Question

可能只是一个简单的答案，所以提前致歉（最少的编码经验）。

我正在尝试从ANY列中删除具有特定字符串（经济7）的任何行，并一直试图退出该线程：

How to drop rows from pandas data frame that contains a particular string in a particular column?

无法正常工作，但在以前的DataFrame（现在的df = energy）上尝试了此代码，尽管现在出现了错误，但它似乎仍然有效：

no_eco = energy[~energy.apply(lambda series: series.str.contains('Economy 7')).any(axis=1)]

AttributeError: ('Can only use .str accessor with string values, which use np.object_ dtype in pandas', 'occurred at index existingProductCodeGas')

有什么建议吗？ ps DataFrame非常大。

谢谢

Answer 1

您只能选择对象列，显然可以选择select_dtypes的字符串：

df = energy.select_dtypes(object)
#added regex=False for improve performance like mentioned @jpp, thank you
mask = ~df.apply(lambda series: series.str.contains('Economy 7', regex=False)).any(axis=1)
no_eco = energy[mask]

示例：

energy = pd.DataFrame({
        'A':list('abcdef'),
         'B':[4,5,4,5,5,4],
         'C':[7,8,9,4,2,3],
         'D':[1,3,5,7,1,0],
         'E':[5,3,6,9,2,4],
         'F':list('adabbb')
})

print (energy)
   A  B  C  D  E  F
0  a  4  7  1  5  a
1  b  5  8  3  3  d
2  c  4  9  5  6  a
3  d  5  4  7  9  b
4  e  5  2  1  2  b
5  f  4  3  0  4  b

df = energy.select_dtypes(object)
mask = ~df.apply(lambda series: series.str.contains('d')).any(axis=1)
no_eco = energy[mask]
print (no_eco)

   A  B  C  D  E  F
0  a  4  7  1  5  a
2  c  4  9  5  6  a
4  e  5  2  1  2  b
5  f  4  3  0  4  b

Answer 2

如果任何列包含特定字符串，我们可以使用to_string方法删除行

df.drop(df[df.apply(lambda row: 'Tony' in row.to_string(header=False), axis=1)].index, inplace=True)

完整的例子是

import pandas as pd

df = pd.DataFrame(columns = ['Name', 'Location'])
df.loc[len(df)] = ['Mathew', 'Houston']
df.loc[len(df)] = ['Tony', 'New York']
df.loc[len(df)] = ['Jerom', 'Los Angeles']
df.loc[len(df)] = ['Aby', 'Dallas']
df.loc[len(df)] = ['Elma', 'Memphis']
df.loc[len(df)] = ['Zack', 'Chicago']
df.loc[len(df)] = ['Lisa', 'New Orleans']
df.loc[len(df)] = ['Nita', 'Las Vegas']

df.drop(df[df.apply(lambda row: 'Tony' in row.to_string(header=False), axis=1)].index, inplace=True)
print(df)

输出：

     Name     Location
0  Mathew      Houston
2   Jerom  Los Angeles
3     Aby       Dallas
4    Elma      Memphis
5    Zack      Chicago
6    Lisa  New Orleans
7    Nita    Las Vegas
[Finished in 1.4s]

使用ANY列中的特定字符串删除/删除pandas DataFrame中的行

2 个答案: