Analyzing and Visualizing Real-Life Data

We can see that only two types of data are presented in the dataframe: object and int64. Columns that have an object data type contain string objects, which makes aggregation impossible for them. Among these columns are Weekly_Sales, Temperature, Fuel_Price, and others. It is obvious that all the mentioned columns must be numerical. We might be interested in comparing the revenue for different dates, but with object data, it's impossible.

Let's solve problems step by step. First, let's remind ourselves what our data looks like by outputting a single row.

Pay close attention to the values and try to find out why most of the columns are considered object columns.

What is wrong with the 'Temperature', 'Fuel_Price', and 'Unemployment' columns?

Select the correct answer

Dot symbols are replaced with commas (14,23 instead of 14.23).

Amounts are written as text (one thousand twenty-five instead of 1025).

Wrong thousand separators are used (156 562 or 156,562 instead of 156562).

Non-numerical symbols are presented ($18.62 instead of 18.62)

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