# 寻找星期几跟股票张得的关系 # 1、先把对应的日期找到星期几 date = pd.to_datetime(data.index).weekday data['week'] = date # 增加一列 # 2、假如把p_change按照大小去分个类0为界限 data['posi_neg'] = np.where(data['p_change'] > 0, 1, 0) # 通过交叉表找寻两列数据的关系 count ...
(total 2 columns): # Column Non-Null Count Dtype --- --- --- --- 0 date 204 non-null object 1 value 204 non-null float64 dtypes: float64(1), object(1) memory usage: 3.3+ KB """ # Convert to datetime df["date"] = pd.to_datetime(df["date"], format = "%Y-%m-%d")...
df["year"] = df["date"].dt.yeardf["month"] = df["date"].dt.monthdf["day"] = df["date"].dt.daydf["calendar"] = df["date"].dt.datedf["hour"] = df["date"].dt.timedf.head()""" date value year month day calendar hour0 1991-07-01 3.526591 1991 7 1 1991-07-01 00...
info() """ <class 'pandas.core.frame.DataFrame'> RangeIndex: 204 entries, 0 to 203 Data columns (total 2 columns): # Column Non-Null Count Dtype --- --- --- --- 0 date 204 non-null datetime64[ns] 1 value 204 non-null float64 dtypes: datetime64[ns](1), float64(1) memory...
from datetime import date def calculate_age(born): today = date.today() try: birthday = born.replace(year=today.year) except ValueError: birthday = born.replace(year=today.year, month=born.month + 1, day=1) if birthday > today: return today.year - born.year - 1 else: return today....
import plotly.graph_objects as goimport numpy as npimport pandas as pd# 读取数据temp = pd.read_csv('2016-weather-data-seattle.csv')# 数据处理, 时间格式转换temp['year'] = pd.to_datetime(temp['Date']).dt.year# 选择几年的数据展示即可year_list = [1950, 1960, 1970, 1980, 1990, 2000...
}', rep)programing = [eval(k[0]) for k in data] # 编程语言dates = [i[1] for i in data]# 正则表达式处理 提取出想要的数据for x in range(len(dates)): name = programing[x] datas = re.findall(r'\[Date.UTC(.*?)\]', dates[x], re.DOTALL) for m in datas: date1 = re....
s._date = datetime(year, month, 1) #每月第一日 s._selection = None #设置为未选中日期 s.G_Frame = ttk.Frame(s.master) s._cal = s.__get_calendar(locale, fwday) s.__setup_styles() # 创建自定义样式 s.__place_widgets() # pack/grid 小部件 ...
Python program to get pandas column index from column name # Importing pandas packageimportpandasaspd# Defining a DataFramesdf=pd.DataFrame(data={'Parle':['Frooti','Krack-jack','Hide&seek'],'Nestle':['Maggie','Kitkat','EveryDay'],'Dabur':['Chawanprash','Honey','Hair oil']})# Displa...
# Column Non-Null Count Dtype --- --- --- --- 0 date 204 non-null object 1 value 204 non-null float64 dtypes: float64(1), object(1) memory usage: 3.3+ KB """# Convert to datetimedf["date"] =pd.to_datetime(df["date"],format="%Y-%m-%d") df.info()""" <class ...