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This time I will show you how python handles the time field of dataframe. What are the precautions for python to handle the time field of dataframe. The following is a practical case, let's take a look.
In the process of machine learning, csv files are usually read through pandas and kept in dadaframe format. However, sometimes it is necessary to model the time field in the dataframe. For example, if the time format is datetime, then it is like general Operating the time field in the same way as the dataframe will result in an error. Therefore, when using the sklearn library for fit and predict, the time field must first be converted to timestamp format. After fit and predict, if matplotlib drawing is required, then The timestamp format is converted into time string , such as 2017-02-01 14:25:14.
The following is a piece of code I have processed, I hope it can help children!
doc_list1 = [] for i in doc1.iloc[:,1:2].values.tolist(): # 转换成了时间戳格式 for j in i: dt = time.strptime(j, "%Y-%m-%d %H:%M:%S") dt_new = time.mktime(dt) doc_list1.append(dt_new) doc_list2 = [] for i in doc_list1: time_local = time.localtime(i) dt = time.strftime("%Y-%m-%d %H:%M:%S",time_local) dt1 = datetime.datetime.strptime(dt, "%Y-%m-%d %H:%M:%S") doc_list2.append(dt1)
X1 = np.mat(doc_list1).T y1= test_target1001 clf = AdaBoostRegressor(DecisionTreeRegressor(max_depth=5),n_estimators=1000, random_state=rng) clf.fit(X1,y1) yhat1 = clf.predict(X1)
Addition: If the value is not in datetime format, it needs to be converted
value = result.iloc[:,1] list = [] for i in value: print(type(i.to_pydatetime().timetuple()),i) list.append(time.mktime(i.to_datetime().timetuple())) print(list)
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