moving averge 滑动平均

import matplotlib.pyplot as plt
import pandas as pd
import requests
import io
import numpy as np
import pylab
pylab.style.use('bmh')
from pylab import rcParams
rcParams['figure.figsize'] = 10, 8

def moving_average(l, N):
sum = 0
result = list( 0 for x in l)
 
for i in range( 0, N ):
       # 从左到右逐渐添加index在N之内的数字
sum = sum + l[i]
result[i] = sum / (i+1)
 
for i in range( N, len(l) ):
       # 加入最右边数字减去最左边数字
sum = sum - l[i-N] + l[i]
result[i] = sum / N
 
return result
 
# 使用效率更高的numpy
# http://stackoverflow.com/questions/13728392/moving-average-or-running-mean
def fast_moving_average(x, N):
return np.convolve(x, np.ones((N,))/N)[(N-1):]
 
url = '铁路客运量.csv'

 
df = pd.read_csv(url)  # python2使用StringIO.StringIO
 
data = np.array(df['铁路客运量_当期值(万人)'])
 
dic = {}
for i in [3,5,10,20]:
    ma_data = moving_average(data, i)
    dic[i] = ma_data
ma_data_df = pd.DataFrame(dic)

ma_data_df.plot()

def fast_moving_average(x, N, mode):
# return np.convolve(x, np.ones((N,))/N, mode='valid')[(N-1):]
return np.convolve(x, np.ones((N,))/N, mode=mode)
dic = {}
modes = ['full', 'same', 'valid']
i = 10
for mode in modes:
    ma_data = fast_moving_average(data, i, mode)
    pylab.plot(ma_data)
pylab.legend(modes)