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 xls.

As before, you'll use the method parse(). This time, however, you'll add the additional arguments skiprows, names and usecols. 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.

This exercise is part of the course

Introduction to Importing Data in Python

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Exercise 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 usecols parameter, skip the first row and rename the column 'Country'. The argument passed to usecols also needs to be of type list.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Parse the first sheet and rename the columns: df1
df1 = xls.parse(____, skiprows=____, names=____)

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

# Parse the first column of the second sheet and rename the column: df2
df2 = xls.parse(____, usecols=____, skiprows=____, names=____)

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

Edit and Run Code