如何在 Pandas DataFrame 中删除具有 NaN 值的行？

NaN 代表 Not A Number，是表示数据中缺失值的常用方法之一。它是一个特殊的浮点值，不能转换为浮点以外的任何其他类型。 NaN 值是数据分析中的主要问题之一。为了得到想要的结果，处理 NaN 是非常必要的。在本文中，我们将讨论如何删除具有 NaN 值的行。
我们可以使用 dropna()函数在 Pandas DataFrame 中删除具有 NaN 值的行

df.dropna() 

也可以使用以下语句删除具有特定列的 NaN 值的行：

df.dropna(subset, inplace=True)

将 in place 设置为 True 并将子集设置为列名列表，以删除这些列下所有带有 NaN 的行。

示例 1：

Python3

# importing libraries
import pandas as pd
import numpy as np
 
num = {'Integers': [10, 15, 30, 40, 55, np.nan,
                    75, np.nan, 90, 150, np.nan]}
 
# Create the dataframe
df = pd.DataFrame(num, columns =['Integers'])
 
# dropping the rows having NaN values
df = df.dropna()
 
# printing the result
df

输出：

注意：我们也可以使用 reset_index() 方法重置索引

df = df.reset_index(drop=True)

示例 2：

Python3

# importing libraries
import pandas as pd
import numpy as np
 
nums = {'Integers_1': [10, 15, 30, 40, 55, np.nan,
                       75, np.nan, 90, 150, np.nan],
           'Integers_2': [np.nan, 21, 22, 23, np.nan,
                          24, 25, np.nan, 26, np.nan,
                          np.nan]}
 
# Create the dataframe
df = pd.DataFrame(nums, columns =['Integers_1', 'Integers_2'])
 
# dropping the rows having NaN values
df = df.dropna()
 
# To reset the indices
df = df.reset_index(drop = True)
 
# Print the dataframe
df

输出：

示例 3：

Python3

# importing libraries
import pandas as pd
import numpy as np
 
nums = {'Student Number': [ 1001, 1111, 1202, 1229, 1330,
                           1335, np.nan, 1400, 1150, np.nan],
           'Seat Number': [np.nan, 15, 22, 43, np.nan, 44,
                           55, np.nan, 57, np.nan]}
 
# Create the dataframe
df = pd.DataFrame(nums, columns =['Student Number', 'Seat Number'])
 
# dropping the rows having NaN values
df = df.dropna()
 
# To reset the indices
df = df.reset_index(drop = True)
 
# Print the dataframe
df

输出：

示例 4：

Python3

# importing libraries
import pandas as pd
import numpy as np
 
car = {'Year of Launch': [ 1999, np.nan, 1986, 2020, np.nan,
                          1991, 2007, 2011, 2001, 2017],
           'Engine Number': [np.nan, 15, 22, 43, 44, np.nan,
                             55, np.nan, 57, np.nan],
        'Chasis Unique Id': [4023, np.nan, 3115, 4522, 3643,
                             3774, 2955, np.nan, 3587, np.nan]}
 
# Create the dataframe
df = pd.DataFrame(car, columns =['Year of Launch', 'Engine Number',
                                 'Chasis Unique Id'])
 
# dropping the rows having NaN values
df = df.dropna()
 
# To reset the indices
df = df.reset_index(drop = True)
 
# Print the dataframe
df

输出：

示例 5：

Python3

# Importing libraries
import pandas as pd
import numpy as np
 
# Creating a dictionary
dit = {'August': [10, np.nan, 34, 4.85, 71.2, 1.1],
       'September': [np.nan, 54, 68, 9.25, pd.NaT, 0.9],
       'October': [np.nan, 5.8, 8.52, np.nan, 1.6, 11],
       'November': [pd.NaT, 5.8, 50, 8.9, 77, pd.NaT]}
 
# Converting it to data frame
df = pd.DataFrame(data=dit)
 
# Dropping the rows having NaN/NaT values
# when threshold of nan values is 2
df = df.dropna(thresh=2)
 
# Resetting the indices using df.reset_index()
df = df.reset_index(drop=True)
 
df

输出：

在上面的示例中，我们在 df.dropna()函数中使用了thresh = 2 ，这意味着它将删除所有 Nan/NaT 值为 2 或大于 2 的行，其他行将保持原样。

示例 6：

Python3

# Importing libraries
import pandas as pd
import numpy as np
 
# Creating a dictionary
dit = {'August': [10, np.nan, 34, 4.85, 71.2, 1.1],
       'September': [np.nan, 54, 68, 9.25, pd.NaT, 0.9],
       'October': [np.nan, 5.8, 8.52, np.nan, 1.6, 11],
       'November': [pd.NaT, 5.8, 50, 8.9, 77, pd.NaT]}
 
# Converting it to data frame
df = pd.DataFrame(data=dit)
 
# Dropping the rowns having NaN/NaT values
# under certain label
df = df.dropna(subset=['October'])
 
# Resetting the indices using df.reset_index()
df = df.reset_index(drop=True)
 
df

输出：

在上面的示例中，我们在 df.dropna()函数中使用了subset = ['October'] ，这意味着它将删除标签 'October' 下所有具有 Nan/NaT 值的行。