Drop/edit rows in dataframe where entry doesn't meet condition

Question

I know this has been asked before but I cannot find an answer that is working for me. I have a dataframe df that contains a column age , but the values are not all integers, some are strings like 35-59 . I want to drop those entries. I have tried these two solutions as suggested by kite but they both give me AttributeError: 'Series' object has no attribute 'isnumeric'

df.drop(df[df.age.isnumeric()].index, inplace=True)

df = df.query("age.isnumeric()")
df = df.reset_index(drop=True)

Additionally is there a simple way to edit the value of an entry if it matches a certain condition? For example instead of deleting rows that have age as a range of values, I could replace it with a random value within that range.

Answer 1

Try with:

df.drop(df[df.age.str.isnumeric() == False].index, inplace=True)

If you check documentation isnumeric is a method of Series.str and not of Series. That's why you get that error.

Also you will need the ==False because you have mixed types and get a series with only booleans.

Answer 2

I'm posting it in case this also helps you with your last question. You can use pandas.DataFrame.at with pandas.DataFrame.Itertuples for iteration over rows of the dataframe and replace values:

for row in df.itertuples():
  # iterate every row and change the value of that column
  if row.age == 'non_desirable_value:
    df.at[row.Index, "age"] = 'desirable_value'

Hence, it could be:

for row in df.itertuples():
  if row.age.str.isnumeric() == False or row.age == 'non_desirable_value':
    df.at[row.Index, "age"] = 'desirable_value'