Private Investment Trends in Aerospace and Defense (2019–2024)

Abstract

This report analyzes private investment activity in the aerospace and defense sectors across North America and Europe from January 2019 to May 2024. Despite a sectoral investment CAGR of -20% since 2019, the market remains dominated by high-value “scaleup” funding rounds exceeding $100M for industry leaders such as SpaceX, Anduril Industries, and Sierra Space. California serves as the primary geographic epicenter for this capital, with Hawthorne and Costa Mesa leading in total money raised. Key investment drivers include space travel, satellite communications, and advanced manufacturing, backed by prominent lead investors like Valor Equity Partners, Sequoia Capital, and BlackRock. Furthermore, the report highlights the disparity in military spending as a percentage of GDP, with the United States maintaining a significantly higher ratio than its European counterparts, providing a stable backdrop for continued private sector innovation.

import pandas as pd
import numpy as np
import adjustText

####### WORKING #######
#!pip install easy-exchange-rates
#!pip install tqdm

df = pd.read_csv("Recipe_CB_Aerospace_and_Defense_Since2019_2.csv")

df.head(5)

	name	money_raised	announced_date	location	industries	description	funding_type	lead_investors	total_funding
0	SpaceX	$1,724,965,480	May 24, 2022	Hawthorne, California, United States, North Am...	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	—	$9,779,343,846
1	Anduril Industries	$1,480,000,000	Dec 2, 2022	Costa Mesa, California, United States, North A...	Aerospace, Government, Military, National Secu...	Anduril Industries is a defense product compan...	Series E	Valor Equity Partners	$2,171,000,000
2	Sierra Space	$1,400,000,000	Nov 19, 2021	Louisville, Colorado, United States, North Ame...	Advanced Materials, Aerospace, Industrial Manu...	Sierra Space is a commercial space company tha...	Series A	Coatue, General Atlantic, Moore Strategic Vent...	$1,690,000,000
3	OneWeb	$1,250,000,000	Mar 18, 2019	London, England, United Kingdom, Europe	Aerospace, Internet, Satellite Communication, ...	OneWeb is building a space-based communication...	Venture - Series Unknown	SoftBank	$4,700,000,000
4	SpaceX	$850,000,000	Feb 16, 2021	Hawthorne, California, United States, North Am...	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	Sequoia Capital	$9,779,343,846

Expand location column to cities, countries etc.

df[['city', 'state', 'country', 'region']] = df['location'].str.split(',', n=3, expand=True)
df.drop(['location'], 
        axis=1, 
        inplace=True)
df['city'] = df['city'].str.strip()
df['state'] = df['state'].str.strip()
df['country'] = df['country'].str.strip()
df['region'] = df['region'].str.strip()

df.head(5)

	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	state	country	region
0	SpaceX	$1,724,965,480	May 24, 2022	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	—	$9,779,343,846	Hawthorne	California	United States	North America
1	Anduril Industries	$1,480,000,000	Dec 2, 2022	Aerospace, Government, Military, National Secu...	Anduril Industries is a defense product compan...	Series E	Valor Equity Partners	$2,171,000,000	Costa Mesa	California	United States	North America
2	Sierra Space	$1,400,000,000	Nov 19, 2021	Advanced Materials, Aerospace, Industrial Manu...	Sierra Space is a commercial space company tha...	Series A	Coatue, General Atlantic, Moore Strategic Vent...	$1,690,000,000	Louisville	Colorado	United States	North America
3	OneWeb	$1,250,000,000	Mar 18, 2019	Aerospace, Internet, Satellite Communication, ...	OneWeb is building a space-based communication...	Venture - Series Unknown	SoftBank	$4,700,000,000	London	England	United Kingdom	Europe
4	SpaceX	$850,000,000	Feb 16, 2021	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	Sequoia Capital	$9,779,343,846	Hawthorne	California	United States	North America

Format date column - announced_date

df['announced_date'] = pd.to_datetime(df['announced_date'], format = '%b %d, %Y')
df['announced_year'] = df['announced_date'].dt.year
df['announced_month'] = df['announced_date'].dt.month
df.head(5)

	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	state	country	region	announced_year	announced_month
0	SpaceX	$1,724,965,480	2022-05-24	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	—	$9,779,343,846	Hawthorne	California	United States	North America	2022	5
1	Anduril Industries	$1,480,000,000	2022-12-02	Aerospace, Government, Military, National Secu...	Anduril Industries is a defense product compan...	Series E	Valor Equity Partners	$2,171,000,000	Costa Mesa	California	United States	North America	2022	12
2	Sierra Space	$1,400,000,000	2021-11-19	Advanced Materials, Aerospace, Industrial Manu...	Sierra Space is a commercial space company tha...	Series A	Coatue, General Atlantic, Moore Strategic Vent...	$1,690,000,000	Louisville	Colorado	United States	North America	2021	11
3	OneWeb	$1,250,000,000	2019-03-18	Aerospace, Internet, Satellite Communication, ...	OneWeb is building a space-based communication...	Venture - Series Unknown	SoftBank	$4,700,000,000	London	England	United Kingdom	Europe	2019	3
4	SpaceX	$850,000,000	2021-02-16	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	Sequoia Capital	$9,779,343,846	Hawthorne	California	United States	North America	2021	2

Fetch (using easy_exchange_rates) an optimised forex rates table, given the date range

from easy_exchange_rates import API
api = API()

df1 = df['announced_date'].unique()
end_date=pd.to_datetime(df1.max(), format="%Y-%m-%d")
start_date=pd.to_datetime(df1.min(), format="%Y-%m-%d")

df_rates = api.get_exchange_rates(
    base_currency="EUR", 
    start_date=start_date.strftime('%Y-%m-%d'),
    end_date=end_date.strftime('%Y-%m-%d'),
    targets=["USD","NOK", "CNY"]
)
print(df_rates.head(5))

                CNY     NOK     USD
TIME_PERIOD                        
2019-01-02   7.8165  9.9108  1.1397
2019-01-03   7.8019  9.9113  1.1348
2019-01-04   7.8280  9.8648  1.1403
2019-01-07   7.8421  9.8018  1.1445
2019-01-08   7.8405  9.7750  1.1440

Backfill missing dates in the rate dataframe

#df_rates.index
# index is of type string, not datetime
#
date_range = pd.date_range(start=start_date.strftime('%Y-%m-%d'), 
                           end=end_date.strftime('%Y-%m-%d'), 
                           freq="D") 
data_reindexed = df_rates.reindex(date_range.strftime('%Y-%m-%d'))  # index is of type string, not datetime
data_reindexed.head(5)

df_rates_backfilled = data_reindexed.fillna(method='bfill')
df_rates_backfilled.head(5)

	CNY	NOK	USD
2019-01-01	7.8165	9.9108	1.1397
2019-01-02	7.8165	9.9108	1.1397
2019-01-03	7.8019	9.9113	1.1348
2019-01-04	7.8280	9.8648	1.1403
2019-01-05	7.8421	9.8018	1.1445

# rate_date = df_rates.iloc[df_rates.index == '2019-10-15']
# print(rate_date['USD'][0])
# print(rate_date['NOK'][0])
# print(rate_date['USD'][0]/rate_date['NOK'][0])
def convert_to_currency(amount, currency, date):
    converted = amount
    
    if currency == '€':
        rate_date = df_rates_backfilled.iloc[df_rates_backfilled.index == date]
        converted = int(amount) * rate_date['USD'][0]
    elif currency == 'NOK':
        rate_date = df_rates_backfilled.iloc[df_rates_backfilled.index == date]
        converted = int(amount) * (rate_date['USD'][0]/rate_date['NOK'][0])
    elif currency == 'CN¥':
        rate_date = df_rates_backfilled.iloc[df_rates_backfilled.index == date]
        converted = int(amount) * (rate_date['USD'][0]/rate_date['CNY'][0])

    return converted

Handle currency columns

currency_pattern = '^([$|€|CN¥|NOK]+)(\d+)'

def transform_money_column(df, column):
    df[column] = df[column].str.replace(',', '')
    df[['part_0', '{}_currency'.format(column) , '{}_amount'.format(column), 'part_3']] = df[column].str.split(currency_pattern, 
                                                                                                        n=-1, 
                                                                                                        expand=True, 
                                                                                                        regex = True)
    df.drop(['part_0', 'part_3'], 
            axis=1, 
            inplace=True)
    df['{}_currency'.format(column)] = df['{}_currency'.format(column)].str.strip()
    df['{}_amount'.format(column)] = df['{}_amount'.format(column)].str.strip()


transform_money_column(df, 'money_raised')
transform_money_column(df, 'total_funding')

df.head(2)

	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	state	country	region	announced_year	announced_month	money_raised_currency	money_raised_amount	total_funding_currency	total_funding_amount
0	SpaceX	$1724965480	2022-05-24	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	—	$9779343846	Hawthorne	California	United States	North America	2022	5	$	1724965480	$	9779343846
1	Anduril Industries	$1480000000	2022-12-02	Aerospace, Government, Military, National Secu...	Anduril Industries is a defense product compan...	Series E	Valor Equity Partners	$2171000000	Costa Mesa	California	United States	North America	2022	12	$	1480000000	$	2171000000

df[df['name'] == 'Nordiq Products AS']

	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	state	country	region	announced_year	announced_month	money_raised_currency	money_raised_amount	total_funding_currency	total_funding_amount
889	Nordiq Products AS	NOK4583879	2022-03-04	Health Diagnostics, Information Technology, Me...	On a mission to help first responders services...	Seed	—	NOK27360096	Oslo	Oslo	Norway	Europe	2022	3	NOK	4583879	NOK	27360096
941	Nordiq Products AS	NOK3538063	2023-04-17	Health Diagnostics, Information Technology, Me...	On a mission to help first responders services...	Seed	—	NOK27360096	Oslo	Oslo	Norway	Europe	2023	4	NOK	3538063	NOK	27360096
965	Nordiq Products AS	NOK2715748	2022-10-21	Health Diagnostics, Information Technology, Me...	On a mission to help first responders services...	Seed	—	NOK27360096	Oslo	Oslo	Norway	Europe	2022	10	NOK	2715748	NOK	27360096

def tx_currency(row, column):
    return convert_to_currency(row['{}_amount'.format(column)],
                          row['{}_currency'.format(column)],
                          row['announced_date'].strftime('%Y-%m-%d'))

df['money_raised_usd'] = df.apply(tx_currency, 
                                  args = ("money_raised",), 
                                  axis=1)
df['total_funding_usd'] = df.apply(tx_currency, 
                                   args = ("total_funding",), 
                                   axis=1)
df.head(5)

	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	state	country	region	announced_year	announced_month	money_raised_currency	money_raised_amount	total_funding_currency	total_funding_amount	money_raised_usd	total_funding_usd
0	SpaceX	$1724965480	2022-05-24	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	—	$9779343846	Hawthorne	California	United States	North America	2022	5	$	1724965480	$	9779343846	1724965480	9779343846
1	Anduril Industries	$1480000000	2022-12-02	Aerospace, Government, Military, National Secu...	Anduril Industries is a defense product compan...	Series E	Valor Equity Partners	$2171000000	Costa Mesa	California	United States	North America	2022	12	$	1480000000	$	2171000000	1480000000	2171000000
2	Sierra Space	$1400000000	2021-11-19	Advanced Materials, Aerospace, Industrial Manu...	Sierra Space is a commercial space company tha...	Series A	Coatue, General Atlantic, Moore Strategic Vent...	$1690000000	Louisville	Colorado	United States	North America	2021	11	$	1400000000	$	1690000000	1400000000	1690000000
3	OneWeb	$1250000000	2019-03-18	Aerospace, Internet, Satellite Communication, ...	OneWeb is building a space-based communication...	Venture - Series Unknown	SoftBank	$4700000000	London	England	United Kingdom	Europe	2019	3	$	1250000000	$	4700000000	1250000000	4700000000
4	SpaceX	$850000000	2021-02-16	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	Sequoia Capital	$9779343846	Hawthorne	California	United States	North America	2021	2	$	850000000	$	9779343846	850000000	9779343846

df['money_raised_usd'] = pd.to_numeric(df['money_raised_usd'], errors='coerce')
df['money_raised_usd'] = df['money_raised_usd'].apply(np.round)

df['total_funding_usd'] = pd.to_numeric(df['total_funding_usd'], errors='coerce')
df['total_funding_usd'] = df['total_funding_usd'].apply(np.round)

df[df['name'] == 'Nordiq Products AS']

	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	state	country	region	announced_year	announced_month	money_raised_currency	money_raised_amount	total_funding_currency	total_funding_amount	money_raised_usd	total_funding_usd
889	Nordiq Products AS	NOK4583879	2022-03-04	Health Diagnostics, Information Technology, Me...	On a mission to help first responders services...	Seed	—	NOK27360096	Oslo	Oslo	Norway	Europe	2022	3	NOK	4583879	NOK	27360096	509335.0	3040103.0
941	Nordiq Products AS	NOK3538063	2023-04-17	Health Diagnostics, Information Technology, Me...	On a mission to help first responders services...	Seed	—	NOK27360096	Oslo	Oslo	Norway	Europe	2023	4	NOK	3538063	NOK	27360096	341882.0	2643798.0
965	Nordiq Products AS	NOK2715748	2022-10-21	Health Diagnostics, Information Technology, Me...	On a mission to help first responders services...	Seed	—	NOK27360096	Oslo	Oslo	Norway	Europe	2022	10	NOK	2715748	NOK	27360096	253312.0	2552018.0

df['aerospace'] = df['industries'].str.lower().str.contains('aerospace')
df['defense'] = df['industries'].str.lower().str.contains('military')
df['announced_date_trunc_month'] = df['announced_date'].dt.to_period('M').dt.to_timestamp()
df['announced_date_trunc_year'] = df['announced_date'].dt.to_period('Y').dt.to_timestamp()
df.head(5)

	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	state	...	money_raised_currency	money_raised_amount	total_funding_currency	total_funding_amount	money_raised_usd	total_funding_usd	aerospace	defense	announced_date_trunc_month	announced_date_trunc_year
0	SpaceX	$1724965480	2022-05-24	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	—	$9779343846	Hawthorne	California	...	$	1724965480	$	9779343846	1.724965e+09	9.779344e+09	True	False	2022-05-01	2022-01-01
1	Anduril Industries	$1480000000	2022-12-02	Aerospace, Government, Military, National Secu...	Anduril Industries is a defense product compan...	Series E	Valor Equity Partners	$2171000000	Costa Mesa	California	...	$	1480000000	$	2171000000	1.480000e+09	2.171000e+09	True	True	2022-12-01	2022-01-01
2	Sierra Space	$1400000000	2021-11-19	Advanced Materials, Aerospace, Industrial Manu...	Sierra Space is a commercial space company tha...	Series A	Coatue, General Atlantic, Moore Strategic Vent...	$1690000000	Louisville	Colorado	...	$	1400000000	$	1690000000	1.400000e+09	1.690000e+09	True	False	2021-11-01	2021-01-01
3	OneWeb	$1250000000	2019-03-18	Aerospace, Internet, Satellite Communication, ...	OneWeb is building a space-based communication...	Venture - Series Unknown	SoftBank	$4700000000	London	England	...	$	1250000000	$	4700000000	1.250000e+09	4.700000e+09	True	False	2019-03-01	2019-01-01
4	SpaceX	$850000000	2021-02-16	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	Sequoia Capital	$9779343846	Hawthorne	California	...	$	850000000	$	9779343846	8.500000e+08	9.779344e+09	True	False	2021-02-01	2021-01-01

5 rows × 24 columns

Plots

from plotnine import ggplot, geom_bar, scale_x_date, scale_y_continuous, aes, stat_smooth, facet_wrap, options, theme_classic, labs, theme, element_text, geom_line, coord_flip, scale_size_continuous, geom_text, geom_label, scale_fill_manual, geom_tile, scale_colour_continuous, theme_bw, scale_colour_manual, scale_color_discrete, geom_point, geom_histogram, after_stat

Europe / US (by parameter)

# Change the parameter to switch between EU and US
region = {
  'EU': {
      'region': 'Europe',
      'region_caption': 'EU'
  },
  'US': {
      'region': 'North America',
      'region_caption': 'US'
  }  
}

# Change here, for subsequent charts
#
current_region = region['US']
#current_region = region['EU']

from IPython.display import Markdown as md
md("### Current Region: **{}**".format(current_region['region']))

Current Region: North America

df2 = (
    df[(df['region'] == current_region['region']) & (df['aerospace'] | df['defense'])]
    .groupby(['region', 'city', 'announced_date_trunc_month', 'aerospace', 'defense'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)

df_europe = df2[df2['total_money_raised'] > 0]
df_europe = df_europe.reset_index()
df_europe.head(5)

	region	city	announced_date_trunc_month	aerospace	defense	total_money_raised
0	North America	Albuquerque	2023-07-01	True	False	3350000.0
1	North America	Alexandria	2021-09-01	True	False	40000000.0
2	North America	Anaheim	2019-10-01	True	False	3750000.0
3	North America	Annapolis	2022-07-01	True	False	9750000.0
4	North America	Annapolis	2022-10-01	True	False	100000000.0

df2_raw = (
    df[(df['region'] == current_region['region']) & (df['aerospace'] | df['defense'])]
)

df_europe_raw = df2_raw[df2_raw['money_raised_usd'] > 0]
df_europe_raw = df_europe_raw.reset_index()
df_europe_raw.head(2)

	index	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	...	money_raised_currency	money_raised_amount	total_funding_currency	total_funding_amount	money_raised_usd	total_funding_usd	aerospace	defense	announced_date_trunc_month	announced_date_trunc_year
0	0	SpaceX	$1724965480	2022-05-24	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	—	$9779343846	Hawthorne	...	$	1724965480	$	9779343846	1.724965e+09	9.779344e+09	True	False	2022-05-01	2022-01-01
1	1	Anduril Industries	$1480000000	2022-12-02	Aerospace, Government, Military, National Secu...	Anduril Industries is a defense product compan...	Series E	Valor Equity Partners	$2171000000	Costa Mesa	...	$	1480000000	$	2171000000	1.480000e+09	2.171000e+09	True	True	2022-12-01	2022-01-01

2 rows × 25 columns

ggplot Helpers / Brand colours

## ggplot helpers
options.figure_size = (840 / options.dpi, 480 / options.dpi)

## Boson research brand 
boson_blue = "#04024B"
#boson_blue_faded = "#9090B7"
boson_blue_faded = "#BBD1EA"
caption = """\
© BOSON RESEARCH 

https://bosonresearch.com/
"""

funding_stages_color_codes = { 
    "Angel": "#bbd1ea", 
    "Convertible Note": "#a1c6ea",
    "Corporate Round": "#c1121f",
    "Equity Crowdfunding": "#e07a5f",
    "Pre-Seed": "#48cae4",
    "Seed": "#00b4d8",
    "Series A": "#0096c7",
    "Series B": "#0077b6",
    "Series C": "#023e8a",
    "Series D": "#03045e",
    "Series E": "#000000",
    "Private Equity": "#fca311",
    "Venture - Series Unknown": "#dae3e5" 
}

startup_stage_color_codes = { 
    "Angel": "#bbd1ea", 
    "Convertible Note": "#a1c6ea",
    "Pre-Seed": "#507dbc",
    "Seed": "#375496",
    "Series A": "#1d2b71",
    "Series B": "#04024b",
    "Venture - Series Unknown": "#dae3e5" 
}

breakout_stage_color_codes = { 
    "Seed": "#a3b18a",
    "Series A": "#588157",
    "Series B": "#3a5a40",
    "Series C": "#131916",
    "Venture - Series Unknown": "#dad7cd" 
}

def add_units(n):
    if n < 1000:
        return str(n)
    elif n < 1e6:
        return f"{round(n/1e3)}k"
    elif n < 1e9:
        return f"{round(n/1e6)}M"
    elif n < 1e12:
        return f"{round(n/1e9)}B"
    elif n < 1e15:
        return f"{round(n/1e12)}T"
    else:
        return "too big!"

Chart 1: Total Money Raised (in USD) over time

df_europe_trend = (
    df_europe_raw
    .groupby(['announced_date_trunc_month'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_europe_trend = df_europe_trend.reset_index()
(
    ggplot(df_europe_trend)
    + geom_bar(aes(x="announced_date_trunc_month", y="total_money_raised"), stat="identity", fill = boson_blue) 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%Y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        subtitle ="Trends, {} - {}".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

from datetime import datetime
def to_sector(df):
    sector = None
    if (df['aerospace'] & df['defense']):
        sector = 'aerospace and defense'
    elif df['aerospace']:
        sector = 'aerospace'
    elif df['defense']:
        sector = 'defense'

    return sector

def to_funding_recency(df):
    funding_recency = "older"
    current_year = datetime.now().year
    if (abs(df['announced_year'] - current_year) <= 2):
        funding_recency = "recent"

    return funding_recency


df_europe['sector'] = df_europe.apply(to_sector, axis = 1)

df_europe_raw['sector'] = df_europe_raw.apply(to_sector, axis = 1)
df_europe_raw['funding_recency'] = df_europe_raw.apply(to_funding_recency, axis = 1)

#df_europe.head(2)
df_europe_raw_desc = df_europe_raw.describe()

df_europe_raw['total_funding_usd_format'] = df_europe_raw['total_funding_usd'].apply(add_units)
df_europe_raw.head(2)

	index	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	...	total_funding_amount	money_raised_usd	total_funding_usd	aerospace	defense	announced_date_trunc_month	announced_date_trunc_year	sector	funding_recency	total_funding_usd_format
0	0	SpaceX	$1724965480	2022-05-24	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	—	$9779343846	Hawthorne	...	9779343846	1.724965e+09	9.779344e+09	True	False	2022-05-01	2022-01-01	aerospace	recent	10B
1	1	Anduril Industries	$1480000000	2022-12-02	Aerospace, Government, Military, National Secu...	Anduril Industries is a defense product compan...	Series E	Valor Equity Partners	$2171000000	Costa Mesa	...	2171000000	1.480000e+09	2.171000e+09	True	True	2022-12-01	2022-01-01	aerospace and defense	recent	2B

2 rows × 28 columns

add_units(df_europe_raw['money_raised_usd'].sum())

'21B'

(
    ggplot(df_europe_raw[df_europe_raw['money_raised_usd']<100000000])
     + geom_histogram(aes(x='money_raised_usd', 
                    y=after_stat("count")))
     + theme_classic()
)

/Users/dbose/anaconda3/envs/py-data/lib/python3.8/site-packages/plotnine/stats/stat_bin.py:109: PlotnineWarning: 'stat_bin()' using 'bins = 32'. Pick better value with 'binwidth'.

df_info = df_europe_raw.describe()
df_info['total_funding_usd_format'] = df_info['total_funding_usd'].apply(add_units)
df_info

	index	announced_date	announced_year	announced_month	money_raised_usd	total_funding_usd	announced_date_trunc_month	announced_date_trunc_year	total_funding_usd_format
count	586.000000	586	586.000000	586.000000	5.860000e+02	5.860000e+02	586	586	586.0
mean	492.153584	2021-10-26 23:18:13.515358464	2021.337884	6.363481	3.524728e+07	2.048825e+08	2021-10-13 12:54:03.686007040	2021-05-04 00:46:41.365187840	205M
min	0.000000	2019-01-01 00:00:00	2019.000000	1.000000	2.000000e+05	2.000000e+05	2019-01-01 00:00:00	2019-01-01 00:00:00	200k
25%	250.500000	2020-10-01 06:00:00	2020.000000	3.000000	1.300000e+06	4.250000e+06	2020-10-01 00:00:00	2020-01-01 00:00:00	4M
50%	477.500000	2021-11-12 12:00:00	2021.000000	6.000000	5.000000e+06	1.551424e+07	2021-11-01 00:00:00	2021-01-01 00:00:00	16M
75%	746.750000	2022-12-31 18:00:00	2022.750000	10.000000	1.772500e+07	6.273066e+07	2022-12-24 06:00:00	2022-10-01 18:00:00	63M
max	999.000000	2024-05-30 00:00:00	2024.000000	12.000000	1.724965e+09	9.779344e+09	2024-05-01 00:00:00	2024-01-01 00:00:00	10B
std	291.937554	NaN	1.478680	3.631669	1.324487e+08	1.080788e+09	NaN	NaN	1B

Overall - by Funding Stage

df_europe_funding_trend_by_funding_stage = (
    df_europe_raw
    .groupby(['funding_type', 'announced_date_trunc_month'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_europe_funding_trend_by_funding_stage = df_europe_funding_trend_by_funding_stage.reset_index()

(
    ggplot(df_europe_funding_trend_by_funding_stage)
    + geom_bar(aes(x="announced_date_trunc_month", 
                   y="total_money_raised", 
                   group = "factor(funding_type)", 
                   fill = "factor(funding_type)"), 
               stat="identity") 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + scale_fill_manual(values = funding_stages_color_codes)
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        fill="Funding stage",
        subtitle ="({} - {}), segmented by funding stages".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

GREY_LIGHT = "#b4aea9"
GREY50 = "#7F7F7F"
adjust_text_dict = {
    'EU': {
        'expand': (2, 2),
        'force_text': (0.2, 2),
        'force_static': (0.5, 1.2),
        'force_explode': (0.2, 1.2),
        'arrowprops': {
            'arrowstyle': '->',
            'color': boson_blue_faded
        }
    },
    'US': {
        'arrowprops': {
            'arrowstyle': '->',
            'color': boson_blue_faded
        }
    }
}
outliers = ['SpaceX', 'Anduril Industries', 'Sierra Space']

df_outlier = df_europe_raw[df_europe_raw['name'].isin(outliers)].groupby('name').agg(total_money_raised = ('money_raised_usd', 'sum')).reset_index()
df_outlier['total_money_raised_format'] = df_outlier['total_money_raised'].apply(add_units)
df_outlier

	name	total_money_raised	total_money_raised_format
0	Anduril Industries	2.130000e+09	2B
1	Sierra Space	1.690000e+09	2B
2	SpaceX	4.358265e+09	4B

options.figure_size = (1048 / options.dpi, 640 / options.dpi)
(
    ggplot(df_europe_raw[~df_europe_raw['name'].isin(outliers)])
    + geom_point(aes(x = "announced_date_trunc_month", 
                     y = "money_raised_usd", 
                     colour = "factor(sector)", 
                     size = "money_raised_usd")) 
    # Annotate startups with >50M funding till date
    + geom_text(aes(x = "announced_date_trunc_month",
                y = "money_raised_usd",
                label = "name"),
                va = "bottom",
                size = 8,
                adjust_text=adjust_text_dict[current_region['region_caption']],
               data=df_europe_raw[(df_europe_raw['money_raised_usd'] > 60000000) & (~df_europe_raw['name'].isin(outliers))])
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + scale_size_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investments in Aerospace & Defense across {}".format(current_region['region_caption']),
        subtitle ="Funding deals (with >=60M, annotated), {} - {}".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        size="Money Raised (range)",
        colour="Aerospace and/or Defense",
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

df_world_trend = (
    df_europe_raw
    .groupby(['funding_type', 'announced_date_trunc_year'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_world_trend = df_world_trend.reset_index()
df_world_trend = df_world_trend.sort_values(by=["announced_date_trunc_year"])
df_world_trend.head(2)

options.figure_size = (1200 / options.dpi, 640 / options.dpi)
(
    ggplot(df_world_trend)
    + geom_bar(aes(x="announced_date_trunc_year", 
                   y="total_money_raised",
                  group="factor(funding_type)",
                  fill="factor(funding_type)"), 
               stat="identity") 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%Y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + scale_fill_manual(values = funding_stages_color_codes)
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        fill="Funding Stages",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        subtitle ="({} - {}) - By funding stages".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
    
)

Early-stage investment : Startup stage ($0-15M rounds)

df_europe_funding_trend_by_funding_stage = (
    df_europe_raw[df_europe_raw['money_raised_usd'] < 15000000]
    .groupby(['funding_type', 'announced_date_trunc_year'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_europe_funding_trend_by_funding_stage = df_europe_funding_trend_by_funding_stage.reset_index()

options.figure_size = (1200 / options.dpi, 640 / options.dpi)
(
    ggplot(df_europe_funding_trend_by_funding_stage)
    + geom_bar(aes(x="announced_date_trunc_year", 
                   y="total_money_raised", 
                   group = "factor(funding_type)", 
                   fill = "factor(funding_type)"), 
               stat="identity") 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + scale_fill_manual(values = startup_stage_color_codes)
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        fill="Funding stage",
        subtitle ="(0-15M rounds) {} - {}, segmented by funding stages".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

/Users/dbose/anaconda3/envs/py-data/lib/python3.8/site-packages/plotnine/scales/scale_manual.py:45: PlotnineWarning: The palette of scale_fill_manual can return a maximum of 7 values. 11 were requested from it.
/Users/dbose/anaconda3/envs/py-data/lib/python3.8/site-packages/plotnine/scales/scale_manual.py:45: PlotnineWarning: The palette of scale_fill_manual can return a maximum of 7 values. 11 were requested from it.

Breakout stage ($15-100M rounds)

df_europe_funding_trend_by_funding_stage = (
    df_europe_raw[(df_europe_raw['money_raised_usd'] > 15000000) & (df_europe_raw['money_raised_usd'] < 100000000)]
    .groupby(['funding_type', 'announced_date_trunc_year'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_europe_funding_trend_by_funding_stage = df_europe_funding_trend_by_funding_stage.reset_index()

options.figure_size = (1200 / options.dpi, 640 / options.dpi)
(
    ggplot(df_europe_funding_trend_by_funding_stage)
    + geom_bar(aes(x="announced_date_trunc_year", 
                   y="total_money_raised", 
                   group = "factor(funding_type)", 
                   fill = "factor(funding_type)"), 
               stat="identity") 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    # https://coolors.co/dad7cd-a3b18a-588157-3a5a40-344e41
    + scale_fill_manual(values = breakout_stage_color_codes)
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        fill="Funding stage",
        subtitle ="(15M-100M rounds) {} - {}, segmented by funding stages".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

/Users/dbose/anaconda3/envs/py-data/lib/python3.8/site-packages/plotnine/scales/scale_manual.py:45: PlotnineWarning: The palette of scale_fill_manual can return a maximum of 5 values. 10 were requested from it.
/Users/dbose/anaconda3/envs/py-data/lib/python3.8/site-packages/plotnine/scales/scale_manual.py:45: PlotnineWarning: The palette of scale_fill_manual can return a maximum of 5 values. 10 were requested from it.

Scaleup ($100M+ rounds)

df_europe_funding_trend_by_funding_stage = (
    df_europe_raw[(df_europe_raw['money_raised_usd'] > 100000000)]
    .groupby(['funding_type', 'announced_date_trunc_year'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_europe_funding_trend_by_funding_stage = df_europe_funding_trend_by_funding_stage.reset_index()

(
    ggplot(df_europe_funding_trend_by_funding_stage)
    + geom_bar(aes(x="announced_date_trunc_year", 
                   y="total_money_raised", 
                   group = "factor(funding_type)", 
                   fill = "factor(funding_type)"), 
               stat="identity") 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    # https://coolors.co/dad7cd-a3b18a-588157-3a5a40-344e41
    + scale_fill_manual(values = funding_stages_color_codes)
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        fill="Funding stage",
        subtitle ="(100M+ rounds) {} - {}, segmented by funding stages".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

df_europe_funding_trend_by_sector = (
    df_europe_raw
    .groupby(['sector', 'announced_date_trunc_month'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_europe_funding_trend_by_sector = df_europe_funding_trend_by_sector.reset_index()

(
    ggplot(df_europe_funding_trend_by_sector)
    + geom_bar(aes(x="announced_date_trunc_month", y="total_money_raised", 
                   group = "factor(sector)", fill = "factor(sector)"), 
               stat="identity") 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        fill="Aerospace and/or Defense",
        subtitle ="Trends {} - {}, segmented by Aerospace/Defense".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

df_europe_funding_trend_by_city = (
    df_europe
    .groupby(['city'])
    .agg(total_money_raised = ('total_money_raised', 'sum'))
)
df_europe_funding_trend_by_city = df_europe_funding_trend_by_city.reset_index()

# Ordering the bar plot (order city by funding)
df_europe_funding_trend_by_city = df_europe_funding_trend_by_city.sort_values(by = ["total_money_raised"], 
                                                                              ascending=False)
city_by_funding = df_europe_funding_trend_by_city["city"].tolist()
df_europe_funding_trend_by_city = df_europe_funding_trend_by_city.assign(
    city_cat=pd.Categorical(df_europe_funding_trend_by_city["city"], categories=city_by_funding)
)

# # assign to a new column in the DataFrame
options.figure_size = (640 / options.dpi, 480 / options.dpi)
(
    ggplot(df_europe_funding_trend_by_city.head(20))
    + geom_bar(aes(x="city_cat", y="total_money_raised"), 
               stat="identity", fill = boson_blue) 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="{} Cities".format(current_region['region_caption']),
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        fill="Aerospace and/or Defense",
        subtitle ="Segmented by cities, {} - {}".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

country_or_state = "state" if current_region['region_caption'] == 'US' else 'country'

df_europe_funding_trend_by_country = (
    df_europe_raw
    .groupby([country_or_state])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_europe_funding_trend_by_country = df_europe_funding_trend_by_country.reset_index()

# Ordering the bar plot (order city by funding)
df_europe_funding_trend_by_country = df_europe_funding_trend_by_country.sort_values(by = ["total_money_raised"], 
                                                                              ascending=False)
country_by_funding = df_europe_funding_trend_by_country[country_or_state].tolist()
df_europe_funding_trend_by_country = df_europe_funding_trend_by_country.assign(
    country_or_state_cat = pd.Categorical(df_europe_funding_trend_by_country[country_or_state], 
                                categories=country_by_funding)
)

# # assign to a new column in the DataFrame
options.figure_size = (640 / options.dpi, 480 / options.dpi)
(
    ggplot(df_europe_funding_trend_by_country.head(20))
    + geom_bar(aes(x="country_or_state_cat", y="total_money_raised"), 
               stat="identity", fill = boson_blue) 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="{} {}".format(current_region['region_caption'], country_or_state),
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        fill="Aerospace and/or Defense",
        subtitle ="Segmented by cities, {} - {}".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

df_europe_funding_trend_by_country['total_money_raised_format'] = df_europe_funding_trend_by_country['total_money_raised'].apply(add_units)
df_europe_funding_trend_by_country.head()

	state	total_money_raised	country_or_state_cat	total_money_raised_format
2	California	1.313909e+10	California	13B
3	Colorado	2.658520e+09	Colorado	3B
31	Texas	1.354833e+09	Texas	1B
33	Vermont	9.491627e+08	Vermont	949M
34	Virginia	4.281782e+08	Virginia	428M

df_europe_raw.describe()

	index	announced_date	announced_year	announced_month	money_raised_usd	total_funding_usd	announced_date_trunc_month	announced_date_trunc_year
count	586.000000	586	586.000000	586.000000	5.860000e+02	5.860000e+02	586	586
mean	492.153584	2021-10-26 23:18:13.515358464	2021.337884	6.363481	3.524728e+07	2.048825e+08	2021-10-13 12:54:03.686007040	2021-05-04 00:46:41.365187840
min	0.000000	2019-01-01 00:00:00	2019.000000	1.000000	2.000000e+05	2.000000e+05	2019-01-01 00:00:00	2019-01-01 00:00:00
25%	250.500000	2020-10-01 06:00:00	2020.000000	3.000000	1.300000e+06	4.250000e+06	2020-10-01 00:00:00	2020-01-01 00:00:00
50%	477.500000	2021-11-12 12:00:00	2021.000000	6.000000	5.000000e+06	1.551424e+07	2021-11-01 00:00:00	2021-01-01 00:00:00
75%	746.750000	2022-12-31 18:00:00	2022.750000	10.000000	1.772500e+07	6.273066e+07	2022-12-24 06:00:00	2022-10-01 18:00:00
max	999.000000	2024-05-30 00:00:00	2024.000000	12.000000	1.724965e+09	9.779344e+09	2024-05-01 00:00:00	2024-01-01 00:00:00
std	291.937554	NaN	1.478680	3.631669	1.324487e+08	1.080788e+09	NaN	NaN

Top companies

df_europe_funding_by_companies = (
    df_europe_raw
    .groupby(['name', 'funding_recency'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'), deals = ('money_raised_usd', 'count'))
)
df_europe_funding_by_companies = df_europe_funding_by_companies.reset_index()

# Ordering the bar plot (order city by funding)
df_europe_funding_by_companies = df_europe_funding_by_companies.sort_values(by = ["total_money_raised"], 
                                                                              ascending=False)
companies_by_funding = df_europe_funding_by_companies["name"].unique()
df_europe_funding_by_companies = df_europe_funding_by_companies.assign(
    company_cat=pd.Categorical(df_europe_funding_by_companies["name"], 
                               categories=companies_by_funding)
)

df6 = df_europe_funding_by_companies.groupby(['name']).agg(total_money_raised = ('total_money_raised', 'sum')).reset_index()
df6['total_money_raised_format'] = df6['total_money_raised'].apply(add_units)
df6_head = df6.sort_values(by=['total_money_raised'], ascending=False).head(15)
','.join([ "{} (${})".format(x,y)  for i, (x, y) in enumerate(zip(df6_head['name'].values, df6_head['total_money_raised_format'].values))])
   

'SpaceX ($4B),Anduril Industries ($2B),Sierra Space ($2B),Relativity Space ($1B),Beta Technologies ($886M),Divergent ($586M),Astranis ($490M),Axiom Space ($487M),Wisk Aero ($450M),Firefly Aerospace ($450M),ABL Space Systems ($419M),HawkEye 360 ($343M),Epirus ($287M),Ursa Major ($264M),ZeroAvia ($240M)'

Top companies >9M (median) and <27M

df6 = df_europe_funding_by_companies.groupby(['name']).agg(total_money_raised = ('total_money_raised', 'sum')).reset_index()
df6['total_money_raised_format'] = df6['total_money_raised'].apply(add_units)
df7 = df6[(df6['total_money_raised']>9000000) & (df6['total_money_raised']<27000000)]
df7 = df7.sort_values(by=['total_money_raised'], ascending=False)
df7 = df7[['name', 'total_money_raised_format']].rename(columns={'name': 'Name', 
                                                           'total_money_raised_format': 'Total Funding (USD)'}).head(10)
','.join([ "{} (${})".format(x,y)  for i, (x, y) in enumerate(zip(df7['Name'].values, df7['Total Funding (USD)'].values))])

'JetZero ($26M),Karman+ ($26M),Vita Inclinata Technologies ($26M),Hydrosat ($25M),World View Enterprises ($25M),Swarm Technologies ($25M),Geminus ($23M),Electric Power Systems ($22M),General Radar Corp. ($22M),Zeno Power ($22M)'

Companies at the tail end

df6.sort_values(by=['total_money_raised'], ascending=False).tail(15)

	name	total_money_raised	total_money_raised_format
115	GoApron	350000.0	350k
157	Launchspace	329254.0	329k
280	United Western Group	310200.0	310k
253	Spike Dynamics	280000.0	280k
146	Kall Morris Inc	279000.0	279k
143	Jetoptera	250000.0	250k
152	Kray Technologies	250000.0	250k
166	MOHOC	250000.0	250k
187	OrbitsEdge	250000.0	250k
231	Shuttle	250000.0	250k
238	Skycom	232608.0	233k
259	Stark Drones Corporation	211000.0	211k
33	Arcana Recovery	200000.0	200k
176	New Frontier Aerospace	200000.0	200k
248	Space Crystals LLC	200000.0	200k

n = 20
df_europe_funding_by_companies_top_n = df_europe_funding_by_companies.drop_duplicates().head(n)
options.figure_size = (1048 / options.dpi, 640 / options.dpi)
(
    # [2:] is to remove the top outlier - OneWeb/SpaceX
    ggplot(df_europe_funding_by_companies_top_n[2:])
    + geom_bar(aes(x="company_cat", 
                   y="total_money_raised", 
                   fill = "factor(funding_recency)"), 
               stat="identity") 
    + scale_fill_manual(values = { "older": boson_blue_faded, "recent": boson_blue })
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Companies",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        fill="Funding deals older than two years ?",
        subtitle ="Top {} companies (total funding) with recency (within 2 years) \nof funding, excluding {} ({} - {})".format(n, df_europe_funding_by_companies_top_n.loc[df_europe_funding_by_companies_top_n.index[0],'name'], start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

# # assign to a new column in the DataFrame

(
    ggplot(df_europe_funding_by_companies.tail(30))
    + geom_bar(aes(x="company_cat", 
                   y="total_money_raised", 
                   fill = "factor(funding_recency)"), 
               stat="identity") 
    + scale_fill_manual(values = { "older": boson_blue_faded, "recent": boson_blue })
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Companies",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        fill="Funding deals older than two years ?",
        subtitle ="Bottom 30 (total funding) companies with recency \n(within 2 years) of funding ({} - {})".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

Lead Investors

df_europe_vc_trend = (
    df_europe_raw
    .groupby(['announced_date_trunc_month', 'lead_investors'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_europe_vc_trend = df_europe_vc_trend.reset_index()
df_europe_vc_trend = df_europe_vc_trend[df_europe_vc_trend['lead_investors'].notna() & (df_europe_vc_trend['lead_investors'] != '—')]

df_europe_vc_trend = df_europe_vc_trend.sort_values(by = ["total_money_raised"], 
                                                    ascending=False)
top_investors = df_europe_vc_trend["lead_investors"].unique()
df_europe_vc_trend = df_europe_vc_trend.assign(
    investors_cat=pd.Categorical(df_europe_vc_trend["lead_investors"], 
                               categories=top_investors)
)
options.figure_size = (1048 / options.dpi, 480 / options.dpi)

(
    ggplot(df_europe_vc_trend.head(20))
    + geom_point(aes(x="announced_date_trunc_month",
                    y="investors_cat",
                    size="total_money_raised"))
    #+ geom_text(aes(label="total_money_raised"), size=9)
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%y") 
    + scale_size_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Funding Date",
        y="Investors",
        title="Private Investment in Aerospace & Defense across {}".format(current_region['region_caption']),
        size="Deal size",
        subtitle ="Top 20 investors",
        caption=caption
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"}),
        figure_size=(8, 8)
    )
    + theme_bw()
)

df8 = df_europe_vc_trend.groupby(['lead_investors']).agg(total_money_raised = ('total_money_raised', 'sum')).reset_index()
df8['total_money_raised_format'] = df8['total_money_raised'].apply(add_units)
df8 = df8.sort_values(by=['total_money_raised'], ascending=False)
df8 = df8[['lead_investors', 'total_money_raised_format']].rename(columns={
                                                            'lead_investors': 'Lead Investors',
                                                            'total_money_raised_format': 'Total Money Invested (USD)'
}).head(10)

','.join([ "{} (${})".format(x,y)  for i, (x, y) in enumerate(zip(df8['Lead Investors'].values, df8['Total Money Invested (USD)'].values))])

'Valor Equity Partners ($1B),Coatue, General Atlantic, Moore Strategic Ventures ($1B),Fidelity ($1B),Sequoia Capital ($850M),BlackRock ($527M),Tiger Global Management ($517M),Elad Gil ($450M),The Boeing Company ($450M),Andreessen Horowitz ($410M),Fidelity, TPG Rise Climate Fund ($375M)'

US / Europe

df_world_raw = (
    df[(df['aerospace'] | df['defense'])]
)

df_world_raw = df_world_raw[df_world_raw['money_raised_usd'] > 0]
df_world_raw = df_world_raw.reset_index()

df_world_raw['sector'] = df_world_raw.apply(to_sector, axis = 1)
df_world_raw['funding_recency'] = df_world_raw.apply(to_funding_recency, axis = 1)

df_world_raw.head(2)

	index	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	...	total_funding_currency	total_funding_amount	money_raised_usd	total_funding_usd	aerospace	defense	announced_date_trunc_month	announced_date_trunc_year	sector	funding_recency
0	0	SpaceX	$1724965480	2022-05-24	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	—	$9779343846	Hawthorne	...	$	9779343846	1.724965e+09	9.779344e+09	True	False	2022-05-01	2022-01-01	aerospace	recent
1	1	Anduril Industries	$1480000000	2022-12-02	Aerospace, Government, Military, National Secu...	Anduril Industries is a defense product compan...	Series E	Valor Equity Partners	$2171000000	Costa Mesa	...	$	2171000000	1.480000e+09	2.171000e+09	True	True	2022-12-01	2022-01-01	aerospace and defense	recent

2 rows × 27 columns

options.figure_size = (1048 / options.dpi, 620 / options.dpi)
df_world_trend = (
    df_world_raw
    .groupby(['announced_date_trunc_year'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_world_trend = df_world_trend.reset_index()
df_world_trend = df_world_trend.sort_values(by=["announced_date_trunc_year"])
df_world_trend.head(2)

	announced_date_trunc_year	total_money_raised
0	2019-01-01	2.778901e+09
1	2020-01-01	1.992270e+09

# Since 2019
first_year_money_raised = df_world_trend.iloc[0, 1]
last_year_money_raised = df_world_trend.iloc[-1, 1]
years = round((df_world_trend.iloc[-1, 0] - df_world_trend.iloc[0, 0])/pd.Timedelta(days=365),2)
CAGR = (last_year_money_raised/first_year_money_raised)**(1./years)-1
"Investment CAGR since 2019: {}%".format(round(CAGR * 100))

'Investment CAGR since 2019: -20%'

# Since 2021
first_year_money_raised = df_world_trend.iloc[2, 1]
last_year_money_raised = df_world_trend.iloc[-1, 1]
years = round((df_world_trend.iloc[-1, 0] - df_world_trend.iloc[0, 0])/pd.Timedelta(days=365),2)
CAGR = (last_year_money_raised/first_year_money_raised)**(1./years)-1
"Investment CAGR since 2021: {}%".format(round(CAGR * 100))

'Investment CAGR since 2021: -38%'

df_t = df_world_trend.copy()
df_t = df_t['total_money_raised'].apply(add_units)
df_t.head(10)

0      3B
1      2B
2     10B
3      8B
4      4B
5    928M
Name: total_money_raised, dtype: object

options.figure_size = (640 / options.dpi, 480 / options.dpi)
(
    ggplot(df_world_trend)
    + geom_bar(aes(x="announced_date_trunc_year", 
                   y="total_money_raised"), 
               stat="identity", 
               fill = boson_blue) 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%Y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across EU & US",
        subtitle ="Trends ({} - {})".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
    + coord_flip()
)

df_world_trend = (
    df_world_raw
    .groupby(['funding_type', 'announced_date_trunc_year'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_world_trend = df_world_trend.reset_index()
df_world_trend = df_world_trend.sort_values(by=["announced_date_trunc_year"])
df_world_trend.head(2)

options.figure_size = (1200 / options.dpi, 640 / options.dpi)
(
    ggplot(df_world_trend)
    + geom_bar(aes(x="announced_date_trunc_year", 
                   y="total_money_raised",
                  group="factor(funding_type)",
                  fill="factor(funding_type)"), 
               stat="identity") 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%Y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + scale_fill_manual(values = { 
        "Angel": "#bbd1ea", 
        "Convertible Note": "#a1c6ea",
        "Corporate Round": "#219ebc",
        "Equity Crowdfunding": "#e07a5f",
        "Pre-Seed": "#48cae4",
        "Seed": "#00b4d8",
        "Series A": "#0096c7",
        "Series B": "#0077b6",
        "Series C": "#023e8a",
        "Series D": "#03045e",
        "Series E": "#000000",
        "Private Equity": "#fca311",
        "Venture - Series Unknown": "#dae3e5" })
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        fill="Funding Stages",
        title="Private Investment in Aerospace & Defense across EU & US",
        subtitle ="({} - {}) - By funding stages".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
    
)

options.figure_size = (1048 / options.dpi, 620 / options.dpi)
df_world_trend = (
    df_world_raw
    .groupby(['region', 'announced_date_trunc_month'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_world_trend = df_world_trend.reset_index()
(
    ggplot(df_world_trend)
    + geom_bar(aes(x="announced_date_trunc_month", 
                   y="total_money_raised"), 
               stat="identity", 
               fill = boson_blue) 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%Y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across EU & US",
        subtitle ="Trends ({} - {})".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
    + facet_wrap("region", nrow=2)
)

df_world_funding_trend_by_sector = (
    df_world_raw
    .groupby(['region', 'sector', 'announced_date_trunc_month'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'))
)
df_world_funding_trend_by_sector = df_world_funding_trend_by_sector.reset_index()

(
    ggplot(df_world_funding_trend_by_sector)
    + geom_bar(aes(x="announced_date_trunc_month", y="total_money_raised", 
                   group = "factor(sector)", fill = "factor(sector)"), 
               stat="identity") 
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investments in Aerospace & Defense across EU & US",
        fill="Aerospace and/or Defense",
        subtitle ="Trends ({} - {}), segmented by Aerospace/Defense".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
    + facet_wrap("region", nrow=2)
)

df_america_raw = df_world_raw[df_world_raw['region'] == 'North America']

GREY_LIGHT = "#b4aea9"
GREY50 = "#7F7F7F"
adjust_text_dict_us = {
    'expand': (2, 2),
    'force_text': (0.2, 0.2),
    'force_static': (0.5, 0.5),
    #'force_explode': (0.2, 0.5),
    'arrowprops': {
        'arrowstyle': '->',
        'color': boson_blue_faded
    }
}

(
    ggplot(df_america_raw)
    + geom_point(aes(x = "announced_date_trunc_month", 
                     y = "money_raised_usd", 
                     colour = "factor(sector)", 
                     size = "money_raised_usd")) 
    + facet_wrap("region", nrow=2)
    # Annotate startups with >50M funding till date
    + geom_text(aes(x = "announced_date_trunc_month",
                y = "money_raised_usd",
                label = "name"),
                va = "bottom",
                size = 8,
                adjust_text=adjust_text_dict_us,
               data=df_america_raw[df_america_raw['money_raised_usd'] > 100000000])
    + scale_x_date(date_breaks = "6 month", date_labels =  "%b-%y") 
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + scale_size_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Funding Date",
        y="Total Money Raised (in USD)",
        title="Private Investments in Aerospace & Defense, across US",
        subtitle ="Funding deals (with >=100M are annotated), {} - {}".format(start_date.strftime('%b %d, %Y'),end_date.strftime('%b %d, %Y')),
        size="Money Raised (range)",
        colour="Aerospace and/or Defense",
        caption=caption,
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

df_world_funding_by_companies = (
    df_world_raw
    .groupby(['region', 'name', 'funding_recency'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'), deals = ('money_raised_usd', 'count'))
)
df_world_funding_by_companies = df_world_funding_by_companies.reset_index()

# Ordering the bar plot (order city by funding)
df_world_funding_by_companies = df_world_funding_by_companies.sort_values(by = ["total_money_raised"], 
                                                                              ascending=False)
world_companies = df_world_funding_by_companies["name"].unique()
df_world_funding_by_companies = df_world_funding_by_companies.assign(
    company_cat=pd.Categorical(df_world_funding_by_companies["name"], 
                               categories=world_companies)
)

options.figure_size = (1024 / options.dpi, 600 / options.dpi)
(
    ggplot(df_world_funding_by_companies.head(50))
    + geom_bar(aes(x="company_cat", 
                   y="total_money_raised", 
                   group="region",
                   fill = "factor(funding_recency)"), 
               stat="identity") 
    + scale_fill_manual(values = { "older": boson_blue_faded, "recent": boson_blue })
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Companies",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across US & Europe",
        fill="Funding deals older than two years ?",
        subtitle ="Top 50 companies (worldwide) with recency (within 2 years) of funding, till {}".format(end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
    + facet_wrap("region")
)

median_funding_world = df_world_funding_by_companies['total_money_raised'].median()
median_funding_world

5953705.0

df_world_limit = df_world_funding_by_companies[(df_world_funding_by_companies['total_money_raised'] > median_funding_world) &
                        (df_world_funding_by_companies['total_money_raised'] < 3*median_funding_world)]

(
    ggplot(df_world_limit.head(30))
    + geom_bar(aes(x="company_cat", 
                   y="total_money_raised", 
                   group="region",
                   fill = "factor(funding_recency)"), 
               stat="identity") 
    + scale_fill_manual(values = { "older": boson_blue_faded, "recent": boson_blue })
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Companies",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across US & Europe",
        fill="Funding deals older than two years ?",
        subtitle ="Companies (worldwide) with {}(median)-{}(3x median) funding, along with recency (within 2 years) of funding \n(till {})".format(add_units(median_funding_world), add_units(3*median_funding_world), end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
    + facet_wrap("region")
)

CAGR in Deal Value - Preparing a derived dataset

df_spacex = df_world_raw[df_world_raw['name'] == 'Hadrian']

'Andreessen Horowitz, Lux Capital,Founders Fund'

# def weighted_average(data):
#     d = {}
#     d['d1_wa'] = np.average(data['d1'], weights=data['weights'])
#     d['d2_wa'] = np.average(data['d2'], weights=data['weights'])
#     return pd.Series(d)
# Call the groupby apply method with our custom function:

# df.groupby('group').apply(weighted_average)

#        d1_wa  d2_wa
# group              
# a        9.0    2.2
# b       58.0   13.2

def first_last_deal_flow(data):
    d = {}
    data_sorted = data.sort_values(by = ["announced_date"])
    d['first_deal_value'] = data_sorted.loc[data_sorted.index[0], 'money_raised_usd']
    d['first_deal_date'] = data_sorted.loc[data_sorted.index[0], 'announced_date']
    d['last_deal_value'] = data_sorted.loc[data_sorted.index[-1], 'money_raised_usd']
    d['last_deal_date'] = data_sorted.loc[data_sorted.index[-1], 'announced_date']
    d['last_deal_year'] = data_sorted.loc[data_sorted.index[-1], 'announced_year']
    d['deal_span_years'] = round((d['last_deal_date'] - d['first_deal_date'])/pd.Timedelta(days=365),2)
    d['total_funding_usd'] = data_sorted.loc[data_sorted.index[0], 'total_funding_usd']
    d['city'] = data_sorted.loc[data_sorted.index[0], 'city']
    d['country'] = data_sorted.loc[data_sorted.index[0], 'country']
    d['region'] = data_sorted.loc[data_sorted.index[0], 'region']
    d['sector'] = data_sorted.loc[data_sorted.index[0], 'sector']
    d['lead_investors'] = ','.join(pd.Series(data_sorted['lead_investors'].str.split(",").explode().unique()).where(lambda x: x != "—").dropna())
    d['industries'] = data_sorted.loc[data_sorted.index[0], 'industries']
    d['deals'] = len(data_sorted)
    d['funding_recency'] = "recent" if (abs(d['last_deal_year'] - datetime.now().year) <= 2) else "older"
    d['deal_growth_cagr'] = round((d['last_deal_value']/d['first_deal_value'])**(1./d['deal_span_years'])-1,
                                  2)*100 if d['deal_span_years'] > 0 else 0
    return pd.Series(d)

df_world_first_last_deal_values = ( 
    df_world_raw
    .groupby(['name'])
    .apply(first_last_deal_flow)
)

# Ignore outliers - top 2 percentile
df_world_first_last_deal_values['deal_growth_cagr'] = df_world_first_last_deal_values['deal_growth_cagr'].clip(upper=df_world_first_last_deal_values['deal_growth_cagr'].quantile(0.98))

# Sort
df_world_first_last_deal_values = df_world_first_last_deal_values.sort_values(by = ["deal_growth_cagr", 
                                                                                    "total_funding_usd", 
                                                                                    "last_deal_date"], 
                                                                              ascending=False)

df_world_first_last_deal_values = df_world_first_last_deal_values.reset_index()
df_world_first_last_deal_values.head(10)

	name	first_deal_value	first_deal_date	last_deal_value	last_deal_date	last_deal_year	deal_span_years	total_funding_usd	city	country	region	sector	lead_investors	industries	deals	funding_recency	deal_growth_cagr
0	Skyryse	2500000.0	2020-05-15	200000000.0	2021-10-27	2021	1.45	240500000.0	El Segundo	United States	North America	aerospace	Fidelity, Monashee Investment Management	[Aerospace, Air Transportation, Internet, Transportation]	2	older	1066.4
1	AeroVanti	9750000.0	2022-07-21	100000000.0	2022-10-19	2022	0.25	109750000.0	Annapolis	United States	North America	aerospace	Network 1 Financial Securities	[Aerospace, Air Transportation, Transportation]	2	recent	1066.4
2	BRINC	2200000.0	2020-10-29	55000000.0	2022-01-01	2022	1.18	82200000.0	Seattle	United States	North America	aerospace	Sam Altman,Index Ventures,Alameda Research	[Aerospace, Drones, Law Enforcement, Public Safety, Robotics]	3	recent	1066.4
3	AKHAN Semiconductor	1949083.0	2021-11-08	20000000.0	2022-02-17	2022	0.28	37919412.0	Gurnee	United States	North America	aerospace and defense		[Aerospace, Automotive, Consumer Electronics, Manufacturing, Military, Semiconductor, Telecommunications]	3	recent	1066.4
4	Phantom Space	875000.0	2020-09-11	21630605.0	2021-11-04	2021	1.15	27655605.0	Tucson	United States	North America	aerospace	Chenel Capital	[Aerospace, Space Travel, Transportation]	3	older	1066.4
5	Karman+	1000000.0	2022-01-01	25000000.0	2023-03-01	2023	1.16	26000000.0	Denver	United States	North America	aerospace		[Aerospace, Robotics]	2	recent	1066.4
6	Apogee Semiconductor	468792.0	2022-07-21	8606581.0	2023-04-19	2023	0.75	10373924.0	Plano	United States	North America	aerospace		[Aerospace, Industrial Manufacturing, Semiconductor]	2	recent	1066.4
7	Radical	500000.0	2023-04-05	4465000.0	2024-01-05	2024	0.75	4965000.0	Seattle	United States	North America	aerospace	Y Combinator,Scout Ventures	[Aerospace, Internet, Telecommunications]	2	recent	1066.4
8	Basalt Tech	500000.0	2024-04-03	3500000.0	2024-05-30	2024	0.16	4000000.0	San Francisco	United States	North America	aerospace	Y Combinator,Initialized Capital	[Aerospace, Industrial Automation, Space Travel]	2	recent	1066.4
9	The Exploration Company	1740300.0	2021-10-05	43576000.0	2023-02-01	2023	1.33	54297360.0	Munich	Germany	Europe	aerospace	Promus Ventures,EQT Ventures, Red River West	[Aerospace, Air Transportation, Manufacturing, Space Travel]	3	recent	1026.0

options.figure_size = (1200 / options.dpi, 780 / options.dpi)

(
    ggplot(df_world_first_last_deal_values.head(50))
    + geom_point(aes(x="name",
                    y="deal_growth_cagr",
                    size="total_funding_usd",
                    group="region",
                    fill="factor(funding_recency)",
                    colour="factor(funding_recency)"))
    #+ geom_text(aes(label="total_money_raised"), size=9)
    + scale_y_continuous(labels = lambda l: ['{}%'.format(v) for v in l])
    + scale_size_continuous(labels = lambda l: [add_units(v) for v in l])
    + scale_fill_manual(values = { "older": boson_blue_faded, "recent": boson_blue })
    + scale_colour_manual(values = { "older": boson_blue_faded, "recent": boson_blue }, guide=False)
    + labs(
        x="Companies",
        y="Deal Value Growth - CAGR",
        title="Private Investment in Aerospace & Defense across World",
        size="Total Funding",
        fill="Funding deals older than two years ?",
        subtitle ="Top 50 companies by Total Funding and Deal Growth CAGR ({} - {})".format(start_date.strftime('%b %d, %Y'), end_date.strftime('%b %d, %Y')),
        caption=caption
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"}),
        figure_size=(8, 8)
    )
    + coord_flip() 
    + facet_wrap("region")
    + theme_bw()
)

options.figure_size = (1200 / options.dpi, 780 / options.dpi)

(
    ggplot(df_world_first_last_deal_values[(df_world_first_last_deal_values['total_funding_usd']>10000000) & 
           (df_world_first_last_deal_values['total_funding_usd']<50000000)].head(50))
    + geom_point(aes(x="name",
                    y="deal_growth_cagr",
                    size="total_funding_usd",
                    group="region",
                    fill="factor(funding_recency)",
                    colour="factor(funding_recency)"))
    #+ geom_text(aes(label="total_money_raised"), size=9)
    + scale_y_continuous(labels = lambda l: ['{}%'.format(v) for v in l])
    + scale_size_continuous(labels = lambda l: [add_units(v) for v in l])
    + scale_fill_manual(values = { "older": boson_blue_faded, "recent": boson_blue })
    + scale_colour_manual(values = { "older": boson_blue_faded, "recent": boson_blue }, 
                          guide=False)
    + labs(
        x="Companies",
        y="Deal Value Growth - CAGR",
        title="Private Investment in Aerospace & Defense across World",
        size="Total Funding",
        fill="Funding deals older than two years ?",
        subtitle ="Companies w/ 10-50M total funding, by Total Funding and Deal Growth CAGR ({} - {})".format(start_date.strftime('%b %d, %Y'), end_date.strftime('%b %d, %Y')),
        caption=caption
    )
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"}),
        figure_size=(8, 8)
    )
    + coord_flip() 
    + facet_wrap("region")
    + theme_bw()
)

df_world_funding_by_stage = (
    df_world_raw
    .groupby(['region', 'funding_type', 'funding_recency'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'), deals = ('money_raised_usd', 'count'))
)
df_world_funding_by_stage = df_world_funding_by_stage.reset_index()

options.figure_size = (1024 / options.dpi, 600 / options.dpi)
(
    ggplot(df_world_funding_by_stage)
    + geom_bar(aes(x="funding_type", 
                   y="total_money_raised", 
                   group="region",
                   fill = "factor(funding_recency)"),
               
               stat="identity") 
    + scale_fill_manual(values = { "older": boson_blue_faded, "recent": boson_blue })
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Funding Stages",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across US & Europe",
        fill="Funding deals older than two years ?",
        subtitle ="Funding by stages ({} - {})".format(start_date.strftime('%b %d, %Y'), end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
    + facet_wrap("region")
)

df_world_funding_by_countries = (
    df_world_raw
    .groupby(['region', 'country', 'funding_recency'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'), deals = ('money_raised_usd', 'count'))
)
df_world_funding_by_countries = df_world_funding_by_countries.reset_index()
df_world_funding_by_countries.head(5)

	region	country	funding_recency	total_money_raised	deals
0	Europe	Austria	older	22542000.0	1
1	Europe	Austria	recent	204462.0	1
2	Europe	Belgium	older	56542900.0	4
3	Europe	Belgium	recent	75873000.0	3
4	Europe	Bulgaria	older	4453420.0	2

Military spending to GDP

Source: https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS?end=2022&start=2022&view=bar

military_to_gdp_df = pd.read_csv("Military_to_GDP.csv")
military_to_gdp_df = military_to_gdp_df[['Country Name', '2022']]
military_to_gdp_df.rename(columns = {"Country Name": "country", "2022": "military_spending_to_gdp"}, 
                          inplace=True)
military_to_gdp_df = military_to_gdp_df.dropna()
military_to_gdp_df.head(5)

	country	military_spending_to_gdp
1	Africa Eastern and Southern	1.001660
3	Africa Western and Central	0.975188
4	Angola	1.328722
5	Albania	1.584881
7	Arab World	4.968286

df_world_funding_and_spending_by_countries = df_world_funding_by_countries.merge(military_to_gdp_df, on = "country")
df_world_funding_and_spending_by_countries['military_spending_to_gdp'] = pd.to_numeric(df_world_funding_and_spending_by_countries['military_spending_to_gdp'])
df_world_funding_and_spending_by_countries.head(5)

	region	country	funding_recency	total_money_raised	deals	military_spending_to_gdp
0	Europe	Austria	older	22542000.0	1	0.772607
1	Europe	Austria	recent	204462.0	1	0.772607
2	Europe	Belgium	older	56542900.0	4	1.179737
3	Europe	Belgium	recent	75873000.0	3	1.179737
4	Europe	Bulgaria	older	4453420.0	2	1.508123

# Ordering the bar plot
df_world_funding_and_spending_by_countries = df_world_funding_and_spending_by_countries.sort_values(by = "military_spending_to_gdp", 
                                                                          ascending=False)
countries_by_funding = df_world_funding_and_spending_by_countries["country"].unique()
df_world_funding_and_spending_by_countries = df_world_funding_and_spending_by_countries.assign(
    country_cat=pd.Categorical(df_world_funding_and_spending_by_countries["country"], 
                               categories=countries_by_funding)
)

options.figure_size = (1024 / options.dpi, 600 / options.dpi)
(
    ggplot(df_world_funding_and_spending_by_countries[1:])
    + geom_bar(aes(x="country_cat",
                   y="military_spending_to_gdp"),
               fill=boson_blue,
               stat="identity") 
    #+ scale_fill_manual(values = { "older": boson_blue_faded, "recent": boson_blue })
    + scale_y_continuous(breaks = list([x * .5 for x in range(20)]), 
                         labels = lambda l: ["{}%".format(v) for v in l])
    + labs(
        x="Countries",
        y="Military Spending to GDP (in %)",
        title="Military Spending to GDP (US & Europe)",
        fill="Funding deals older than two years ?",
        subtitle ="Source: https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS?end=2022&start=2022&view=bar",
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

df_world_funding_and_spending_by_countries.head(2)

	region	country	funding_recency	total_money_raised	deals	military_spending_to_gdp	country_cat
28	Europe	Ukraine	older	3.500000e+05	1	33.546573	Ukraine
32	North America	United States	recent	1.026982e+10	273	3.454920	United States

df_world_funding_by_countries = (
    df_world_funding_and_spending_by_countries
    .groupby(["funding_recency", "country"])
    .agg(total_money_raised = ('total_money_raised', 'sum'))
    .sort_values(by=["total_money_raised"], 
                 ascending=False)
)
df_world_funding_by_countries = df_world_funding_by_countries.reset_index()
df_world_funding_by_countries.head(5)

	funding_recency	country	total_money_raised
0	older	United States	1.038508e+10
1	recent	United States	1.026982e+10
2	older	United Kingdom	3.027402e+09
3	recent	Germany	1.091632e+09
4	older	Germany	7.439148e+08

# Ordering the bar plot
df_world_funding_by_countries = df_world_funding_by_countries.sort_values(by = "total_money_raised", 
                                                                          ascending=False)
countries_by_funding = df_world_funding_by_countries["country"].unique()
df_world_funding_by_countries = df_world_funding_by_countries.assign(
    country_cat=pd.Categorical(df_world_funding_by_countries["country"], 
                               categories=countries_by_funding)
)

options.figure_size = (1024 / options.dpi, 600 / options.dpi)

# Annotate >1B countries
df_labels = df_world_funding_by_countries[df_world_funding_by_countries['total_money_raised'] > 1000000000]
df_labels = (
    df_labels
    .groupby(["country_cat"])
    .agg(total_money_raised = ('total_money_raised', 'sum'))
    .reset_index()
)
df_labels['total_money_raised_format'] = df_labels['total_money_raised'].apply(add_units)

(
    ggplot(df_world_funding_by_countries)
    + geom_bar(aes(x="country_cat", 
                   y="total_money_raised",
                  fill="factor(funding_recency)"),
               stat="identity") 
    + geom_text(aes(x = "country_cat",
                    y = "total_money_raised - 300000000",
                    label = "total_money_raised_format"),
                color="white",
                va = "top",
                size = 8,
               data=df_labels)
    + scale_fill_manual(values = { "older": boson_blue_faded, "recent": boson_blue })
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Countries",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across US & Europe",
        fill="Funding deals older than two years ?",
        subtitle ="Funding by countries ({} - {})".format(start_date.strftime('%b %d, %Y'), end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

df_world_funding_by_countries = (
    df_world_raw
    .groupby(['region', 'country', 'announced_year'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'), deals = ('money_raised_usd', 'count'))
)
df_world_funding_by_countries = df_world_funding_by_countries.reset_index()

df_europe_funding_by_countries = (
    df_world_funding_by_countries[
        (df_world_funding_by_countries['region'] == current_region['region']) & 
        ((df_world_funding_by_countries['announced_year'] >= 2022))
    ]
    .groupby(["country", "announced_year"])
    .agg(total_money_raised = ('total_money_raised', 'sum'))
    .sort_values(by=["total_money_raised"], 
                 ascending=False)
)
df_europe_funding_by_countries = df_europe_funding_by_countries.reset_index()

# Ordering the bar plot
df_europe_funding_by_countries = df_europe_funding_by_countries.sort_values(by = "total_money_raised", 
                                                                          ascending=False)
countries_by_funding = df_europe_funding_by_countries["country"].unique()
df_europe_funding_by_countries = df_europe_funding_by_countries.assign(
    country_cat=pd.Categorical(df_europe_funding_by_countries["country"], 
                               categories=countries_by_funding)
)

df_labels = (
    df_world_raw[
        (df_world_raw['region'] == current_region['region']) & 
        (df_world_raw['announced_year'] >= 2022)
    ]
    .groupby(['country', 'name'])
    .agg(total_money_raised = ('money_raised_usd', 'sum'), deals = ('money_raised_usd', 'count'))
)
df_labels = df_labels.reset_index()
df_labels = df_labels.assign(
    country_cat=pd.Categorical(df_labels["country"], 
                               categories=countries_by_funding)
)


(
    ggplot(df_europe_funding_by_countries)
    + geom_bar(aes(x="country_cat", 
                   y="total_money_raised"),
               stat="identity",
              fill=boson_blue)
    # + geom_text(aes(x = "country_cat",
    #             y = "total_money_raised * 1.2 + 1000000",
    #             label = "name"),
    #             va = "bottom",
    #             size = 8,
    #             nudge_y = 0.5,
    #             colour="#780000",
    #             #adjust_text=adjust_text_dict,
    #            data=df_labels[df_labels['total_money_raised'] > 100000000])
    + scale_y_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Countries",
        y="Total Money Raised (in USD)",
        title="Private Investment in Aerospace & Defense across Europe",
        subtitle ="Funding by countries (2022-2024)",
        caption=caption,
    )
    + coord_flip()
    + facet_wrap("announced_year", ncol=3)
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

df_labels.sort_values(by=['total_money_raised'], ascending=False).head(20)

	country	name	total_money_raised	deals	country_cat
157	United States	SpaceX	1.974965e+09	2	United States
17	United States	Anduril Industries	1.480000e+09	1	United States
55	United States	Divergent	4.900000e+08	3	United States
195	United States	Wisk Aero	4.500000e+08	1	United States
37	United States	Beta Technologies	3.750000e+08	1	United States
67	United States	Firefly Aerospace	3.750000e+08	2	United States
32	United States	Axiom Space	3.500000e+08	1	United States
150	United States	Sierra Space	2.900000e+08	1	United States
62	United States	Epirus	2.000000e+08	1	United States
24	United States	Astranis	2.000000e+08	1	United States
81	United States	Hadrian	1.820000e+08	2	United States
77	United States	Gecko Robotics	1.730000e+08	2	United States
42	United States	Capella Space	1.570000e+08	2	United States
202	United States	ZeroAvia	1.460000e+08	2	United States
124	United States	Overair	1.450000e+08	1	United States
178	United States	Ursa Major	1.380000e+08	2	United States
173	United States	True Anomaly	1.330000e+08	3	United States
6	United States	AeroVanti	1.097500e+08	2	United States
83	United States	Hermeus	1.000000e+08	1	United States
165	United States	Stoke Space	1.000000e+08	1	United States

df_world_spending_by_countries = (
    df_world_funding_and_spending_by_countries
    .groupby(["country"])
    .agg(military_spending_to_gdp = ('military_spending_to_gdp', 'sum'))
    .sort_values(by=["military_spending_to_gdp"], 
                 ascending=False)
)
df_world_spending_by_countries = df_world_spending_by_countries.reset_index()

countries_by_spending = df_world_spending_by_countries["country"].unique()
df_world_spending_by_countries = df_world_spending_by_countries.assign(
    country_cat=pd.Categorical(df_world_spending_by_countries["country"], 
                               categories=countries_by_spending)
)
df_world_spending_by_countries.head(5)

	country	military_spending_to_gdp	country_cat
0	Ukraine	33.546573	Ukraine
1	United States	6.909840	United States
2	Lithuania	5.045247	Lithuania
3	United Kingdom	4.454368	United Kingdom
4	France	3.877447	France

(
    ggplot(df_world_spending_by_countries[1:])
    + geom_bar(aes(x="country_cat", 
                   y="military_spending_to_gdp"),
               stat="identity",
            fill=boson_blue) 
    #+ scale_fill_manual(values = { "older": boson_blue_faded, "recent": boson_blue })
    + scale_y_continuous(labels = lambda l: ['{}%'.format(v) for v in l])
    + labs(
        x="Countries",
        y="Military Spending to GDP (%()",
        title="Military Spending to GDP",
        fill="Funding deals older than two years ?",
        subtitle ="Funding by countries ({} - {})".format(start_date.strftime('%b %d, %Y'), end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_classic()
)

df_world_raw.head(2)

	index	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	...	total_funding_currency	total_funding_amount	money_raised_usd	total_funding_usd	aerospace	defense	announced_date_trunc_month	announced_date_trunc_year	sector	funding_recency
0	0	SpaceX	$1724965480	2022-05-24	Advanced Materials, Aerospace, Manufacturing, ...	SpaceX is an aviation and aerospace company th...	Venture - Series Unknown	—	$9779343846	Hawthorne	...	$	9779343846	1.724965e+09	9.779344e+09	True	False	2022-05-01	2022-01-01	aerospace	recent
1	1	Anduril Industries	$1480000000	2022-12-02	Aerospace, Government, Military, National Secu...	Anduril Industries is a defense product compan...	Series E	Valor Equity Partners	$2171000000	Costa Mesa	...	$	2171000000	1.480000e+09	2.171000e+09	True	True	2022-12-01	2022-01-01	aerospace and defense	recent

2 rows × 27 columns

df_world_raw['industries'] = df_world_raw['industries'].str.split(",")

df_world_raw_exploded = df_world_raw.explode('industries')
df_world_raw_exploded['industries'] = df_world_raw_exploded['industries'].str.strip()

df_world_raw_exploded_agg_by_industries = (
    df_world_raw_exploded
    .groupby(['industries'])
    .agg(money_raised_usd = ('money_raised_usd', 'sum'))
)
df_world_raw_exploded_agg_by_industries = (
    df_world_raw_exploded_agg_by_industries
    .reset_index()
)
# df_world_raw_exploded_agg_by_industries.head(2)


df_world_raw_exploded_agg_by_industries['money_raised_usd_format'] = df_world_raw_exploded_agg_by_industries['money_raised_usd'].apply(add_units)
df_world_raw_exploded_agg_by_industries.head(5)

	industries	money_raised_usd	money_raised_usd_format
0	3D Printing	6.628812e+08	663M
1	3D Technology	1.342485e+09	1B
2	Advanced Materials	6.570686e+09	7B
3	Aerospace	2.641824e+10	26B
4	AgTech	7.591975e+07	76M

import plotly.express as px
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

boson_blue = "#04024B"
boson_royal = "#1d2b71"
boson_yinmin = '#375496'
boson_glaucous = '#507dbc'
boson_powder_blue = '#a1c6ea'
boson_columbia_blue = '#bbd1ea'
boson_blue_faded = '#dae3e5'

fig = px.treemap(df_world_raw_exploded_agg_by_industries[20:], 
                 path=['industries'],
                 values='money_raised_usd',
                 color='money_raised_usd',
                
                 # https://coolors.co/generate
                 # https://coolors.co/04024b-1d2b71-375496-507dbc-a1c6ea-bbd1ea-dae3e5
                 color_continuous_scale=[(0,boson_blue_faded),
                                         (0.14, boson_columbia_blue),
                                         (0.28, boson_powder_blue),
                                         (0.42, boson_glaucous),
                                         (0.56, boson_yinmin),
                                         (0.75, boson_royal),
                                         (1,boson_blue)],

                 custom_data=['money_raised_usd_format'],
                 
                 width=1200, 
                 height=650)
fig.update_layout(margin = dict(t=20, l=0, r=0, b=0))
fig.update_traces(marker = dict(
                    line = dict(width = 0)
                  ),
                 tiling = dict(pad = 0))
fig.data[0].texttemplate = "<b>%{label}</b><br>%{customdata[0]}"
fig.show()

CAGR by sub-sectors

df_world_first_last_deal_values.head(5)

	name	first_deal_value	first_deal_date	last_deal_value	last_deal_date	last_deal_year	deal_span_years	total_funding_usd	city	country	region	sector	lead_investors	industries	deals	funding_recency	deal_growth_cagr
0	Skyryse	2500000.0	2020-05-15	200000000.0	2021-10-27	2021	1.45	240500000.0	El Segundo	United States	North America	aerospace	—	Aerospace, Air Transportation, Internet, Trans...	2	older	1066.4
1	AeroVanti	9750000.0	2022-07-21	100000000.0	2022-10-19	2022	0.25	109750000.0	Annapolis	United States	North America	aerospace	Network 1 Financial Securities	Aerospace, Air Transportation, Transportation	2	recent	1066.4
2	BRINC	2200000.0	2020-10-29	55000000.0	2022-01-01	2022	1.18	82200000.0	Seattle	United States	North America	aerospace	Sam Altman	Aerospace, Drones, Law Enforcement, Public Saf...	3	recent	1066.4
3	AKHAN Semiconductor	1949083.0	2021-11-08	20000000.0	2022-02-17	2022	0.28	37919412.0	Gurnee	United States	North America	aerospace and defense	—	Aerospace, Automotive, Consumer Electronics, M...	3	recent	1066.4
4	Phantom Space	875000.0	2020-09-11	21630605.0	2021-11-04	2021	1.15	27655605.0	Tucson	United States	North America	aerospace	—	Aerospace, Space Travel, Transportation	3	older	1066.4

#df_world_first_last_deal_values['industries'] = df_world_first_last_deal_values['industries'].str.split(",")

df_world_first_last_deal_values_exploded = df_world_first_last_deal_values.explode('industries')
df_world_first_last_deal_values_exploded['industries'] = df_world_first_last_deal_values_exploded['industries'].str.strip()

df_world_cagr_exploded_agg_by_industries = (
    df_world_first_last_deal_values_exploded
    .groupby(['industries'])
    .agg(avg_funding_usd = ('total_funding_usd', 'mean'),
        deal_growth_cagr = ('deal_growth_cagr', 'mean'))
)
df_world_cagr_exploded_agg_by_industries = (
    df_world_cagr_exploded_agg_by_industries
    .reset_index()
)
#df_world_cagr_exploded_agg_by_industries['deal_growth_cagr'] = df_world_cagr_exploded_agg_by_industries['deal_growth_cagr']*100
# df_world_cagr_exploded_agg_by_industries.head(20)

df_world_cagr_exploded_agg_by_industries = df_world_cagr_exploded_agg_by_industries.sort_values(by = "deal_growth_cagr",
                                                                                                ascending=False)
industries = df_world_cagr_exploded_agg_by_industries["industries"].unique()
df_world_cagr_exploded_agg_by_industries = df_world_cagr_exploded_agg_by_industries.assign(
    industry_cat=pd.Categorical(df_world_cagr_exploded_agg_by_industries["industries"], 
                               categories=industries)
)

options.figure_size = (1024 / options.dpi, 600 / options.dpi)
(
    ggplot(df_world_cagr_exploded_agg_by_industries.head(30))
    + geom_point(aes(x="industry_cat", 
                     y="deal_growth_cagr",
                     size="avg_funding_usd"),
               stat="identity",
              fill=boson_blue) 
    + scale_y_continuous(breaks = list(range(0,800,50)),
                         labels = lambda l: ["{}%".format(v) for v in l])
    + scale_size_continuous(labels = lambda l: [add_units(v) for v in l])
    + labs(
        x="Industries",
        y="Deal Value Growth - CAGR",
        title="Private Investment in Aerospace & Defense across US & Europe",
        size="Avg Funding (USD)",
        subtitle ="CAGR and Avg. Funding by industries ({} - {})".format(start_date.strftime('%b %d, %Y'), end_date.strftime('%b %d, %Y')),
        caption=caption,
    )
    + coord_flip()
    + theme(
        # left justify the caption and have one line of space between it and
        # the x-axis label
        plot_caption=element_text(ha="left", margin={"t": 1, "units": "lines"})
    )
    + theme_bw()
)

Sub-industry: Transportation / Air Transportation

df_world_first_last_deal_values_exploded.head(2)

	name	first_deal_value	first_deal_date	last_deal_value	last_deal_date	last_deal_year	deal_span_years	total_funding_usd	city	country	region	sector	lead_investors	industries	deals	funding_recency	deal_growth_cagr
0	Skyryse	2500000.0	2020-05-15	200000000.0	2021-10-27	2021	1.45	240500000.0	El Segundo	United States	North America	aerospace	—	Aerospace, Air Transportation, Internet, Trans...	2	older	1066.4
1	AeroVanti	9750000.0	2022-07-21	100000000.0	2022-10-19	2022	0.25	109750000.0	Annapolis	United States	North America	aerospace	Network 1 Financial Securities	Aerospace, Air Transportation, Transportation	2	recent	1066.4

def make_pretty(styler):
    #styler.set_caption(caption)
    #styler.format(recency)
    styler.background_gradient(axis=None, cmap="Blues")
    return styler

df_world_aerospace_transportaion = (
    df_world_first_last_deal_values_exploded[
    (
        (df_world_first_last_deal_values_exploded['industries'] == 'Transportation') |
        (df_world_first_last_deal_values_exploded['industries'] == 'Air Transportation')
    ) & 
    (
        df_world_first_last_deal_values_exploded['deal_growth_cagr']>0
    )]
    .sort_values(by=['last_deal_year', 'total_funding_usd', 'deals'], ascending=False)
)
df_world_aerospace_transportaion['total_funding_usd_format'] = df_world_aerospace_transportaion['total_funding_usd'].apply(add_units)

# >10M funding
df_world_aerospace_transportaion = df_world_aerospace_transportaion[df_world_aerospace_transportaion['total_funding_usd'] > 10000000] 
df_world_aerospace_transportaion = df_world_aerospace_transportaion[['name', 'last_deal_year', 'total_funding_usd_format', 'country', 'region', 'deals', 'deal_growth_cagr']].rename(columns={
    'name': 'Name',
    'last_deal_year': 'Last Deal Year',
    'total_funding_usd_format': 'Total Funding (USD)',
    'country': 'Country',
    'region': 'Region',
    'deals': 'Number of Funding Deals',
    'deal_growth_cagr': 'Deal CAGR'
})

df_world_aerospace_transportaion.drop_duplicates().head(20).style.pipe(make_pretty)

	Name	Last Deal Year	Total Funding (USD)	Country	Region	Number of Funding Deals	Deal CAGR

Pick: Elroy Air

','.join(df_world_raw[df_world_raw['name']=='Elroy Air']['lead_investors'].str.replace('-', ''))

'—,Lockheed Martin Ventures, Marlinspike Capital, Prosperity7 Ventures,—,Catapult Ventures'

df_world_aerospace_transportaion[df_world_aerospace_transportaion['Region']=='Europe'].drop_duplicates().head(20).style.pipe(make_pretty)

	Name	Last Deal Year	Total Funding (USD)	Country	Region	Number of Funding Deals	Deal CAGR

Pick: Wingcopter

','.join(df_world_raw[df_world_raw['name']=='Wingcopter']['lead_investors'].str.replace('-', ''))

'—,Futury Capital, Xplorer Capital'

Recent funding rounds with >0 CAGR

df_world_first_last_deal_values.head(2)

	name	first_deal_value	first_deal_date	last_deal_value	last_deal_date	last_deal_year	deal_span_years	total_funding_usd	city	country	region	sector	lead_investors	industries	deals	funding_recency	deal_growth_cagr
0	Skyryse	2500000.0	2020-05-15	200000000.0	2021-10-27	2021	1.45	240500000.0	El Segundo	United States	North America	aerospace	—	Aerospace, Air Transportation, Internet, Transportation	2	older	1066.4
1	AeroVanti	9750000.0	2022-07-21	100000000.0	2022-10-19	2022	0.25	109750000.0	Annapolis	United States	North America	aerospace	Network 1 Financial Securities	Aerospace, Air Transportation, Transportation	2	recent	1066.4

pd.set_option('max_colwidth', 700)
df_recent_current_region = df_world_first_last_deal_values[
    (
        (df_world_first_last_deal_values['last_deal_year']==2024) | 
        (df_world_first_last_deal_values['last_deal_year']==2023)
    ) & 
    (df_world_first_last_deal_values['region']==current_region['region']) & 

    (df_world_first_last_deal_values['deal_growth_cagr'] > 0) & 

    (df_world_first_last_deal_values['total_funding_usd'] > 15000000) & 

    (df_world_first_last_deal_values['total_funding_usd'] < 100000000)
].sort_values(by=['total_funding_usd', 'deals', 'deal_growth_cagr'], ascending=False)

df_recent_current_region['total_funding_usd_format'] = df_recent_current_region['total_funding_usd'].apply(add_units)
df_recent_current_region['deal_growth_cagr'] = df_recent_current_region['deal_growth_cagr'].round()
df_recent_current_region['industries_concat'] = df_recent_current_region['industries'].apply(lambda x: ",".join(x))
df_recent_current_region = df_recent_current_region[['name', 'total_funding_usd_format', 'last_deal_year', 'city', 'deals', 'deal_growth_cagr', 'lead_investors', 'industries_concat']].rename(columns={
    'name': 'Name',
    'total_funding_usd_format': 'Total Funding (USD)',
    'last_deal_year': 'Last Deal',
    'city': 'City',
    'deals': '# of Deals',
    'deal_growth_cagr': 'Deal Growth (CAGR)',
    'lead_investors': 'Investors',
    'industries_concat': 'Industries'
}).head(30)

df_recent_current_region.style.pipe(make_pretty)

	Name	Total Funding (USD)	Last Deal	City	# of Deals	Deal Growth (CAGR)	Investors	Industries
29	Vannevar Labs	87M	2023	Palo Alto	2	270.000000	Costanoa Ventures, Point72 Ventures,Felicis	Aerospace, Artificial Intelligence (AI), GovTech, National Security, Software
132	Impulse Space	75M	2023	El Segundo	2	36.000000	Founders Fund, Lux Capital,Alumni Ventures, RTX Ventures	Aerospace, Manufacturing, Space Travel
44	Pixxel	71M	2023	El Segundo	5	177.000000	growX ventures, Techstars,Blume VenBlume Venture Advisors Private Limitedtures, growX ventures, Lightspeed India Partners,Radical Ventures,Google	Aerospace, Geospatial, Information Technology, Satellite Communication
17	Saronic	70M	2023	Austin	2	595.000000	8VC,Caffeinated Capital	Artificial Intelligence (AI), Drones, Government, Military, National Security
88	Portside	67M	2023	San Francisco	2	89.000000	Tiger Global Management,Insight Partners	Aerospace, Air Transportation, Analytics, Software, Transportation
133	Benchmark Space Systems	56M	2024	South Burlington	5	36.000000		Aerospace, Hardware, Manufacturing
153	Ramon.Space	44M	2023	Los Altos	2	21.000000	StageOne Ventures,Ingrasys, Strategic Development Fund	Aerospace, Computer, Electronics, Machine Learning, Semiconductor, Software
93	Orbit Fab	42M	2023	Lafayette	3	84.000000	Asymmetry Ventures,8090 Industries	Aerospace, Energy, Infrastructure, Manufacturing, Space Travel
53	Whisper Aero	40M	2023	Crossville	2	143.000000	Capricorn Investment Group, Connor Capital SB, EVE Atlas, Menlo Ventures	Aerospace, Air Transportation, GreenTech
81	Venus Aerospace	39M	2023	Houston	3	98.000000	Prime Movers Lab,Airbus Ventures, Alumni Ventures	Aerospace, Product Research, Transportation
32	Onebrief	37M	2024	Honolulu	3	232.000000		Big Data, Computer, Military, Productivity Tools, Software
168	Biofire	37M	2024	Broomfield	4	1.000000	Founders Fund	Biometrics, Consumer Electronics, Government, Law Enforcement, Military, Public Safety, Security
135	Vaya Space	36M	2023	Cocoa	3	33.000000		Aerospace, Manufacturing, Transportation
50	Hydrosat	36M	2023	Washington	4	150.000000	Techstars,Cultivation Capital,OTB Ventures,Statkraft Ventures	Aerospace, AgTech, Analytics, Artificial Intelligence (AI), Big Data, Geospatial, Information Services, Machine Learning
25	Starfish Space	34M	2023	Seattle	4	303.000000	MaC Venture Capital, NFX,Spacecadet Ventures,Munich Re Ventures, Toyota Ventures	Aerospace, Manufacturing, Space Travel, Transportation
31	Aerodome	28M	2024	New York	2	248.000000	2048 Ventures, Andreessen Horowitz,CRV	Aerospace, Drones, Law Enforcement, Military, Public Safety
120	Cambium	27M	2023	Mojave	2	48.000000	8VC	Advanced Materials, Aerospace, Law Enforcement, National Security, Renewable Energy
64	Apex - Spacecraft Manufacturing	27M	2023	Los Angeles	2	123.000000	Andreessen Horowitz, Shield Capital	Aerospace, Commercial, Manufacturing
5	Karman+	26M	2023	Denver	2	1066.000000		Aerospace, Robotics
129	Geminus	23M	2024	Cambridge	3	40.000000	The Hive,Lam Capital,SLB	Aerospace, Artificial Intelligence (AI), Chemical, Clean Energy, Energy Efficiency, Energy Storage, Oil and Gas, Renewable Energy, Semiconductor
33	Atomos Space	22M	2023	Denver	2	232.000000	Cantos, Yamauchi-No.10 Family Office	Aerospace, Logistics, Nuclear, Robotics, Space Travel
14	Castelion	20M	2023	El Segundo	2	713.000000	Lavrock Ventures,Andreessen Horowitz, Lavrock Ventures	Aerospace, Government, Manufacturing, Military, National Security
115	Agile Space Industries	18M	2023	Durango	2	53.000000	Caruso Ventures	3D Printing, Aerospace, Air Transportation, Space Travel
74	Inertial Labs	18M	2024	Paeonian Springs	2	107.000000		Aerospace, Autonomous Vehicles, Electronics, Geospatial, GPS, Manufacturing, Military, Navigation
28	Interlune	17M	2024	Estes Park	2	274.000000	Seven Seven Six	Aerospace, Information Technology, Space Travel
86	Wilder Systems	16M	2023	Austin	2	91.000000	Cortado Ventures	Aerospace, Air Transportation, Robotics
72	Urban Sky	16M	2023	Denver	4	108.000000	New Stack Ventures, TenOneTen Ventures,Catapult Ventures, Techstars, Union Labs Ventures,Lavrock Ventures, Lerer Hippeau, New Legacy Group	Aerospace, Geospatial, Mapping Services, Remote Sensing, Smart Cities
126	Frontier Aerospace	16M	2023	Simi Valley	2	42.000000	AEI Horizon X	Aerospace, Geospatial, Space Travel

print(','.join([ "{} (${})".format(x,y)  for i, (x, y) in enumerate(zip(df_recent_current_region['Name'].values, df_recent_current_region['Total Funding (USD)'].values))]))

Vannevar Labs ($87M),Impulse Space ($75M),Pixxel ($71M),Saronic ($70M),Portside ($67M),Benchmark Space Systems ($56M),Ramon.Space ($44M),Orbit Fab ($42M),Whisper Aero ($40M),Venus Aerospace ($39M),Onebrief ($37M),Biofire ($37M),Vaya Space ($36M),Hydrosat ($36M),Starfish Space ($34M)

df_world_raw[df_world_raw['name'] == 'Hydrosat']

	index	name	money_raised	announced_date	industries	description	funding_type	lead_investors	total_funding	city	...	total_funding_currency	total_funding_amount	money_raised_usd	total_funding_usd	aerospace	defense	announced_date_trunc_month	announced_date_trunc_year	sector	funding_recency
229	281	Hydrosat	$15000000	2023-04-25	[Aerospace, AgTech, Analytics, Artificial Intelligence (AI), Big Data, Geospatial, Information Services, Machine Learning]	Hydrosat provides geospatial intelligence for food security, critical infrastructure, and the environment.	Series A	Statkraft Ventures	$35623002	Washington	...	$	35623002	15000000.0	35623002.0	True	False	2023-04-01	2023-01-01	aerospace	recent
372	468	Hydrosat	$5000000	2021-11-16	[Aerospace, AgTech, Analytics, Artificial Intelligence (AI), Big Data, Geospatial, Information Services, Machine Learning]	Hydrosat provides geospatial intelligence for food security, critical infrastructure, and the environment.	Seed	OTB Ventures	$35623002	Washington	...	$	35623002	5000000.0	35623002.0	True	False	2021-11-01	2021-01-01	aerospace	older
373	469	Hydrosat	$5000000	2021-06-17	[Aerospace, AgTech, Analytics, Artificial Intelligence (AI), Big Data, Geospatial, Information Services, Machine Learning]	Hydrosat provides geospatial intelligence for food security, critical infrastructure, and the environment.	Seed	Cultivation Capital	$35623002	Washington	...	$	35623002	5000000.0	35623002.0	True	False	2021-06-01	2021-01-01	aerospace	older
723	911	Hydrosat	$470000	2019-07-15	[Aerospace, AgTech, Analytics, Artificial Intelligence (AI), Big Data, Geospatial, Information Services, Machine Learning]	Hydrosat provides geospatial intelligence for food security, critical infrastructure, and the environment.	Pre-Seed	Techstars	$35623002	Washington	...	$	35623002	470000.0	35623002.0	True	False	2019-07-01	2019-01-01	aerospace	older

4 rows × 27 columns