# Drop Order Region column# (axis=0 for rows and axis=1 for columns)df = df.drop('Order Region', axis=1)# Drop Order Region column without having to reassign df (using inplace=True)df.drop('Order Region', axis=1, inplace=True)# Drop by column number instead of by column labeldf...
# Check what percentage of the data frame these 3 missing values ••represent print(f"3 missing values represents {(df['Customer Zipcode'].isnull().sum() / df.shape[0] * 100).round(4)}% of the rows in our DataFrame.") Zipcode列中有3个缺失值 dropna() 可以删除包含至少一个缺失...
Remove all rows with NULL values: import pandas as pddf = pd.read_csv('data.csv')df.dropna(inplace = True) print(df.to_string()) Try it Yourself » Note: Now, the dropna(inplace = True) will NOT return a new DataFrame, but it will remove all rows containing NULL values from...
(axis=0 for rows and axis=1 for columns) # Note: inplace=True modifies the DataFrame rather than creating a new one df.dropna(inplace=True) # Drop all the columns where at least one element is missing df.dropna(axis=1, inplace=True) # Drop rows with missing values in specific ...
dropna(axis=1, inplace=True) # Drop rows with missing values in specific columns df.dropna(subset = ['Additional Order items', 'Customer Zipcode'], inplace=True) fillna()也可以用更合适的值替换缺失的值,例如平均值、中位数或自定义值。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # ...
# Drop rows with missing values in specific columns df.dropna(subset = ['Additional Order items', 'Customer Zipcode'], inplace=True) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. fillna() 1. 也可以用更合适的值替换缺失的值,例如平均值、中位数或自定义值。
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百度试题 结果1 题目删除包含空单元格的行,正确的Pandas方法是什么? remove ___ null( )dropna( )delete ___ null 相关知识点: 试题来源: 解析 dropna() 反馈 收藏
Drop Rows that NaN/None/Null Values While working with analytics you would often be required to clean up the data that hasNone,Null&np.NaNvalues. By usingdf.dropna()you can remove NaN values from DataFrame. # Delete rows with Nan, None & Null Values ...