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 sqlalchemy import Column, Integer, String, Date, UniqueConstraint from sqlalchemy.ext.declarative import declarative_base Base = declarative_base() class DBBook(Base): __tablename__ = 'books' book_id = Column(Integer, primary_key=True) title = Column(String, nullable=False) author = C...
一:pandas简介 Pandas 是一个开源的第三方 Python 库,从 Numpy 和 Matplotlib 的基础上构建而来,享有数据分析“三剑客之一”的盛名(NumPy、Matplotlib、Pandas)。Pandas 已经成为 Python 数据分析的必备高级工具,它的目标是成为强大、
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....
}', 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....
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...
e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column. # dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo' # === '''A:在读取文件时,将str类型的date列转化成dateTime类型''' '''B:并将时间列作为索引列''' ...
(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")...
df2[column] 筛选之后是一个Series,在这个数据上做修改会影响到原数据。 df2[[column]] 这个属于花式索引,两层中括号,筛选之后赋值给变量是一个DataFrame,它有自己的原数据,因为做任何修改不会影响到原数据。 3.2 删除 df.drop() 通过指定label或者index,还有轴方向axis来控制删除的范围和方向。 df2.drop( labe...