我已将具有两列的数据集导入python，现在我想向此数据集添加6列，这些数据列是使用python计算的，并添加了具有不同设置的均值。
数据集称为final.csv，大约有620万行数据。
这是一个我正在尝试自学python和机器学习的项目，希望对您有所帮助。
我使用
导入数据库 import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#import dataset
data = pd.read_csv("Final.csv")
那很好。
问题是我可以生成均值，但是它在我只希望在索引1上的两列上都完成了，然后创建了6个其他数据集。
我在这里做什么错了？
平均值的代码如下：
exp1 = data.ewm(span=21, adjust=False).mean()
exp2 = data.ewm(span=13, adjust=False).mean()
exp3 = data.ewm(span=8, adjust=False).mean()
rolling_mean = data.rolling(window=21).mean()
rolling_mean2 = data.rolling(window=13).mean()
rolling_mean3 = data.rolling(window=8).mean()
如果有人可以解释这样做的正确方法，我将不胜感激。
编辑
所以目前，当我执行导入时，它显示为
0.88484, 0.88515
0.88484, 0.88515
0.88464, 0.88495
0.88484, 0.88515
0.88474, 0.88505
0.88454, 0.88485
0.88434, 0.88465
0.88434, 0.88465
这是从我的数据集中导入的标准内容。
然后我平均了，我得到了
0.88484 , 0.88515
0.88484 , 0.88515
0.884821818181818 , 0.8851318181818182
0.88482347107438 , 0.8851334710743801
0.8848158827948909 , 0.8851258827948909
0.8847908025408099 , 0.8851008025408098
0.8847498204916454 , 0.8850598204916452
0.884712564083314 , 0.8850225640833138
这是我运行的每个脚本的结尾，因此以6个新的数据集结尾，两列都位于我希望的位置
Original1,Original2,Mean1, Mean2, Mean3, RollingMean1,RollingMean2,RollingMean3
0.88484, 0.88515, 0.88515, 0.88515, 0.88515, 0.884778571, 0.88485, 0.88495
0.88484, 0.88515, 0.88515, 0.88515, 0.88515, 0.884778571, 0.88485, 0.88495
0.88464, 0.88495, 0.885131818, 0.885121429, 0.885105556, 0.884778571, 0.88485, 0.88495
0.88484, 0.88515, 0.885133471, 0.88512551, 0.885115432, 0.884778571, 0.88485, 0.88495
0.88474, 0.88505, 0.885125883, 0.885114723, 0.885100892, 0.884778571, 0.88485, 0.88495
0.88454, 0.88485, 0.885100803, 0.885076905, 0.885045138, 0.884778571, 0.88485, 0.88495
0.88434, 0.88465, 0.88505982, 0.885015919, 0.88495733, 0.884778571, 0.88485, 0.88495
0.88434, 0.88465, 0.885022564, 0.884963645, 0.884889034, 0.884778571, 0.88485, 0.88495
0.88434, 0.88465, 0.884988695, 0.884918838, 0.884835915, 0.884778571, 0.88485, 0.8848875
0.88434, 0.88465, 0.884957904, 0.884880433, 0.884794601, 0.884778571, 0.88485, 0.884825
0.88444, 0.88475, 0.884939004, 0.8848618, 0.88478469, 0.884778571, 0.88485, 0.8848
0.88444, 0.88475, 0.884921822, 0.884845828, 0.884776981, 0.884778571, 0.88485, 0.88475
0.88434, 0.88465, 0.884897111, 0.884817853, 0.884748763, 0.884778571, 0.88485, 0.8847
0.88434, 0.88465, 0.884874646, 0.884793874, 0.884726816, 0.884778571, 0.884811538, 0.884675
0.88434, 0.88465, 0.884854224, 0.88477332, 0.884709745, 0.884778571, 0.884773077, 0.884675
0.88444, 0.88475, 0.884844749, 0.884769989, 0.884718691, 0.884778571, 0.884757692, 0.8846875
0.88434, 0.88465, 0.884827044, 0.884752848, 0.884703426, 0.884778571, 0.884719231, 0.8846875
0.88434, 0.88465, 0.884810949, 0.884738155, 0.884691554, 0.884778571, 0.884688462, 0.8846875
0.88454, 0.88485, 0.884814499, 0.884754133, 0.884726764, 0.884778571, 0.884688462, 0.8847
0.88434, 0.88465, 0.884799545, 0.884739257, 0.884709705, 0.884778571, 0.884688462, 0.8846875
0.88414, 0.88445, 0.884767768, 0.884697934, 0.884651993, 0.884778571, 0.884673077, 0.8846625
0.88424, 0.88455, 0.884747971, 0.884676801, 0.884629328, 0.88475, 0.884665385, 0.88465
0.88414, 0.88445, 0.884720883, 0.884644401, 0.884589477, 0.884716667, 0.88465, 0.884625
0.88434, 0.88465, 0.884714439, 0.884645201, 0.884602927, 0.884702381, 0.884642308, 0.8846125
0.88444, 0.88475, 0.884717672, 0.884660172, 0.88463561, 0.884683333, 0.884642308, 0.884625
0.88434, 0.88465, 0.88471152, 0.884658719, 0.884638808, 0.884664286, 0.884642308, 0.884625
0.88444, 0.88475, 0.884715018, 0.884671759, 0.884663517, 0.884659524, 0.88465, 0.8846125
0.88444, 0.88475, 0.884718198, 0.884682936, 0.884682735, 0.884664286, 0.884657692, 0.884625
0.88444, 0.88475, 0.884721089, 0.884692517, 0.884697683, 0.884669048, 0.884657692, 0.8846625
0.88434, 0.88465, 0.884714627, 0.884686443, 0.884687087, 0.884669048, 0.884657692, 0.884675
0.88444, 0.88475, 0.884717842, 0.884695523, 0.884701068, 0.88467381, 0.884665385, 0.8847125
0.88434, 0.88465, 0.884711675, 0.884689019, 0.884689719, 0.884669048, 0.88465, 0.8847125
0.88454, 0.88485, 0.88472425, 0.884712017, 0.884725337, 0.88467381, 0.884665385, 0.884725
0.88424, 0.88455, 0.884708409, 0.884688871, 0.884686373, 0.884669048, 0.884673077, 0.8847125
0.88424, 0.88455, 0.884694008, 0.884669033, 0.884656068, 0.884664286, 0.884673077, 0.8846875
0.88424, 0.88455, 0.884680916, 0.884652028, 0.884632497, 0.884659524, 0.884680769, 0.8846625
0.88424, 0.88455, 0.884669015, 0.884637453, 0.884614165, 0.88465, 0.884673077, 0.8846375
0.88424, 0.88455, 0.884658195, 0.884624959, 0.884599906, 0.884645238, 0.884657692, 0.884625
0.88444, 0.88475, 0.884666541, 0.884642822, 0.88463326, 0.88465, 0.884665385, 0.884625
编辑2
我有一个带有两列的数据集 0.88424、0.88455， 然后，使用第二列计算出从先前的x值计算得出的平均值，因此对于上述值，它是： 0.884708409 0.884688871 0.884686373 0.884669048 0.884673077 0.8847125
当我运行代码以获取均值时，我只需要在第2列即索引1处获得第1列和第2列的均值，那么我不希望将它们作为其他数据集，而是将它们组合在一起，因此最终产品将是< / p>
0.88424, 0.88455, 0.884708409, 0.884688871, 0.884686373, 0.884669048, 0.884673077, 0.8847125
而不是拥有
dataset
0.88424, 0.88455
Mean1
0.884708409
Mean2
0.884688871
Mean3
0.884686373
RollingMean1
0.884669048
RollingMEan2
0.884673077
RollingMean3
0.8847125
每个都作为自己的数据集