英文:
Updating MULTIPLE values in a column in pandas dataframe using ffill (or other methods)
问题
Here is the translated code portion you provided:
#line 1
scores_full['Event_BOLD_Duration'] = scores_full['Event'].where(scores_full['Event'].isin(['SRNeg', 'SINeg'])).ffill(limit=7).fillna(scores_full['Event'])
#line 2
scores_full['Event_BOLD_Duration'] = scores_full['Event'].where(scores_full['Event'].isin(['ArrowTask'])).ffill(limit=4).fillna(scores_full['Event'])
#line 3
scores_full['Event_BOLD_Duration'] = scores_full['Event'].where(scores_full['Event'].isin(['Rating'])).ffill(limit=1).fillna(scores_full['Event'])
Please note that these lines of code are meant to fill NaN values in the 'Event_BOLD_Duration' column based on conditions from the 'Event' column. If you have any further questions or need assistance with this code, feel free to ask.
英文:
I have a dataframe that can be simplified like this:
TR = [17,18,19,20,21,22,23,24,25,26,27,28,29]
Event = ['SRNeg', np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, 'Rating', np.NaN, np.NaN, 'ArrowTask', np.NaN]
df = pd.DataFrame({'Event':Event,'TR':TR})
I want to fill in some NaNs after the event value with that value. Note that not all NaNs are filled. Here is the ideal output:
TR = [17,18,19,20,21,22,23,24,25,26,27,28,29]
Event = ['SRNeg', np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, 'Rating', np.NaN, np.NaN, 'ArrowTask', np.NaN]
Event_BOLD_Duration = ['SRNeg', 'SRNeg', 'SRNeg', 'SRNeg', 'SRNeg', 'SRNeg', 'SRNeg', 'SRNeg', 'Rating', 'Rating', np.NaN, 'ArrowTask', 'ArrowTask']
df = pd.DataFrame({'Event':Event,'Event_BOLD_Duration':Event_BOLD_Duration,'TR':TR})
Here is the code I have so far to complete the above task.
#line 1
scores_full['Event_BOLD_Duration'] = scores_full['Event'].where(scores_full['Event'].isin(['SRNeg', 'SINeg'])).ffill(limit=7).fillna(scores_full['Event'])
#line 2
scores_full['Event_BOLD_Duration'] = scores_full['Event'].where(scores_full['Event'].isin(['ArrowTask'])).ffill(limit=4).fillna(scores_full['Event'])
#line 3
scores_full['Event_BOLD_Duration'] = scores_full['Event'].where(scores_full['Event'].isin(['Rating'])).ffill(limit=1).fillna(scores_full['Event'])
However, each line seems to override the previous line's output. How can I fix this issue?
Thank you!
答案1
得分: 1
你需要创建具有通用模式 "
limits = {'SRNeg': 7, 'SINeg': 7, 'ArrowTask': 4, 'Rating': 1}
forward_fill = lambda x: x.ffill(limit=limits[x.iloc[0]]) \
if x.iloc[0] in limits else x
df['Event_BOLD_Duration'] = (
df.groupby(df['Event'].notna().cumsum())['Event']
.transform(forward_fill)
)
输出：
>>> df
Event TR Event_BOLD_Duration
0 SRNeg 17 SRNeg
1 NaN 18 SRNeg
2 NaN 19 SRNeg
3 NaN 20 SRNeg
4 NaN 21 SRNeg
5 NaN 22 SRNeg
6 NaN 23 SRNeg
7 NaN 24 SRNeg
8 Rating 25 Rating
9 NaN 26 Rating
10 NaN 27 NaN
11 ArrowTask 28 ArrowTask
12 NaN 29 ArrowTask
英文:
You have to create individual group with the common pattern "<condition>.cumsum()" then fill values on each group. To help us, create a dict of limits:
limits = {'SRNeg': 7, 'SINeg': 7, 'ArrowTask': 4, 'Rating': 1}
forward_fill = lambda x: x.ffill(limit=limits[x.iloc[0]]) \
if x.iloc[0] in limits else x
df['Event_BOLD_Duration'] = (
df.groupby(df['Event'].notna().cumsum())['Event']
.transform(forward_fill)
)
Output:
>>> df
Event TR Event_BOLD_Duration
0 SRNeg 17 SRNeg
1 NaN 18 SRNeg
2 NaN 19 SRNeg
3 NaN 20 SRNeg
4 NaN 21 SRNeg
5 NaN 22 SRNeg
6 NaN 23 SRNeg
7 NaN 24 SRNeg
8 Rating 25 Rating
9 NaN 26 Rating
10 NaN 27 NaN
11 ArrowTask 28 ArrowTask
12 NaN 29 ArrowTask
答案2
得分: 0
尝试这样做：
```python
df['Event_BOLD_Duration']=df['Event'].fillna(method='ffill')
df.loc[10,'Event_BOLD_Duration']=np.nan
print(df)
输出
Event TR Event_BOLD_Duration
0 SRNeg 17 SRNeg
1 NaN 18 SRNeg
2 NaN 19 SRNeg
3 NaN 20 SRNeg
4 NaN 21 SRNeg
5 NaN 22 SRNeg
6 NaN 23 SRNeg
7 NaN 24 SRNeg
8 Rating 25 Rating
9 NaN 26 Rating
10 NaN 27 NaN
11 ArrowTask 28 ArrowTask
12 NaN 29 ArrowTask
英文:
try this:
df['Event_BOLD_Duration']=df['Event'].fillna(method='ffill')
df.loc[10,'Event_BOLD_Duration']=np.nan
print(df)
Output
Event TR Event_BOLD_Duration
0 SRNeg 17 SRNeg
1 NaN 18 SRNeg
2 NaN 19 SRNeg
3 NaN 20 SRNeg
4 NaN 21 SRNeg
5 NaN 22 SRNeg
6 NaN 23 SRNeg
7 NaN 24 SRNeg
8 Rating 25 Rating
9 NaN 26 Rating
10 NaN 27 NaN
11 ArrowTask 28 ArrowTask
12 NaN 29 ArrowTask
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