Please consider the following example:
I have a DataFrame
| Index | Speaker | Word |
|---|---|---|
| 0 | spk_0 | can |
| 1 | spk_0 | you |
| 2 | spk_0 | see |
| 3 | spk_0 | my |
| 4 | spk_0 | screen |
| 5 | spk_0 | now |
| 6 | spk_0 | ? |
| 7 | spk_1 | yes |
| 0 | spk_1 | , |
| 8 | spk_1 | now |
| 9 | spk_1 | I |
| 10 | spk_1 | can |
| 11 | spk_1 | see |
| 12 | spk_1 | your |
| 13 | spk_1 | screen |
| 14 | spk_1 | . |
| 15 | spk_0 | Let |
| 16 | spk_0 | me |
| 17 | spk_0 | start |
| 18 | spk_0 | then |
| 19 | spk_2 | yes |
| 20 | spk_2 | sure |
I want to combine the Word column such that it should look like the following:
| Index | Speaker | Sentence |
|---|---|---|
| 0 | spk_0 | can you see my screen now ? |
| 1 | spk_1 | yes , now I can see your screen . |
| 2 | spk_0 | let me start then . |
| 3 | spk_2 | Yes sure . |
Can someone please help me find a solution to this problem? I already had tried group by but didn't work.
CodePudding user response：
You can group by consecutive values of Speaker column created by comapred shifted value with cumulative sum and aggregate join:
g = df['Speaker'].ne(df['Speaker'].shift()).cumsum()
df = df.groupby(['Speaker', g],sort=False)['Word'].agg(' '.join).droplevel(-1).reset_index()
print (df)
Speaker Word
0 spk_0 can you see my screen now ?
1 spk_1 yes , now I can see your screen .
2 spk_0 Let me start then
3 spk_2 yes sure