# Customizing your spreadsheet import

# Here, you'll parse your spreadsheets and use additional arguments to 
# skip rows, rename columns and select only particular columns.

# The spreadsheet 'battledeath.xlsx' is already loaded as xl.

# As before, you'll use the method parse(). This time, however, you'll 
# add the additional arguments skiprows, names and parse_cols. These 
# skip rows, name the columns and designate which columns to parse, 
# respectively. All these arguments can be assigned to lists containing 
# the specific row numbers, strings and column numbers, as appropriate.

# Instructions

# Parse the first sheet by index. In doing so, skip the first row of 
# data and name the columns 'Country' and 'AAM due to War (2002)' using 
# the argument names. The values passed to skiprows and names all need 
# to be of type list.

# Parse the second sheet by index. In doing so, parse only the first 
# column with the parse_cols parameter, skip the first row and rename 
# the column 'Country'. The argument passed to parse_cols also needs to 
# be of type list.

# Import pandas
import pandas as pd

# Assign spreadsheet filename: file
file = 'battledeath.xlsx'

# Load spreadsheet: xl
xl = pd.ExcelFile(file)

# Parse the first sheet and rename the columns: df1
df1 = xl.parse('2002', skiprows=[0], names=['Country', 'AAM due to War (2002)'])

# Print the head of the DataFrame df1
print(df1.head())

# Parse the first column of the second sheet and rename the column: df2
df2 = xl.parse('2004', parse_cols=[0], skiprows=[0], names=['Country'])

# Print the head of the DataFrame df2
print(df2.head())