From the course: Data Analysis with Python and Pandas
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Array aggregation
From the course: Data Analysis with Python and Pandas
Array aggregation
- [Instructor] All right, so let's take a look at array aggregation methods. So array aggregation methods allow us to calculate things like sum, mean, min and max. Later on, we'll be able to group by and aggregate by categories in our data when we start working with multiple columns of data. But when we're talking about a single piece of information or a single array, we're going to be grouping by all the values in our array and just return a single value. So if I wanted to calculate the sum of all the values in our array, we could use the sum method. So array.sum will return the sum of all values in an array. So the sum of all the values in this array is 3,763. There are 10 elements in this array. So if I wanted to calculate the mean, we could divide that sum by 10, or we could use the mean method. So sales_array.mean will return the average of the values in our array. The max method will return the largest value in an array. In this case, it's 1,827. And the min, not surprisingly…
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Contents
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pandas and NumPy intro2m 53s
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NumPy arrays and array properties7m 41s
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Challenge: Array basics1m 47s
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Solution: Array basics2m 2s
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Array creation8m 13s
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Random number generation5m 58s
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Challenge: Array creation1m 30s
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Solution: Array creation4m 22s
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Indexing and slicing arrays9m 9s
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Challenge: Indexing and slicing arrays1m 6s
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Solution: Indexing and slicing arrays2m 23s
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Array operations7m 45s
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Challenge: Array operations2m 6s
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Solution: Array operations4m 16s
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Filtering arrays and modifying array values10m 56s
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The where() function4m
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Challenge: Filtering and modifying arrays1m 57s
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Solution: Filtering and modifying arrays3m 11s
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Array aggregation6m 51s
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Array functions7m 41s
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Sorting arrays3m 51s
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Challenge: Aggregation and sorting1m 11s
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Solution: Aggregation and sorting1m 35s
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Vectorization4m 19s
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Broadcasting7m 8s
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Challenge: Bringing it all together2m 45s
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Solution: Bringing it all together6m 18s
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Key takeaways1m 56s
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