Pandas Series Series.map() 功能
Minahil Noor
2023年1月30日
-
pandas.Series.map()语法 -
示例代码：
Series.map() -
示例代码：
Series.map()传递一个字典作为arg参数 -
示例代码：
Series.map()传递一个函数作为arg参数 -
示例代码：
Series.map()应用于DataFrame
Python Pandas Series.map() 函数替换一个 Series 的值。被替换的值可能来自于 Series、字典或一个函数。这个函数只对一个 Series 有效。如果我们将此函数应用于 DataFrame，那么它将产生一个 AttributeError。
pandas.Series.map() 语法
Series.map(arg, na_action=None)
参数
arg |
它是函数，字典或 Series。要替换的值来自这个函数、字典或 Series。 |
na_action |
该参数接受两个值。None 和 ignore。它的默认值是 None。如果它的值是 ignore，那么它就不会将派生值映射到 NaN 值。它忽略 NaN 值。 |
返回值
它返回一个与调用者具有相同索引的 Series。
示例代码：Series.map()
我们将生成一个包含 NaN 值的 Series，以检查传递 na_action 参数后的输出。
import pandas as pd
import numpy as np
series = pd.Series(['Rose',
'Lili',
'Tulip',
np.NaN,
'Orchid',
'Hibiscus',
'Jasmine',
'Daffodil',
np.NaN ,
'SunFlower',
'Daisy'])
print(series)
示例 Series 是：
0 Rose
1 Lili
2 Tulip
3 NaN
4 Orchid
5 Hibiscus
6 Jasmine
7 Daffodil
8 NaN
9 SunFlower
10 Daisy
dtype: object
我们使用 NumPy 来生成 NaN 值。
参数 arg 是一个强制性参数。如果它没有被传递，那么函数会产生一个 TypeError。我们将首先传递一个 Series 作为 arg 参数。
为了映射两个 Series，第一个 Series 的最后一列应该与第二个 Series 的索引相同。
import pandas as pd
import numpy as np
first_series = pd.Series(
[
"Rose",
"Lili",
"Tulip",
np.NaN,
"Orchid",
"Hibiscus",
"Jasmine",
"Daffodil",
np.NaN,
"SunFlower",
"Daisy",
]
)
second_series = pd.Series(
[23, 34, 67, 90, 21, 45, 29, 70, 56],
index=[
"Rose",
"Lili",
"Tulip",
"Orchid",
"Hibiscus",
"Jasmine",
"Daffodil",
"SunFlower",
"Daisy",
],
)
series1 = first_series.map(second_series)
print(series1)
输出：
0 23.0
1 34.0
2 67.0
3 NaN
4 90.0
5 21.0
6 45.0
7 29.0
8 NaN
9 70.0
10 56.0
dtype: float64
请注意，函数在比较了两个 Series 之后，已经替换了这些值。
示例代码：Series.map() 传递一个字典作为 arg 参数
import pandas as pd
import numpy as np
series = pd.Series(
[
"Rose",
"Lili",
"Tulip",
np.NaN,
"Orchid",
"Hibiscus",
"Jasmine",
"Daffodil",
np.NaN,
"SunFlower",
"Daisy",
]
)
dictionary = {
"Rose": "One",
"Lili": "Two",
"Orchid": "Three",
"Jasmine": "Four",
"Daisy": "Five",
}
series1 = series.map(dictionary)
print(series1)
输出：
0 One
1 Two
2 NaN
3 NaN
4 Three
5 NaN
6 Four
7 NaN
8 NaN
9 NaN
10 Five
dtype: object
Series 中不在字典中的值会被 NaN 值代替。
示例代码：Series.map() 传递一个函数作为 arg 参数
现在我们将传递一个函数作为参数。
import pandas as pd
import numpy as np
series = pd.Series(
[
"Rose",
"Lili",
"Tulip",
np.NaN,
"Orchid",
"Hibiscus",
"Jasmine",
"Daffodil",
np.NaN,
"SunFlower",
"Daisy",
]
)
series1 = series.map("The name of the flower is {}.".format)
print(series1)
输出：
0 The name of the flower is Rose.
1 The name of the flower is Lili.
2 The name of the flower is Tulip.
3 The name of the flower is nan.
4 The name of the flower is Orchid.
5 The name of the flower is Hibiscus.
6 The name of the flower is Jasmine.
7 The name of the flower is Daffodil.
8 The name of the flower is nan.
9 The name of the flower is SunFlower.
10 The name of the flower is Daisy.
dtype: object
这里，我们传递了 string.format() 函数作为参数。请注意，该函数也被应用于 NaN 值。如果我们不想将该函数应用于 NaN 值，那么我们将把 ignore 值传递给 na_action 参数。
import pandas as pd
import numpy as np
series = pd.Series(
[
"Rose",
"Lili",
"Tulip",
np.NaN,
"Orchid",
"Hibiscus",
"Jasmine",
"Daffodil",
np.NaN,
"SunFlower",
"Daisy",
]
)
series1 = series.map("The name of the flower is {}.".format, na_action="ignore")
print(series1)
输出：
0 The name of the flower is Rose.
1 The name of the flower is Lili.
2 The name of the flower is Tulip.
3 NaN
4 The name of the flower is Orchid.
5 The name of the flower is Hibiscus.
6 The name of the flower is Jasmine.
7 The name of the flower is Daffodil.
8 NaN
9 The name of the flower is SunFlower.
10 The name of the flower is Daisy.
dtype: object
上面的示例代码已经忽略了 NaN 值。
示例代码：Series.map() 应用于 DataFrame
import pandas as pd
dataframe = pd.DataFrame(
{
"Attendance": {0: 60, 1: 100, 2: 80, 3: 75, 4: 95},
"Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
"Obtained Marks": {0: 56, 1: 75, 2: 82, 3: 64, 4: 67},
}
)
dataframe1 = dataframe.map("The flower name is {}.".format)
print(dataframe1)
输出：
AttributeError: 'DataFrame' object has no attribute 'map'
函数产生了 AttributeError。