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Python中使用条件的时间序列分析

[英]Time series analysis in Python using conditions

I have the following data (sample)我有以下数据(样本)

Symbol Sections      iBid     Bid                Date
0    O.U20       O1  99.73167  99.730 2020-06-29 16:32:25
1    O.Z20       O1  99.70250  99.700 2020-06-29 16:32:25
2    O.H21       O1       NaN  99.795 2020-06-29 16:32:25
3    O.M21       O1  99.81167  99.810 2020-06-29 16:32:25
4    O.U21       O2  99.81667  99.815 2020-06-29 16:32:25
5    O.Z21       O2       NaN  99.795 2020-06-29 16:32:25
6    O.H22       O2  99.81000  99.810 2020-06-29 16:32:25
7    O.M22       O2  99.79500  99.795 2020-06-29 16:32:25
16  F3.U26       F3       NaN   1.000 2020-06-29 16:32:25
17  F3.Z26       F3       NaN  -3.000 2020-06-29 16:32:25
18  F3.H27       F3       NaN  -1.000 2020-06-29 16:32:25
19  F6.H26       F6  -1.75000     NaN 2020-06-29 16:32:25
20  F6.M26       F6  -4.50000     NaN 2020-06-29 16:32:25
21  F6.U26       F6  -5.50000     NaN 2020-06-29 16:32:25
22  F9.U20       F9  -8.50000  -9.000 2020-06-29 16:32:25
23   O.U20       O3  99.73167  99.730 2020-06-29 16:32:26
24   O.Z20       O3  99.70250  99.700 2020-06-29 16:32:26
25   O.H21       O3       NaN  99.795 2020-06-29 16:32:26
26   O.M21       O3  99.81167  99.810 2020-06-29 16:32:26
27   O.U21       O4  99.81667  99.815 2020-06-29 16:32:26
28   O.Z21       O4       NaN  99.795 2020-06-29 16:32:26
29   O.H22       O4  99.81000  99.810 2020-06-29 16:32:26
30   O.M22       O4  99.79500  99.795 2020-06-29 16:32:26

What I want to do is draw a scatterplot or a line chart or any kind of chart that is suitable for such an analysis that can analyze the trend over time if a condition is met.我想要做的是绘制散点图或折线图或任何适合这种分析的图表,如果满足条件,可以分析随时间变化的趋势。 For example, I want to see how many times iBid is higher than Bid overtime for each symbol like (O,S,F) and also for sections (O1,F3 etc)例如,我想查看每个符号(O、S、F)以及部分(O1、F3 等)的 iBid 比 Bid 加班高多少倍

I know I'm required to present some working but I'm not sure if such a chart is even possible?我知道我需要展示一些工作,但我不确定这样的图表是否可能? So far I can only do is sperate the data based on Symbol到目前为止,我只能根据 Symbol 对数据进行拆分

df_O = df[df['Contract'].str.contains('O')]

and filter out the results like并过滤掉类似的结果

IbidgreaterBid = big_frame[(big_frame.iBid > big_frame.Bid)]

Is it possible to obtain a graph that can analyze when is Ibid > Bid with Date column as x axis?是否可以获得可以分析 Ibid > Bid 何时以 Date 列为 x 轴的图表? (Date column has thousand of rows with the only difference of seconds) (日期列有千行,只有秒的差异)

It's not clear what you mean by a graph that can analyze when ibid > bid.当同上>出价时可以分析的图表不清楚您的意思。 However, I can suggest a way to distinguish data based on Ibid >/< Bid.但是,我可以建议一种基于 Ibid >/< Bid 来区分数据的方法。 In the following example, red scatter points indicate data points where Ibid > Bid, blue for otherwise.在以下示例中,红色散点表示 Ibid > Bid 的数据点,蓝色表示其他情况。 Moreover, because the difference is only on the second's scale, I've made use of mdates date-formatter to set xticks to show HMS only.此外,由于差异仅在秒的范围内,我使用mdates日期格式化程序将 xticks 设置为仅显示 HMS。

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from matplotlib.offsetbox import AnchoredText
import matplotlib.dates as mdates
from datetime import timedelta
plt.style.use('seaborn-whitegrid')

n_sections=df['Sections'].nunique()
cols=2
rows=int(round(n_sections/2.0))
#setup the plot
fig, ax = plt.subplots(rows, cols, figsize=(16,8),sharex=False,sharey=False) # if you want to turn off sharing axis.
row=0 #to iterate over rows/cols
col=0 #to iterate over rows/cols


for index, Section in df.groupby('Sections'):
    ax[row][col].scatter(np.array(Section['Datetime']),Section['iBid'] , color='blue')
    ax[row][col].scatter(np.array(Section['Datetime'][Section['iBid']>Section['Bid']]),Section['iBid'][Section['iBid']>Section['Bid']] , color='red')
    ax[row][col].set_xlim([min(Section['Datetime'])-timedelta(seconds=5), max(Section['Datetime'])+timedelta(seconds=5)])
    ax[row][col].set_xlabel('Date Time',fontsize=20)
    ax[row][col].set_ylabel('iBid',fontsize=20)
    anchored_text = AnchoredText("{}".format(Section['Sections'].unique()[0]), loc=4,prop=dict(size=20))
    ax[row][col].add_artist(anchored_text)

    ax[row][col].xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
    ax[row][col].tick_params(axis='both', direction='in', which='major', length=5, width=2,labelsize=16)
    
    row=row+1
    if row==rows:
        row=0
        col=col+1

在此处输入图像描述

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