Python 如何使用列的格式字符串显示浮点数的 Pandas DataFrame？
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/20937538/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
How to display pandas DataFrame of floats using a format string for columns?
提问by Jason S
I would like to display a pandas dataframe with a given format using print()and the IPython display(). For example:
我想使用print()IPython显示具有给定格式的 Pandas 数据框display()。例如：
df = pd.DataFrame([123.4567, 234.5678, 345.6789, 456.7890],
index=['foo','bar','baz','quux'],
columns=['cost'])
print df
cost
foo 123.4567
bar 234.5678
baz 345.6789
quux 456.7890
I would like to somehow coerce this into printing
我想以某种方式将其强制打印
cost
foo 3.46
bar 4.57
baz 5.68
quux 6.79
without having to modify the data itself or create a copy, just change the way it is displayed.
无需修改数据本身或创建副本，只需更改其显示方式即可。
How can I do this?
我怎样才能做到这一点？
采纳答案by unutbu
import pandas as pd
pd.options.display.float_format = '${:,.2f}'.format
df = pd.DataFrame([123.4567, 234.5678, 345.6789, 456.7890],
index=['foo','bar','baz','quux'],
columns=['cost'])
print(df)
yields
产量
cost
foo 3.46
bar 4.57
baz 5.68
quux 6.79
but this only works if you want everyfloat to be formatted with a dollar sign.
但这仅适用于您希望每个浮点数都使用美元符号进行格式化的情况。
Otherwise, if you want dollar formatting for some floats only, then I think you'll have to pre-modify the dataframe (converting those floats to strings):
否则，如果您只想为某些浮点数设置美元格式，那么我认为您必须预先修改数据框（将这些浮点数转换为字符串）：
import pandas as pd
df = pd.DataFrame([123.4567, 234.5678, 345.6789, 456.7890],
index=['foo','bar','baz','quux'],
columns=['cost'])
df['foo'] = df['cost']
df['cost'] = df['cost'].map('${:,.2f}'.format)
print(df)
yields
产量
cost foo
foo 3.46 123.4567
bar 4.57 234.5678
baz 5.68 345.6789
quux 6.79 456.7890
回答by Chris Moore
If you don't want to modify the dataframe, you could use a custom formatter for that column.
如果您不想修改数据框，则可以为该列使用自定义格式化程序。
import pandas as pd
pd.options.display.float_format = '${:,.2f}'.format
df = pd.DataFrame([123.4567, 234.5678, 345.6789, 456.7890],
index=['foo','bar','baz','quux'],
columns=['cost'])
print df.to_string(formatters={'cost':'${:,.2f}'.format})
yields
产量
cost
foo 3.46
bar 4.57
baz 5.68
quux 6.79
回答by sedeh
Similar to unutbu above, you could also use applymapas follows:
与上面的 unutbu 类似，您也可以使用applymap如下：
import pandas as pd
df = pd.DataFrame([123.4567, 234.5678, 345.6789, 456.7890],
index=['foo','bar','baz','quux'],
columns=['cost'])
df = df.applymap("${0:.2f}".format)
回答by Jason S
As of Pandas 0.17 there is now a styling systemwhich essentially provides formatted views of a DataFrame using Python format strings:
从 Pandas 0.17 开始，现在有一个样式系统，它基本上使用Python 格式字符串提供 DataFrame 的格式化视图：
import pandas as pd
import numpy as np
constants = pd.DataFrame([('pi',np.pi),('e',np.e)],
columns=['name','value'])
C = constants.style.format({'name': '~~ {} ~~', 'value':'--> {:15.10f} <--'})
C
which displays
显示
This is a view object; the DataFrame itself does not change formatting, but updates in the DataFrame are reflected in the view:
这是一个视图对象；DataFrame 本身不会更改格式，但 DataFrame 中的更新会反映在视图中：
constants.name = ['pie','eek']
C
However it appears to have some limitations:
但是它似乎有一些限制：
Adding new rows and/or columns in-place seems to cause inconsistency in the styled view (doesn't add row/column labels):
constants.loc[2] = dict(name='bogus', value=123.456) constants['comment'] = ['fee','fie','fo'] constants
就地添加新行和/或列似乎会导致样式视图不一致（不添加行/列标签）：
constants.loc[2] = dict(name='bogus', value=123.456) constants['comment'] = ['fee','fie','fo'] constants
which looks ok but:
看起来不错，但是：
C
Formatting works only for values, not index entries:
constants = pd.DataFrame([('pi',np.pi),('e',np.e)], columns=['name','value']) constants.set_index('name',inplace=True) C = constants.style.format({'name': '~~ {} ~~', 'value':'--> {:15.10f} <--'}) C
格式化仅适用于值，不适用于索引条目：
constants = pd.DataFrame([('pi',np.pi),('e',np.e)], columns=['name','value']) constants.set_index('name',inplace=True) C = constants.style.format({'name': '~~ {} ~~', 'value':'--> {:15.10f} <--'}) C
回答by Selah
I like using pandas.apply() with python format().
我喜欢将 pandas.apply() 与 python format() 结合使用。
import pandas as pd
s = pd.Series([1.357, 1.489, 2.333333])
make_float = lambda x: "${:,.2f}".format(x)
s.apply(make_float)
Also, it can be easily used with multiple columns...
此外，它可以很容易地与多列一起使用......
df = pd.concat([s, s * 2], axis=1)
make_floats = lambda row: "${:,.2f}, ${:,.3f}".format(row[0], row[1])
df.apply(make_floats, axis=1)
回答by Carson
summary:
概括：
df = pd.DataFrame({'money': [100.456, 200.789], 'share': ['100,000', '200,000']})
print(df)
print(df.to_string(formatters={'money': '${:,.2f}'.format}))
for col_name in ('share',):
df[col_name] = df[col_name].map(lambda p: int(p.replace(',', '')))
print(df)
"""
money share
0 100.456 100,000
1 200.789 200,000
money share
0 0.46 100,000
1 0.79 200,000
money share
0 100.456 100000
1 200.789 200000
"""
回答by Vlad Bezden
You can also set locale to your region and set float_format to use a currency format. This will automatically set $ sign for currency in USA.
您还可以将语言环境设置为您所在的地区，并将 float_format 设置为使用货币格式。这将自动为美国的货币设置 $ 符号。
import locale
locale.setlocale(locale.LC_ALL, "en_US.UTF-8")
pd.set_option("float_format", locale.currency)
df = pd.DataFrame(
[123.4567, 234.5678, 345.6789, 456.7890],
index=["foo", "bar", "baz", "quux"],
columns=["cost"],
)
print(df)
cost
foo 3.46
bar 4.57
baz 5.68
quux 6.79
