找出给定数据集中各列中缺失值的百分比 [英] Find out the percentage of missing values in each column in the given dataset

问题描述

import pandas as pd
df = pd.read_csv('https://query.data.world/s/Hfu_PsEuD1Z_yJHmGaxWTxvkz7W_b0')
percent= 100*(len(df.loc[:,df.isnull().sum(axis=0)>=1 ].index) / len(df.index))
print(round(percent,2))

输入为 https://query.data.world/s/Hfu_PsEuD1Z_yJHmGaxWTxvkz7W_b0

，输出应为

Ord_id                 0.00
Prod_id                0.00
Ship_id                0.00
Cust_id                0.00
Sales                  0.24
Discount               0.65
Order_Quantity         0.65
Profit                 0.65
Shipping_Cost          0.65
Product_Base_Margin    1.30
dtype: float64

推荐答案

这个怎么样?我想我实际上曾经在这里找到过类似的东西，但是现在看不到...

How about this? I think I actually found something similar on here once before, but I'm not seeing it now...

percent_missing = df.isnull().sum() * 100 / len(df)
missing_value_df = pd.DataFrame({'column_name': df.columns,
                                 'percent_missing': percent_missing})

如果要对丢失的百分比进行排序，请按照上述步骤操作:

And if you want the missing percentages sorted, follow the above with:

missing_value_df.sort_values('percent_missing', inplace=True)

如评论中所述，您也许也可以仅通过上面我的代码中的第一行即可:

As mentioned in the comments, you may also be able to get by with just the first line in my code above, i.e.:

percent_missing = df.isnull().sum() * 100 / len(df)

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