时间戳

首页 > 解决方案 > 从右列中删除 NaN 值，同时在左列中保留值

python - 从右列中删除 NaN 值，同时在左列中保留值

问题描述

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

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 列中删除重复项，然后是最后三列，最后是中间列：

#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：

+------+-----+------+----+-------+---------+---------+---------+
|  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我的 NaN 值并将值向上移动。所以我希望我的最终结果看起来像：

+------+-----+------+----+-------+---------+---------+---------+
|  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 |
+------+-----+------+----+-------+---------+---------+---------+

我尝试将这些列分成两个不同的数据框，删除 NA，然后将它们组合起来，但是我的数据由于索引而被丢弃。

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

任何帮助或想法都会很棒！

非常感谢，

要旨

标签： pythonpandasnumpydataframe

解决方案

然后我从前 4 列中删除重复项，然后是最后三列，最后是中间列：

假设这些是您想要执行的步骤，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'])

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

推荐阅读