Create A Program to Implement Data Frames and Machine Learning in Python Assignment Solution
- Instructions
- Requirements and Specifications
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))
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