This document discusses using Pandas and Numpy to calculate stock statistics and historical price yield (HPY). It covers importing stock data from Yahoo Finance, refining the data, calculating statistics like mean, variance, and standard deviation using Numpy functions, and combining data to calculate and print the HPY value for Apple stock. The key steps involve understanding Pandas and Numpy, importing and refining stock data, using Numpy functions to calculate statistics, entering the HPY formula, combining date data with a DataFrame, and printing the final HPY value for Apple.

1. Using Pandas,Numpy calculate
stock statics and HPY
WI258@NAVER.COM

2. First need to know concept
Python concept and code
Pandas (data refinement package)
Numpy (data array and statics package)
statistics and HPY formula

3. statistics and HPY formula
mean : HPY(HPR) :
variance :
standard deviation:

4. Import stock data and refinement
First download stock
file on yahoo. And
then refine Date,
Open , Close Data,
Print it
In second one
have loc function .
Loc function
refine line data

5. mean , standard deviation , variance
Using Numpy statistic
function calculate statistics
value.

6. Numpy statistics function
함수
np.sum Calculate data sum
np.mean Calculate data mean
np.std Calculate data standard deviation
np.var Calculate data variance
np.min , np.max Calculate data minium and maxium
np.argmin, np.argmax Calculate data aggregate minium and maxium
np.cumsum Calculate every data cumulative sum(from 0
increasing)
np.cumprod Calculate every data cumulative multiply(from
1 multiplying)

7. Calculate HPY
First enter HPY formula
and then print function.
But in data no Date, so
need to refine Date data

8. Finally, will combine two date. For combining two
data, will use DataFrame function. Because
DataFrame use for two-dimentional matrix. If just
have just one data, it is first two-dimentional
matrix.

9. Finally ,print Apple HPY value

10. Thank you !
If have problem, please send to me email