访问和删除 Pandas Series 中的元素
现在我们来了解如何访问或修改 Pandas Series 中的元素。Pandas Series 的一大优势是我们能够以很多不同的方式访问数据。我们可以通过在方括号 内添加索引标签或数字索引访问元素，就像访问 NumPy ndarray 中的元素一样。因为我们可以使用数字索引，因此可以使用正整数从 Series 的开头访问数据，或使用负整数从末尾访问。因为我们可以通过多种方式访问元素，为了清晰地表明我们指代的是索引标签还是数字索引，Pandas Series 提供了两个属性 .loc 和 .iloc，帮助我们清晰地表明指代哪种情况。属性 .loc 表示 位置 ，用于明确表明我们使用的是标签索引（也就是用前面的Data指引 ）。同样，属性 .iloc 表示整型位置 ，用于明确表明我们使用的是数字索引（也就是用后面的Index指引）。我们来看一些示例：
python
# We access elements in Groceries using index labels:
# We use a single index label
print('How many eggs do we need to buy:', groceries['eggs'])
print()
# we can access multiple index labels
print('Do we need milk and bread:\n', groceries[['milk', 'bread']])
print()
# we use loc to access multiple index labels
print('How many eggs and apples do we need to buy:\n', groceries.loc[['eggs', 'apples']])
print()
# We access elements in Groceries using numerical indices:
# we use multiple numerical indices
print('How many eggs and apples do we need to buy:\n', groceries[[0, 1]])
print()
# We use a negative numerical index
print('Do we need bread:\n', groceries[[-1]])
print()
# We use a single numerical index
print('How many eggs do we need to buy:', groceries[0])
print()
# we use iloc to access multiple numerical indices
print('Do we need milk and bread:\n', groceries.iloc[[2, 3]])
vbnet
How many eggs do we need to buy: 30
Do we need milk and bread:
milk Yes
bread No
dtype: object
How many eggs and apples do we need to buy:
eggs 30
apples 6
dtype: object
How many eggs and apples do we need to buy:
eggs 30
apples 6
dtype: object
Do we need bread:
bread No
dtype: object
How many eggs do we need to buy: 30
Do we need milk and bread:
milk Yes
bread No
dtype: object和 NumPy ndarray 一样，Pandas Series 也是可变的，也就是说，创建好 Pandas Series 后，我们可以更改其中的元素。例如，我们更改下购物清单中的鸡蛋购买数量
python
# We display the original grocery list
print('Original Grocery List:\n', groceries)
# We change the number of eggs to 2
groceries['eggs'] = 2
# We display the changed grocery list
print()
print('Modified Grocery List:\n', groceries)
yaml
Original Grocery List:
eggs 30
apples 6
milk Yes
bread No
dtype: object
Modified Grocery List:
eggs 2
apples 6
milk Yes
bread No
dtype: object我们还可以使用 .drop() 方法删除 Pandas Series 中的条目。Series.drop(label) 方法会从给定 Series 中删除给定的 label。请注意，Series.drop(label) 方法不在原地地从 Series 中删除元素，即不会更改被修改的原始 Series。我们来看看代码编写方式
python
# We display the original grocery list
print('Original Grocery List:\n', groceries)
# We remove apples from our grocery list. The drop function removes elements out of place
print()
print('We remove apples (out of place):\n', groceries.drop('apples'))
# When we remove elements out of place the original Series remains intact. To see this
# we display our grocery list again
print()
print('Grocery List after removing apples out of place:\n', groceries)
yaml
Original Grocery List:
eggs 30
apples 6
milk Yes
bread No
dtype: object
We remove apples (out of place):
eggs 30
milk Yes
bread No
dtype: object
Grocery List after removing apples out of place:
eggs 30
apples 6
milk Yes
bread No
dtype: object我们可以通过在 .drop() 方法中将关键字 inplace 设为 True，原地地从 Pandas Series 中删除条目。我们来看一个示例：
python
# We display the original grocery list
print('Original Grocery List:\n', groceries)
# We remove apples from our grocery list in place by setting the inplace keyword to True
groceries.drop('apples', inplace = True)
# When we remove elements in place the original Series its modified. To see this
# we display our grocery list again
print()
print('Grocery List after removing apples in place:\n', groceries)
yaml
Original Grocery List:
eggs 30
apples 6
milk Yes
bread No
dtype: object
Grocery List after removing apples in place:
eggs 30
milk Yes
bread No
dtype: object