英文:
Array_Split with grouped string indices
问题
我有一个数据框，我想在其中创建子数组（即分块），基于索引中的字符串值组。我已经阅读了如何将字符串值列表作为np.array_split中的indices变量传递，但我的情况有点更复杂，我不确定最佳方法。
从下表/数组中，我想要有2个子数组：一个包括索引字符串值"Alpha"和"Bravo"，第二个包括值"Charlie"和"Delta"。
示例表格：
| 索引 | 列1 | 列2 |
|---|---|---|
| Alpha | 样本 | 12 |
| Alpha | 样本 | 13 |
| Alpha | 样本 | 14 |
| Bravo | 样本 | 15 |
| Charlie | 样本 | 16 |
| Charlie | 样本 | 17 |
| Delta | 样本 | 18 |
| Delta | 样本 | 19 |
| Delta | 样本 | 20 |
| Delta | 样本 | 21 |
英文:
I have a dataframe that I would like to create sub-arrays within (i.e. chunk) based on groups of string values within the index. I've read how you can pass a list of string values as the indices variable in np.array_split, but my scenario is a bit more complicated and I'm unsure on best approach.
From the below table/array, I'd like to have 2 sub-arrays: one array which includes index string values "Alpha" and "Bravo", the second with values "Charlie" and "Delta"
Example table:
| Index | Column1 | Column2 |
|---|---|---|
| Alpha | sample | 12 |
| Alpha | sample | 13 |
| Alpha | sample | 14 |
| Bravo | sample | 15 |
| Charlie | sample | 16 |
| Charlie | sample | 17 |
| Delta | sample | 18 |
| Delta | sample | 19 |
| Delta | sample | 20 |
| Delta | sample | 21 |
答案1
得分: 1
假设有一个DataFrame，并且您想要按自定义分组拆分：
groups = ['Alpha', 'Bravo'], ['Charlie', 'Delta']dfs = [g for _, g in df.groupby(df['Index'].map({k: v for v, l in enumerate(groups) for k in l}))]
输出：
dfs[0]Index Column1 Column20 Alpha sample 121 Alpha sample 132 Alpha sample 143 Bravo sample 15dfs[1]Index Column1 Column24 Charlie sample 165 Charlie sample 176 Delta sample 187 Delta sample 198 Delta sample 209 Delta sample 21
或者，如果 "Index" 实际上是索引：
groups = ['Alpha', 'Bravo'], ['Charlie', 'Delta']dfs = [df.loc[l] for l in groups]
输出：
dfs[0]Column1 Column2Alpha sample 12Alpha sample 13Alpha sample 14Bravo sample 15dfs[1]Column1 Column2Charlie sample 16Charlie sample 17Delta sample 18Delta sample 19Delta sample 20Delta sample 21
最后，如果您没有明确的组合想法，只想要2个值的组（按顺序），那么可以使用：
dfs = [g for _, g in df.groupby(pd.factorize(df['Index'])[0] // 2)]
英文:
Assuming a DataFrame and that you want to split by custom groups:
groups = [['Alpha', 'Bravo'], ['Charlie', 'Delta']]dfs = [g for _, g in df.groupby(df['Index'].map({k: v for v,l in enumerate(groups) for k in l}))]
Output:
dfs[0]Index Column1 Column20 Alpha sample 121 Alpha sample 132 Alpha sample 143 Bravo sample 15dfs[1]Index Column1 Column24 Charlie sample 165 Charlie sample 176 Delta sample 187 Delta sample 198 Delta sample 209 Delta sample 21
Or, if "Index" is actually the index:
groups = [['Alpha', 'Bravo'], ['Charlie', 'Delta']]dfs = [df.loc[l] for l in groups]
Output:
dfs[0]Column1 Column2Alpha sample 12Alpha sample 13Alpha sample 14Bravo sample 15dfs[1]Column1 Column2Charlie sample 16Charlie sample 17Delta sample 18Delta sample 19Delta sample 20Delta sample 21
Finally, if you don't have explicit combinations in mind but just want groups of 2 values (in order), then use:
dfs = [g for _,g in df.groupby(pd.factorize(df['Index'])[0]//2)]
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