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Exercise

Loading a CSV into a table

You've done a great job so far at inserting data into tables! You're now going to learn how to load the contents of a CSV file into a table.

One way to do that would be to read a CSV file line by line, create a dictionary from each line, and then use insert(), like you did in the previous exercise.

But there is a faster way using pandas. You can read a CSV file into a DataFrame using the read_csv() function (this function should be familiar to you, but you can run help(pd.read_csv) in the console to refresh your memory!). Then, you can call the .to_sql() method on the DataFrame to load it into a SQL table in a database. The columns of the DataFrame should match the columns of the SQL table.

.to_sql() has many parameters, but in this exercise we will use the following:

  • name is the name of the SQL table (as a string).
  • con is the connection to the database that you will use to upload the data.
  • if_exists specifies how to behave if the table already exists in the database; possible values are "fail", "replace", and "append".
  • index (True or False) specifies whether to write the DataFrame's index as a column.

In this exercise, you will upload the data contained in the census.csv file into an existing table "census". The connection to the database has already been created for you.

Instructions 1/2
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  • Use pd.read_csv() to load the "census.csv" file into a DataFrame. Set the header parameter to None since the file doesn't have a header row.
  • Rename the columns of census_df to "state", "sex", age, "pop2000", and "pop2008" to match the columns of the "census" table in the database.
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