Python 有没有一种简单的方法可以将 Pandas 数据框中的一列是/否更改为 1/0？
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Is there a simple way to change a column of yes/no to 1/0 in a Pandas dataframe?
提问by Mushu909
I read a csv file into a pandas dataframe, and would like to convert the columns with binary answers from strings of yes/no to integers of 1/0. Below, I show one of such columns ("sampleDF" is the pandas dataframe).
我将一个 csv 文件读入一个 Pandas 数据帧，并希望将带有二进制答案的列从是/否字符串转换为 1/0 的整数。下面，我展示了其中一列（“sampleDF”是熊猫数据框）。
In [13]: sampleDF.housing[0:10]
Out[13]:
0 no
1 no
2 yes
3 no
4 no
5 no
6 no
7 no
8 yes
9 yes
Name: housing, dtype: object
Help is much appreciated!
非常感谢帮助！
回答by piRSquared
method 1
方法一
sample.housing.eq('yes').mul(1)
method 2
方法二
pd.Series(np.where(sample.housing.values == 'yes', 1, 0),
sample.index)
method 3
方法三
sample.housing.map(dict(yes=1, no=0))
method 4
方法四
pd.Series(map(lambda x: dict(yes=1, no=0)[x],
sample.housing.values.tolist()), sample.index)
method 5
方法五
pd.Series(np.searchsorted(['no', 'yes'], sample.housing.values), sample.index)
All yield
所有产量
0 0
1 0
2 1
3 0
4 0
5 0
6 0
7 0
8 1
9 1
timing
given sample
定时
给定样本
timing
long samplesample = pd.DataFrame(dict(housing=np.random.choice(('yes', 'no'), size=100000)))
定时
长样本sample = pd.DataFrame(dict(housing=np.random.choice(('yes', 'no'), size=100000)))
回答by gold_cy
Try this:
尝试这个：
sampleDF['housing'] = sampleDF['housing'].map({'yes': 1, 'no': 0})
回答by 3novak
# produces True/False
sampleDF['housing'] = sampleDF['housing'] == 'yes'
The above returns True/False values which are essentially 1/0, respectively. Booleans support sum functions, etc. If you really need it to be 1/0 values, you can use the following.
上面分别返回 True/False 值，它们基本上是 1/0。Booleans 支持 sum 函数等，如果你真的需要它是 1/0 值，你可以使用下面的。
housing_map = {'yes': 1, 'no': 0}
sampleDF['housing'] = sampleDF['housing'].map(housing_map)
回答by SriramKRaju
%timeit
sampleDF['housing'] = sampleDF['housing'].apply(lambda x: 0 if x=='no' else 1)
1.84 ms ± 56.2 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
每个循环 1.84 ms ± 56.2 μs（7 次运行的平均值 ± 标准偏差，每次 1000 次循环）
Replaces 'yes' with 1, 'no' with 0 for the df column specified.
对于指定的 df 列，将“是”替换为 1，将“否”替换为 0。
回答by praveen kumar
yes there is you can change yes/no values of your column to 1/0 by using following code snippet
是的，您可以使用以下代码片段将列的是/否值更改为 1/0
sampleDF = sampleDF.replace(to_replace = ['yes','no'],value = ['1','0'])
sampleDF
by using first line you can replace the values with 1/0 by using second line you can see the changes by printing it
通过使用第一行，您可以使用第二行将值替换为 1/0，您可以通过打印来查看更改
回答by Siddaram H
Generic way:
通用方式：
import pandas as pd
string_data = string_data.astype('category')
numbers_data = string_data.cat.codes
reference: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.astype.html
参考：https: //pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.astype.html
回答by jpp
You can convert a series from Boolean to integer explicitly:
您可以显式地将系列从布尔值转换为整数：
sampleDF['housing'] = sampleDF['housing'].eq('yes').astype(int)
回答by Eslamspot
The easy way to do that use pandas as below:
使用熊猫的简单方法如下：
housing = pd.get_dummies(sampleDF['housing'],drop_first=True)
after that drop this filed from main df
之后从主 df 中删除此文件
sampleDF.drop('housing',axis=1,inplace=True)
now merge new one in you df
现在在你 df 中合并新的
sampleDF= pd.concat([sampleDF,housing ],axis=1)
回答by Freek Nortier
回答by Josmy Faure
A simple and intuitive way to convert the whole dataframe to 0's and 1's might be:
将整个数据帧转换为 0 和 1 的一种简单直观的方法可能是：
sampleDF = sampleDF.replace(to_replace = "yes", value = 1)
sampleDF = sampleDF.replace(to_replace = "no", value = 0)
