Introducing the project dataset

For the final chapter, you'll be looking at some of the Gapminder datasets combined into one tidy file called "gapminder_tidy.csv". This data set is available as a pandas DataFrame under the variable name data.

It is always a good idea to begin with some Exploratory Data Analysis. Pandas has a number of built-in methods that help with this. For example, data.head() displays the first five rows/entries of data, while data.tail() displays the last five rows/entries. data.shape gives you information about how many rows and columns there are in the data set. Another particularly useful method is data.info(), which provides a concise summary of data, including information about the number of entries, columns, data type of each column, and number of non-null entries in each column.

Use the IPython Shell and the pandas methods mentioned above to explore this data set. How many entries and columns does this data set have?

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7 entries, 10111 columns.
10111 entries, 7 columns.
9000 entries, 7 columns.

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Exercise

Introducing the project dataset

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