NaN 即将覆盖 pandas 中相同数据帧的另一个子集的数据帧子集

Question

我有一个数据帧的子集 x

>> x.loc[1030:1036,'Priority order':]
      Priority order|bike_id|    lat|       lon|        median_idle_time
|    |              |
|1030|  1           | 52447|    13.027514   77.723228   31.904444
|1031|  1           | 52447|    13.027514   77.723228   31.904444
|1032|  1           | 52447|    13.027514   77.723228   31.904444
|1033|  1           | 52447|    13.027514   77.723228   31.904444
|1034|  1           | 52447|    13.027514   77.723228   31.904444
|1035|  1           | 52447|    13.027514   77.723228   31.904444
|1036|  1           | 52447|    13.027514   77.723228   31.904444

当我尝试用同一父 Dataframe x 的另一个子集更改它时

>> x.loc[157:330,'Priority order':'median_idle_time']  
|   |Priority |bike_id  |lat       |lon       |median_idle_time
      order 
|157    |158    51281   13.053876   77.550079   2.017778
|158    |159    53283   12.975240   77.479927   2.004306
|159    |160    48387   12.824695   77.673805   2.000556
|160    |161    52320   12.993121   77.576515   1.984722
|161    |162    35281   12.934137   77.686356   1.958333
|...    |...    ... ... ... ...
|326    |327    48103   12.985148   77.618721   0.464167
|327    |328    48577   12.959455   77.651382   0.443611
|328    |329    54039   12.982092   77.639412   0.311944
|329    |330    49586   13.011577   77.607353   0.213889
|330    |331    53100   12.971110   77.640602   0.199167

x.loc[1030:1036,'Priority order':'median_idle_time'] = x.loc[157:330,'Priority order':'median_idle_time']

在数据框 x

的子集位置给我 NaN 值

    |Priority   |drop hex   |avg_daily_sessions |idle_bike_count    |drop lat   |drop lon   |avg_daily_sessions/supply  |Priority order |bike_id    |lat    |lon    |median_idle_time
|0      | 1 |8960144b4dbffff    |122.071429 |0  |13.084574  |77.48569   |122.071429 |1.0    |52447.0    |13.027514  |77.723228  |31.904444
|1030   |1  |8960144b4dbffff    |122.071429 |0  |13.084574  |77.48569   |122.071429 |NaN    |NaN    |NaN    |NaN    |NaN
|1031   |1  |8960144b4dbffff    |122.071429 |0  |13.084574  |77.48569   |122.071429 |NaN    |NaN    |NaN    |NaN    |NaN
|1032   |1  |8960144b4dbffff    |122.071429 |0  |13.084574  |77.48569   |122.071429 |NaN    |NaN    |NaN    |NaN    |NaN
|1033   |1  |8960144b4dbffff    |122.071429 |0  |13.084574  |77.48569   |122.071429 |NaN    |NaN    |NaN    |NaN    |NaN
|1034   |1  |8960144b4dbffff    |122.071429 |0  |13.084574  |77.48569   |122.071429 |NaN    |NaN    |NaN    |NaN    |NaN
|1035   |1  |8960144b4dbffff    |122.071429 |0  |13.084574  |77.48569   |122.071429 |NaN    |NaN    |NaN    |NaN    |NaN
|1036   |1  |8960144b4dbffff    |122.071429 |0  |13.084574  |77.48569   |122.071429 |NaN    |NaN    |NaN    |NaN    |NaN

如果有人能指出我哪里搞砸了，我将不胜感激？

Answer 1

这一行有两个问题x.loc[1030:1036,'Priority order':'median_idle_time'] = x.loc[157:330,'Priority order':'median_idle_time']

#1，它们的形状不匹配。 L.H.S。有 7 行，但 R.H.S。有 174 行。请匹配它们。

#2，pandas赋值index-wise，因为L.H.S之间没有index-overlap。和 R.H.S.，你会看到所有这些 NaN。您可以转换 R.H.S。通过 x.loc[157:330,'Priority order':'median_idle_time'].values

到数组

NaN 即将覆盖 pandas 中相同数据帧的另一个子集的数据帧子集

NaN coming on overwriting subset of dataframe from another subset of same dataframe in pandas

python

subset

dataframe

pandas