Pandas filtering based on 2 different columns conditions-CodePudding

So lets say, I have the following dataframe.

data = pd.DataFrame({'Name': ['RACHEL', 'MONICA', 'PHOEBE', 'ROSS', 'CHANDLER', 'JOEY', 'RACHEL', 'RACHEL'],
                        
                      'Age': [30, 35, 37, 33, 34, 30, 30, 15],
                        
                      'Salary': [100000, 93000, 88000, 120000, 94000, 95000, 100000, 10],
                        
                      'Job': ['DESIGNER', 'CHEF', 'MASUS', 'PALENTOLOGY',
                              'IT', 'ARTIST', 'DESIGNER', 'CHEF']})

which gives:

Name    Age Salary  Job
RACHEL  30  100000  DESIGNER
MONICA  35  93000   CHEF
PHOEBE  37  88000   MASUS
ROSS    33  120000  PALENTOLOGY
CHANDLER    34  94000   IT
JOEY    30  95000   ARTIST
RACHEL  30  100000  DESIGNER
RACHEL  15  10  CHEF

What I want to do it pretty simple, I want to filter(get rows) and get rows where Name != 'RACHEL' and Job != 'CHEF';

Expected result set:

Name    Age Salary  Job
RACHEL  30  100000  DESIGNER
MONICA  35  93000   CHEF
PHOEBE  37  88000   MASUS
ROSS    33  120000  PALENTOLOGY
CHANDLER    34  94000   IT
JOEY    30  95000   ARTIST
RACHEL  30  100000  DESIGNER

Note that the last entry is removed.

What i have tried so far is:

data = data.loc[ (data.Name != 'RACHEL') & (data.Job != 'CHEF') ]

This filters other rows Where Name = "RACHEL" OR Job = "CHEF". I only want to filter the last row where Name = 'RACHEL' and in the same row the Job = "CHEF".

Any help is appreciated. Thanks.

CodePudding user response：

Use this:

data = data.loc[ ~((data.Name == 'RACHEL') & (data.Job == 'CHEF')) ]

You want to remove all the rows that have both Name = RACHEL and Job = CHEF. So just write that condition and invert it to filter them out.

CodePudding user response：

rachefs = df[~(df["Name"] == "RACHEL") | ~(df["Job"] == "CHEF")]

The | usually meaning OR turns into an AND because the negatives we use.

https://en.wikipedia.org/wiki/De_Morgan's_laws#