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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。