Python 排序数据框后更新索引
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Update index after sorting data-frame
提问by Lemming
Take the following data-frame:
取以下数据框：
x = np.tile(np.arange(3),3)
y = np.repeat(np.arange(3),3)
df = pd.DataFrame({"x": x, "y": y})
x y
0 0 0
1 1 0
2 2 0
3 0 1
4 1 1
5 2 1
6 0 2
7 1 2
8 2 2
I need to sort it by xfirst, and only second by y:
我需要对它进行排序x第一，也是唯一由二y：
df2 = df.sort(["x", "y"]) x y
0 0 0
3 0 1
6 0 2
1 1 0
4 1 1
7 1 2
2 2 0
5 2 1
8 2 2
How can I change the index such that it is ascending again. I.e. how do I get this:
如何更改索引以使其再次上升。即我如何得到这个：
x y
0 0 0
1 0 1
2 0 2
3 1 0
4 1 1
5 1 2
6 2 0
7 2 1
8 2 2
I have tried the following. Unfortunately, it doesn't change the index at all:
我尝试了以下方法。不幸的是，它根本不会改变索引：
df2.reindex(np.arange(len(df2.index)))
采纳答案by joris
You can resetthe index using reset_indexto get back a default index of 0, 1, 2, ..., n-1 (and use drop=Trueto indicate you want to drop the existing index instead of adding it as an additional column to your dataframe):
您可以使用重置索引reset_index来恢复默认索引 0, 1, 2, ..., n-1 （并用于drop=True指示您要删除现有索引而不是将其作为附加列添加到数据帧中） ：
In [19]: df2 = df2.reset_index(drop=True)
In [20]: df2
Out[20]:
x y
0 0 0
1 0 1
2 0 2
3 1 0
4 1 1
5 1 2
6 2 0
7 2 1
8 2 2
回答by Gregg
回答by aaronpenne
df.sort()is deprecated, use df.sort_values(...): https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html
df.sort()已弃用，请使用df.sort_values(...)：https: //pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html
Then follow joris' answer by doing df.reset_index(drop=True)
然后按照乔里斯的回答做 df.reset_index(drop=True)
回答by David
Since pandas 1.0.0 df.sort_valueshas a new parameter ignore_indexwhich does exactly what you need:
由于 pandas 1.0.0df.sort_values有一个新参数ignore_index，它完全符合您的需要：
In [1]: df2 = df.sort_values(by=['x','y'],ignore_index=True)
In [2]: df2
Out[2]:
x y
0 0 0
1 0 1
2 0 2
3 1 0
4 1 1
5 1 2
6 2 0
7 2 1
8 2 2
