python - pandas 数据框将 INT64 列转换为布尔值

Question

数据框 df 中的某些列 df.column 存储为数据类型 int64。

这些值都是 1 或 0。

有没有办法用布尔值替换这些值？

Answer 1

df['column_name'] = df['column_name'].astype('bool')

例如：

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.random_integers(0,1,size=5), 
                  columns=['foo'])
print(df)
#    foo
# 0    0
# 1    1
# 2    0
# 3    1
# 4    1

df['foo'] = df['foo'].astype('bool')
print(df)

产量

     foo
0  False
1   True
2  False
3   True
4   True

---

给定一个列表column_names，您可以使用以下方法将多个列转换为booldtype：

df[column_names] = df[column_names].astype(bool)

如果您没有列名列表，但希望转换所有数字列，那么您可以使用

column_names = df.select_dtypes(include=[np.number]).columns
df[column_names] = df[column_names].astype(bool)

Answer 2

参考：Stack Overflow unutbu（1 月 9 日 13:25），BrenBarn（2017 年 9 月 18 日）

我有像年龄和 ID 这样的数字列，我不想将其转换为布尔值。因此，在识别出 unutbu 向我们展示的数字列之后，我过滤掉了最大值大于 1 的列。

# code as per unutbu
column_names = df.select_dtypes(include=[np.number]).columns 

# re-extracting the columns of numerical type (using awesome np.number1 :)) then getting the max of those and storing them in a temporary variable m.
m=df[df.select_dtypes(include=[np.number]).columns].max().reset_index(name='max')

# I then did a filter like BrenBarn showed in another post to extract the rows which had the max == 1 and stored it in a temporary variable n.
n=m.loc[m['max']==1, 'max']

# I then extracted the indexes of the rows from n and stored them in temporary variable p.
# These indexes are the same as the indexes from my original dataframe 'df'.
p=column_names[n.index]

# I then used the final piece of the code from unutbu calling the indexes of the rows which had the max == 1 as stored in my variable p.
# If I used column_names directly instead of p, all my numerical columns would turn into Booleans.
df[p] = df[p].astype(bool)