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I have a data frame which has the structure as follows

code      value
1          red
2          blue 
3          yellow
1
4          
4          pink
2          blue

so basically i want to update the value column so that the blank rows are filled with values from other rows. So I know the code 4 refers to value pink, I want it to be updated in all the rows where that value is not present.

jpp
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Prachi Verma
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  • Possible duplicate of [How can I replace all the NaN values with Zero's in a column of a pandas dataframe](https://stackoverflow.com/questions/13295735/how-can-i-replace-all-the-nan-values-with-zeros-in-a-column-of-a-pandas-datafra) – Plasma Aug 29 '18 at 15:57

5 Answers5

4

Using groupby and ffill and bfill

df.groupby('code').value.ffill().bfill()


0       red
1      blue
2    yellow
3       red
4      pink
5      pink
6      blue
Name: value, dtype: object
user3483203
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4

You could use first value of the given code group

In [379]: df.groupby('code')['value'].transform('first')
Out[379]:
0       red
1      blue
2    yellow
3       red
4      pink
5      pink
6      blue
Name: value, dtype: object

To assign back

In [380]: df.assign(value=df.groupby('code')['value'].transform('first'))
Out[380]:
   code   value
0     1     red
1     2    blue
2     3  yellow
3     1     red
4     4    pink
5     4    pink
6     2    blue

Or

df['value'] = df.groupby('code')['value'].transform('first')
Zero
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3

You can create a series of your code-value pairs, and use that to map:

my_map = df[df['value'].notnull()].set_index('code')['value'].drop_duplicates()

df['value'] = df['code'].map(my_map)

>>> df
   code   value
0     1     red
1     2    blue
2     3  yellow
3     1     red
4     4    pink
5     4    pink
6     2    blue

Just to see what is happening, you are passing the following series to map:

>>> my_map
code
1       red
2      blue
3    yellow
4      pink
Name: value, dtype: object

So it says: "Where you find 1, give the value red, where you find 2, give blue..."

sacuL
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    `df.dropna().set_index('code')['value']` would do too. – Zero Aug 29 '18 at 15:59
  • `df.dropna().set_index('code')['value'].drop_duplicates()`, because you still have to make sure there are no duplicate indices when you pass to `map` – sacuL Aug 29 '18 at 16:01
  • @sacul you could use `to_dict` to remove the duplicates and map using the dictionary – user3483203 Aug 29 '18 at 16:01
  • I am still getting 2 rows for one of the code value pair, where one value is blank and other has the correct value. Is there a way to check if that field anything apart from Null, may be blank space? This is in the my_map – Prachi Verma Aug 29 '18 at 16:18
  • you can try starting with `df.replace({'':pd.np.nan, ' ':pd.np.nan}, inplace=True)` to get rid of those cases from the start – sacuL Aug 29 '18 at 16:20
2

You can sort_values, ffill and then sort_index. The last step may not be necessary if order is not important. If it is, then the double sort may be unreasonably expensive.

df = df.sort_values(['code', 'value']).ffill().sort_index()

print(df)

   code   value
0     1     red
1     2    blue
2     3  yellow
3     1     red
4     4    pink
5     4    pink
6     2    blue
jpp
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2

Using reindex

df.dropna().drop_duplicates('code').set_index('code').reindex(df.code).reset_index()
Out[410]: 
   code   value
0     1     red
1     2    blue
2     3  yellow
3     1     red
4     4    pink
5     4    pink
6     2    blue
BENY
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  • this works, but with one problem. The first value of code 4 is null, so the result has all the value column for row with code 4 update to null. – Prachi Verma Aug 29 '18 at 16:32