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Pandas檢查dataFrame中的NaN實(shí)現(xiàn)

更新時(shí)間：2023年01月19日 09:29:07 作者：uncle_ll

本文主要介紹了Pandas檢查dataFrame中的NaN實(shí)現(xiàn)，文中通過示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧

檢查Pandas DataFrame中的NaN值

在Pandas DataFrame中檢查NaN的方法如下：

使用isnull().values.any()方法檢查NaN
使用isnull().sum()方法統(tǒng)計(jì)NaN
使用isnull().sum().any()方法檢查NaN
使用isnull().sum().sum()方法統(tǒng)計(jì)NaN

方法1：使用isnull().values.any()方法

# 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'])

# Applying the method
check_nan = df['Integers'].isnull().values.any()

# printing the result
print(check_nan)
# 輸出 True

可以通過從isnull().values.any()中刪除.values.any()來獲得NaN值所在的確切位置。

df['Integers'].isnull()

1
0     False
1     False
2     False
3     False
4     False
5      True
6     False
7      True
8     False
9     False
10     True
Name: Integers, dtype: bool

方法2：使用isnull().sum()方法

# 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'])

# applying the method
count_nan = df['Integers'].isnull().sum()

# printing the number of values present
# in the column
print('Number of NaN values present: ' + str(count_nan))

Number of NaN values present: 3

方法3：使用isnull().sum().any()方法

# 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'])

# applying the method
nan_in_df = df.isnull().sum().any()

# Print the dataframe
print(nan_in_df)
# 輸出 True

可以通過從isnull().sum().any()中刪除.sum().any()來獲得NaN值所在的確切位置。

方法4：使用isnull().sum().sum()方法

# 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'])

# applying the method
nan_in_df = df.isnull().sum().sum()

# printing the number of values present in
# the whole dataframe
print('Number of NaN values present: ' + str(nan_in_df))