首页 \ 问答 \ 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)

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      3

Let me explain a bit of how the data should be filled. Consider the group 2. The values of data are 0,np.nan,2 . The np.nan is the missing value from the array [0,1,2]. So the data to be filled inplace of nan is 1.

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
更新时间:2024-03-08 08:03

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