Updating a DataFrame based on another DataFrame

Question

Given DataFrame df:

    Id Sex  Group  Time  Time!
0  21   M      2  2.31    NaN
1   2   F      2  2.29    NaN

and 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, Sex and Group and either update Time! with Time value (from the update df) if match, or insert if a new record.

Here is how I do it:

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

if i in df.index:
    ...
else:
    ...

working obviously wrong (it would proceed to else and vice-verse where it shouldn't, I guess, this MultiIndex may be the cause somehow).

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?

I think there is a mistake in your expected, the 2.09 row, doesn't have group=1 in the update. — Andy Hayden
– Andy Hayden, Commented Feb 25, 2014 at 23:28
I think your expected output has a typo btw. There's no (2, F, 1) in the examples you provided — TomAugspurger
– TomAugspurger, Commented Feb 25, 2014 at 23:29

Andy Hayden · Accepted Answer · 2014-02-25 23:26:52Z

4

I think I would do this with a merge, and then update the columns with a where. First remove the Time column from up:

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:

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

answered Feb 25, 2014 at 23:26

Andy Hayden

378k110 gold badges640 silver badges546 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

Updating a DataFrame based on another DataFrame

1 Answer 1

Comments

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Linked

Related