def initial_look(data):
    """
    This function initally shows the number of unique and null values for each feature.
    A simple algorithm tries to classify the feaures as categoric and continuous.

    Parameters:
    data: raw data

    Output:
    dataframe with results 

    """
    import pandas as pd
    typelist = data.dtypes.to_dict()
    initial_look = pd.DataFrame(index=data.columns, columns=['Count',
        'Null', 'Null (%)', 'Unique', 'Unique (%)', 'Type', 'Categoric'])
    for i in data.columns:
        initial_look.loc[i, 'Count'] = data[i].count()
        initial_look.loc[i, 'Null'] = data[i].isnull().sum()
        initial_look.loc[i, 'Null (%)'] = round(data[i].isnull().sum() / (
            data[i].count() + data[i].isnull().sum()) * 100)
        initial_look.loc[i, 'Unique'] = data[i].nunique()
        initial_look.loc[i, 'Unique (%)'] = round(data[i].nunique() / data[
            i].count() * 100)
        initial_look.loc[i, 'Categoric'] = 0
        if typelist[i] == 'object':
            initial_look.loc[i, 'Categoric'] = 1
        if initial_look.loc[i, 'Unique (%)'] < 5:
            initial_look.loc[i, 'Categoric'] = 1
        for i in pd.DataFrame(data.dtypes).index:
            initial_look.loc[i, 'Type'] = pd.DataFrame(data.dtypes).loc[i, 0]
    return initial_look
