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

# Convert the data frame to be suitable for time series analysis

# 1. Create date range for quarters (already completed) date_range = pd.date_range(start='1/1/1985', end='12/31/1989', freq='Q')

# 2. Recreate the shipments_df dataframe but drop the first column (already completed) # Format for filtering dataframes --> # [rowStart:rowEnd, columnStart:columnEnd] shipments_df = shipments_df.iloc[0: , 1:2]

# 3. Add the date_range column to the shipments_df dataframe and make sure it is a DATETIME datatype # MISSING: one line of code needed. # HINT: use the pd.to_datetime() function

# 4. Convert the Pandas dataframe called shipments_df into a pandas series called shipment_ts # - Make the date column the Index # - Use the Shipments column .values as the data # MISSING: one line of code needed.

#5. Print the resulting Pandas series # MISSING: one line of code needed.

output:

Out[26]: Date 1985-03-31 1985-06-30 1985-09-30 1985-12-31 1986-03-31 1986-06-30 1986-09-30 1986-12-31 1987-03-31 1987-06-30 1987-09-30 1987-12-31 1988-03-31 1988-06-30 1988-09-30 1988-12-31 1989-03-31 1989-06-30 1989-09-30 1989-12-31 dtype: int64 4009 4321 4224 3944 4123 4522 4657 4030 4493 4806 4551 4485 4595 4799 4417 4258 4245 4900 4585 4533