如何使用 pandas 将列中的空值替换为特定列中非空值的平均值答案

【问题标题】：How to replace null values in a column with mean of non null values in a particular column by using pandas如何使用 pandas 将列中的空值替换为特定列中非空值的平均值
【发布时间】：2022-11-20 03:38:31
【问题描述】：

我采用的数据集有 country,coal_ 列生产_改变pct,gasprodchangepct,year.煤中有空值prod change pct 和 gas prod change pct，我想用煤炭 prod change pct 非空值和 gas prod change pct 非空值的平均值替换空值。数据框如下图所示。

[{"metadata":{"trusted":true},"cell_type":"code","source":"sample_df.loc[490:500,['country','coal_prod_change_pct','year','gas_prod_change_pct']]","execution_count":79,"outputs":[{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"                  country  coal_prod_change_pct  year  gas_prod_change_pct\n490               Ukraine              2.737000  2018             1.463000\n491               Ukraine             -2.299000  2019            -0.481000\n492               Ukraine             -4.111211  2020             1.197368\n493  United Arab Emirates                   NaN  2001             2.553000\n494  United Arab Emirates                   NaN  2002            10.239000\n495  United Arab Emirates                   NaN  2003             3.227000\n496  United Arab Emirates                   NaN  2004             3.349000\n497  United Arab Emirates                   NaN  2005             3.240000\n498  United Arab Emirates                   NaN  2006             2.092000\n499  United Arab Emirates                   NaN  2007             3.074000\n500  United Arab Emirates                   NaN  2008            -0.099000","text/html":"\n\n\n  \n    \n      \n      \n      \n      \n      \n    \n  \n  \n    \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n    \n    \n      \n      \n      \n      \n      \n    \n  \ncountrycoal_prod_change_pctyeargas_prod_change_pct490Ukraine2.73700020181.463000491Ukraine-2.2990002019-0.481000492Ukraine-4.11121120201.197368493United Arab EmiratesNaN20012.553000494United Arab EmiratesNaN200210.239000495United Arab EmiratesNaN20033.227000496United Arab EmiratesNaN20043.349000497United Arab EmiratesNaN20053.240000498United Arab EmiratesNaN20062.092000499United Arab EmiratesNaN20073.074000500United Arab EmiratesNaN2008-0.099000\n"},"metadata":{}}]}]


country_grp = sample_df.groupby('country')

country_grp\['coal_prod_change_pct'\].fillna(country_grp\['coal_prod_change_pct'\].mean())

country_grp\['coal_prod_change_pct'\].apply(lambda x: x.fillna(x.mean()))

但是在第二种方法中没有 inplace = true 因为我们应用方法

【问题讨论】：

标签： pandas data-cleaning

【解决方案1】：

我们通常做transform

filler = country_grp['coal_prod_change_pct'].transform('mean')
sample_df['coal_prod_change_pct'].fillna(filler, inplace=True)

【讨论】：