如何根据列和数组中的数据填充值?(How to fill values based on data present in column and an array? Pandas)
假设我在每个组中都有nans的数据帧
df = pd.DataFrame({'data':[0,1,2,0,np.nan,2,np.nan,0,1],'group':[1,1,1,2,2,2,3,3,3]})
和像numpy数组一样
x = np.array([0,1,2])
现在基于组如何填充我所拥有的numpy数组中的缺失值ie
df = pd.DataFrame({'data':[0,1,2,0,1,2,2,0,1],'group':[1,1,1,2,2,2,3,3,3]})
data group 0 0 1 1 1 1 2 2 1 3 0 2 4 1 2 5 2 2 6 2 3 7 0 3 8 1 3让我解释一下如何填充数据。 考虑组2.
data
的值为0,np.nan,2。np.nan
是数组[0,1,2]
缺少的值。 所以要填写的纳米数据是1
。对于多个nan值,取一个例如具有数据[np.nan,0,np.nan]的组,现在要填充的值代替nan是1和2.得到
[1,0,2]
。Lets say I have dataframe with nans in each group like
df = pd.DataFrame({'data':[0,1,2,0,np.nan,2,np.nan,0,1],'group':[1,1,1,2,2,2,3,3,3]})
and a numpy array like
x = np.array([0,1,2])
Now based on groups how to fill the missing values that are in the numpy array I have i.e
df = pd.DataFrame({'data':[0,1,2,0,1,2,2,0,1],'group':[1,1,1,2,2,2,3,3,3]})
data group 0 0 1 1 1 1 2 2 1 3 0 2 4 1 2 5 2 2 6 2 3 7 0 3 8 1 3Let me explain a bit of how the data should be filled. Consider the group 2. The values of
data
are 0,np.nan,2 . Thenp.nan
is the missing value from the array[0,1,2]
. So the data to be filled inplace of nan is1
.For multiple nan values, take a group for example that has data [np.nan,0,np.nan] now the values to be filled in place of nan are 1 and 2. resulting in
[1,0,2]
.
原文:https://stackoverflow.com/questions/46937481
最满意答案
您是否知道可以从SharePoint设计器中删除导航节点?
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