The file ApplianceShipments.csv contains the series of quarterly shipments (in millions of dollars) of US household appliances between 1985 and 1989.

Quarter	Shipments
Q1-1985	4009
Q2-1985	4321
Q3-1985	4224
Q4-1985	3944
Q1-1986	4123
Q2-1986	4522
Q3-1986	4657
Q4-1986	4030
Q1-1987	4493
Q2-1987	4806
Q3-1987	4551
Q4-1987	4485
Q1-1988	4595
Q2-1988	4799
Q3-1988	4417
Q4-1988	4258
Q1-1989	4245
Q2-1989	4900
Q3-1989	4585
Q4-1989	4533

Using Python, create one chart with four separate lines, one line for each of Q1, Q2, Q3, and Q4. In Python, this can be achieved by adding columns for quarter and year. Then group the data frame by quarter and then plot shipment versus year for each quarter as a separate series on a line graph. Zoom in to the range of 35005000 on the y-axis. Does there appear to be a difference between quarters?

# Create separate time series for each quarter # Line 1 Pandas Series Q1 = shipments_df.iloc[[0, 4, 8, 12, 16], :] Q1_ts = pd.Series(Q1.Shipments.values, index=Q1.Date)

#Line 2, 3, 4 Pandas Series # MISSING: 6 lines of code needed. # Hint: Copy the above code 3 more times and change the quaters from q1 to the appropriate quarter

#Plot each of the 4 Pandas Series from above #Plot 1 plt.plot(Q1_ts, data=Q1_ts, label='Q1') plt.legend()

# Plots 2, 3, 4 # MISSING: 6 lines of code needed. # Hint: Copy the above code 3 more times and change the quaters from q1 to the appropriate quarter

#Add lables to the x and y axis # MISSING: 2 lines of code needed. "Using MATPLOTLIB"

output:

Out[9]: Text(0, 0.5, Shipments (in millon dollars)') 4800 |||| Q1 Q2 Q3 Q3 4600 Shipments (in millon dollars) 4400 4200 4000 1986 1987 1989 1990 1988 Quarter