pandas 基于另一个 DataFrame 更新 DataFrame
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Updating a DataFrame based on another DataFrame
提问by nutship
Given DataFrame df:
给定数据帧df：
Id Sex Group Time Time!
0 21 M 2 2.31 NaN
1 2 F 2 2.29 NaN
and update:
和update：
Id Sex Group Time
0 21 M 2 2.36
1 2 F 2 2.09
2 3 F 1 1.79
I want to match on Id, Sexand Groupand either update Time!with Timevalue (from the updatedf) if match, or insert if a new record.
我想匹配Id，Sex并且Group，要么更新Time!与Time值（从update如果要是一个新的记录，DF）的比赛，或插入。
Here is how I do it:
这是我如何做到的：
df = df.set_index(['Id', 'Sex', 'Group'])
update = update.set_index(['Id', 'Sex', 'Group'])
for i, row in update.iterrows():
if i in df.index: # update
df.ix[i, 'Time!'] = row['Time']
else: # insert new record
cols = up.columns.values
row = np.array(row).reshape(1, len(row))
_ = pd.DataFrame(row, index=[i], columns=cols)
df = df.append(_)
print df
Time Time!
Id Sex Group
21 M 2 2.31 2.36
2 F 2 2.29 2.09
3 F 1 1.79 NaN
The code seem to work and my wished result matches with the above. However, I have noticed this behaving faultily on a big data set, with the conditional
该代码似乎有效，我希望的结果与上述相符。但是，我注意到这在大数据集上表现不佳，条件是
if i in df.index:
...
else:
...
working obviously wrong (it would proceed to elseand vice-verse where it shouldn't, I guess, this MultiIndex may be the cause somehow).
工作显然是错误的（它会继续进行else，反之亦然，我想，这个 MultiIndex 可能是某种原因）。
So my question is, do you know any other way, or a more robust version of mine, to update one df based on another df?
所以我的问题是，你知道任何其他方式，或者我的更强大的版本，基于另一个 df 更新一个 df 吗？
回答by Andy Hayden
I think I would do this with a merge, and then update the columns with a where. First remove the Time column from up:
我想我会通过合并来做到这一点，然后用 where 更新列。首先从上面删除时间列：
In [11]: times = up.pop('Time') # up = the update DataFrame
In [12]: df1 = df.merge(up, how='outer')
In [13]: df1
Out[13]:
Id Sex Group Time Time!
0 21 M 2 2.31 NaN
1 2 F 2 2.29 NaN
2 3 F 1 NaN NaN
Update Time if it's not NaN and Time! if it's NaN:
如果不是 NaN 和时间，则更新时间！如果是 NaN：
In [14]: df1['Time!'] = df1['Time'].where(df1['Time'].isnull(), times)
In [15]: df1['Time'] = df1['Time'].where(df1['Time'].notnull(), times)
In [16]: df1
Out[16]:
Id Sex Group Time Time!
0 21 M 2 2.31 2.36
1 2 F 2 2.29 2.09
2 3 F 1 1.79 NaN
