Python/Pandas：如果值为 NaN 或 0，则用同一行中下一列的值填充

Question

我浏览了几篇文章，它们要么只适用于具有一列的示例，要么适用于只有 NaN 或 0 值的示例 - 但不能同时适用于两者。

我的 df 看起来像这样。我想用在它右边的四列中找到的非缺失或非零字符串填充列 'Main'。

当前 df =

import pandas as pd

d = {'Main': ['','','',''], 'col2': ['Big','','',0], 'col3': [0,'Medium',0,''], 'col4': ['','','Small',''], 'col5':['',0,'','Vsmall']}
df = pd.DataFrame(data=d)

+------+------+--------+-------+--------+
| Main | Col2 | Col3   | Col4  | Col5   |
+------+------+--------+-------+--------+
|      | Big  | 0      | ...   |        |
+------+------+--------+-------+--------+
|      | ...  | Medium | ...   | 0      |
+------+------+--------+-------+--------+
|      |      | 0      | Small |        |
+------+------+--------+-------+--------+
|      | 0    | ...    | ...   | Vsmall |
+------+------+--------+-------+--------+

期望的输出 df

+--------+------+--------+-------+--------+
| Main   | Col2 | Col3   | Col4  | Col5   |
+--------+------+--------+-------+--------+
| Big    | Big  | 0      | ...   |        |
+--------+------+--------+-------+--------+
| Medium | ...  | Medium | ...   | 0      |
+--------+------+--------+-------+--------+
| Small  |      | 0      | Small |        |
+--------+------+--------+-------+--------+
| Vsmall | 0    | ...    | ...   | Vsmall |
+--------+------+--------+-------+--------+

提前致谢！

Answer 1

根据您提供的示例数据，我认为您想要实现的是解码单热编码数据（机器学习中将分类数据转换为数值数据的经典技术）。

这里是实现解码的代码：

import pandas as pd

d = {'Main': [0,0,0,0], 'col2': ['Big','','',0], 'col3': [0,'Medium',0,''], 'col4': ['','','Small',''], 'col5':['',0,'','Vsmall']}
df = pd.DataFrame(data=d)

def reduce_function(row):
    for col in ['col2','col3','col4','col5']:
        if not pd.isnull(row[col]) and row[col] != 0 and row[col] != '':
            return row[col]

df['Main']=df.apply(reduce_function, axis=1)

注意：始终考虑在数据帧上使用缩减（即 apply()）而不是迭代行。

Answer 2

想法是将 0 和空字符串替换为 DataFrame.mask 缺失值，然后填充缺失的行和最后 select 第一列：

c = ['col2','col3','col4','col5']
df['Main'] = df[c].mask(df.isin(['0','',0])).bfill(axis=1).iloc[:, 0]
print (df)
     Main col1    col2   col3
0     Big  Big    None       
1  Medium    0  Medium   None
2   Small            0  Small

如果可能，创建所有可能提取的字符串的列表，将所有其他值替换为 DataFrame.where:

['col2','col3','col4','col5']
df['Main'] = df[c].where(df.isin(['Big','Medium','Small','Vsmall'])).bfill(axis=1).iloc[:,0]
print (df)
     Main col1    col2   col3
0     Big  Big    None       
1  Medium    0  Medium   None
2   Small            0  Small

详情:

print (df[c].mask(df.isin(['0','',0])))
#print (df[c].where(df.isin(['Big','Medium','Small','Vsmall'])))

   col1    col2   col3
0  Big    None    NaN
1  NaN  Medium   None
2  NaN     NaN  Small

print (df[c].mask(df.isin(['0','',0])).bfill(axis=1))
     col1    col2   col3
0     Big     NaN    NaN
1  Medium  Medium   None
2   Small   Small  Small

Python/Pandas：如果值为 NaN 或 0，则用同一行中下一列的值填充

Python/Pandas: if value is NaN or 0 then fill with the value from the next column within the same row

python

fill

dataframe

pandas