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I have a dataframe that looks like this.

+----+-------+
| ID | Value |
+----+-------+
|  1 | 23    |
|  1 | NA    |
|  1 | NA    |
|  1 | NA    |
|  2 | 24    |
|  2 | NA    |
|  2 | NA    |
+----+-------+

For each ID value in a group, I either have one value or NA. I wanted to apply this non NA value to the NA values in that specific group as below.

Desired Output :

+----+-------+
| ID | Value |
+----+-------+
|  1 | 23    |
|  1 | 23    |
|  1 | 23    |
|  1 | 23    |
|  2 | 24    |
|  2 | 24    |
|  2 | 24    |
+----+-------+

How can we achieve this in pandas?

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  • 2
    df = df.sort_values(['ID', 'Value']).fffill() Commented Aug 23, 2020 at 16:36
  • This worked. Thanks. Just one change. df = df.sort_values(['ID', 'Value']).ffill() Commented Aug 23, 2020 at 16:53

2 Answers 2

Reset to default
2

Check with

df.Value.fillna(df.groupby('ID').Value.transform('first'), inplace=True)
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2 Comments

Heyy benyo, Good seeing you here. Thanks for your cumsum solution, it really did the trick. ~ And wishing you Happy Coding as well.
@High-Octane happy coding ~
1

say your dataframe variable is df

Then,

df.value.fillna(method='ffill')

Should do the trick.

Documentation for your reference :

Forward Fill Documentation Reference

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1 Comment

thanks. RichieV's answer worked for me

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