How to fill missing values based on the current values using Python?

Question

My data is like this:

a=pd.DataFrame({'id':[0,1,2,3,4,5,6,7,8,9],
                'value':[np.nan,np.nan,0,np.nan,np.nan,1,2,np.nan,3,np.nan]})

I want to fill the missing values based on the previous known values. If there is no previous values, then fill -1. So, the result should look like:

id    value
0     -1
1     -1
2     0
3     0
4     0
5     1
6     2
7     2
8     3
9     3

My current way is to find all the known values and their positions, then scan the whole table. But there should be a better way which I am not aware of. What can I try here?

Mayank Porwal · Accepted Answer · 2020-10-22 05:09:52Z

3

Use df.ffill() and fillna():

In [1587]: a.ffill().fillna(-1)
Out[1587]: 
   id  value
0   0   -1.0
1   1   -1.0
2   2    0.0
3   3    0.0
4   4    0.0
5   5    1.0
6   6    2.0
7   7    2.0
8   8    3.0
9   9    3.0

answered Oct 22, 2020 at 5:09

Mayank Porwal

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Andy L. · Accepted Answer · 2020-10-22 05:09:38Z

2

You need a ffill and a fillna

a['value'] = a.value.ffill().fillna(-1)

Out[935]:
   id  value
0   0   -1.0
1   1   -1.0
2   2    0.0
3   3    0.0
4   4    0.0
5   5    1.0
6   6    2.0
7   7    2.0
8   8    3.0
9   9    3.0

answered Oct 22, 2020 at 5:09

Andy L.

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