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  3. Streamlined Data Ingestion with pandas
  • 1

    Importing Data from Flat Files

    Free

    Practice using pandas to get just the data you want from flat files, learn how to wrangle data types and handle errors, and look into some U.S. tax data along the way.

  • 2

    Importing Data From Excel Files

    Automate data imports from that staple of office life, Excel files. Import part or all of a workbook and ensure boolean and datetime data are properly loaded, all while learning about how other people are learning to code.

  • 3

    Importing Data from Databases

    Combine pandas with the powers of SQL to find out just how many problems New Yorkers have with their housing. This chapter features introductory SQL topics like WHERE clauses, aggregate functions, and basic joins.

  • 4

    Importing JSON Data and Working with APIs

    Learn how to work with JSON data and web APIs by exploring a public dataset and getting cafe recommendations from Yelp. End by learning some techniques to combine datasets once they have been loaded into data frames.

Initializing

Exercise

Merge dataframes

In the last exercise, you built a dataset of the top 100 cafes in New York City according to Yelp. Now, you'll combine that with demographic data to investigate which neighborhood has the most good cafes per capita.

To do this, you'll merge two datasets with the DataFrame merge() method. The first,crosswalk, is a crosswalk between ZIP codes and Public Use Micro Data Sample Areas (PUMAs), which are aggregates of census tracts and correspond roughly to NYC neighborhoods. Then, you'll merge in pop_data, which contains 2016 population estimates for each PUMA.

pandas (as pd) has been imported, as has the cafes dataframe from last exercise.

Instructions 1/3

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Question

Explore the cafes and crosswalk dataframes in the console. Which columns should be used as join keys?

Possible answers