Summing strings and concatenating numbers

In the previous exercise, you were able to identify that category is the correct data type for user_type and convert it in order to extract relevant statistical summaries that shed light on the distribution of user_type.

Another common data type problem is importing what should be numerical values as strings, as mathematical operations such as summing and multiplication lead to string concatenation, not numerical outputs.

In this exercise, you'll be converting the string column duration to the type int. Before that however, you will need to make sure to strip "minutes" from the column in order to make sure pandas reads it as numerical. The pandas package has been imported as pd.

This exercise is part of the course

Cleaning Data in Python

Exercise instructions

Use the .strip() method to strip duration of "minutes" and store it in the duration_trim column.
Convert duration_trim to int and store it in the duration_time column.
Write an assert statement that checks if duration_time's data type is now an int.
Print the average ride duration.

Hands-on interactive exercise

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

# Strip duration of minutes
ride_sharing['duration_trim'] = ride_sharing['duration'].____.____()

# Convert duration to integer
ride_sharing['duration_time'] = ____

# Write an assert statement making sure of conversion
assert ride_sharing['____'].____ == '____'

# Print formed columns and calculate average ride duration 
print(ride_sharing[['duration','duration_trim','duration_time']])
print(____)

Edit and Run Code

This exercise is part of the course

Cleaning Data in Python

IntermediateSkill Level

4.8+

Start Course for Free

In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

Exercise 1: Data type constraints Exercise 2: Common data types Exercise 3: Numeric data or ... ?Exercise 4: Summing strings and concatenating numbers

Current Exercise

Exercise 5: Data range constraints Exercise 6: Tire size constraints Exercise 7: Back to the future Exercise 8: Uniqueness constraints Exercise 9: How big is your subset?Exercise 10: Finding duplicates Exercise 11: Treating duplicates

Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

Exercise 1: Membership constraints Exercise 2: Members only Exercise 3: Finding consistency Exercise 4: Categorical variables Exercise 5: Categories of errors Exercise 6: Inconsistent categories Exercise 7: Remapping categories Exercise 8: Cleaning text data Exercise 9: Removing titles and taking names Exercise 10: Keeping it descriptive

In this chapter, you'll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You'll also gain invaluable skills that will help you verify that values have been added correctly, and that missing values don't negatively impact your analyses.

Exercise 1: Uniformity Exercise 2: Ambiguous dates Exercise 3: Uniform currencies Exercise 4: Uniform dates Exercise 5: Cross field validation Exercise 6: Cross field or no cross field?Exercise 7: How's our data integrity?Exercise 8: Completeness Exercise 9: Is this missing at random?Exercise 10: Missing investors Exercise 11: Follow the money

Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you'll then use your new skills to join two restaurant review datasets into one clean master dataset.

Exercise 1: Comparing strings Exercise 2: Minimum edit distance Exercise 3: The cutoff point Exercise 4: Remapping categories II Exercise 5: Generating pairs Exercise 6: To link or not to link?Exercise 7: Pairs of restaurants Exercise 8: Similar restaurants Exercise 9: Linking DataFrames Exercise 10: Getting the right index Exercise 11: Linking them together!Exercise 12: Congratulations!