# -*- coding: utf-8 -*-
"""
Created on Sun Oct 15 19:42:13 2017
@author: wnma3
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pywt
from pandas import DataFrame, Series
from scipy.interpolate import lagrange
from scipy.io import loadmat # mat yes MATLAB Special format for , call loadmat Method reading
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
"""
Code instructions ：
ployinterp_column--> Lagrangian fill value
programmer_1--> Filter exception data （ Include NaN） Fill in
programmer_2--> Minimum - Maximum normalization 、 zero - Mean normalization 、 Standardization of decimal scale
programmer_4--> Basic dataframe operation
programmer_5--> Using wavelet analysis （？？？） Conduct feature analysis
programmer_6--> utilize PCA Calculate the eigenvector , For dimension reduction analysis
"""
path = os.getcwd()
def programmer_1():
inputfile = path + '/data/catering_sale.xls'
outputfile = path + '/tmp/sales.xls'
data = pd.read_excel(inputfile)
data[(dat
