Example of python deleting single or multiple contents from a specified column or columns

When working with data in python, it is common to encounter some elements whose content is not needed. They need to be deleted or replaced. This post explores in detail the deletion methods under various data types (series, dataframe)

Randomly create a DataFrame of data

import pandas as pd
import numpy as np
data=((10,size=(5,3)),columns=['a','b','c'])
>>>
 a b c
0 3 8 2
1 9 9 5
2 4 5 1
3 2 7 5
4 1 2 8

Series：

The isin inverse function removes unwanted partial elements of a column, suitable for large quantities:

S data type directly use isin will be elected to the column contains the specified content, our demand is to delete the specified content need to use isin inverse function. But python currently does not have a function like isnotin, so we need to use the - sign to realize isnotin method

! = Comparison operator approach, suitable for a small number of cases or for use with conditions that satisfy both a and b.

isin:

Scenes from Series

print(data['c'][data['c'].isin([1])])
>>>
2 1
Name: c, dtype: int64

print(data['c'][-data['c'].isin([1])])
>>>
0 2
1 5
3 5
4 8
Name: c, dtype: int64

print(data['c'][-data['c'].isin([1,2])])
>>>
1 5
3 5
4 8
Name: c, dtype: int64

DataFrame Scene:

print(data[-([1,2])])#Operate df by Series logic to find that it will appear that the NAN is not removed
>>>
 a b c
0 3.0 8.0 NaN
1 9.0 9.0 5.0
2 4.0 5.0 NaN
3 NaN 7.0 5.0
4 NaN NaN 8.0
print(data[-([1,2])].dropna())#We just need to add another dropna to remove nulls
>>>
a b c
1 9.0 9.0 5.0

! = Comparison operator:

Scenes from Series.

print(data['c'][data['c']!=1])
>>>
0 2
1 5
3 5
4 8
Name: c, dtype: int64

print(data['c'][(data['c']!=1)&((data['c']!=2))])
>>>
1 5
3 5
4 8
Name: c, dtype: int64

DataFrame Scene:

Delete data with different conditions for a and b respectively

print(data[(data['a']!=1)&(data['c']!=2)]
>>>
 a b c
1 9 9 5
2 4 5 1
3 2 7 5

print(data[(data!=1)&(data!=2)].dropna()) # Same principle as isin
 a b c
1 9.0 9.0 5.0

The above python delete specified column or multiple columns single or multiple content example is all that I have shared with you, I hope to give you a reference, and I hope you support me more.