Importing non-flat files from the web

Congrats! You've just loaded a flat file from the web into a DataFrame without first saving it locally using the pandas function pd.read_csv(). This function is super cool because it has close relatives that allow you to load all types of files, not only flat ones. In this interactive exercise, you'll use pd.read_excel() to import an Excel spreadsheet.

The URL of the spreadsheet is

'https://assets.datacamp.com/course/importing_data_into_r/latitude.xls'

Your job is to use pd.read_excel() to read in all of its sheets, print the sheet names and then print the head of the first sheet using its name, not its index.

Note that the output of pd.read_excel() is a Python dictionary with sheet names as keys and corresponding DataFrames as corresponding values.

This exercise is part of the course

Intermediate Importing Data in Python

View Course

Exercise instructions

Assign the URL of the file to the variable url.
Read the file in url into a dictionary xls using pd.read_excel() recalling that, in order to import all sheets you need to pass None to the argument sheet_name.
Print the names of the sheets in the Excel spreadsheet; these will be the keys of the dictionary xls.
Print the head of the first sheet using the sheet name, not the index of the sheet! The sheet name is '1700'

Hands-on interactive exercise

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

# Import package
import pandas as pd

# Assign url of file: url


# Read in all sheets of Excel file: xls


# Print the sheetnames to the shell


# Print the head of the first sheet (using its name, NOT its index)

Edit and Run Code