How to convert numbers represented as characters f

问题:

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 to correct numeric representation?

This shows the data frame:

import pandas as pd
data_csv = pd.read_csv('https://biz.yahoo.com/p/csv/422conameu.csv')
data_csv

This is a sample value:

data_csv['Market Cap'][0]
type(data_csv['Market Cap'][0])

I tried this:

data_csv.loc[data_csv['Market Cap'].str.contains('B'), 'Market Cap'] = data_csv['Market Cap'].str.replace('B', '').astype(float).fillna(0.0)
data_csv

But unfortunately there are also values with 'M' at the end which denotes Millions. It returns error as follows:

ValueError: invalid literal for float(): 6.46M

How can I replace both B and M with appropriate values in this column? Is there a better way to do it?

回答1:

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.

回答2:

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))

回答3:

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.