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Homework 5
Import modules
from datetime import datetime
import pandas as pd
import matplotlib.pyplot as pyplot
Consider the following data points:
date tick_numbers
2016-05-01 10:23:05.069722 3213
2016-05-01 10:23:05.119994 4324
2016-05-02 10:23:05.178768 2132
2016-05-02 10:23:05.230071 43242
2016-05-02 10:23:05.230071 4234
2016-05-02 10:23:05.280592 4234
2016-05-03 10:23:05.332662 4324
2016-05-03 10:23:05.385109 1245
2016-05-04 10:23:05.436523 1555
2016-05-04 10:23:05.486877 543345
Create a dataframe ‘ts’
ts=
print ts
date tick_numbers
0 2016-05-01 10:23:05.069722 3213
1 2016-05-01 10:23:05.119994 4324
2 2016-05-02 10:23:05.178768 2132
3 2016-05-02 10:23:05.230071 43242
4 2016-05-02 10:23:05.230071 4234
5 2016-05-02 10:23:05.280592 4234
6 2016-05-03 10:23:05.332662 4324
7 2016-05-03 10:23:05.385109 1245
8 2016-05-04 10:23:05.436523 1555
9 2016-05-04 10:23:05.486877 543345
Convert ts['date'] from string to datetime. You can use ts.index.
ts.index=Delete useless column with the command del
del
print ts
In [17]: print ts
tick_numbers
date
2016-05-01 10:23:05.069722 3213
2016-05-01 10:23:05.119994 4324
2016-05-02 10:23:05.178768 2132
2016-05-02 10:23:05.230071 43242
2016-05-02 10:23:05.230071 4234
2016-05-02 10:23:05.280592 4234
2016-05-03 10:23:05.332662 4324
2016-05-03 10:23:05.385109 1245
2016-05-04 10:23:05.436523 1555
2016-05-04 10:23:05.486877 543345
Print all data from 2016
Print all data from May 2016
Data after May 3rd, 2016
Remove all the data after May 2nd, 2016 using truncateCount the number of data per timestamp
Mean value of ticks per day. You will use resample with a period of D
and a method of mean.
Total value ticks per day. You will use sum and a period of D
Plot of the total of ticks per day
Create another dataframe
np.random.seed(12345)
# create a dictionary
# df[‘ARCA’] = store np.random.randint(low=20000, high=30000, size=62)
# df[‘BARX’] = store np.random.randint(low=20000, high=30000, size=62)
# index = pd.date_range('4/1/2012', '6/1/2012')
# create the dataframe with the 3 components above
Print (df)pd.DataFrame(volume,index=index).head()
Out[90]:
ARCA BARX
2012-04-01 24578 28633
2012-04-02 22177 26542
2012-04-03 23492 26554
2012-04-04 24094 21707
2012-04-05 24478 25568
Truncate the dataframe to get data (before='2012-04-04',after='2012-05-24')
Change the offset of the dataframe by pd.DateOffset(months=1, days=1)
Shift the dataframe by 1 day
Lag a variable 1 day
Aggregate into 2W-SUN (bi-weekly starting by Sunday) by summing up
the value of each daily volumwAggregate into weeks by averaging up the value of each daily volume

辅导matplotlib、讲解Python程序、辅导dataframe、Python语言程序讲解 讲解数据库SQL|辅导Python编程

辅导matplotlib、讲解Python程序、辅导dataframe、Python语言程序讲解讲解数据库SQL|辅导Python编程