您可以尝试：

lst=['adam','beth']
out=pd.concat([df['Name'].str.contains(x,case=False).map({True:x})  for x in lst],axis=1)
df['Name corrected']=out.bfill(axis=1).iloc[:,0]

# Finally:

df['Name corrected']=df['Name corrected'].ffill()

# but In certain condition ffill() gives you wrong values

解释：

lst=['adam','beth']

# created a list of words

out=pd.concat([df['Name'].str.contains(x,case=False).map({True:x})  for x in lst],axis=1)

# checking If the 'Name' column contain the word one at a time that are inside the list and that will give a boolean series of True and False and then we are mapping The value of that particular element that is inside list so True becomes that value and False become NaN and then we are concatinating both list of Series on axis=1 so that It becomes a Dataframe

df['Name corrected']=out.bfill(axis=1).iloc[:,0]

# Backword filling values on axis=1 and getting the 1st column

# Finally:

df['Name corrected']=df['Name corrected'].ffill()

# Forward filling the missing values