pandas 将字符串列转换为整数
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Convert string column to integer
提问by billboard
I have a dataframe like below
我有一个如下所示的数据框
a b
0 1 26190
1 5 python
2 5 580
I want to make column bto host only integers, but as you can see pythonis not int convertible, so I want to delete the row at index 1. My expected out put has to be like
我想让列b只承载整数，但正如你所看到的，python它不是 int 可转换的，所以我想删除 index 处的行1。我的预期输出必须像
a b
0 1 26190
1 5 580
How to filter and remove using pandas in python?
如何在python中使用pandas过滤和删除？
回答by jezrael
You can use to_numericwith notnulland filter by boolean indexing:
您可以使用to_numericwithnotnull和过滤方式boolean indexing：
print (pd.to_numeric(df.b, errors='coerce'))
0 26190.0
1 NaN
2 580.0
Name: b, dtype: float64
print (pd.to_numeric(df.b, errors='coerce').notnull())
0 True
1 False
2 True
Name: b, dtype: bool
df = df[pd.to_numeric(df.b, errors='coerce').notnull()]
print (df)
a b
0 1 26190
2 5 580
Another solution by comment of Boud- use to_numericwith dropnaand last convert to intby astype:
Boud评论的另一个解决方案- 使用to_numericwithdropna并最后转换为intby astype：
df.b = pd.to_numeric(df.b, errors='coerce')
df = df.dropna(subset=['b'])
df.b = df.b. astype(int)
print (df)
a b
0 1 26190
2 5 580
If need check all rows with bad data use isnull- filter all data where after applying function to_numericget NaN:
如果需要检查所有具有错误数据的行，请使用isnull- 在应用函数to_numericget后过滤所有数据NaN：
print (pd.to_numeric(df.b, errors='coerce').isnull())
0 False
1 True
2 False
Name: b, dtype: bool
print (df[pd.to_numeric(df.b, errors='coerce').isnull()])
a b
1 5 python
回答by conor
This should work
这应该工作
import pandas as pd
import numpy as np
df = pd.DataFrame({'a' : [1, 5, 5],
'b' : [26190, 'python', 580]})
df
a b
0 1 26190
1 5 python
2 5 580
df['b'] = np.where(df.b.str.contains('[a-z]') == True, np.NaN, df.b)
df
a b
0 1 26190
1 5 NaN
2 5 580
df = df.dropna()
df
a b
0 1 26190
2 5 580
You use the regex to identify strings, then convert these to np.NaNusing np.wherethen drop them from the df with df.dropna().
您使用正则表达式来识别字符串，然后将它们转换为np.NaNusingnp.where然后将它们从 df 中删除df.dropna()。
