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

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))