# change monthly freq to daily freq In [387]: pi.astype("period[D]") Out[387]: PeriodIndex(['2016-01-31', '2016-02-29', '2016-03-31'], dtype='period[D]') # convert to DatetimeIndex In [388]: pi.astype("datetime64[ns]") Out[388]: DatetimeIndex(['2016-01-01', '2016-02...
pandas.to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False, origin='unix', cache=True)[source] 将参数转换为datetime。 参数: arg:integer,float,string,datetime, list,tuple,1-d array(一维数组...
in DatetimeIndex._maybe_cast_slice_bound(self, label, side) 637 if isinstance(label, dt.date) and not isinstance(label, dt.datetime): 638 # Pandas supports slicing with dates, treated as datetimes at
# @Time : 2022/1/12 19:57 # @Author : 南黎 # @FileName: 3..py import pandas as pd ###显示中文宋体字体导入,如果使用中文加上这段代码### import matplotlib as plt plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False ### import pandas as pd...
* 要选择字符串, 必须使用object, 但这样返回的结果可能会包含格式未正确转化的其他列 * 要选择datetime, 应使用np.datetime64 或 'datetime' 或 'datetime64' * 要选择timedelta, 应使用np.timedelta64 或 'timedelta' 或 'timedelta64' * 要选择 pandas的 categorical数据类型, 应使用'category' ...
ignore: suppress exceptions. On error return original object. Returns: same type as caller See also to_datetime Convert argument to datetime. to_timedelta Convert argument to timedelta. to_numeric Convert argument to a numeric type. numpy.ndarray.astype ...
str or datetime-like, default NoneLeft bound for generating dates.end : str or datetime-like, default NoneRight bound for generating dates.periods : int, default NoneNumber of periods to generate.freq : str or DateOffset, default 'B' (business daily)Frequency strings can have multiples, e....
你可以使用pct_change方法计算每个股票的收益率,并将其作为因子进行分析: import akshare as ak import pandas as pd import statsmodels.api as sm # 定义股票代码列表 stock_symbols = ['600570', '600313'] # 初始化一个空的DataFrame来存储合并的数据 combined_df = pd.DataFrame() # 为每个股票代码获取...
to_datetime('20200701'))]# 数值筛选df[(df['popularity'] > 3) & (df['popularity'] < 7)]# 按数据类型选择列df = pd.DataFrame({'a': [1, 2] * 3, 'b': [True, False] * 3, 'c': [1.0, 2.0] * 3})print('df:', df)# 输出包含 bool 数据类型的列print('输出包含 bool 数据...
‘’’ [RangeIndex(start=0, stop=100, step=1), Index([‘name’, ‘team’, ‘Q1’, ‘Q2’, ‘Q3’, ‘Q4’], dtype=‘object’)]‘’’ Series 只显示列索引,就是它的索引: s.axes [RangeIndex(start=0, stop=100, step=1)] 其他信息 注:以下信息操作,DataFrame 和 Series 一般都支持...