Python 用另一个值替换熊猫数据框列中的几个值
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Replacing few values in a pandas dataframe column with another value
提问by Pulkit Jha
I have a pandas dataframe df as illustrated below:
我有一个熊猫数据框 df 如下图所示：
BrandName Specialty
A H
B I
ABC J
D K
AB L
I want to replace 'ABC' and 'AB' in column BrandName by A. Can someone help with this?
我想用 A 替换 BrandName 列中的“ABC”和“AB”。有人可以帮忙吗？
采纳答案by Alex Riley
The easiest way is to use the replacemethod on the column. The arguments are a list of the things you want to replace (here ['ABC', 'AB']) and what you want to replace them with (the string 'A'in this case):
最简单的方法是replace在色谱柱上使用方法。参数是要替换的内容（此处['ABC', 'AB']）以及要替换的内容（'A'在本例中为字符串）的列表：
>>> df['BrandName'].replace(['ABC', 'AB'], 'A')
0 A
1 B
2 A
3 D
4 A
This creates a new Series of values so you need to assign this new column to the correct column name:
这将创建一个新的系列值，因此您需要将此新列分配给正确的列名：
df['BrandName'] = df['BrandName'].replace(['ABC', 'AB'], 'A')
回答by I159
Replace
代替
DataFrameobject has powerful and flexible replacemethod:
DataFrameobject 具有强大而灵活的replace方法：
DataFrame.replace(
to_replace=None,
value=None,
inplace=False,
limit=None,
regex=False,
method='pad',
axis=None)
Note, if you need to make changes in place, use inplaceboolean argument for replacemethod:
请注意，如果您需要进行适当的更改，请inplace对replace方法使用布尔参数：
Inplace
到位
inplace: boolean, default
FalseIfTrue, in place. Note: this will modify any other views on this object (e.g. a column form a DataFrame). Returns the caller if this isTrue.
就地：布尔值，默认
False如果True，就地。注意：这将修改此对象上的任何其他视图（例如，DataFrame 中的列）。如果是 ，则返回调用者True。
Snippet
片段
df['BrandName'].replace(
to_replace=['ABC', 'AB'],
value='A',
inplace=True
)
回答by Namrata Tolani
This solution will change the existing dataframe itself:
此解决方案将更改现有数据帧本身：
mydf = pd.DataFrame({"BrandName":["A", "B", "ABC", "D", "AB"], "Speciality":["H", "I", "J", "K", "L"]})
mydf["BrandName"].replace(["ABC", "AB"], "A", inplace=True)
回答by Saurabh
回答by shubham ranjan
Created the Data frame:
创建数据框：
import pandas as pd
dk=pd.DataFrame({"BrandName":['A','B','ABC','D','AB'],"Specialty":['H','I','J','K','L']})
Now use DataFrame.replace()function:
现在使用DataFrame.replace()功能：
dk.BrandName.replace(to_replace=['ABC','AB'],value='A')
回答by Claudiu Creanga
Just wanted to show that there is no performancedifference between the 2 main ways of doing it:
只是想表明两种主要方法之间没有性能差异：
df = pd.DataFrame(np.random.randint(0,10,size=(100, 4)), columns=list('ABCD'))
def loc():
df1.loc[df1["A"] == 2] = 5
%timeit loc
19.9 ns ± 0.0873 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
def replace():
df2['A'].replace(
to_replace=2,
value=5,
inplace=True
)
%timeit replace
19.6 ns ± 0.509 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
