CODEWITHSUNDEEP
pythonpandas
SalazarSid
SalazarSid

Reputation: 64

Python : Removing the contents of a cell based on a specific condition

I have a *.xlsx file as below -

           A                              B                        C
                                   [['Neutral']]                ['nan']
                                   [['Neutral']]                ['nan']
          Bad                      [['Negative']]               ['Bad']
          Meh                      [['Neutral']]                ['Meh']
                                   [['Neutral']]                ['nan']

I'm trying to remove all the [['Neutral']] and ['nan'] values in column B and C ONLY if their value is null.

This is my code -

df1 = pd.read_excel(path)

for i, row in df1.iterrows():
    if pd.isnull(row[0]):
        # del row[1]
        # del row[2]
        row[1] = 0
        row[2] = 0

My code is finding all the null values perfectly but is not able to clear out the B and C column variable s.What am I doing wrong?

Expected output is-

       A                              B                        C


      Bad                      [['Negative']]               ['Bad']
      Meh                      [['Neutral']]                ['Meh']

And yes the blank spaces/cells should still exist.

Upvotes: 1

Views: 1500

Answers (2)

Mohamed Thasin ah
Mohamed Thasin ah

Reputation: 11192

try this,

mask=df['A'].isnull()
df.loc[mask]=''

Output:

     A               B        C
0                              
1                              
2  Bad  [['Negative']]  ['Bad']
3  Meh   [['Neutral']]  ['Meh']
4

For this problem you don't need to use for loop in pandas,

Explanation

  1. Find index of wherever A is blank

  2. replace empty at selected index

EDIT:

To remove from specific columns,

df.loc[mask,['B','C']]=''

Upvotes: 2

Naga kiran
Naga kiran

Reputation: 4607

You can simply assign it by taking the indexes of null values

df.loc[df.A.isnull()] = ''

Out:

        A   B   C
0           
1           
2   Bad [['Negative']]  ['Bad']
3   Meh [['Neutral']]   ['Meh']
4

Upvotes: 1

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