[python] Update a dataframe in pandas while iterating row by row

I have a pandas data frame that looks like this (its a pretty big one)

           date      exer exp     ifor         mat  
1092  2014-03-17  American   M  528.205  2014-04-19 
1093  2014-03-17  American   M  528.205  2014-04-19 
1094  2014-03-17  American   M  528.205  2014-04-19 
1095  2014-03-17  American   M  528.205  2014-04-19    
1096  2014-03-17  American   M  528.205  2014-05-17 

now I would like to iterate row by row and as I go through each row, the value of ifor in each row can change depending on some conditions and I need to lookup another dataframe.

Now, how do I update this as I iterate. Tried a few things none of them worked.

for i, row in df.iterrows():
    if <something>:
        row['ifor'] = x
    else:
        row['ifor'] = y

    df.ix[i]['ifor'] = x

None of these approaches seem to work. I don't see the values updated in the dataframe.

You should assign value by df.ix[i, 'exp']=X or df.loc[i, 'exp']=X instead of df.ix[i]['ifor'] = x.

Otherwise you are working on a view, and should get a warming:

-c:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_index,col_indexer] = value instead

But certainly, loop probably should better be replaced by some vectorized algorithm to make the full use of DataFrame as @Phillip Cloud suggested.

for i, row in df.iterrows():
    if <something>:
        df.at[i, 'ifor'] = x
    else:
        df.at[i, 'ifor'] = y

A method you can use is itertuples(), it iterates over DataFrame rows as namedtuples, with index value as first element of the tuple. And it is much much faster compared with iterrows(). For itertuples(), each row contains its Index in the DataFrame, and you can use loc to set the value.

for row in df.itertuples():
    if <something>:
        df.at[row.Index, 'ifor'] = x
    else:
        df.at[row.Index, 'ifor'] = x

    df.loc[row.Index, 'ifor'] = x

Under most cases, itertuples() is faster than iat or at.

Thanks @SantiStSupery, using .at is much faster than loc.

You can assign values in the loop using df.set_value:

for i, row in df.iterrows():
    ifor_val = something
    if <condition>:
        ifor_val = something_else
    df.set_value(i,'ifor',ifor_val)

If you don't need the row values you could simply iterate over the indices of df, but I kept the original for-loop in case you need the row value for something not shown here.

update

df.set_value() has been deprecated since version 0.21.0 you can use df.at() instead:

for i, row in df.iterrows():
    ifor_val = something
    if <condition>:
        ifor_val = something_else
    df.at[i,'ifor'] = ifor_val

It's better to use lambda functions using df.apply() -

df["ifor"] = df.apply(lambda x: {value} if {condition} else x["ifor"], axis=1)

Pandas DataFrame object should be thought of as a Series of Series. In other words, you should think of it in terms of columns. The reason why this is important is because when you use pd.DataFrame.iterrows you are iterating through rows as Series. But these are not the Series that the data frame is storing and so they are new Series that are created for you while you iterate. That implies that when you attempt to assign tho them, those edits won't end up reflected in the original data frame.

Ok, now that that is out of the way: What do we do?

Suggestions prior to this post include:

1. pd.DataFrame.set_value is deprecated as of Pandas version 0.21
2. pd.DataFrame.ix is deprecated
3. pd.DataFrame.loc is fine but can work on array indexers and you can do better

My recommendation
Use pd.DataFrame.at

for i in df.index:
    if <something>:
        df.at[i, 'ifor'] = x
    else:
        df.at[i, 'ifor'] = y

You can even change this to:

for i in df.index:
    df.at[i, 'ifor'] = x if <something> else y

Response to comment

and what if I need to use the value of the previous row for the if condition?

for i in range(1, len(df) + 1):
    j = df.columns.get_loc('ifor')
    if <something>:
        df.iat[i - 1, j] = x
    else:
        df.iat[i - 1, j] = y

Well, if you are going to iterate anyhow, why don't use the simplest method of all, df['Column'].values[i]

df['Column'] = ''

for i in range(len(df)):
    df['Column'].values[i] = something/update/new_value

Or if you want to compare the new values with old or anything like that, why not store it in a list and then append in the end.

mylist, df['Column'] = [], ''

for <condition>:
    mylist.append(something/update/new_value)

df['Column'] = mylist

Increment the MAX number from a column. For Example :

df1 = [sort_ID, Column1,Column2]
print(df1)

My output :

Sort_ID Column1 Column2
12         a    e
45         b    f
65         c    g
78         d    h

MAX = df1['Sort_ID'].max() #This returns my Max Number 

Now , I need to create a column in df2 and fill the column values which increments the MAX .

Sort_ID Column1 Column2
79      a1       e1
80      b1       f1
81      c1       g1
82      d1       h1

_{Note : df2 will initially contain only the Column1 and Column2 . we need the Sortid column to be created and incremental of the MAX from df1 .}