代写matplotlib、代做Python程序、代写dataframe、Python语言程序代做

日期：2018-10-15 10:42

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 truncate

Count 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 volumw

Aggregate into weeks by averaging up the value of each daily volume