I have a column in my data frame which has values like \'3.456B\' which actually stands for 3.456 Billion (and similar notation for Million). How to convert this string form
I'd use a dictionary to replace the strings then evaluate as float.
mapping = dict(K='E3', M='E6', B='E9')
df['Market Cap'] = pd.to_numeric(df['Market Cap'].replace(mapping, regex=True))
First extract units as last character in strings. Then convert values without units to floats and multiply where needed:
df = pd.DataFrame({'Market Cap':['6.46M','2.25B','0.23B']})
units = df['Market Cap'].str[-1]
df['Market Cap'] = df['Market Cap'].str[:-1].astype(float)
df.loc[units=='M','Market Cap'] *= 0.001
# Market Cap
# 0 0.00646
# 1 2.25000
# 2 0.23000
Now everything is in billions.
Assuming all entries have a letter at the end, you can do this:
d = {'K': 1000, 'M': 1000000, 'B': 1000000000}
df.loc[:, 'Market Cap'] = pd.to_numeric(df['Market Cap'].str[:-1]) * \
df['Market Cap'].str[-1].replace(d)
This converts everything but the last character into a numeric value, then multiplies it by the number equivalent to the letter in the last character.