def helpme():
    print('help is needed')
    '''
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    plt.style.use('ggplot') # ggplot - a data visualisation package
    from faker import Faker # Faker - library which generates fake data for us

    fake = Faker()
    # To make the results are reproducible

    Faker.seed(4321)
    name_list = []
    fake = Faker()

    for _ in range(100):
    name_list.append(fake.name())
    # len(name_list) 
    # print(name_list)
    np.random.seed(7)

    salary_list = []

    for _ in range(100):
    sal = np.random.randint(1000,3000)
    salary_list.append(sal)
    # len(salary) 

    # CREATE A DATAFRAME 
    salary_df = pd.DataFrame(
        {'person':name_list,
        'salary':salary_list})

    # DISPLAY THE DATAFRAME
    print(salary_df.head())


    # Next is creating the outlier of our own
    # Purposefully generating two anomalies
    salary_df.at[16,'salary'] = 30
    salary_df.at[70,'salary'] = 50
    print(salary_df['salary'].plot(kind='box'))

    # the black circle represents the anomaly


    # THE OUTLIER IS ALSO PROVEN USING THE HISTOGRAM

    ax = salary_df['salary'].plot(kind='hist')
    ax.set_xlabel('Salary')
    ax


    print('min salary '+str(salary_df['salary'].min()))
    print('max salary '+str(salary_df['salary'].max()))


    # CREATING A RAW SALARY DATA

    salary_raw = salary_df['salary'].values
    # print(salary_raw)
    salary_raw = salary_raw.reshape(-1,1)
    # print(salary_raw)
    salary_raw = salary_raw.astype('float64')


    # TO DO OUTLIER REDUCTION, K-MEANS IS CALLED

    from scipy.cluster.vq import vq, kmeans # vq - vection quantisation

    codebook, distortion = kmeans(salary_raw,4)
    print('codebook=',codebook,',distortion=',distortion)


    groups,cdist = vq(salary_raw,codebook)
    plt.scatter(salary_raw, np.arange(100), c=groups) #Return evenly spaced values within a given interval
    plt.xlabel('Salary')
    plt.ylabel('Indices')
    plt.title('19bee0167 - Swarup Tripathy')
    plt.show()
    '''