随机播放DataFrame行 [英] Shuffle DataFrame rows

问题描述

我有以下DataFrame:

I have the following DataFrame:

    Col1  Col2  Col3  Type
0      1     2     3     1
1      4     5     6     1
...
20     7     8     9     2
21    10    11    12     2
...
45    13    14    15     3
46    16    17    18     3
...

从csv文件读取DataFrame.所有具有Type 1的行都位于最上面，然后是具有Type 2的行，然后是具有Type 3的行，等等.

The DataFrame is read from a csv file. All rows which have Type 1 are on top, followed by the rows with Type 2, followed by the rows with Type 3, etc.

我想重新排列DataFrame行的顺序，以便混合所有Type.可能的结果可能是:

I would like to shuffle the order of the DataFrame's rows, so that all Type's are mixed. A possible result could be:

    Col1  Col2  Col3  Type
0      7     8     9     2
1     13    14    15     3
...
20     1     2     3     1
21    10    11    12     2
...
45     4     5     6     1
46    16    17    18     3
...

我该如何实现?

推荐答案

使用Pandas的惯用方法是使用

The idiomatic way to do this with Pandas is to use the .sample method of your dataframe to sample all rows without replacement:

df.sample(frac=1)

frac关键字参数指定随机样本中要返回的行的分数，因此frac=1表示返回所有行(以随机顺序).

The frac keyword argument specifies the fraction of rows to return in the random sample, so frac=1 means return all rows (in random order).

注意: 如果您希望就地改组数据框并重置索引，则可以执行例如

Note: If you wish to shuffle your dataframe in-place and reset the index, you could do e.g.

df = df.sample(frac=1).reset_index(drop=True)

在这里，指定drop=True可以防止.reset_index创建包含旧索引条目的列.

Here, specifying drop=True prevents .reset_index from creating a column containing the old index entries.

后续说明:尽管上面的操作看起来不是就地 ，但python/pandas足够聪明，因此无需为改组后的代码做另外的malloc目的.也就是说，即使 reference 对象已更改(我的意思是id(df_old)与id(df_new)不同)，底层的C对象仍然相同.为了证明确实如此，您可以运行一个简单的内存分析器:

Follow-up note: Although it may not look like the above operation is in-place, python/pandas is smart enough not to do another malloc for the shuffled object. That is, even though the reference object has changed (by which I mean id(df_old) is not the same as id(df_new)), the underlying C object is still the same. To show that this is indeed the case, you could run a simple memory profiler:

$ python3 -m memory_profiler .\test.py
Filename: .\test.py

Line #    Mem usage    Increment   Line Contents
================================================
     5     68.5 MiB     68.5 MiB   @profile
     6                             def shuffle():
     7    847.8 MiB    779.3 MiB       df = pd.DataFrame(np.random.randn(100, 1000000))
     8    847.9 MiB      0.1 MiB       df = df.sample(frac=1).reset_index(drop=True)

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