# Check for missing values in the dataframedf.isnull()# Check the number of missing values in the dataframedf.isnull().sum().sort_values(ascending=False)# Check for missing values in the 'Customer Zipcode' columndf['Customer Zipcode'].isnull().sum()# Check what percentage of the data ...
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 # ...
print("Value Range Check (MedInc):") print(value_range_check) 1. 2. 3. 4. 5. 也可以尝试选择其他的数值特征。但可以看到,MedInc列中的所有数值都在预期范围内: 复制 Output >>> Value Range Check (MedInc): Empty DataFrame Columns: [MedInc, HouseAge, AveRooms, AveBedrms, Population, AveO...
between(*valid_range)] print("Value Range Check (MedInc):") print(value_range_check) 也可以尝试选择其他的数值特征。但可以看到,MedInc列中的所有数值都在预期范围内: Output >>> Value Range Check (MedInc): Empty DataFrame Columns: [MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup...
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值的任何行或列。 # Drop all the rows where at least one element is missing df = df.dropna() # or df.dropna(axis=0) **(axis=0 for rows and axis=1 for columns) # Note: ...
quantile(0.75) IQR = Q3 - Q1 lower_bound = Q1 - 1.5 * IQR upper_bound = Q3 + 1.5 * IQR outliers = data[(data[column] < lower_bound) | (data[column] > upper_bound)] return outliers # 对每个指定的列查找带有异常值的记录 outliers_dict = {} for column in columns_to-check: ...
Thesubset=['A', 'B']parameter drops rows with missing values in columns 'A' or 'B'. This is useful for targeted cleaning. Dropping Rows with a Threshold of Non-Missing Values This example shows how to drop rows with fewer than a specified number of non-missing values. ...
In [13]: df2 Out[13]: A a 0 a 1 b 2 In [14]: df2.index.is_unique Out[14]: False In [15]: df2.columns.is_unique Out[15]: True 注意 检查索引是否唯一对于大型数据集来说有点昂贵。pandas 会缓存此结果,因此在相同的索引上重新检查非常快。 Index.duplicated()将返回一个布尔数组,指...
# check which columns contain only NaN valuescolumns_with_nan = df.columns[df.isnull().all()]# drop the columns containing only NaN valuesdf = df.drop(columns=columns_with_nan) print(df) Run Code Output A B D 0 1 5.0 9 1 2 6.0 10 ...
print('变量 "{}" \t 共有 {} 笔缺失值\t 占比为 {:.4f}%'.format(k,v,v/all_count)) 感谢 https://www.jianshu.com/p/9f583668f386 defcheck_missing_data(df): returndf.isnull().sum().sort_values(ascending=False) 感谢 https://www.cnblogs.com/Mrzhang3389/p/11166800.html...