从右列删除NaN值，同时保留左列中的值

Question

我将三个数据框合并在一起，然后从中删除重复的数据框。 但是，当我从最后三列中删除重复项时，在要删除的数据框顶部获得NaN值，但似乎找不到解决方法。

到目前为止，这是我的代码：

bDF=pd.read_csv(bRaw)
pDF=pd.read_csv(pRaw)
mDF=pd.read_csv(mRaw)
del bRaw,pRaw,mRaw

#Merge Together Datarames on the Value Role Name
dfs=[bDF,pDF,mDF]
df_merged = reduce(lambda  left,right: pd.merge(left,right,on=['R1'],
                                            how='outer'), dfs)
del bDF,pDF,mDF,dfs

#Rearrange Columns
cols=df_merged.columns.tolist()
cols=cols[0:1]+cols[-3:]+cols[1:5]
df_merged=df_merged[cols]

合并后的输出：

+------+-----+------+----+--------+--------+--------+--------+
|  R   |  C  |  D   | JC |   R    |   PM   |  Nme   |  Vle   |
+------+-----+------+----+--------+--------+--------+--------+
| JMAC | 305 | 3302 | I6 | Cofow  | Value1 | Value1 | Value1 |
| JMAC | 305 | 3915 | R6 | Cofow  | Value1 | Value1 | Value1 |
| JMAC | 301 | 3302 | I6 | Cofow  | Value1 | Value1 | Value1 |
| JMAC | 301 | 3915 | R6 | Cofow  | Value1 | Value1 | Value1 |
| JMAC | 305 | 3302 | I6 | Cofow  | Value2 | Value2 | Value2 |
| JMAC | 305 | 3915 | R6 | Cofow  | Value2 | Value2 | Value2 |
| JMAC | 301 | 3302 | I6 | Cofow  | Value2 | Value2 | Value2 |
| JMAC | 301 | 3915 | R6 | Cofow  | Value2 | Value2 | Value2 |
| JMAC | 305 | 3302 | I6 | Cofow  | Value3 | Value3 | Value3 |
| JMAC | 305 | 3915 | R6 | Cofow  | Value3 | Value3 | Value3 |
| JMAC | 301 | 3302 | I6 | Cofow  | Value3 | Value3 | Value3 |
| JMAC | 301 | 3915 | R6 | Cofow  | Value3 | Value3 | Value3 |
| JMAC | 305 | 3302 | I6 | Cofow  | Value4 | Value4 | Value4 |
| JMAC | 305 | 3915 | R6 | Cofow  | Value4 | Value4 | Value4 |
| JMAC | 301 | 3302 | I6 | Cofow  | Value4 | Value4 | Value4 |
| JMAC | 301 | 3915 | R6 | Cofow  | Value4 | Value4 | Value4 |
| JMAP | 301 | 3315 | I6 | Cofowd | Value6 | Value6 | Value6 |
| JMAP | 301 | 3916 | R6 | Cofowd | Value6 | Value6 | Value6 |
| JMAP | 305 | 3314 | I6 | Cofowd | Value6 | Value6 | Value6 |
| JMAP | 305 | 3315 | R6 | Cofowd | Value6 | Value6 | Value6 |
| JMAP | 305 | 3916 | R6 | Cofowd | Value6 | Value6 | Value6 |
| JMAP | 301 | 3315 | I6 | Cofowd | Value7 | Value7 | Value7 |
| JMAP | 301 | 3916 | R6 | Cofowd | Value7 | Value7 | Value7 |
| JMAP | 305 | 3314 | I6 | Cofowd | Value7 | Value7 | Value7 |
| JMAP | 305 | 3315 | R6 | Cofowd | Value7 | Value7 | Value7 |
| JMAP | 305 | 3916 | R6 | Cofowd | Value7 | Value7 | Value7 |
| JMAP | 301 | 3315 | I6 | Cofowd | Value8 | Value8 | Value8 |
| JMAP | 301 | 3916 | R6 | Cofowd | Value8 | Value8 | Value8 |
| JMAP | 305 | 3314 | I6 | Cofowd | Value8 | Value8 | Value8 |
| JMAP | 305 | 3315 | R6 | Cofowd | Value8 | Value8 | Value8 |
| JMAP | 305 | 3916 | R6 | Cofowd | Value8 | Value8 | Value8 |
| JMAP | 301 | 3315 | I6 | Cofowd | Value9 | Value9 | Value9 |
| JMAP | 301 | 3916 | R6 | Cofowd | Value9 | Value9 | Value9 |
| JMAP | 305 | 3314 | I6 | Cofowd | Value9 | Value9 | Value9 |
| JMAP | 305 | 3315 | R6 | Cofowd | Value9 | Value9 | Value9 |
| JMAP | 305 | 3916 | R6 | Cofowd | Value9 | Value9 | Value9 |
+------+-----+------+----+--------+--------+--------+--------+

然后，我从前4列，后三列，最后是中间列中删除重复项：

#Remove Duplicate Values
df_merged[cols[0:-3]]=df_merged[cols[0:-3]].mask(df_merged[cols[:-3]].duplicated())
df_merged[cols[-3:]]=df_merged[cols[-3:]].mask(df_merged[cols[-3:]].duplicated())
df_merged[cols[4:5]]=df_merged[cols[4:5]].mask(df_merged[cols[4:5]].duplicated())
df_merged=df_merged.dropna(how='all')

我的输出接近最终形式所需的形式：

+------+-----+------+----+-------+---------+---------+---------+
|  R   |  C  |  D   | JC |   R   |   PM    |   Nme   |   Vle   |
+------+-----+------+----+-------+---------+---------+---------+
| JMAC | 305 | 3302 | I6 | Cofow | Value1  | Value1  | Value1  |
| JMAC | 305 | 3915 | R6 |       | NaN     | NaN     | NaN     |
| JMAC | 301 | 3302 | I6 |       | NaN     | NaN     | NaN     |
| JMAC | 301 | 3915 | R6 |       | NaN     | NaN     | NaN     |
|      |     |      |    |       | Value2  | Value2  | Value2  |
|      |     |      |    |       | Value3  | Value3  | Value3  |
|      |     |      |    |       | Value4  | Value4  | Value4  |
|      |     |      |    |       | Value6  | Value6  | Value6  |
|      |     |      |    |       | Value7  | Value7  | Value7  |
| JMAP | 301 | 3315 | I6 | Cofow | Value8  | Value8  | Value8  |
| JMAP | 301 | 3916 | R6 |       | NaN     | NaN     | NaN     |
| JMAP | 305 | 3314 | I6 |       | NaN     | NaN     | NaN     |
| JMAP | 305 | 3315 | R6 |       | NaN     | NaN     | NaN     |
| JMAP | 305 | 3916 | R6 |       | NaN     | NaN     | NaN     |
|      |     |      |    |       | Value9  | Value9  | Value9  |
|      |     |      |    |       | Value10 | Value10 | Value10 |
|      |     |      |    |       | Value11 | Value11 | Value11 |
|      |     |      |    |       | Value12 | Value12 | Value12 |
|      |     |      |    |       | Value13 | Value13 | Value13 |
+------+-----+------+----+-------+---------+---------+---------+

我的问题是我想摆脱NaN值并向上移动值。 所以我希望最终结果看起来像这样：

+------+-----+------+----+-------+---------+---------+---------+
|  R   |  C  |  D   | JC |   R   |   PM    |   Nme   |   Vle   |
+------+-----+------+----+-------+---------+---------+---------+
| JMAC | 305 | 3302 | I6 | Cofow | Value1  | Value1  | Value1  |
| JMAC | 305 | 3915 | R6 |       | Value2  | Value2  | Value2  |
| JMAC | 301 | 3302 | I6 |       | Value3  | Value3  | Value3  |
| JMAC | 301 | 3915 | R6 |       | Value4  | Value4  | Value4  |
|      |     |      |    |       | Value6  | Value6  | Value6  |
|      |     |      |    |       | Value7  | Value7  | Value7  |
| JMAP | 301 | 3315 | I6 | Cofow | Value8  | Value8  | Value8  |
| JMAP | 301 | 3916 | R6 |       | Value9  | Value9  | Value9  |
| JMAP | 305 | 3314 | I6 |       | Value10 | Value10 | Value10 |
| JMAP | 305 | 3315 | R6 |       | Value11 | Value11 | Value11 |
| JMAP | 305 | 3916 | R6 |       | Value12 | Value12 | Value12 |
|      |     |      |    |       | Value13 | Value13 | Value13 |
+------+-----+------+----+-------+---------+---------+---------+

我曾尝试将列分为两个不同的数据帧，删除NA，然后将它们合并，但是由于索引，我的数据被丢弃了。

df3=pd.concat([df2,df1], axis=1, ignore_index=False)

任何帮助或想法都将很棒！

非常感谢，

要旨

Answer 1

然后，我从前4列，后三列，最后是中间列中删除重复项：

假设您要执行这些步骤，请尝试drop_duplicates 。 这是一个示例，它将在一个命令中按您的顺序执行此操作：

df = df.drop_duplicates(
    subset=['col1', 'col2', 'col3', 'col4']).drop_duplicates(
    subset=['col6', 'col7', 'col8']).drop_duplicates(
    subset=['col5'])

您也可以使用keep参数（例如， keep='first' first'vs keep='last' ）来更改要删除/保留的行。