What does "dataframe[each]" represents in Pandas?-CodePudding

I am having an issue understanding a line of code which has been used for one-hot encoding using Pandas in python language

dummies = pd.get_dummies(dataframe[each], prefix=each, drop_first=False)

I am totally new to this due to which can't figure out the code snippet. I tried looking in the pandas documentation, but didn't find any specific answer. Please if you have the understanding of this line, do let me know. TIA

Here is the One-Hot encoding section.

def one_hot(dataframe, col):
for each in col:
    dummies = pd.get_dummies(dataframe[each], prefix=each, drop_first=False)
    dataframe = pd.concat([dataframe, dummies], axis=1)
    dataframe = dataframe.drop(each, 1)
return dataframe

CodePudding user response：

I add comments for explain code:

def one_hot(dataframe, col):
    #loop by columns names in varible col
    #in another words - for each column processing code bellow
    for each in col:
        #for each column call get_dummies method
        dummies = pd.get_dummies(dataframe[each], prefix=each, drop_first=False)
        #add dummies DataFrame to original DataFrame
        dataframe = pd.concat([dataframe, dummies], axis=1)
        #remove column
        dataframe = dataframe.drop(each, 1)
    return dataframe

CodePudding user response：

You are looping through each of the columns. "each" indicates one column each time which will perform one-hot encoding.

Also, you are getting a Name Error because your function is not intended correctly

def one_hot(dataframe, col):
    for each in col:
        dummies = pd.get_dummies(dataframe[each], prefix=each, drop_first=False)
        dataframe = pd.concat([dataframe, dummies], axis=1)
        dataframe = dataframe.drop(each, 1)
    return dataframe