逐行编辑pandas数据帧

Question

蟒蛇的大熊猫很整洁.我正在尝试用pandas-dataframe替换字典列表.但是,我想知道有一种方法可以在for循环中逐行更改值吗？

这是非熊猫dict-version:

trialList = [
    {'no':1, 'condition':2, 'response':''},
    {'no':2, 'condition':1, 'response':''},
    {'no':3, 'condition':1, 'response':''}
]  # ... and so on

for trial in trialList:
    # Do something and collect response
    trial['response'] = 'the answer!'

...现在trialList包含更新的值,因为它trial引用了那个.非常便利!但是这些名单是非常不方便的,特别是因为我希望能够以列为单位计算大熊猫擅长的东西.

所以从上面给出了trialList,我可以通过做一些类似熊猫的事情来做得更好:

import pandas as pd    
dfTrials = pd.DataFrame(trialList)  # makes a nice 3-column dataframe with 3 rows

for trial in dfTrials.iterrows():
   # do something and collect response
   trials[1]['response'] = 'the answer!'

......但trialList在这里保持不变.有没有一种简单的方法可以逐行更新值,也许等同于dict-version？重要的是它是逐行的,因为这是一个实验,其中参与者被呈现大量的试验并且在每个单独的试验中收集各种数据.

Answer 1

如果你真的想要逐行操作,你可以使用iterrows和loc:

>>> for i, trial in dfTrials.iterrows():
...     dfTrials.loc[i, "response"] = "answer {}".format(trial["no"])
...     
>>> dfTrials
   condition  no  response
0          2   1  answer 1
1          1   2  answer 2
2          1   3  answer 3

[3 rows x 3 columns]

更好的是,当你可以矢量化:

>>> dfTrials["response 2"] = dfTrials["condition"] + dfTrials["no"]
>>> dfTrials
   condition  no  response  response 2
0          2   1  answer 1           3
1          1   2  answer 2           3
2          1   3  answer 3           4

[3 rows x 4 columns]

而且总是apply:

>>> def f(row):
...     return "c{}n{}".format(row["condition"], row["no"])
... 
>>> dfTrials["r3"] = dfTrials.apply(f, axis=1)
>>> dfTrials
   condition  no  response  response 2    r3
0          2   1  answer 1           3  c2n1
1          1   2  answer 2           3  c1n2
2          1   3  answer 3           4  c1n3

[3 rows x 5 columns]