数据分析-时序序列、调频、补值

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108

# -*- coding:utf-8 -*- from datetime import datetime import json import numpy as np import pandas as pd import matplotlib.pyplot as plt       ResultData = "[\"[\\\"asset-8nmfb4hb\\\",0,0,\\\"2022-04-28 15:57:00.000\\\"]\",\"[\\\"asset-8nmfb4hb\\\",0,0,\\\"2022-04-28 15:56:30.000\\\"]\",\"[\\\"asset-8nmfb4hb\\\",0,1,\\\"2022-04-28 15:56:00.000\\\"]\",\"[\\\"asset-8nmfb4hb\\\",0,0.3333333,\\\"2022-04-28 15:55:30.000\\\"]\"]" ResultSchema = "[\"{\\\"Name\\\":\\\"c_deviceid\\\",\\\"Type\\\":\\\"String\\\"}\",\"{\\\"Name\\\":\\\"integer1\\\",\\\"Type\\\":\\\"Int64\\\"}\",\"{\\\"Name\\\":\\\"float1\\\",\\\"Type\\\":\\\"Float32\\\"}\",\"{\\\"Name\\\":\\\"c_time\\\",\\\"Type\\\":\\\"DateTime64(3, 'Asia/Shanghai')\\\"}\"]"     def lists_format_to_json(ResultSchema, ResultData):     # 字符串数据格式化     result_data = json.loads(ResultData)     result_schema = json.loads(ResultSchema)     # print(result_data)     # print(result_schema)       # 取出表结构字段     key_schema = []     for schema in result_schema:         # unicode码点str转bytes流str（python默认处理的对象是bytes流str）         #key_schema.append(schema.encode('utf-8'))         schema = json.loads(schema)    # 把字符串直接序列化         key_schema.append(schema['Name'])       #print(key_schema)       # 数据跟表头一一对应     data_schema = []     for data in result_data:         data = json.loads(data)         key_data = {}         for i in range(0, len(key_schema)):             key_data[key_schema[i]] = data[i]         data_schema.append(key_data)     print('data_schema：',data_schema)     return data_schema     def generate_sequence_data(data_schema, c_time, key):     # 生成时间序列数据     index = []     value = []     for data in data_schema:         # 字符串类型str转换为dateTime类型         p = data[c_time]         dateTime_p = datetime.strptime(p, '%Y-%m-%d %H:%M:%S.%f')         #print(dateTime_p)         index.append(dateTime_p)         value.append(data[key])       index.reverse()     #print(index)     value.reverse()       sequence_data = pd.Series(value, index=index)     print('sequence_data：',sequence_data)     return sequence_data     def adjust_time_frequent(sequence_data, frequent):     """     H   hourly frequency     T, min  minutely frequency     S   secondly frequency     :param sequence_data: 时间序列数     :param frequent: 调频间隔     :return:     """     frequent_data = sequence_data.asfreq(frequent)     print('frequent_data：',frequent_data)     return frequent_data     def fill_front_data(frequent_data):     front_data = frequent_data.fillna(method="ffill")     print('front_data: ', front_data)   def fill_back_data(frequent_data):     back_data = frequent_data.fillna(method="bfill")     print('back_data: ', back_data)     def draw_picture():     v = np.random.randn(20)     tx = pd.Series(v)     tx.index = pd.date_range('2018-12-01', periods=20, freq="d")     # print "tx", "-" * 20, "\n", tx     rm = tx.rolling(window=5, center=False).mean()     rm.plot()     tx.plot()     plt.show()   if __name__ == '__main__':     data_schema = lists_format_to_json(ResultSchema, ResultData)     c_time = 'c_time'     key = 'float1'     sequence_data = generate_sequence_data(data_schema, c_time, key)     #print('-----------------')     # print(sequence_data['2022-04-28 15:57:00.000'])     frequent = '15S'     frequent_data = adjust_time_frequent(sequence_data, frequent)     fill_front_data(frequent_data)     fill_back_data(frequent_data)     draw_picture()