**kwargs) end_time = time.time() print(f"{func.__name__} took {end_time - start_time:.2f} seconds to execute.") return result return wrapper@timerdef my_data_processing_function(): # Your data processing code here 将...
import pandas as pd import cudf import time # 使用 Pandas 加载数据 start = time.time() df_pandas = pd.read_csv('ecommerce_data.csv') pandas_load_time = time.time() - start # 使用 cuDF.pandas 加载数据 start = time.time() df_cudf = cudf.read_csv('ecommerce_data.csv') cudf_load...
calculate_average([1, 2, 3, 4, 5]) 此装饰器将在调用calculate_average函数时自动记录日志。 3.2.1.2 性能分析装饰器 这里展示一个计算函数执行时间的装饰器: import time def timing_decorator(original_function): @functools.wraps(original_function) def wrapper(*args, **kwargs): start_time = time....
created = Time(tz.localize(created)) modified = dt.fromtimestamp(os.path.getmtime(source)) modified = Time(tz.localize(modified)) accessed = dt.fromtimestamp(os.path.getatime(source)) accessed = Time(tz.localize(accessed))print("Source\n===")print("Created: {}\nModified: {}\nAccessed:...
def calculate_global_ranking(characters: List['GameCharacter']): # 这是一个不依赖于实例的排序方法 pass3.3数据一致性与比较 为了确保数据的一致性和正确性,dataclasses提供了丰富的选项来定制数据类实例间的比较行为。 3.3.1eq、order与frozen选项
if delay_time is not None: return delay_time delay_time = get_active_intime_delay(active_in_time) if delay_time is not None: return delay_time logging.warning("Activation delay time is invalid!") return None def sha256_calc(file_path, is_config_file=False): """Calculate the SHA256 ...
df = df.drop_duplicates() df.shape (220320, 53) def calculate_percentage_NAs(df): nans = pd.DataFrame(df.isnull().sum().sort_values(ascending=False)/len(df), columns=['percent']) idx = nans['percent'] > 0 return nans[idx] calculate_percentage_NAs(df).head(10) percent sensor...
Python脚本文件是两种中间文件格式中的一种。设备通过运行Python脚本来下载版本文件。 Python脚本文件的文件名必须以“.py”作为后缀名,格式如Python脚本文件示例所示。详细脚本文件解释请见Python脚本文件解释。 Python脚本文件示例 该脚本文件仅作为样例,支持SFTP协议进行文件传输,用户可以根据实际开局场景进行修改。
代码:defcalculate_ichimoku_cloud(df):# Tenkan-sen (Conversion Line)nine_period_high = df['high'].rolling(window=9).max() nine_period_low = df['low'].rolling(window=9).min() df['tenkan_sen'] = (nine_period_high + nine_period_low) /2# Kijun-sen (Base Line)twenty_six_period_hi...
chronometer_start = 1670000000 #timestamp start_recording = 1673280570 #timestamp gap_in_seconds = start_recording - chronometer_start # given that the recordings are of 5 minutes each but with 2.5 minutes overlap, # I can calculate how many Null values to add at the beginning of the recordi...