Pandas Series Series.map() 功能
Minahil Noor
2023年1月30日
2020年11月7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
。