Using pandas to import flat files as DataFrames (2)

In the last exercise, you were able to import flat files into a pandas DataFrame. As a bonus, it is then straightforward to retrieve the corresponding numpy array using the attribute values. You'll now have a chance to do this using the MNIST dataset, which is available as digits.csv.

This exercise is part of the course

Importing Data in Python

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Exercise instructions

Import the first 5 rows of the file into a DataFrame using the function pd.read_csv() and assign the result to data. You'll need to use the arguments nrows and header (there is no header in this file).
Build a numpy array from the resulting DataFrame in data and assign to data_array.
Execute print(type(data_array)) to print the datatype of data_array.

Hands-on interactive exercise

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

# Assign the filename: file
file = 'digits.csv'

# Read the first 5 rows of the file into a DataFrame: data


# Build a numpy array from the DataFrame: data_array


# Print the datatype of data_array to the shell
print(type(data_array))

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