x_data.append(time.time())
时间: 2024-05-31 13:07:32 浏览: 20
This line of code appends the current time (in seconds since January 1, 1970) to a list called `x_data`.
This is often used in data collection or plotting applications, where time is an important variable. By appending the current time to `x_data` at regular intervals, we can track how data changes over time.
For example, if we were collecting temperature data every minute, we could use `x_data` to keep track of when each temperature reading was taken. We could then plot the temperature over time using `x_data` as the x-axis.
相关问题
def data_processing(data): # 日期缺失,补充 data.fillna(method='ffill', inplace=True) date_history = pd.DataFrame(data.iloc[:, 0]) data_history = pd.DataFrame(data.iloc[:, 1]) date_history = np.array(date_history) data_history = [x for item in np.array(data_history).tolist() for x in item] # 缺失值处理 history_time_list = [] for date in date_history: date_obj = datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') #将字符串转为 datetime 对象 history_time_list.append(date_obj) start_time = history_time_list[0] # 起始时间 end_time = history_time_list[-1] # 结束时间 delta = datetime.timedelta(minutes=15) #时间间隔为15分钟 time_new_list = [] current_time = start_time while current_time <= end_time: time_new_list.append(current_time) current_time += delta # 缺失位置记录 code_list = [] for i in range(len(time_new_list)): code_list = code_list history_time_list = history_time_list while (time_new_list[i] - history_time_list[i]) != datetime.timedelta(minutes=0): history_time_list.insert(i, time_new_list[i]) code_list.append(i) for i in code_list: data_history.insert(i, data_history[i - 1]) # 输出补充好之后的数据 data = pd.DataFrame({'date': time_new_list, 'load': data_history}) return data 优化代码
可以尝试将循环语句进行向量化,使用 pandas 库提供的数据处理函数。具体来说,可以将以下部分进行优化:
```
history_time_list = []
for date in date_history:
date_obj = datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M')
history_time_list.append(date_obj)
```
可以改写为:
```
history_time_list = pd.to_datetime(date_history.iloc[:, 0], format='%Y/%m/%d %H:%M')
```
同样地,可以将以下部分进行优化:
```
time_new_list = []
current_time = start_time
while current_time <= end_time:
time_new_list.append(current_time)
current_time += delta
```
可以改写为:
```
time_new_list = pd.date_range(start=start_time, end=end_time, freq='15min')
```
最后,可以使用 pandas 库提供的函数 `fillna()` 进行缺失值处理,而无需使用循环语句。
优化后的代码如下:
```
def data_processing(data):
# 日期缺失,补充
data.fillna(method='ffill', inplace=True)
# 转换时间格式
date_history = pd.to_datetime(data.iloc[:, 0], format='%Y/%m/%d %H:%M')
# 缺失值处理
start_time = date_history.iloc[0]
end_time = date_history.iloc[-1]
time_new_list = pd.date_range(start=start_time, end=end_time, freq='15min')
data_history = data.iloc[:, 1].fillna(method='ffill')
data_history = data_history.reindex(code_list, method='ffill')
# 输出补充好之后的数据
data = pd.DataFrame({'date': time_new_list, 'load': data_history})
return data
```
解释下这段代码watch_time_record = openpyxl.load_workbook("eye_movement_record_43.xlsx") #这里要改文件名,最后头要改储存文件名 images_workbook = openpyxl.load_workbook("images.xlsx") fix_record = watch_time_record["FIX"] images_info = images_workbook["Sheet1"] # watch_time_record.close() # images_workbook.close() # 注意openpyxl读取excel的时候,行从1开始,列仍从0开始 out_data_workbook = ow.Workbook(write_only=True) out_data_sheet = out_data_workbook.create_sheet("watch_time_stat_fix") # out_data_sheet.append(["start_time", "end_time", "duration", "coordinate_x", "coordinate_y", "pupil_size"]) out_data_sheet.append( ["trail_index", "img", "trail_start_time", "trail_end_time", "object1_duration", "object1_times", "object2_duration", "object2_times"])
这段代码的作用是使用openpyxl库导入Excel文件,并在其中读取指定的工作表信息,同时也创建了一个新的Excel文件,以待写入数据。
具体解释如下:
1. `watch_time_record = openpyxl.load_workbook("eye_movement_record_43.xlsx")`:使用`openpyxl`库中的`load_workbook()`函数读取名为`eye_movement_record_43.xlsx`的Excel文件,并将其存储到`watch_time_record`变量中,以便后续读取工作表信息。
2. `images_workbook = openpyxl.load_workbook("images.xlsx")`:同样使用`openpyxl`库中的`load_workbook()`函数读取名为`images.xlsx`的Excel文件,并将其存储到`images_workbook`变量中,以便后续读取工作表信息。
3. `fix_record = watch_time_record["FIX"]`:使用`watch_time_record`变量中打开的Excel文件中的`"FIX"`工作表,并将其存储到`fix_record`变量中,以便后续读取信息。
4. `images_info = images_workbook["Sheet1"]`:使用`images_workbook`变量中打开的Excel文件中的`"Sheet1"`工作表,并将其存储到`images_info`变量中,以便后续读取信息。
5. `out_data_workbook = ow.Workbook(write_only=True)`:创建一个新的Excel文件,并将其存储到`out_data_workbook`变量中,以待写入数据。
6. `out_data_sheet = out_data_workbook.create_sheet("watch_time_stat_fix")`:在`out_data_workbook`变量中创建一个名为`"watch_time_stat_fix"`的工作表,并将其存储到`out_data_sheet`变量中,以待写入数据。
7. `out_data_sheet.append(["trail_index", "img", "trail_start_time", "trail_end_time", "object1_duration", "object1_times", "object2_duration", "object2_times"])`:向`out_data_sheet`工作表中添加一行数据,该行包含了多个列名,用于在后续的数据写入中进行参考。
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