Import the NumPy library under the alias `np`

.

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```

Create a NumPy array called `my_array`

that contains the following elements: `1`

, `3`

, and `5`

.

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```

Create a two-dimensional NumPy array with 9 elements. The array should be called `my_matrix`

and should have 3 columns and three rows. The matrix can contain whatever values you'd like.

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```

Use NumPy's `arange`

method to generate the following output:

```
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20])
```

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```

Use NumPy's `arange`

method to generate the following output:

`array([ 1, 4, 7, 10, 13])`

Hint: You will need to use `arange`

's third argument.

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```

Generate a NumPy array that contains 20 zeros.

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Generate a NumPy array that contains 50 ones.

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Using NumPy, divide the space between 0 and 100 into 1000 even intervals.

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```

Create a 10x10 identity matrix using NumPy.

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```

Returns a random sample of numbers with 10 values where each value is between 0 and 1.

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```

Returns a random sample of numbers with 10 values where each value is between 0 and 10.

Hint: Use the `random.rand`

method combined with a multiplication operation.

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Generate a random sample of 15 numbers from a normal distribution.

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Generate a random sample of 7 integers that range between 5 and 10.

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```

Reshape the following one-dimensional NumPy array into a two-dimensional Numpy array with 3 rows and 3 columns.

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```
arr = np.array([0,1,2,3,4,5,6,7,8])
```

Print the minimum and maximum values of the following NumPy array.

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```
arr = np.array([0,1,2,3,4,5,6,7,8])
```

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```
#Print the minimum value here.
```

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```
#Print the maximum value here.
```

For the following NumPy array, print the index of the minimum and maximum values.

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```
my_array = np.array([6, 7, 0, 2])
```

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```
#Print the minimum value's index here.
```

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```
#Print the maximum value's index here.
```