2

使用此语句时，这会在单个语句中显示多个警告：

Internaldfdeny=pd.DataFrame({'Count':Internaldf[Internaldf['Status']=='deny'][Internaldf['SrcIP']!="NA"][Internaldf['DstIP']!="NA"][Internaldf['TimeStamp']-Internaldf['TimeStamp'].iloc[0]<pd.tslib.Timedelta(minutes=30)].groupby(['DstPort','SrcIP']).size()}).reset_index().pivot_table('Count',['DstPort'],'SrcIP').fillna(0).to_sparse(fill_value=0)

警告是：

/home/lubuntu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1：用户警告：布尔系列键将被重新索引以匹配 DataFrame 索引。"""启动 IPython 内核的入口点。/home/lubuntu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index. """启动 IPython 内核的入口点。/home/lubuntu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1：FutureWarning：pandas.tslib 已弃用，将在未来版本中删除。您可以访问 Timedelta 作为 pandas.Timedelta """启动 IPython 内核的入口点。/home/lubuntu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: 布尔系列键将被重新索引以匹配 DataFrame 索引。"""启动 IPython 内核的入口点。

我找不到任何其他旋转表格的方法：

我检查过没有 to_sparse(0) 但它仍然显示它！这是一个重要的警告吗？我一直忽略它。我一直在使用通过 anaconda 安装的 Jupyter Notebook Python v3.6，如果这完全相关的话。

编辑：

Internaldf.head()

节目

                   TimeStamp          SrcIP          DstIP  DstPort Status
0 2018-03-31 03:48:13.731929  192.168.52.43  166.62.28.228       80  close
1 2018-03-31 03:48:13.749007  10.208.23.136    96.45.33.73     8888   deny
2 2018-03-31 03:48:13.799235    10.208.2.56   14.142.64.16     8081   deny
3 2018-03-31 03:48:13.799235  10.208.35.193  13.75.119.102      443  close
4 2018-03-31 03:48:13.799235    10.208.2.70   10.208.3.255      137   deny
4

1 回答 1

0

我认为需要：

m1 = Internaldf['Status']=='deny'
m2 = Internaldf['SrcIP']!="NA"
#if want check non NaNs
#m2 = Internaldf['SrcIP'].notnull()
m3 = Internaldf['DstIP']!="NA"
#if want check non NaNs
#m3 = Internaldf['DstIP'].notnull()
m4 = Internaldf['TimeStamp']-Internaldf['TimeStamp'].iloc[0] < pd.Timedelta(minutes=30)

#chain condition with & for AND or by | for OR, for column use reset_index 
df=Internaldf[m1 & m2 & m3 & m4].groupby(['DstPort','SrcIP']).size().reset_index(name='Count')

Internaldfdeny=df.pivot_table('Count','DstPort','SrcIP').fillna(0).to_sparse(fill_value=0)
print (Internaldfdeny)
SrcIP    10.208.2.56  10.208.2.70  10.208.23.136
DstPort                                         
137              0.0          1.0            0.0
8081             1.0          0.0            0.0
8888             0.0          0.0            1.0
于 2018-06-27T06:31:07.480 回答