在 DataFrame 中将行转换为整数时删除数组中的字符

Question

我有一个如下所示的数据框

每行的长度不同。

我正在尝试运行下面的代码

for i in range(len(df)):
  df['ColumnA'][i] = df['ColumnA'][i].astype(int)

我收到错误消息，因为某些行具有“64B”、“64A”等字符串值。

例如，

df['ColumnA'][37]

输出：

array(['34', '35', '36', '38', '39', '40', '41', '56', '58', '59', '60',
       '61', '62', '62A', '62B', '63', '64', '65', '88', '90', '94', '98'],
      dtype='<U3')

您可以轻松查看“62A”、“62B”字符串值。

我的目标 是将所有字符串转换为 int （例如从 62A 到 62）。删除旁边的字符。

Answer 1

您可以使用内置 regex module 中的 sub 函数替换每个 non-digit 字符（\D 在正则表达式中选择 non-digits）空白 "".

列表推导式可让您将相同的方法应用于列表中的每个项目。

您需要的最后一个组件是 pandas 中的 apply 方法，它将对系列中的每个项目应用相同的功能。

import re

import pandas as pd


# sample data
df = pd.Series([
    [61, '62a', 70, 'z8z8z'],
    [61, '62hello', 70],
], name='columnA').to_frame()

# show sample data
df
#>                 columnA
#> 0  [61, 62a, 70, z8z8z]
#> 1     [61, 62hello, 70]

# select the column and apply the function to each element
df.columnA.apply(
    lambda row: [
        int(re.sub("\D", "", str(x)))  # cast to string, replace any non-digit character, cast to int
        for x in row  # for every item in the list
    ]
)
#> 0    [61, 62, 70, 88]
#> 1        [61, 62, 70]
#> Name: columnA, dtype: object

这将导致一个列表存储为系列“columnA”中的每个项目。如果您希望这些是 numpy 数组，您可以将列表理解包装在对 np.array.

的调用中

Answer 2

您可以通过使用列表理解来做到这一点：

import re
import pandas as pd
import numpy as np

df = pd.DataFrame({'colA': [np.array(['34A', '35', '36A', '38', '39', '40', '41', '56', '58', '59', '60',
       '61', '62', '62A', '62B', '63', '64', '65', '88', '90', '94', '98'],
       dtype='<U3') for _ in range(10)]})

print('before : \n', df)

df['colA'] = [[int(re.sub('[^0-9]','', el)) for el in row] for row in df['colA']]

print('after : \n', df)

输出：

before : 
                                                 colA
0  [34A, 35, 36A, 38, 39, 40, 41, 56, 58, 59, 60,...
1  [34A, 35, 36A, 38, 39, 40, 41, 56, 58, 59, 60,...
2  [34A, 35, 36A, 38, 39, 40, 41, 56, 58, 59, 60,...
3  [34A, 35, 36A, 38, 39, 40, 41, 56, 58, 59, 60,...
4  [34A, 35, 36A, 38, 39, 40, 41, 56, 58, 59, 60,...
5  [34A, 35, 36A, 38, 39, 40, 41, 56, 58, 59, 60,...
6  [34A, 35, 36A, 38, 39, 40, 41, 56, 58, 59, 60,...
7  [34A, 35, 36A, 38, 39, 40, 41, 56, 58, 59, 60,...
8  [34A, 35, 36A, 38, 39, 40, 41, 56, 58, 59, 60,...
9  [34A, 35, 36A, 38, 39, 40, 41, 56, 58, 59, 60,...
after :
                                                 colA
0  [34, 35, 36, 38, 39, 40, 41, 56, 58, 59, 60, 6...
1  [34, 35, 36, 38, 39, 40, 41, 56, 58, 59, 60, 6...
2  [34, 35, 36, 38, 39, 40, 41, 56, 58, 59, 60, 6...
3  [34, 35, 36, 38, 39, 40, 41, 56, 58, 59, 60, 6...
4  [34, 35, 36, 38, 39, 40, 41, 56, 58, 59, 60, 6...
5  [34, 35, 36, 38, 39, 40, 41, 56, 58, 59, 60, 6...
6  [34, 35, 36, 38, 39, 40, 41, 56, 58, 59, 60, 6...
7  [34, 35, 36, 38, 39, 40, 41, 56, 58, 59, 60, 6...
8  [34, 35, 36, 38, 39, 40, 41, 56, 58, 59, 60, 6...
9  [34, 35, 36, 38, 39, 40, 41, 56, 58, 59, 60, 6...

在 DataFrame 中将行转换为整数时删除数组中的字符

Deleting characters in arrays while converting rows to integer in DataFrame

python

arrays

dataframe

type-conversion