pooledInvestmentFunds[
        "offeringData.industryGroup.investmentFundInfo.investmentFundType"
    ]
    .value_counts()
    .to_frame()
)
pooledFundTypes["count"] = pooledFundTypes["count"].apply(lambda x: f"{x:,}")
pooledFundTypes.index.name = "Pooled Investment Fund Type"

print("Offerings per Type of Pooled Investment Fund (2008 - 2024)")
pooledFundTypes
```

```
Offerings per Type of Pooled Investment Fund (2008 - 2024)
```

Out[91]:

|  | count |
| --- | --- |
| Pooled Investment Fund Type |  |
| Venture Capital Fund | 42,098 |
| Other Investment Fund | 39,549 |
| Private Equity Fund | 39,214 |
| Hedge Fund | 28,857 |

```python
pooledFundTypesPiv = (
    form_d_pooledInvestmentFunds.groupby(
        ["offeringData.industryGroup.investmentFundInfo.investmentFundType", "year"]
    )
    .size()
    .unstack()
    .fillna(0)
)
pooledFundTypesPiv.index.name = "Pooled Investment Fund Type"
pooledFundTypesPiv["total"] = pooledFundTypesPiv.sum(axis=1)
pooledFundTypesPiv = pooledFundTypesPiv.sort_values(by="total", ascending=False)

print("Offerings by Pooled Investment Fund Type per Year")
pooledFundTypesPiv.apply(lambda x: x.apply(lambda y: f"{y:,}"), axis=1)
```

```
Offerings by Pooled Investment Fund Type per Year
```

Out:

| year | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | total |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Pooled Investment Fund Type |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| Venture Capital Fund | 5 | 131 | 232 | 328 | 365 | 474 | 682 | 936 | 896 | 925 | 1,266 | 2,252 | 3,300 | 8,631 | 9,295 | 6,003 | 6,377 | 42,098 |
| Other Investment Fund | 14 | 803 | 968 | 974 | 1,055 | 1,257 | 1,648 | 1,987 | 2,088 | 2,541 | 3,172 | 2,722 | 3,261 | 5,458 | 5,001 | 3,197 | 3,403 | 39,549 |
| Private Equity Fund | 7 | 527 | 840 | 1,034 | 1,083 | 1,390 | 1,582 | 1,706 | 1,936 | 2,256 | 2,794 | 2,948 | 3,194 | 4,827 | 4,736 | 3,922 | 4,432 | 39,214 |
| Hedge Fund | 42 | 1,946 | 2,142 | 1,968 | 1,952 | 2,111 | 1,932 | 1,824 | 1,701 | 1,765 | 1,870 | 1,491 | 1,600 | 2,014 | 1,641 | 1,378 | 1,480 | 28,857 |

```python
fig, ax = plt.subplots(figsize=(4, 3))

pooledFundTypesPiv.T.loc[2009:2024, :].plot(kind="bar", stacked=True, ax=ax)

ax.set_title("Offerings by Type of Pooled Investment Fund")
ax.set_xlabel("Year")
ax.set_ylabel("Number of Offerings")
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter("{x:,.0f}"))
ax.grid(axis="x")
ax.set_axisbelow(True)
handles, labels = ax.get_legend_handles_labels()
ax.legend(
    reversed(handles),
    reversed(labels),
    title="Fund Type",
    labelspacing=0.15,
)
plt.xticks(rotation=90)
plt.tight_layout()
plt.show()
```

## Issuer Size: Revenue Range and Net Asset Values

```python
revenueRange = form_d["offeringData.issuerSize.revenueRange"].value_counts().to_frame()
revenueRange["count"] = revenueRange["count"].apply(lambda x: f"{x:,}")
revenueRange.index.name = "Revenue Range"
print("Issuer Size by Revenue Range")
revenueRange
```

```
Issuer Size by Revenue Range
```

Out[28]:

|  | count |
| --- | --- |
| Revenue Range |  |
| Decline to Disclose | 254,194 |
| No Revenues | 39,942 |
| 1−1,000,000 | 21,831 |
| Not Applicable | 9,899 |
| 1,000,001−5,000,000 | 8,643 |
| 5,000,001−25,000,000 | 5,307 |
| 25,000,001−100,000,000 | 2,228 |
| Over $100,000,000 | 2,063 |

```python
netAssetRange = form_d["offeringData.issuerSize.aggregateNetAssetValueRange"].value_counts().to_frame()
netAssetRange["count"] = netAssetRange["count"].apply(lambda x: f"{x:,}")
netAssetRange.index.name = "Net Asset Range"
print("Issuer Size by Net Asset Range")
netAssetRange
```

```
Issuer Size by Net Asset Range
```

Out[29]:

|  | count |
| --- | --- |
| Net Asset Range |  |
| Decline to Disclose | 60,605 |
| 1−5,000,000 | 2,294 |
| 5,000,001−25,000,000 | 1,397 |
| No Aggregate Net Asset Value | 1,281 |
| Not Applicable | 1,120 |
| Over $100,000,000 | 773 |
| 25,000,001−50,000,000 | 501 |
| 50,000,001−100,000,000 | 435 |