Pandas - 如何在 DataFrame 系列中用零值替换字符串？
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Pandas - How to replace string with zero values in a DataFrame series?
提问by Steve Maughan
I'm importing some csv data into a Pandas DataFrame (in Python). One series is meant to be all numerical values. However, it also contains some spurious "$-" elements represented as strings. These have been left over from previous formatting. If I just import the series, Pandas reports it as a series of 'object'.
我正在将一些 csv 数据导入 Pandas DataFrame（在 Python 中）。一个系列意味着所有数值。但是，它还包含一些以字符串表示的虚假“$-”元素。这些是从以前的格式中遗留下来的。如果我只是导入该系列，Pandas 会将其报告为一系列“对象”。
What's the best way to replace these "$-" strings with zeros? Or more generally, how can I replace all the strings in a series (which is predominantly numerical), with a numerical value, and convert the series to a floating point type?
用零替换这些“$-”字符串的最佳方法是什么？或者更一般地说，如何用数值替换系列中的所有字符串（主要是数字），并将系列转换为浮点类型？
- Steve
- 史蒂夫
采纳答案by hellpanderr
Use Series.str.replaceand Series.astype
使用Series.str.replace和Series.astype
df = pd.Series(['2$-32$-4','123$-12','00123','44'])
df = pd.Series(['2$-32$-4','123$-12','00123','44'])
df.str.replace(r'\$-','0').astype(float)
df.str.replace(r'\$-','0').astype(float)
0 203204
1 123012
2 123
3 44
dtype: float64
回答by tmdavison
You can use the convert_objectsmethod of the DataFrame, with convert_numeric=Trueto change the strings to NaNs
您可以使用, with的convert_objects方法将字符串更改为DataFrameconvert_numeric=TrueNaNs
From the docs:
从文档：
convert_numeric: If True, attempt to coerce to numbers (including strings), with unconvertible values becoming NaN.
convert_numeric：如果为 True，则尝试强制转换为数字（包括字符串），不可转换的值变为 NaN。
In [17]: df
Out[17]:
a b c
0 1. 2. 4
1 sd 2. 4
2 1. fg 5
In [18]: df2 = df.convert_objects(convert_numeric=True)
In [19]: df2
Out[19]:
a b c
0 1 2 4
1 NaN 2 4
2 1 NaN 5
Finally, if you want to convert those NaNsto 0's, you can use df.replace
最后，如果你想将它们转换NaNs为0's，你可以使用df.replace
In [20]: df2.replace('NaN',0)
Out[20]:
a b c
0 1 2 4
1 0 2 4
2 1 0 5
