Pandas df swap column value-CodePudding

I have dataframe with column which has 2 values: good and bad.

I want to know if there is a way that I can swap them. For example whenever value is good should be changed to bad or vice versa. Is there a way to swap the values?

Edit: I want to swap values for certain indices after for loop. Example:

 for i in np.arange(0,len(df)-1):
     if(df.loc[i,'othercolumnname']>=100:
      # swap the values here

CodePudding user response：

How about using Series.map:

import pandas as pd

df = pd.DataFrame({'x': ['good', 'bad', 'bad', 'good']})
print(df)
df['x'] = df.x.map({'good': 'bad', 'bad': 'good'})
print(df)

      x           x
0  good     0   bad
1   bad  →  1  good
2   bad     2  good
3  good     3   bad

For the edited part of the question, you can combine Series.map and Series.where:

import pandas as pd

df = pd.DataFrame({'x': ['good', 'bad', 'bad', 'good'], 'y': [100, 0, 100, 0]})
print(df)
df['x'] = df.x.map({'good': 'bad', 'bad': 'good'}).where(df.y >= 100, df.x)
print(df)

The idea is to swap the values globally (using map) and then revert back (using where) to the original x where the condition df.y >= 100 is False.

Alternatively, you can use DataFrame.apply:

swap = {'good': 'bad', 'bad': 'good'}
df['x'] = df.apply(lambda row: swap[row.x] if row.y >= 100 else row.x, axis=1)

      x    y           x    y
0  good  100     0   bad  100
1   bad    0  →  1   bad    0
2   bad  100     2  good  100
3  good    0     3  good    0

CodePudding user response：

You can just use the pandas function replace that can make dict-like replacements.

df.replace({'YourColumnName': {'good': 'bad',  
                               'bad': 'good'}})

CodePudding user response：

You can use replace to achieve this...

df = pd.DataFrame({'A': ["good", "good", "bad", "bad", "good"],
                   'B': [5, 6, 7, 8, 9],
                   'C': ['a', 'b', 'c', 'd', 'e'],
                   'D': ["good", "good", "bad", "bad", "good"],})


print(df.replace({'A': {"good": "bad", "bad": "good"}}))

This will limit to only column A. If you want all columns, you can unnest the dictionary in replace. and just pass {"good": "bad", "bad": "good"}

CodePudding user response：

Using np.where and @j1-lee's dataframe:

import numpy as np

df = pd.DataFrame({'x': ['good', 'bad', 'bad', 'good']})
df['y'] = np.where(df['x'].eq('good'),'bad','good')

      x     y
0  good   bad
1   bad  good
2   bad  good
3  good   bad

CodePudding user response：

Here's a trick that will work as expected for two unique values:

df['x'].replace({k: v for k, v in zip(df['x'].unique(), df['x'].unique()[::-1])})

Output:

>>> df
      x
0  good
1   bad
2   bad
3  good

>>> df['x'] = df['x'].replace({k: v for k, v in zip(df['x'].unique(), df['x'].unique()[::-1])})
>>> df
0     bad
1    good
2    good
3     bad