如何根据条件删除python数据框中的行？

Question

I have the following data:
df =

   Emp_Name   Leaves   Leave_Type   Salary   Performance
0  Christy      20      sick        3000.0    56.6
1  Rocky        10      Casual      kkkk      22.4
2  jenifer      50      Emergency   2500.6   '51.6'
3  Tom          10      sick        Nan       46.2
4  Harry        nn      Casual      1800.1   '58.3'
5  Julie        22      sick        3600.2   'unknown'
6  Sam          5       Casual      Nan       47.2
7  Mady         6       sick        unknown   Nan

Output:

   Emp_Name   Leaves   Leave_Type   Salary   Performance
0  Christy      20      sick        3000.0    56.6
1  jenifer      50      Emergency   2500.6    51.6
2  Tom          10      sick        Nan       46.2
3  Sam          5       Casual      Nan       47.2
4  Mady         6       sick        unknown   Nan

I want to delete records where there is datatype error in numerical columns(Leaves,Salary,Performance).
If numerical columns contains strings then that row show be deleted from data frame?

df[['Leaves','Salary','Performance']].apply(pd.to_numeric, errors = 'coerce')

but this will covert values to Nan.

Answer 1

Let's start from a note concerning your sample data:

It contains Nan strings, which are not among strings automatically recognized as NaN s. To treat them as NaN , I read the source text with read_fwf , passing na_values=['Nan'] .

And now get down to the main task:

Define a function to check whether a cell is acceptable:

def isAcceptable(cell):
    if pd.isna(cell) or cell == 'unknown':
        return True
    return all(c.isdigit() or c == '.' for c in cell)

I noticed that you accept NaN values. You also a cell if it contains only unknown string, but you don't accept a cell if such word is enclosed between eg quotes.

If you change your mind about what is / is not acceptable, change the above function accordingly.

Then, to leave only rows with all acceptable values in all 3 mentioned columns, run:

df[df[['Leaves', 'Salary', 'Performance']].applymap(isAcceptable).all(axis=1)]