When using a pandas dataframe, how do I add column if does not exist?

Question

I'm new to using pandas and am writing a script where I read in a dataframe and then do some computation on some of the columns.

Sometimes I will have the column called "Met":

df = pd.read_csv(File, 
  sep='\t', 
  compression='gzip', 
  header=0, 
  names=["Chrom", "Site", "coverage", "Met"]
)

Other times I will have:

df = pd.read_csv(File, 
  sep='\t', 
  compression='gzip', 
  header=0, 
  names=["Chrom", "Site", "coverage", "freqC"]
)

I need to do some computation with the "Met" column so if it isn't present I will need to calculate it using:

df['Met'] = df['freqC'] * df['coverage'] 

is there a way to check if the "Met" column is present in the dataframe, and if not add it?

Answer 1

You check it like this:

if 'Met' not in df:
    df['Met'] = df['freqC'] * df['coverage'] 

Answer 2

When interested in conditionally adding columns in a method chain , consider using pipe() with a lambda :

df.pipe(lambda d: (
    d.assign(Met=d['freqC'] * d['coverage'])
    if 'Met' not in d else d
))

Answer 3

If you were creating the dataframe from scratch, you could create the missing columns without a loop merely by passing the column names into the pd.DataFrame() call:

cols = ['column 1','column 2','column 3','column 4','column 5']
df = pd.DataFrame(list_or_dict, index=['a',], columns=cols)

Answer 4

Alternatively you can use get :

df['Met'] = df.get('Met', df['freqC'] * df['coverage'])    

If the column Met exists, the values inside this column are taken. Otherwise freqC and coverage are multiplied.