How to build a normalized tf dataframe?

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I want to apply this into my tf function. enter image description here But unable to build the function.

My dataset looks like this enter image description here

I have tried to buield the function like this

def term_document_matrix(data, vocab_list = None, doc_index= 'ID', text= 'text'):
      tf_matirx = pd.DataFrame(columns=df[document_index], index= vocab).fillna(0)
    a = int(input("enter the value"))
    for word in tf_matrix.index:
    
    for doc in data[document_index]:
        
        result = a + (1-a)*[data[data[document_index] == doc][text].values[0].count(word)/X]
        X = ????????
        tf_matrix.loc[word,doc] = result
return tf_matrix

But unable to build this completely.

Here parameters are described as below

parameter: 

    data: DataFrame. 
    Frequency of word calculated against the data.
    
    vocab_list: list of strings.
    Vocabulary of the documents    
    
    doc_index: str.
    Column name for document index in DataFrame passed.
    
    text: str
    Column name containing text for all documents in DataFrame,
    
returns:
    tf_matrix: DataFrame.
    DataFrame containing term document matrix.
    """

My goal is to get a dataframe like this enter image description here

stanford-nlp information-retrieval nlp python
1 Answers

You can determine tf dataframe by using CountVectorizer. Then divide each value by max value of it's column and repeat this process for every column in your dataframe

 df_1st = df.apply(lambda col: col / col.max())

and then just multiply and add a scaler for each element in your dataframe.

df_2nd = df_1st.apply(lambda col: lambda + col*(1-lambda))
tf_matrix = df_2nd
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