在 pandas 数据框中将数字和字母的字符串转换为int/float [英] Converting string of numbers and letters to int/float in pandas dataframe

问题描述

我觉得必须快速解决我的问题，我使用多个列表推导方法破解了一个实施不佳的解决方案，但这并不理想.也许有人可以在这里帮忙.

I feel like there has to be a quick solution to my problem, I hacked out a poorly implemented solution using multiple list comprehensions which is not ideal whatsoever. Maybe someone could help out here.

我有一组字符串值(例如3.2B，1.5M，1.1T)，其中最后一个字符自然表示一百万，十亿，万亿.在该集合内，还应保留NaN/'none'值.我希望将它们转换为浮点数或整数，因此在给定的示例中(3200000000、1500000、1100000000000)

I have a set of values which are strings (e.g. 3.2B, 1.5M, 1.1T) where naturally the last character denotes million, billion, trillion. Within the set there are also NaN/'none' values which should remain untouched. I wish to convert these to floats or ints, so in the given example (3200000000, 1500000, 1100000000000)

TIA

推荐答案

您可以创建一个函数:和

You could create a function: and applymap it to every entry in the dataframe:

powers = {'B': 10 ** 9, 'M': 10 ** 6, 'T': 10 ** 12}
# add some more to powers as necessary

def f(s):
    try:
        power = s[-1]
        return int(s[:-1]) * powers[power]
    except TypeError:
        return s

df.applymap(f)

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