pandas dataframe dataframe.dropna() 函数-ag捕鱼王app官网

pandas.dataframe.dropna() 函数通过丢弃包含空值的行或列，从 dataframe 中删除空值（缺失值）。

nan（not a number）和 nat（not a time）代表空值。dataframe.dropna() 检测这些值并相应地过滤 dataframe。

pandas.dataframe.dropna() 语法

dataframe.dropna(axis, how, thresh, subset, inplace)

参数

返回值

它根据传递的参数返回一个过滤后的 dataframe，其中包含删除的行或列。

示例代码：dataframe.dropna() 删除行

默认情况下，轴为 0，即行，所以所有的输出都有行掉。

import pandas as pd
dataframe=pd.dataframe({'attendance': {0: 60, 1: none, 2: 80,3: none, 4: 95},
                    'name': {0: 'olivia', 1: 'john', 2: 'laura',3: 'ben',4: 'kevin'},
                    'obtained marks': {0: none, 1: 75, 2: 82, 3: 64, 4: none}})
print(dataframe)

示例 dataframe 如下。

   attendance    name  obtained marks
0        60.0  olivia             nan
1         nan    john            75.0
2        80.0   laura            82.0
3         nan     ben            64.0
4        95.0   kevin             nan

这个函数的所有参数都是可选的。如果我们不传递任何参数，那么函数将丢弃所有包含一个空值的行。

import pandas as pd
dataframe = pd.dataframe(
    {
        "attendance": {0: 60, 1: none, 2: 80, 3: none, 4: 95},
        "name": {0: "olivia", 1: "john", 2: "laura", 3: "ben", 4: "kevin"},
        "obtained marks": {0: none, 1: 75, 2: 82, 3: 64, 4: none},
    }
)
dataframe1 = dataframe.dropna()
print(dataframe1)

输出：

   attendance   name  obtained marks
2        80.0  laura            82.0

丢弃所有包含一个缺失值的行。

示例代码：dataframe.dropna() 删除列

import pandas as pd
dataframe = pd.dataframe(
    {
        "attendance": {0: 60, 1: none, 2: 80, 3: none, 4: 95},
        "name": {0: "olivia", 1: "john", 2: "laura", 3: "ben", 4: "kevin"},
        "obtained marks": {0: none, 1: 75, 2: 82, 3: 64, 4: none},
    }
)
dataframe1 = dataframe.dropna(axis=1)
print(dataframe1)

输出：

     name
0  olivia
1    john
2   laura
3     ben
4   kevin

因为我们在 dataframe.dropna() 方法中设置了 axis=1，所以它删除了所有包含一个缺失值的列。

示例代码：dataframe.dropna() 与 how=all

import pandas as pd
dataframe = pd.dataframe(
    {
        "attendance": {0: 60, 1: none, 2: 80, 3: none, 4: 95},
        "name": {0: "olivia", 1: "john", 2: "laura", 3: "ben", 4: "kevin"},
        "obtained marks": {0: none, 1: 75, 2: 82, 3: 64, 4: none},
    }
)
dataframe1 = dataframe.dropna(axis=1, how="all")
print(dataframe1)

输出：

   attendance    name  obtained marks
0        60.0  olivia             nan
1         nan    john            75.0
2        80.0   laura            82.0
3         nan     ben            64.0
4        95.0   kevin             nan

包含缺失值的行没有被删除，因为 how 参数的值被设置为 all，这意味着该行的所有值都应该是空的。

如果在指定的轴上缺少所有的值，那么 dataframe.dropna() 方法会丢弃该轴，即使 how 被设置为 all。

import pandas as pd
dataframe = pd.dataframe(
    {
        "attendance": {0: 60, 1: none, 2: 80, 3: none, 4: 95},
        "name": {0: "olivia", 1: "john", 2: "laura", 3: "ben", 4: "kevin"},
        "obtained marks": {0: none, 1: none, 2: none, 3: none, 4: none},
    }
)
print(dataframe)
print("--------")
dataframe1 = dataframe.dropna(axis=1, how="all")
print(dataframe1)

输出：

   attendance    name obtained marks
0        60.0  olivia           none
1         nan    john           none
2        80.0   laura           none
3         nan     ben           none
4        95.0   kevin           none   attendance    name
0        60.0  olivia
1         nan    john
2        80.0   laura
3         nan     ben
4        95.0   kevin

示例代码：dataframe.dropna() 与指定的子集或阈值

import pandas as pd
dataframe = pd.dataframe(
    {
        "attendance": {0: 60, 1: none, 2: 80, 3: none, 4: 95},
        "name": {0: "olivia", 1: "john", 2: "laura", 3: "ben", 4: "kevin"},
        "obtained marks": {0: none, 1: 75, 2: 82, 3: 64, 4: none},
    }
)
dataframe1 = dataframe.dropna(thresh=3)
print(dataframe1)

输出：

   attendance   name  obtained marks
2        80.0  laura            82.0

thresh 的值是 3，这意味着为了防止掉落，至少需要 3 个非空值。

我们也可以指定 subset。

import pandas as pd
dataframe = pd.dataframe(
    {
        "attendance": {0: 60, 1: none, 2: 80, 3: none, 4: 95},
        "name": {0: "olivia", 1: "john", 2: "laura", 3: "ben", 4: "kevin"},
        "obtained marks": {0: none, 1: 75, 2: 82, 3: 64, 4: none},
    }
)
dataframe1 = dataframe.dropna(subset=["attendance", "name"])
print(dataframe1)

输出：

   attendance    name  obtained marks
0        60.0  olivia             nan
2        80.0   laura            82.0
4        95.0   kevin             nan

根据 attendance 和 name 列，删除缺失值的行。如果只有其他列中的值比如这里的 obtained marks 列有缺失值，它就不会删除记录。

示例代码：dataframe.dropna() 与 inplace=true

dataframe.dropna() 如果 inplace 被设置为 true，则调用者 dataframe 就地改变。

import pandas as pd
dataframe = pd.dataframe(
    {
        "attendance": {0: 60, 1: none, 2: 80, 3: none, 4: 95},
        "name": {0: "olivia", 1: "john", 2: "laura", 3: "ben", 4: "kevin"},
        "obtained marks": {0: none, 1: 75, 2: 82, 3: 64, 4: none},
    }
)
dataframe1 = dataframe.dropna(inplace=true)
print(dataframe1)

输出：

none

该参数对调用者 dataframe 进行了原地修改，返回 none。