Change first element of each group in pandas DataFrame

user_4913108 0
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I want to ensure that the first value of val2 corresponding to each vintage is NaN. Currently two are already NaN, but I want to ensure that 0.53 also changes to NaN. 

df = pd.DataFrame({
        'vintage': ['2017-01-01', '2017-01-01', '2017-01-01', '2017-02-01', '2017-02-01', '2017-03-01'],
        'date': ['2017-01-01', '2017-02-01', '2017-03-01', '2017-02-01', '2017-03-01', '2017-03-01'],
        'val1': [0.59, 0.68, 0.8, 0.54, 0.61, 0.6],
        'val2': [np.nan, 0.66, 0.81, 0.53, 0.62, np.nan]
    })

Here's what I've tried so far:

df.groupby('vintage').first().val2 #This gives the first non-NaN values, as shown below

vintage
2017-01-01    0.66
2017-02-01    0.53
2017-03-01     NaN

df.groupby('vintage').first().val2 = np.nan #This doesn't change anything
df.val2

0     NaN
1    0.66
2    0.81
3    0.53
4    0.62
5     NaN
pandas dataframe python
3 Answers

I think you could also write:

def h(x):
 x['val2'].iloc[0] = np.NaN
 return x

df = df.groupby("vintage").apply(h)
user_192189610
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