How to iterate and modify row values using pandas dataframe

Question

I want to iterate and modify the values under a column 'B', which have repeated values.

So for example if my data frame is like

A   B    
1   null,null    
2   null    
3   null,null,null    
4   null,apples    
5   null,apples,null    
6   null,apples,apples

Now I want to modify this so that a new column C is created in the data frame, depending on the column values from B. It will do the following:

Replace all the multiple "Nulls" with just a single "null" value
If there is an occurrence of the word apples, then we store "apples" in the new column instead of null.

Desired output-

A   B                  C

1   null,null          null

2   null               null

3   null,null,null     null

4   null,apples         apples

5   null,apples,null    apples

6   null,apples,apples  apples

Do you have null string or None in your column?

Sociopath
– Sociopath

2019-06-13 13:37:00 +00:00
Commented Jun 13, 2019 at 13:37 — Sociopath
– Sociopath, Commented Jun 13, 2019 at 13:37
"Null" is the string value

Rafeh Ishtiaq
– Rafeh Ishtiaq

2019-06-13 13:39:34 +00:00
Commented Jun 13, 2019 at 13:39 — Rafeh Ishtiaq
– Rafeh Ishtiaq, Commented Jun 13, 2019 at 13:39

Quang Hoang · Accepted Answer · 2019-06-13 13:44:29Z

3

Try

df['C'] = (df.B.str.split(',',expand=True)  # split the string and put them as columns
            .replace('null',np.nan)         # replace all the null with nan values
            .bfill(axis=1)[0]               # fill the nan to the left and choose the first column
            .fillna('null')                 # replace the nan values with string 'null'
          )

edited Jun 13, 2019 at 13:44

answered Jun 13, 2019 at 13:41

Quang Hoang

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1 Comment

Elmex80s Over a year ago

+1, but I would define / wrap it in a seperated function. Easier to understand, easier to test. The question has not much to do with data-frames. It is about a mapping which maps (from my understanding) strings like null,apples,null to apples. The data-frame is just to hold the originals and images.

BENY · Accepted Answer · 2019-06-13 14:07:53Z

2

Using get_dummies

s=df.B.str.get_dummies(',').drop('null',1)

df['New']=s.dot(s.columns).replace('','null')
df
Out[143]: 
   A                   B     New
0  1           null,null    null
1  2                null    null
2  3      null,null,null    null
3  4         null,apples  apples
4  5    null,apples,null  apples
5  6  null,apples,apples  apples

answered Jun 13, 2019 at 14:07

BENY

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1 Comment

Will Over a year ago

quite a clever answer

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