将Pandas Dataframe转换为以逗号浮动时出错

Question

so I got a Dataframe with at least 2-3 columns with numbers running from 1 to 3000, and the numbers have comma. I need to convert the numbers to float or int in all the relevant columns.this is an example for my Dataframe:

data = pd.read_csv('exampleData.csv')
data.head(10)
 Out[179]:
 Rank     Total
  1         2
  20        40
  1,200    1,400
  NaN       NaN

as you can see from the example, my Dataframe consists of numbers, numbers with comma and some NaNs.I've read several posts here about converting to float or int, but I always get error messages such as: 'str' object has no attribute 'astype'. my approach is as follows for several columns:

cols = ['Rank', 'Total']
data[cols] = data[cols].apply(lambda x: pd.to_numeric(x.astype(str)
                                               .str.replace(',',''), errors='coerce'))

Answer 1

使用参数thousands 。

pd.read_csv('exampleData.csv', thousands=',')

Answer 2

John's solution won't work for numbers with multiple commas, like 1,384,496.

A more scalable solution would be to just do

data = data.replace({",":""}, regex=True)

Then convert the strings to numeric.

Answer 3

Pandas read_csv() takes many arguments which allow you to control how fields are converted. From the documentation :

decimal : str, default ‘.’
    Character to recognize as decimal point (e.g. use ‘,’ for European data).

So, here's a crazy idea: convert the numerical fields using the keyword argument, "decimal = ',' ". Then, multiply the numerical fields by 1000.