Python Pandas - 将特定 iloc 的值添加到新的数据框列中
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Pandas - add value at specific iloc into new dataframe column
提问by Rob
I have a large dataframe containing lots of columns.
我有一个包含大量列的大型数据框。
For each row/index in the dataframe I do some operations, read in some ancilliary ata, etc and get a new value. Is there a way to add that new value into a new column at the correct row/index?
对于数据帧中的每一行/索引，我执行一些操作，读取一些辅助数据等并获得一个新值。有没有办法将该新值添加到正确行/索引处的新列中？
I can use .assign to add a new column but as I'm looping over the rows and only generating the data to add for one value at a time (generating it is quite involved). When it's generated I'd like to immediately add it to the dataframe rather than waiting until I've generated the entire series.
我可以使用 .assign 添加一个新列，但是当我遍历行并且一次只生成要添加一个值的数据时（生成它非常复杂）。当它生成时，我想立即将它添加到数据框中，而不是等到我生成了整个系列。
This doesn't work and gives a key error:
这不起作用并给出一个关键错误：
df['new_column_name'].iloc[this_row]=value
Do I need to initialise the column first or something?
我需要先初始化列吗？
回答by RumbleFish
There are two steps to created & populate a new column using only a row number... (in this approach ilocis not used)
仅使用行号创建和填充新列有两个步骤......（在这种方法中不使用iloc）
First, get the row indexvalue by using the row number
首先，通过行号获取行索引值
rowIndex = df.index[someRowNumber]
Then, use row indexwith the locfunction to reference the specific row and add the new column / value
然后，使用带有loc函数的行索引来引用特定行并添加新列/值
df.loc[rowIndex, 'New Column Title'] = "some value"
These two steps can be combine into one line as follows
这两个步骤可以合并为一行，如下所示
df.loc[df.index[someRowNumber], 'New Column Title'] = "some value"
回答by Bharath
If you have a dataframe like
如果你有一个像
import pandas as pd
df = pd.DataFrame(data={'X': [1.5, 6.777, 2.444, pd.np.NaN], 'Y': [1.111, pd.np.NaN, 8.77, pd.np.NaN], 'Z': [5.0, 2.333, 10, 6.6666]})
Instead of iloc,you can use .locwith row index and column name like df.loc[row_indexer,column_indexer]=value
您可以使用.loc行索引和列名代替 iloc，例如df.loc[row_indexer,column_indexer]=value
df.loc[[0,3],'Z'] = 3
Output:
输出：
X Y Z 0 1.500 1.111 3.000 1 6.777 NaN 2.333 2 2.444 8.770 10.000 3 NaN NaN 3.000
