对于一维数据的分析,最常见的就是计算平均值(Mean)、方差(Variance)和标准差(Standard Deviation)。在做【特征工程】的时候,会出现缺失值,那么经常会用到使用 平均值 或者 中位数等进行填充。 平均值 平均值的概念很简单:所有数据之和除以数据点的个数,以此表示数据集的平均大小;其数学定义为 以下面5个数据为例
Output: --- Measure Variance --- Apple 367.900000 Orange 52.666667 Banana 134.266667 Pear 211.866667 dtype: float64 --- Standard Deviation --- Apple 19.180719 Orange 7.257180 Banana 11.587349 Pear 14.555640 dtype: float64 计算DataFrame 列之间的协方差 import pandas as pd df = pd.DataFrame([[10,...
non_zero_range、verify_series 函数frompandas_ta.utilsimportget_offset, non_zero_range, verify_series# 定义一个函数 massi,用于计算 Mass Index(MASSI)defmassi(high, low, fast=None, slow=None, offset=None, **kwargs):"""Indicator: Mass
df.fillna(method=kwargs["fill_method"], inplace=True)# 准备返回的 DataFramedf.name =f"{_props}"df.category ="statistics"returndf# 设定函数的文档字符串tos_stdevall.__doc__ = \"""TD Ameritrade's Think or Swim Standard Deviation All (TOS_STDEV) A port of TD Ameritrade's Think or Sw...
--- Measure Variance --- Apple 367.900000 Orange 52.666667 Banana 134.266667 Pear 211.866667 dtype: float64 --- Standard Deviation --- Apple 19.180719 Orange 7.257180 Banana 11.587349 Pear 14.555640 dtype: float64 84计算 DataFrame 列之间的协方差 import pandas as pd df = pd.DataFrame([[10, 20...
std() Returns the standard deviation of the values in the specified axis sum() Returns the sum of the values in the specified axis sub() Subtracts the values of a DataFrame with the specified value(s) swaplevel() Swaps the two specified levels T Turns rows into columns and columns into ...
---Measure Variance---Apple367.900000Orange52.666667Banana134.266667Pear211.866667dtype:float64---Standard Deviation---Apple19.180719Orange7.257180Banana11.587349Pear14.555640dtype:float64 84计算 DataFrame 列之间的协方差 importpandasaspd df=pd.DataFrame([[10,20,30,40...
get_offset、verify_series 函数from pandas_ta.utils import get_offset, verify_series# 定义函数 tos_stdevall,计算 Think or Swim 标准偏差def tos_stdevall(close, length=None, stds=None, ddof=None, offset=None, **kwargs):"""Indicator: TD Ameritrade's Think or Swim Standard Deviation All"""...
get_offset(offset) mode_tal = bool(talib) if isinstance(talib, bool) else True if close is None: return # 计算结果 if Imports["talib"] and mode_tal: from talib import BBANDS upper, mid, lower = BBANDS(close, length, std, std, tal_ma(mamode)) else: standard_deviation = stdev(...
(50% quantile) of values mad Mean absolute deviation from mean value prod Product of all values var Sample variance of values std Sample standard deviation of values skew Sample skewness (third moment) of values kurt Sample kurtosis (fourth moment) of values cumsum Cumulative sum of values ...