Python Pandas—访问对象列的快速方式'；属性

假设我有一个python中的自定义类，它具有属性

val

。如果我有一个包含这些对象的列的dataframe，如何访问此属性并使用此值创建一个新列

示例数据：

df
Out[46]: 
row   custom_object
1     foo1
2     foo2
3     foo3
4     foo4
Name: book, dtype: object

其中自定义对象属于Foo类：

class Foo:
    def __init__(self, val):
        self.val = val

据我所知，使用实例属性创建新列的唯一方法是使用

apply

和

lambda

组合，这在大型数据集上速度较慢：

df['custom\u val']=df['custom\u object'].应用（lambda x:x.val）

---

有没有更有效的方法？

您可以使用列表理解：

df['custom_val'] = [foo.val for foo in df['custom_object']]

计时

# Set-up 100k Foo objects.
vals = [np.random.randn() for _ in range(100000)]
foos = [Foo(val) for val in vals]
df = pd.DataFrame(foos, columns=['custom_object'])

# 1) OP's apply method.
%timeit df['custom_object'].apply(lambda x: x.val)
# 10 loops, best of 3: 26.7 ms per loop

# 2) Using a list comprehension instead.
%timeit [foo.val for foo in df['custom_object']]
# 100 loops, best of 3: 11.7 ms per loop

# 3) For reference with the original list of objects (slightly faster than 2) above).
%timeit [foo.val for foo in foos]
# 100 loops, best of 3: 9.79 ms per loop

# 4) And just on the original list of raw values themselves.
%timeit [val for val in vals]
# 100 loops, best of 3: 4.91 ms per loop

如果您有原始的值列表，您可以直接分配它们：

# 5) Direct assignment to list of values.
%timeit df['v'] = vals
# 100 loops, best of 3: 5.88 ms per loop

设置代码：

导入操作符
随机输入
从数据类导入数据类
将numpy作为np导入
作为pd进口熊猫
@数据类
类SomeObj：
val:int
df=pd.DataFrame（data={f“col_1”：[SomeObj（random.randint（0，10000）），用于范围（10000000）]）

---

解决方案1

df['col_1'].map（lambda elem:elem.val）

时间：~3.2秒

# Set-up 100k Foo objects.
vals = [np.random.randn() for _ in range(100000)]
foos = [Foo(val) for val in vals]
df = pd.DataFrame(foos, columns=['custom_object'])

# 1) OP's apply method.
%timeit df['custom_object'].apply(lambda x: x.val)
# 10 loops, best of 3: 26.7 ms per loop

# 2) Using a list comprehension instead.
%timeit [foo.val for foo in df['custom_object']]
# 100 loops, best of 3: 11.7 ms per loop

# 3) For reference with the original list of objects (slightly faster than 2) above).
%timeit [foo.val for foo in foos]
# 100 loops, best of 3: 9.79 ms per loop

# 4) And just on the original list of raw values themselves.
%timeit [val for val in vals]
# 100 loops, best of 3: 4.91 ms per loop

解决方案2

df['col_1'].map（operator.attrgetter（'val'））

时间：~2.7秒

# Set-up 100k Foo objects.
vals = [np.random.randn() for _ in range(100000)]
foos = [Foo(val) for val in vals]
df = pd.DataFrame(foos, columns=['custom_object'])

# 1) OP's apply method.
%timeit df['custom_object'].apply(lambda x: x.val)
# 10 loops, best of 3: 26.7 ms per loop

# 2) Using a list comprehension instead.
%timeit [foo.val for foo in df['custom_object']]
# 100 loops, best of 3: 11.7 ms per loop

# 3) For reference with the original list of objects (slightly faster than 2) above).
%timeit [foo.val for foo in foos]
# 100 loops, best of 3: 9.79 ms per loop

# 4) And just on the original list of raw values themselves.
%timeit [val for val in vals]
# 100 loops, best of 3: 4.91 ms per loop

解决方案3

[elem.val用于df中的元素['col_1']]

时间：~1.4秒

# Set-up 100k Foo objects.
vals = [np.random.randn() for _ in range(100000)]
foos = [Foo(val) for val in vals]
df = pd.DataFrame(foos, columns=['custom_object'])

# 1) OP's apply method.
%timeit df['custom_object'].apply(lambda x: x.val)
# 10 loops, best of 3: 26.7 ms per loop

# 2) Using a list comprehension instead.
%timeit [foo.val for foo in df['custom_object']]
# 100 loops, best of 3: 11.7 ms per loop

# 3) For reference with the original list of objects (slightly faster than 2) above).
%timeit [foo.val for foo in foos]
# 100 loops, best of 3: 9.79 ms per loop

# 4) And just on the original list of raw values themselves.
%timeit [val for val in vals]
# 100 loops, best of 3: 4.91 ms per loop

注意：请记住，此解决方案会产生不同的结果类型，这在某些情况下可能是一个问题

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