import matplotlib.lines as mlines # Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv") df.sort_values('pct_2014', inplace=True) df.reset_index(inplace=True) # Func to draw line segment def newline(p1, p2, color='black'): ax = ...
change_stu(stu_list) elif number == str(4): query_stu(stu_list) elif number == str(5): query_all_stu(stu_list) elif number == str(6): whether_to_quit = input("是否退出(y/n)") while True: if whether_to_quit == 'y' or whether_to_quit == 'Y' or whether_to_quit == ...
(self): # 用户级别设置 self.new_obj.user = self.request.user flag = self.org_obj is None and 'create' or 'change' if flag == 'create': # 对密码字段进行加密 self.new_obj.password = encrypt_oracle(self.new_obj.password) elif flag == 'change': if 'password' in self.change_...
"" logging.info("Set the next startup saved-configuration file " "to {}...".format(file_path)) uri = '/restconf/operations/huawei-cfg:set-startup' req_data = '' if exportcfg is not None: exportcfg_change = ops.opscharacterEncode(exportcfg) items = {'filename': file_path, '...
er_window =10fast_period =2slow_period =30df['change'] = df['close'] - df['close'].shift(1) df['volatility'] =abs(df['close'] - df['close'].shift(er_window)) df['er'] = df['change'] / df['volatility'] sc_fast =2/ (fast_period +1) ...
['Test Statistic','p-value','#Lags Used','NumberofObservations Used']) for key,value in dftest[4].items(): dfoutput['CriticalValue(%s)'%key] = value return dfoutput # 自相关和偏相关图,默认阶数为31阶 def draw_acf_pacf(ts, lags=31): f = plt.figure(facecolor='white')ax1=f....
earn up to $100,000 a day"] print("%-20s %s" % ("Query", "Best Match")) print("-" * 50) for query in ("feel good story", "climate change", "health", "war", "wildlife", "asia", "north america", "dishonest junk"): # Get index of best section that best mat...
# we have automagically already created an index (in the first section) In [418]: i = store.root.df.table.cols.index.index In [419]: i.optlevel, i.kind Out[419]: (6, 'medium') # change an index by passing new parameters In [420]: store.create_table_index("df", optlevel=9...
(type='month')127 128 # 预测结果还原129 predict_ts = model.properModel.predict()130 diff_shift_ts = ts_diff_1.shift(1)131 diff_recover_1 = predict_ts.add(diff_shift_ts)132 rol_shift_ts = rol_mean.shift(1)133 diff_recover = diff_recover_1.add(rol_shift_ts)134 rol_sum = ts...
number2=number2+1 print(changeInt:number2=,number2) #调⽤ number1=2 chanageInt(number1) print(number:,number1) 打印结果哪项是正确的(B) AchangeInt:number2=3number:3 BchangeInt:number2=3number:2 Cnumber:2changeInt:number2=2 Dnumber:2changeInt:number2=3 18.关于python类说法错误的是...