pandas 为熊猫数据帧中的整数格式化千位分隔符

Formatting thousand separator for integers in a pandas dataframe

提问by Javier Cárdenas

I'm trying to use '{:,}'.format(number)like the example below to format a number in a pandas dataframe:

我正在尝试使用'{:,}'.format(number)下面的示例来格式化 Pandas 数据框中的数字：

# This works for floats and integers
print '{:,}'.format(20000)
# 20,000
print '{:,}'.format(20000.0)
# 20,000.0

The problem is that with a dataframe that has integers does not work, and in a dataframe with float works ok. See the examples:

问题是具有整数的数据帧不起作用，而在具有浮点数的数据帧中可以正常工作。请参阅示例：

# Does not work. The format stays the same, does not show thousands separator
df_int = DataFrame({"A": [20000, 10000]})
print df_int.to_html(float_format=lambda x: '{:,}'.format(x))

# Example of result
# <tr>
#   <th>0</th>
#   <td> 20000</td>
# </tr

# Works OK
df_float = DataFrame({"A": [20000.0, 10000.0]})
print df_float.to_html(float_format=lambda x: '{:,}'.format(x))

# Example of result
# <tr>
#   <th>0</th>
#   <td>20,000.0</td>
# </tr>

What i'm doing wrong?

我在做什么错？

采纳答案by chrisb

The formattersparameter in to_htmlwill take a dictionary of column names mapped to a formatting function. Below has an example of a function to build a dict that maps the same function to both floats and ints.

该formatters参数to_html将映射到一个格式化函数列名称的字典。下面有一个函数示例，用于构建一个将相同函数映射到浮点数和整数的 dict。

In [250]: num_format = lambda x: '{:,}'.format(x)

In [246]: def build_formatters(df, format):
     ...:     return {column:format 
     ...:               for (column, dtype) in df.dtypes.iteritems()
     ...:               if dtype in [np.dtype('int64'), np.dtype('float64')]}
     ...: 

In [247]: formatters = build_formatters(df_int, num_format)


In [249]: print df_int.to_html(formatters=formatters)
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>A</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>20,000</td>
    </tr>
    <tr>
      <th>1</th>
      <td>10,000</td>
    </tr>
  </tbody>
</table>

回答by kynan

pandas (as of 0.20.1) does not allow overriding the default integer format in an easy way. It is hard coded in pandas.io.formats.format.IntArrayFormatter(the labmdafunction):

pandas（从 0.20.1 开始）不允许以简单的方式覆盖默认的整数格式。它被硬编码在pandas.io.formats.format.IntArrayFormatter（labmda函数）中：

class IntArrayFormatter(GenericArrayFormatter):

    def _format_strings(self):
        formatter = self.formatter or (lambda x: '% d' % x)
        fmt_values = [formatter(x) for x in self.values]
        return fmt_values

I'm assuming is what you're actually asking for is how you can override the format for all integers: replace ("monkey patch") the IntArrayFormatterto print integer values with thousands separated by comma as follows:

我假设您实际要求的是如何覆盖所有整数的格式：replace ("monkey patch")IntArrayFormatter以用逗号分隔的千位打印整数值，如下所示：

import pandas

class _IntArrayFormatter(pandas.io.formats.format.GenericArrayFormatter):

    def _format_strings(self):
        formatter = self.formatter or (lambda x: ' {:,}'.format(x))
        fmt_values = [formatter(x) for x in self.values]
        return fmt_values

pandas.io.formats.format.IntArrayFormatter = _IntArrayFormatter

Note:

笔记：

• before 0.20.0, the formatters were in pandas.formats.format.
• before 0.18.1, the formatters were in pandas.core.format.

• 在 0.20.0 之前，格式化程序在pandas.formats.format.
• 在 0.18.1 之前，格式化程序在pandas.core.format.

Aside

在旁边

For floats you do not need to jump through those hoops since there is a configuration optionfor it:

对于花车，您不需要跳过这些箍，因为它有一个配置选项：

display.float_format: The callable should accept a floating point number and return a string with the desired format of the number. This is used in some places like SeriesFormatter. See core.format.EngFormatterfor an example.

display.float_format: callable 应该接受一个浮点数并返回一个具有所需数字格式的字符串。这在某些地方使用，例如SeriesFormatter. 参见core.format.EngFormatter示例。