# 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
# 方式1:直接转换后设置索引 df.index = pd.to_datetime(df.pop('timestamp_column')) # 方式2:链式操作(推荐) df = df.set_index(pd.to_datetime(df['raw_time'])).drop(columns=['raw_time']) 1. 2. 3. 4. 5. 2.2 智能切片操作 # 部分字符串匹配(自动解析) jan_data = df['2025-01'...
>>>importpandasaspd>>>column_subset=[..."id",..."make",..."model",..."year",..."cylinders",..."fuelType",..."trany",..."mpgData",..."city08",..."highway08"...]>>>df=pd.read_csv(..."https://www.fueleconomy.gov/feg/epadata/vehicles.csv",...usecols=column_subset...
方式1:直接转换后设置索引 df.index = pd.to_datetime(df.pop('timestamp_column')) 方式2:链式操作(推荐) df = df.set_index(pd.to_datetime(df['raw_time'])).drop(columns=['raw_time']) 2.2 智能切片操作 部分字符串匹配(自动解析) jan_data = df['2025-01'] # 提取2025年1月所有数据 跨...
你也可以指定columns的顺序,如果你指定的Column不存在时默认用NaN填充。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 In [64]: frame = DataFrame(data,columns=['year','state','pop','exit'],index=['a','b','c','d','k']) In [65]: frame Out[65]: year state pop exit a 2000 ...
drinks.select_dtypes(include=['number','object','category','datetime']).head() #用 exclude 关键字排除指定的数据类型 drinks.select_dtypes(exclude=['number']).head() 7.字符串转换为数值 df = pd.DataFrame({'列1':['1.1','2.2','3.3'], '列2':['4.4','5.5','6.6'], '列3':['7.7...
RangeIndex: 6 entries, 0 to 5 Data columns (total 6 columns): # Column Non-Null Count Dtype 0 id 6 non-null int64 1 date 6 non-null datetime64[ns] 2 city 6 non-null object 3 category 6 non-null object 4 age 6 non-null int64 5 price 4 non-null float64 dtypes: datetime64ns...
dtype='datetime64[ns]', freq='D') #以时间序列为index,以“ABCD”为列明,用24个符合正态分布的随机数作为数值df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list("ABCD")) df A B C D2023-02-03 -1.688539-0.687145-0.087825-0.1137402023-02-04 -0.483402-2.333871-1.0787781.78...
# 按大体类型推定m = ['1', 2, 3]s = pd.to_numeric(s) # 转成数字pd.to_datetime(m) # 转成时间pd.to_timedelta(m) # 转成时间差pd.to_datetime(m, errors='coerce') # 错误处理pd.to_numeric(m, errors='ignore')pd.to_numeric(m errors='coerce'...
df[columnname]:标示一个Series df[[columnname]]:标示一个DataFrame DataFrame可以用join函数进行拼接,而Series则不行 六。df拼接:join df.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False) 将df 和other按列合并, on:None代表是按照索引index进行匹配合并 columnsname:按照列进行...