Data Analytics with Python for beginner: Pandas (9/18)
Notes for Module 9:
https://drive.google.com/file/d/1e5VA...
Files:
https://drive.google.com/file/d/1NVbY...
https://docs.google.com/spreadsheets/...
Solution:
1. Code
import pandas as pd
data = [['Jack',20,12],['Helen',8,11.5],['Catherine',15,14.2],['Rick',-3,13.7]]
info = ['Manager','Returns(%)','Standard Deviation(%)']
fund = ['Fund A','Fund B','Fund C','Fund D']
df = pd.DataFrame(data, index = fund, columns = info)
2. Code
from pandas_datareader import DataReader as pdr
from datetime import datetime as dt
start = dt(2018,1,1)
end = dt(2021,12,31)
fb = pdr("META","stooq",start,end)
amzn = pdr("AMZN","stooq",start,end)
aapl = pdr("AAPL","stooq",start,end)
nflx = pdr("NFLX","stooq",start,end)
goog = pdr("GOOG","stooq",start,end)
fb_return = fn['Close'].pct_change().iloc[1:]
amzn_return = amzn['Close'].pct_change().iloc[1:]
aapl_return = aapl['Close'].pct_change().iloc[1:]
nflx_return = nflx['Close'].pct_change().iloc[1:]
goog_return = goog['Close'].pct_change().iloc[1:]
fb_return.describe()
amzn_return.describe()
aapl_return.describe()
nflx_return.describe()
goog_return.describe()
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