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...
下面是一个例子：```pythonimport requestsimport pandas as pdimport time# 设置代理IP池# 理IP池的最大长度MAX_PROXY_NUM = 100# 代理IP池proxy_pool = []# 获取代理IP并验证，如果可用则加入代理IP池中def get_and_verify_proxy(url):try:# 设置headers，模拟浏览器访问headers = {'User-Agent': 'Moz...
此处取apple组 # 方法 1 df_grouped.get_group('apple') # 方法 2 for i,
"Parch","Embarked"] df_coded = pd.get_dummies( df_train, # 要转码的列 columns=needcode_cat_columns, # 生成的列名的前缀 prefix=needcode_cat_columns, # 把空值也做编码 dummy_na=True, # 把1 of k移除(dummy variable trap) drop_first=True )...
Series# 从 .stdev 模块中导入 stdev 别名为 stdevfrom.stdevimportstdevasstdev# 从 pandas_ta.utils 模块中导入 get_offset、verify_series 函数frompandas_ta.utilsimportget_offset, verify_series# 定义函数 tos_stdevall,计算 Think or Swim 标准偏差deftos_stdevall(close, length=None, stds=None, ddof=...
(fast, slow, lookback)) # 获取偏移量 offset = get_offset(offset) # 如果 kwargs 中包含 "length" 键,则移除它 if "length" in kwargs: kwargs.pop("length") # 如果未提供 close 参数,则返回空值 if close is None: return # 计算快速移动平均线和慢速移动平均线 fast_ma = ma(mamode, ...
Write a Pandas program to get the datatypes of columns of a DataFrame. Sample data: Original DataFrame: attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 ... 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 Data types of the columns of the said DataFrame: attempts int64 ...
36defget_weather_data(city='hangzhou',time_func_name=get_month_period,*args):37begin,end=time_func_name(*args)38print(begin,end)39# 获得需要爬取的日期区间40date_list=[date.strftime("%Y%m")fordateinpd.date_range(begin,end,freq='M')]41# 构建url42url_list=["http://www.tianqihoubao...
limit=limit) con= db.get_engine(current_app,'tm_new_hfjy') with con.connect() as conn: df1= pd.read_sql(sql, con=conn) df2= pd.read_sql("SELECT FOUND_ROWS() as total;", con=conn)#df1 = df1.drop(index=[0])#print(df1["update_time"].head())#print(df1.dtypes)#df1["crea...
copy() tmp['d'] = 4 # Altering data associated with D-Tale process # FYI: this will clear any front-end settings you have at the time for this process (filter, sorts, formatting) d.data = tmp # Get raw dataframe w/ any sorting or edits made through the UI d.data # Get raw ...