CODEWITHSUNDEEP
pythonpandasdataframe
Mostafa Alishahi
Mostafa Alishahi

Reputation: 340

Change a column in python data frame according to previous values of the same column

I have a data frame in pandas python as following data frame

<table style="width:100%">
  <tr>
    <th>ID</th>
    <th>AGE</th> 
    <th>GENDER</th>
    <th>TIME</th>
    <th>CODE</th>
  </tr>
  <tr>
    <td>1</td>
    <td>66</td> 
    <td>M</td>
    <td>1</td>
    <td>1</td>
  </tr>
  <tr>
    <td>1</td>
    <td>66</td> 
    <td>M</td>
    <td>2</td>
    <td>1</td>
  </tr>
  <tr>
    <td>1</td>
    <td>66</td> 
    <td>M</td>
    <td>3</td>
    <td>1</td>
  </tr>
  <tr>
    <td>1</td>
    <td>66</td> 
    <td>M</td>
    <td>4</td>
    <td>1</td>
  </tr>
    <tr>
    <td>2</td>
    <td>20</td> 
    <td>F</td>
    <td>1</td>
    <td>0</td>
  </tr>
  <tr>
    <td>2</td>
    <td>20</td> 
    <td>F</td>
    <td>2</td>
    <td>0</td>
    <tr>
    <td>2</td>
    <td>20</td> 
    <td>F</td>
    <td>3</td>
    <td>0</td> 
  </tr>
  <tr>
    <td>3</td>
    <td>18</td> 
    <td>F</td>
    <td>1</td>
    <td>1</td>
  </tr>
    <tr>
    <td>3</td>
    <td>18</td> 
    <td>F</td>
    <td>2</td>
    <td>1</td>
  </tr>
  <tr>
    <td>3</td>
    <td>18</td> 
    <td>F</td>
    <td>3</td>
    <td>1</td>
  </tr>
  <tr>
    <td>3</td>
    <td>18</td> 
    <td>F</td>
    <td>4</td>
    <td>1</td>
  </tr>
  <tr>
    <td>3</td>
    <td>18</td> 
    <td>F</td>
    <td>5</td>
    <td>1</td>
  </tr>
</table>

1 66 M 1 1 1 66 M 2 1 1 66 M 3 1 2 20 F 1 0 2 20 F 2 0 2 20 F 3 0 2 20 F 4 0 3 18 F 1 1 3 18 F 2 1 3 18 F 3 1 3 18 F 4 1

and I need to change the last column according to following(wherever the 'CODE' column is 1 keep the last row of That ID as 1 and change the previous rows to zero)

<table style="width:100%">
  <tr>
    <th>ID</th>
    <th>AGE</th> 
    <th>GENDER</th>
    <th>TIME</th>
    <th>CODE</th>
  </tr>
  <tr>
    <td>1</td>
    <td>66</td> 
    <td>M</td>
    <td>1</td>
    <td>0</td>
  </tr>
  <tr>
    <td>1</td>
    <td>66</td> 
    <td>M</td>
    <td>2</td>
    <td>0</td>
  </tr>
  <tr>
    <td>1</td>
    <td>66</td> 
    <td>M</td>
    <td>3</td>
    <td>0</td>
  </tr>
  <tr>
    <td>1</td>
    <td>66</td> 
    <td>M</td>
    <td>4</td>
    <td>1</td>
  </tr>
    <tr>
    <td>2</td>
    <td>20</td> 
    <td>F</td>
    <td>1</td>
    <td>0</td>
  </tr>
  <tr>
    <td>2</td>
    <td>20</td> 
    <td>F</td>
    <td>2</td>
    <td>0</td>
    <tr>
    <td>2</td>
    <td>20</td> 
    <td>F</td>
    <td>3</td>
    <td>0</td> 
  </tr>
  <tr>
    <td>3</td>
    <td>18</td> 
    <td>F</td>
    <td>1</td>
    <td>0</td>
  </tr>
    <tr>
    <td>3</td>
    <td>18</td> 
    <td>F</td>
    <td>2</td>
    <td>0</td>
  </tr>
  <tr>
    <td>3</td>
    <td>18</td> 
    <td>F</td>
    <td>3</td>
    <td>0</td>
  </tr>
  <tr>
    <td>3</td>
    <td>18</td> 
    <td>F</td>
    <td>4</td>
    <td>0</td>
  </tr>
  <tr>
    <td>3</td>
    <td>18</td> 
    <td>F</td>
    <td>5</td>
    <td>1</td>
  </tr>
</table>

how is it possible to do it using pandas?

after looking it up I found this line of code which removes the last row of each group dfnew = (df.groupby('ID').apply(lambda x: x.iloc[:-1] if len(x)>1 else x))

Thanks in advance

Upvotes: 1

Views: 70

Answers (1)

jezrael
jezrael

Reputation: 863611

Get index by filtering by 1 and remove dupes by ID by drop_duplicates:

i = df[df['CODE'] == 1].drop_duplicates(subset=['ID'], keep='last').index

Set column to 0 first and then replace by i:

df['CODE'] = 0
df.loc[i, 'CODE'] = 1

Another solution is create boolean mask and convert it to ints:

m = (df['CODE'] == 1) & ~df['ID'].duplicated(keep='last')
print (m)
0     False
1     False
2      True
3     False
4     False
5     False
6     False
7     False
8     False
9     False
10     True
dtype: bool

df['CODE'] = m.astype(int)

print (df)
    ID  AGE GENDER  TIME  CODE
0    1   66      M     1     0
1    1   66      M     2     0
2    1   66      M     3     1
3    2   20      F     1     0
4    2   20      F     2     0
5    2   20      F     3     0
6    2   20      F     4     0
7    3   18      F     1     0
8    3   18      F     2     0
9    3   18      F     3     0
10   3   18      F     4     1

Upvotes: 1

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