Data Curation at Universidade Nova De Lisboa | Flashcards & Summaries

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# Lernmaterialien für Data Curation an der Universidade Nova de Lisboa

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The comparable operation of reshape is typically known as flattening or raveling

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False

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Every array has a shape, a tuple indicating the size of each dimension, and a dtype, an object describing the data type of the array

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True

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What will be the output of the following:

arrd = np.array( [ [ 1, 2, 3] , [ 4, 5, 6] , [ 7, 8, 9 ] ] )

arrd[ 0 ][ 2 ]

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3

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ndarray is a generic multidimensional container for heterogenous data; that is, all of the elements can have different types

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​False

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What does NOT apply to NumPy

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Use typically same amount of memory than built-in Python objects

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Transposing is a special form of reshaping with the transpose method and also the special T attribute

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True

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A set is an unordered and unindexed collection of unique elements. You can think of them like dicts, but keys only, no values

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True

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In np.array nested sequences, like a list of equal-length lists, will be converted into a unidimensional array

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False

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Functions like mean and sum take an optional axis argument that computes the statistic over the given axis, resulting in an array with one fewer dimension

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True

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astype() method can be use to convert an array from one dtype to another

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True

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What operation does np.dot perform between two-dimensional arrays?

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Multiplication

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You can subset/slice, count and check the value in the tuple similar as list, but you cannot modify elements in the tuple because it is immutable

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True

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Q:

The comparable operation of reshape is typically known as flattening or raveling

A:

False

Q:

Every array has a shape, a tuple indicating the size of each dimension, and a dtype, an object describing the data type of the array

A:

True

Q:

What will be the output of the following:

arrd = np.array( [ [ 1, 2, 3] , [ 4, 5, 6] , [ 7, 8, 9 ] ] )

arrd[ 0 ][ 2 ]

A:

3

Q:

ndarray is a generic multidimensional container for heterogenous data; that is, all of the elements can have different types

A:

​False

Q:

What does NOT apply to NumPy

A:

Use typically same amount of memory than built-in Python objects

Q:

Transposing is a special form of reshaping with the transpose method and also the special T attribute

A:

True

Q:

A set is an unordered and unindexed collection of unique elements. You can think of them like dicts, but keys only, no values

A:

True

Q:

In np.array nested sequences, like a list of equal-length lists, will be converted into a unidimensional array

A:

False

Q:

Functions like mean and sum take an optional axis argument that computes the statistic over the given axis, resulting in an array with one fewer dimension

A:

True

Q:

astype() method can be use to convert an array from one dtype to another

A:

True

Q:

What operation does np.dot perform between two-dimensional arrays?

A:

Multiplication

Q:

You can subset/slice, count and check the value in the tuple similar as list, but you cannot modify elements in the tuple because it is immutable

A:

True

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