在列pandas python中获取非数字行 [英] get non numerical rows in a column pandas python

问题描述

我检查了以下帖子:在熊猫? 但这并不能真正回答我的问题.

I checked this post: finding non-numeric rows in dataframe in pandas? but it doesn't really answer my question.

我的样本数据:

import pandas as pd


d = {
 'unit': ['UD', 'UD', 'UD', 'UD', 'UD','UD'],
 'N-D': [ 'Q1', 'Q2', 'Q3', 'Q4','Q5','Q6'],
 'num' : [ -1.48, 1.7, -6.18, 0.25, 'sum(d)', 0.25]

}
df = pd.DataFrame(d)

它看起来像这样:

  N-D   num   unit
0  Q1  -1.48   UD
1  Q2   1.70   UD
2  Q3  -6.18   UD
3  Q4   0.25   UD
4  Q5   sum(d) UD
5  Q6   0.25   UD

我只想过滤掉"NUM"列中非数字的行.我只希望所有包含"num"列非数字值的行的所有列.

I want to filter out only the rows in column 'num' that are NON-NUMERIC. I want all of the columns for only the rows that contain non-numeric values for column 'num'.

所需的输出:

  N-D   num   unit
4  Q5   sum(d) UD

我的尝试:

nonnumeric=df[~df.applymap(np.isreal).all(1)] #didn't work, it pulled out everything, besides i want the condition to check only column 'num'. 

nonnumeric=df['num'][~df.applymap(np.isreal).all(1)] #didn't work, it pulled out all the rows for column 'num' only.

推荐答案

使用 to_numeric + isnull
注意:此解决方案无法找到或过滤保存为字符串的数字:例如'1'或'22'

Use boolean indexing with mask created by to_numeric + isnull
Note: This solution does not find or filter numbers saved as strings: like '1' or '22'

print (pd.to_numeric(df['num'], errors='coerce'))
0   -1.48
1    1.70
2   -6.18
3    0.25
4     NaN
5    0.25
Name: num, dtype: float64

print (pd.to_numeric(df['num'], errors='coerce').isnull())
0    False
1    False
2    False
3    False
4     True
5    False
Name: num, dtype: bool

print (df[pd.to_numeric(df['num'], errors='coerce').isnull()])
  N-D     num unit
4  Q5  sum(d)   UD

具有isinstance和apply的另一种解决方案:

Another solution with isinstance and apply:

print (df[df['num'].apply(lambda x: isinstance(x, str))])
  N-D     num unit
4  Q5  sum(d)   UD

这篇关于在列pandas python中获取非数字行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！