How do I select specific values in column and add character before

Question

How could I select in column 'Funding' all the values ending with "M" and then eliminate M,$ and add "0," before value.

ex. from $535M to 0,535

That's beacuase I have Billion and Million values, I've decided to formatting the column in billion so, values in millions must be 0,...

df.head(10).to_dict()  

{'Company': {0: 'Bytedance',
1: 'SpaceX',
2: 'SHEIN',
3: 'Stripe',
4: 'Klarna',
5: 'Canva',
6: 'Checkout.com',
7: 'Instacart',
8: 'JUUL Labs',
9: 'Databricks'},
'Valuation': {0: '$180B',
1: '$100B',
2: '$100B',
3: '$95B',
4: '$46B',
5: '$40B',
6: '$40B',
7: '$39B',
8: '$38B',
9: '$38B'},
'Date Joined': {0: '2017-04-07',
1: '2012-12-01',
2: '2018-07-03',
3: '2014-01-23',
4: '2011-12-12',
5: '2018-01-08',
6: '2019-05-02',
7: '2014-12-30',
8: '2017-12-20',
9: '2019-02-05'},
'Industry': {0: 'Artificial intelligence',
1: 'Other',
2: 'E-commerce & direct-to-consumer',
3: 'Fintech',
4: 'Fintech',
5: 'Internet software & services',
6: 'Fintech',
7: 'Supply chain, logistics, & delivery',
8: 'Consumer & retail',
9: 'Data management & analytics'},
'City': {0: 'Beijing',
1: 'Hawthorne',
2: 'Shenzhen',
3: 'San Francisco',
4: 'Stockholm',
5: 'Surry Hills',
6: 'London',
7: 'San Francisco',
8: 'San Francisco',
9: 'San Francisco'},
'Country': {0: 'China',
1: 'United States',
2: 'China',
3: 'United States',
4: 'Sweden',
5: 'Australia',
6: 'United Kingdom',
7: 'United States',
8: 'United States',
9: 'United States'},
'Continent': {0: 'Asia',
1: 'North America',
2: 'Asia',
3: 'North America',
4: 'Europe',
5: 'Oceania',
6: 'Europe',
7: 'North America',
8: 'North America',
9: 'North America'},
'Year Founded': {0: 2012,
1: 2002,
2: 2008,
3: 2010,
4: 2005,
5: 2012,
6: 2012,
7: 2012,
8: 2015,
9: 2013},
'Funding': {0: '$8B',
1: '$7B',
2: '$2B',
3: '$2B',
4: '$4B',
5: '$572M',
6: '$2B',
7: '$3B',
8: '$14B',
9: '$3B'},
'Select Investors': {0: 'Sequoia Capital China, SIG Asia Investments, Sina Weibo, Softbank Group', 1: 'Founders Fund, Draper Fisher Jurvetson, Rothenberg Ventures', 2: 'Tiger Global Management, Sequoia Capital China, Shunwei Capital Partners', 3: 'Khosla Ventures, LowercaseCapital, capitalG', 4: 'Institutional Venture Partners, Sequoia Capital, General Atlantic', 5: 'Sequoia Capital China, Blackbird Ventures, Matrix Partners', 6: 'Tiger Global Management, Insight Partners, DST Global', 7: 'Khosla Ventures, Kleiner Perkins Caufield & Byers, Collaborative Fund', 8: 'Tiger Global Management', 9: 'Andreessen Horowitz, New Enterprise Associates, Battery Ventures'}}

I did a similar manipulation with Valuation, here is how I did. I hope it's right.

df['Valuation'] = df['Valuation'].str.replace(
    "B","").str.replace(
    "$","").astype(int)

I've tried in several way but none of them works. Here are some of them:

df['Funding'] = np.where(df.Funding.str.contain("M"), 
                         df['Funding'] = ('0,'+ df['Funding']),
                        pass)

df['Funding'] = df['Funding'].str.replace(
    "B", "").str.replace(
    "$","").str.replace(
    "M","0,")

if df['Funding'].str.contains("M").any():
    df['Funding'] = df['Funding'].str.replace("M", "") 

asd = "M"
if any(("M" in asd) for M in df['Funding']):
    df['Funding'].join((df['Funding'][:0],'0,',df['Funding'][0:])) and replace("M", "")

Thank to all who want to help me. It's my first time with Python, I'm more familiare with R

Answer 1

If you want all your column values in billions, you can use:

df["Valuation"] = df["Funding"].str[1:-1].astype(int).where(df["Funding"].str.endswith("B"),df["Funding"].str[1:-1].astype(int).div(1000))

>>> df
  Funding  Valuation
0     $8B      8.000
1     $2B      2.000
2   $535M      0.535

Input df:

df = pd.DataFrame({"Funding": ["$8B", "$2B", "$535M"]})