将函数应用于依赖于pandas中列名称的DataFrame中的每个单元格 [英] Apply function to each cell in DataFrame that depends on the column name in pandas

问题描述

如何将函数应用于取决于列名称的DataFrame中的每个单元格?

How can I apply function to each cell in a DataFrame that depends on the column name?

我知道 pandas.DataFrame. applymap ，但根据列名似乎不允许:

I'm aware of pandas.DataFrame.applymap but it doesn't seem to allow depending on the column name:

import numpy as np
import pandas as pd
np.random.seed(1)
frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), 
                     index=['Utah', 'Ohio', 'Texas', 'Oregon'])
print(frame)
format = lambda x: '%.2f' % x
frame = frame.applymap(format)
print(frame)

返回:

               b         d         e
Utah    1.624345 -0.611756 -0.528172
Ohio   -1.072969  0.865408 -2.301539
Texas   1.744812 -0.761207  0.319039
Oregon -0.249370  1.462108 -2.060141

            b      d      e
Utah     1.62  -0.61  -0.53
Ohio    -1.07   0.87  -2.30
Texas    1.74  -0.76   0.32
Oregon  -0.25   1.46  -2.06

相反，我希望应用到每个单元格的函数将当前单元格的列名用作参数.

Instead, I want the function that I applied to each cell to use the column name of the current cell as an argument.

我不想遍历每一列，例如:

I don't want to have to loop myself over each column, like:

def format2(cell_value, column_name):
    return '{0}_{1:.2f}'.format(column_name, cell_value)

for column_name in frame.columns.values:
    print('column_name: {0}'.format(column_name))
    frame[column_name]=frame[column_name].apply(format2, args=(column_name))
print(frame)

返回:

              b        d        e
Utah     b_1.62  d_-0.61  e_-0.53
Ohio    b_-1.07   d_0.87  e_-2.30
Texas    b_1.74  d_-0.76   e_0.32
Oregon  b_-0.25   d_1.46  e_-2.06

(这只是一个例子.我想在单元格上应用的功能可能不只是添加列名而已)

(This is just one example. The functions I want to apply on the cells may do more than just appending the column name)

推荐答案

我改进了另一个答案，默认情况下axis=0是因此可以省略:

I bit improved another answer, axis=0 is by default so can be omit:

a = frame.apply(lambda x: x.apply(format2,args=(x.name)))
print (a)
              b        d        e
Utah     b_1.62  d_-0.61  e_-0.53
Ohio    b_-1.07   d_0.87  e_-2.30
Texas    b_1.74  d_-0.76   e_0.32
Oregon  b_-0.25   d_1.46  e_-2.06

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