Python 抑制熊猫中的科学记数法？
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Suppressing scientific notation in pandas?
提问by user1244215
I have a DataFrame in pandas where some of the numbers are expressed in scientific notation (or exponent notation) like this:
我在 Pandas 中有一个 DataFrame，其中一些数字用科学记数法（或指数记数法）表示，如下所示：
id value
id 1.00 -4.22e-01
value -0.42 1.00e+00
percent -0.72 1.00e-01
played 0.03 -4.35e-02
money -0.22 3.37e-01
other NaN NaN
sy -0.03 2.19e-04
sz -0.33 3.83e-01
And the scientific notation makes what should be an easy comparison, needlessly difficult. I assume it's the 21900 value that's screwing it up for the others. I mean 1.0 is encoded. ONE!
科学记数法使本应很容易的比较变得不必要地困难。我认为是 21900 的值让其他人搞砸了。我的意思是 1.0 已编码。一！
This doesn't work:
这不起作用：
np.set_printoptions(supress=True)
And pandas.set_printoptionsdoesn't implement suppress either, and I've looked all at pd.describe_options()in despair, and pd.core.format.set_eng_float_format()only seems to turn it on for all the other float values, with no ability to turn it off.
并且pandas.set_printoptions也没有实现抑制，我pd.describe_options()绝望地看着所有内容，pd.core.format.set_eng_float_format()似乎只为所有其他浮点值打开它，无法关闭它。
采纳答案by Jeff
Your data is probably objectdtype. This is a direct copy/paste of your data. read_csvinterprets it as the correct dtype. You should normally only have objectdtype on string-like fields.
您的数据可能是objectdtype。这是您数据的直接复制/粘贴。read_csv将其解释为正确的 dtype。您通常应该只object在类似字符串的字段上使用dtype。
In [5]: df = read_csv(StringIO(data),sep='\s+')
In [6]: df
Out[6]:
id value
id 1.00 -0.422000
value -0.42 1.000000
percent -0.72 0.100000
played 0.03 -0.043500
money -0.22 0.337000
other NaN NaN
sy -0.03 0.000219
sz -0.33 0.383000
check if your dtypes are object
检查您的 dtypes 是否是 object
In [7]: df.dtypes
Out[7]:
id float64
value float64
dtype: object
This converts this frame to objectdtype (notice the printing is funny now)
这会将这个框架转换为objectdtype（注意现在打印很有趣）
In [8]: df.astype(object)
Out[8]:
id value
id 1 -0.422
value -0.42 1
percent -0.72 0.1
played 0.03 -0.0435
money -0.22 0.337
other NaN NaN
sy -0.03 0.000219
sz -0.33 0.383
This is how to convert it back (astype(float)) also works here
这是如何将它转换回来 ( astype(float)) 也适用于这里
In [9]: df.astype(object).convert_objects()
Out[9]:
id value
id 1.00 -0.422000
value -0.42 1.000000
percent -0.72 0.100000
played 0.03 -0.043500
money -0.22 0.337000
other NaN NaN
sy -0.03 0.000219
sz -0.33 0.383000
This is what an objectdtype frame would look like
这就是objectdtype 框架的样子
In [10]: df.astype(object).dtypes
Out[10]:
id object
value object
dtype: object
回答by citynorman
quick temporary: df.round(4)
快速临时： df.round(4)
global: pd.options.display.float_format = '{:20,.2f}'.format
全球的： pd.options.display.float_format = '{:20,.2f}'.format
回答by evil242
If you would like to use the values as formated string in a list, say as part of csvfile csv.writier, the numbers can be formated before creating a list:
如果您想将值用作列表中的格式化字符串，例如作为 csvfile csv.writier 的一部分，则可以在创建列表之前对数字进行格式化：
df['label'].apply(lambda x: '%.17f' % x).values.tolist()
