Python Data Science Pandas Problems

Need help answering the following python problems using pandas. Any help is appreciated.

# Step 1

# Create the Stock class

#

class Stock:

"""

Stock class of a publicly traded stock on a major market

"""

def __init__(self, symbol, name, init_filepath) :

"""

:param symbol: stock symbol

:param name: company name

:param init_filepath: locate the file for date, price (eod) and volume lists

"""

# note that the complete list of properties/attributes below has more than items than

# the numnber of arguments of the constructor. That's perfectly fine.

# Some property values are to be assigned later after instantiation.

self.symbol = symbol.upper()

self.name = name

self.data = self.import_history(init_filepath) # this is the pandas df, make sure import_history() returns a pd dataframe

# the pandas df self.data will have columns price, volume, delta1, delta2, and index is date

self.init_delta1() # Calculate the daily change values from stock price itself, append to df

self.init_delta2() # Calculate the daily values second derivative, append to df

self.firstdate = self.data.index[-1]

self.lastdate = self.data.index[0]

def import_history(self, filepath):

"""

import stock history from csv file, with colunms date, eod_price, volume, and save them to data as pd.dataframe

"""

return pd.read_csv( filepath, index_col=0 ) # use date as index

def init_delta1(self):

"""

compute the daily change from price_eod, append to data as new column as delta1

"""

# notice that:

# aapl['price'] returns a pandas series

# aapl[['price']] returns a pandas dataframe

# aapl['price'].values returns a numpy array of the values only

self.data['delta1'] = 0 # initialize a new column with 0s

self.data['delta1'] = self.data['price'][0:-1] - self.data.price.values[1:] # self.data['price'] is same as self.price for df

# the first term on the right is the full pd series with index attached. Second one is a simple numpy array without the date

# index. That way, the broadcasting will not try to match the indices/indexes on the two df

return # you can choose to return self

def init_delta2(self):

"""

compute the daily change for the entire list of delta1, essentially the second derivatives for price_eod

"""

# essentially the same function as init_delta1.

# ###### QUESTION 2 QUESTION 2 QUESTION 2 ##########

# write your codes here

# ###### END of QUESTION 2 ### END of QUESTION 2 ##########

return # you can choose to return self

def add_newday(self, newdate, newprice, newvolume):

"""

add a new data point at the beginning of data df

"""

# Make plans

# insert a new row to self.data with

# (date, price, volume, delta1, delta2) to the pandas df,

# and also should update self.lastdate

#

# update self.lastdate

# ###### QUESTION 3 QUESTION 3 QUESTION 3 ##########

# write your codes here, should be just one line

# ###### END of QUESTION 3 ### END of QUESTION 3 ##########

# get ready a new row, in the form of a pandas dataframe.

# Pandas dataframe does not have an insert function. The usual method is to use .append()

# and .append() is most efficient to append a df to another df of the same columns.

newRow = self.setNewRow(newdate, newprice, newvolume) # we do this quite a lot: assume it's done already, then implement it later, as long as it doesn't break the codes

# need this function setNewRow() to return a dataframe

self.data = newRow.append(self.data) # this will put the new row on top, and push self.data after the new data

return self

def setNewRow(self, newdate, newprice, newvolume):

df = self.data[0,:].copy() # first create a true copy of the first row

df.index[0] = newdate # then put in the new values

df.price[0] = newprice

# ###### QUESTION 4 QUESTION 4 QUESTION 4 ##########

# write your codes here

# set volume value

# set delta1 value

# set delta2 value

# ###### END of QUESTION 4 ### END of QUESTION 4 ##########

return df # return the dataframe with one one row of data

def nday_change_percent(self,n):

"""

calculate the percentage change in the last n days, returning a percentage between 0 and 100

"""

# ###### QUESTION 5 QUESTION 5 QUESTION 5 ##########

# change = ??

# percent = ??

# ###### END of QUESTION 5 ### END of QUESTION 5 ##########

print(self.symbol,": Percent change in",n,"days is {0:.2f}".format(percent))

return percent

def nday_max_price(self,n):

"""

find the highest price within the last n days

"""

# ###### QUESTION 6 QUESTION 6 QUESTION 6 ##########

# return ?? # you can try to use the .max() function of a pandas dataframe

# ###### END of QUESTION 6 ### END of QUESTION 6 ##########

def nday_min_price(self,n):

"""

find the lowest price within the last n days

"""

# ###### QUESTION 7 QUESTION 7 QUESTION 7 ##########

# return ??

# ###### END of QUESTION 7 ### END of QUESTION 7 ##########