用逗号格式化数字以在 Python 中分隔千位
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Format a number with commas to separate thousands in Python
提问by Sandy Tumma
I have a large dataframe which has a column called Lead Rev. This column is a field of numbers such as (100000 or 5000 etc.) I want to know how to format these numbers to show commas as thousand separators. The dataset has over 200,000 rows.
我有一个大型数据框，其中有一列名为Lead Rev. 此列是一个数字字段，例如（100000 或 5000 等）我想知道如何格式化这些数字以将逗号显示为千位分隔符。该数据集有超过 200,000 行。
Is it something like: '{:,}'.format('Lead Rev')
是不是像这样： '{:,}'.format('Lead Rev')
which gives this error:
这给出了这个错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-182-5fe9c827d80b> in <module>()
----> 1 '{:,}'.format('Lead Rev')
ValueError: Cannot specify ',' or '_' with 's'.
回答by jeffhale
To make all your floats show comma separators by default in pandas versions 0.23 through 0.25 set the following:
要使所有浮点数在 0.23 到 0.25 版本的 Pandas 中默认显示逗号分隔符，请设置以下内容：
pd.options.display.float_format = '{:,}'.format
https://pandas.pydata.org/pandas-docs/version/0.23.4/options.html
https://pandas.pydata.org/pandas-docs/version/0.23.4/options.html
In pandas version 1.0 this leads to some strange formatting in some cases.
在 pandas 1.0 版中，这在某些情况下会导致一些奇怪的格式。
回答by flivan
You can use apply() to get the desired result. This works with floating too
您可以使用 apply() 来获得所需的结果。这也适用于浮动
import pandas as pd
series1 = pd.Series({'Value': 353254})
series2 = pd.Series({'Value': 54464.43})
series3 = pd.Series({'Value': 6381763761})
df = pd.DataFrame([series1, series2, series3])
print(df.head())
Value
0 3.532540e+05
1 5.446443e+04
2 6.381764e+09
df['Value'] = df.apply(lambda x: "{:,}".format(x['Value']), axis=1)
print(df.head())
Value
0 353,254.0
1 54,464.43
2 6,381,763,761.0
回答by Ran Feldesh
df.head().style.format("{:,.0f}")(for all columns)
df.head().style.format("{:,.0f}")（对于所有列）
df.head().style.format({"col1": "{:,.0f}", "col2": "{:,.0f}"})(per column)
df.head().style.format({"col1": "{:,.0f}", "col2": "{:,.0f}"})（每列）
回答by user2357112 supports Monica
回答by Sassaba
You can use apply or stack method
您可以使用 apply 或 stack 方法
df.apply(lambda x: x.str.replace(',','.'))
df.stack().str.replace(',','.').unstack()
