Create A Program to Implement Data Frames and Machine Learning in Python Assignment Solution

Instructions

Objective

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Requirements and Specifications

Source Code

import pandas as pd ### 1) Read Data df = pd.read_csv("SP500_Constituents.csv") ### 2) Display First Row df.head(1) ### 3) Determine how many rows are in the data print("There are " + str(len(df)) + " rows in the data") ### 4) Which columns have missing values? The columns with True value are the ones that have missing values df.isna().any() ### 5) Use loc[] method to display the information about Microsoft (symbol=MSFT) df.loc[df["Symbol"] == "MSFT",:] ### 6) Use filter expression to display the rows where the company is in the Communication Services sector For this case, we use a filter and locate the rows where the column **GICS Sector** has a value equal to **Communication Services** comm_serv = df[df["GICS Sector"] == "Communication Services"] comm_serv.head(len(comm_serv)) # From question 4, we saw that the column 'Date first added' has NaN values, So, delete these rows comm_serv = comm_serv[comm_serv['Date first added'].notna()] ### 7) Use sort_values() to order to data for Communication Services companies, sorting based on the 'Date first added'. What company was added first, what company was added most recently? # First, sort the data comm_serv = comm_serv.sort_values(by=['Date first added']) **Display the row for the company added first** comm_serv.head(1) **Display the row for the company added most recently** comm_serv.tail(1) ### 8) Using isna() and column selection, display the Symbol, Security, and Founded for those companies that are missing data for 'Date first added'. For this part, use all stocks in the S&P500 (instead of just those in the Communication Services sector). # Pick rows where 'Date first added' is na data = df[df['Date first added'].isna()] # Now pick the desired columns data = data[['Symbol', 'Security', 'Founded']] # Now, display all rows data.head(len(data))