![\"numpy](\"https:\/\/www.pythontutorial.net\/wp-content\/uploads\/2022\/08\/numpy-tutorial.svg\" "\\"numpy")
<\/figure>\n<\/div>\n\n\nWhat you’ll learn #<\/a><\/h2>\n\n\n\n- Create single and multi-dimensional NumPy arrays<\/li>
- Effectively use NumPy built-in functions & methods<\/li>
- Perform mathematical operations on arrays<\/li>
- Extract elements from arrays using slicing and indexing<\/li>
- Select elements of arrays conditionally.<\/li><\/ul>\n\n\n\n\n
Section 1. Getting started #<\/a><\/h2>\n\n\n\n- What is NumPy<\/a> – learn what NumPy is and what it can do for you.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 2. Creating arrays #<\/a><\/h2>\n\n\n\n- Creating arrays<\/a> – show you how to create numpy arrays.<\/li>
- zeros()<\/a> – create a numpy array of a given shape whose elements are filled with zeros.<\/li>
- ones()<\/a> – create a numpy array of a given shape whose elements are filled with ones.<\/li>
- arange()<\/a> – create a numpy array with evenly spaced numbers.<\/li>
- linspace()<\/a> – create a new numpy array with evenly spaced numbers of a specified interval.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 3. Array indexing & slicing #<\/a><\/h2>\n\n\n\n- Indexing<\/a> – learn how to select elements using indexing.<\/li>
- Slicing<\/a> – show you how to use slices to extract elements from an array.<\/li>
- Fancy indexing<\/a> – learn how to index a numpy array using another numpy array.<\/li>
- Boolean indexing<\/a> – guide you on how to index an array using another array of boolean values.<\/li>
- View vs. copy<\/a> – show you the difference between a view & copy of an array and how to use the copy() method to make a copy of an array.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 4. Aggregate functions #<\/a><\/h2>\n\n\n\n- sum()<\/a>– return the sum of all elements<\/li>
- mean()<\/a> – return the average of all elements in an array.<\/li>
- var() <\/a>– return the variance of all elements in an array.<\/li>
- std()<\/a> – calculate the standard deviation of elements of an array.<\/li>
- prod()<\/a> – return the product of all elements.<\/li>
- amin()<\/a> – return the minimum value in an array.<\/li>
- amax()<\/a> – return the maximum value in an array.<\/li>
- all()<\/a> – return
True<\/code> if all elements in an array evaluate to True<\/code>.<\/li>- any()<\/a> – return True if any of the elements in an array is nonzero.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 5. Array operations #<\/a><\/h2>\n\n\n\n- reshape()<\/a> – give an array a new shape while keeping the same elements.<\/li>
- transpose()<\/a> – return a view of an array with axes transposed.<\/li>
- sort()<\/a> – return a sorted copy of an array.<\/li>
- flatten()<\/a> – return a copy of an array collapsed into one dimension.<\/li>
- ravel()<\/a> – return a contiguous flattened array.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 6. Arithmetic operations #<\/a><\/h2>\n\n\n\n- add()<\/a> – return the sum of two equal-sized arrays.<\/li>
- subtract()<\/a> – return the difference between two equal-sized arrays.<\/li>
- multiply()<\/a> – return the product of two equal-sized arrays.<\/li>
- divide()<\/a> – return the quotient of two equal-sized arrays.<\/li>
- Broadcasting<\/a> – show you how NumPy uses broadcasting to perform arithmetic operations on arrays with different shapes.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 7. Joining & splitting arrays #<\/a><\/h2>\n\n\n\n- concatenate()<\/a> – join two or more arrays along an existing axis.<\/li>
- stack()<\/a> – join two or more arrays along a new axis.<\/li>
- vstack()<\/a> – join two or more arrays vertically.<\/li>
- hstack()<\/a> – join two or more arrays horizontally.<\/li>
- split()<\/a> – split an array into subarrays.<\/li><\/ul>\n<\/div><\/div>\n\n\t
\n\t\tWas this tutorial helpful ?<\/div>\n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t Yes <\/span>\n\t\t\t<\/button>\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t No <\/span>\n\t\t\t<\/button>\n\t\t<\/div>\n\t<\/header>\n\n\t\n\t\t\n\t\t\t<\/div>\n\t\t\t
\nSection 1. Getting started #<\/a><\/h2>\n\n\n\n
Section 1. Getting started #<\/a><\/h2>\n\n\n\n- What is NumPy<\/a> – learn what NumPy is and what it can do for you.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 2. Creating arrays #<\/a><\/h2>\n\n\n\n- Creating arrays<\/a> – show you how to create numpy arrays.<\/li>
- zeros()<\/a> – create a numpy array of a given shape whose elements are filled with zeros.<\/li>
- ones()<\/a> – create a numpy array of a given shape whose elements are filled with ones.<\/li>
- arange()<\/a> – create a numpy array with evenly spaced numbers.<\/li>
- linspace()<\/a> – create a new numpy array with evenly spaced numbers of a specified interval.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 3. Array indexing & slicing #<\/a><\/h2>\n\n\n\n- Indexing<\/a> – learn how to select elements using indexing.<\/li>
- Slicing<\/a> – show you how to use slices to extract elements from an array.<\/li>
- Fancy indexing<\/a> – learn how to index a numpy array using another numpy array.<\/li>
- Boolean indexing<\/a> – guide you on how to index an array using another array of boolean values.<\/li>
- View vs. copy<\/a> – show you the difference between a view & copy of an array and how to use the copy() method to make a copy of an array.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 4. Aggregate functions #<\/a><\/h2>\n\n\n\n- sum()<\/a>– return the sum of all elements<\/li>
- mean()<\/a> – return the average of all elements in an array.<\/li>
- var() <\/a>– return the variance of all elements in an array.<\/li>
- std()<\/a> – calculate the standard deviation of elements of an array.<\/li>
- prod()<\/a> – return the product of all elements.<\/li>
- amin()<\/a> – return the minimum value in an array.<\/li>
- amax()<\/a> – return the maximum value in an array.<\/li>
- all()<\/a> – return
True<\/code> if all elements in an array evaluate to True<\/code>.<\/li>- any()<\/a> – return True if any of the elements in an array is nonzero.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 5. Array operations #<\/a><\/h2>\n\n\n\n- reshape()<\/a> – give an array a new shape while keeping the same elements.<\/li>
- transpose()<\/a> – return a view of an array with axes transposed.<\/li>
- sort()<\/a> – return a sorted copy of an array.<\/li>
- flatten()<\/a> – return a copy of an array collapsed into one dimension.<\/li>
- ravel()<\/a> – return a contiguous flattened array.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 6. Arithmetic operations #<\/a><\/h2>\n\n\n\n- add()<\/a> – return the sum of two equal-sized arrays.<\/li>
- subtract()<\/a> – return the difference between two equal-sized arrays.<\/li>
- multiply()<\/a> – return the product of two equal-sized arrays.<\/li>
- divide()<\/a> – return the quotient of two equal-sized arrays.<\/li>
- Broadcasting<\/a> – show you how NumPy uses broadcasting to perform arithmetic operations on arrays with different shapes.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 7. Joining & splitting arrays #<\/a><\/h2>\n\n\n\n- concatenate()<\/a> – join two or more arrays along an existing axis.<\/li>
- stack()<\/a> – join two or more arrays along a new axis.<\/li>
- vstack()<\/a> – join two or more arrays vertically.<\/li>
- hstack()<\/a> – join two or more arrays horizontally.<\/li>
- split()<\/a> – split an array into subarrays.<\/li><\/ul>\n<\/div><\/div>\n\n\t
\n\t\tWas this tutorial helpful ?<\/div>\n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t Yes <\/span>\n\t\t\t<\/button>\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t No <\/span>\n\t\t\t<\/button>\n\t\t<\/div>\n\t<\/header>\n\n\t\n\t\t\n\t\t\t<\/div>\n\t\t\t
\nSection 2. Creating arrays #<\/a><\/h2>\n\n\n\n
Section 2. Creating arrays #<\/a><\/h2>\n\n\n\n- Creating arrays<\/a> – show you how to create numpy arrays.<\/li>
- zeros()<\/a> – create a numpy array of a given shape whose elements are filled with zeros.<\/li>
- ones()<\/a> – create a numpy array of a given shape whose elements are filled with ones.<\/li>
- arange()<\/a> – create a numpy array with evenly spaced numbers.<\/li>
- linspace()<\/a> – create a new numpy array with evenly spaced numbers of a specified interval.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 3. Array indexing & slicing #<\/a><\/h2>\n\n\n\n- Indexing<\/a> – learn how to select elements using indexing.<\/li>
- Slicing<\/a> – show you how to use slices to extract elements from an array.<\/li>
- Fancy indexing<\/a> – learn how to index a numpy array using another numpy array.<\/li>
- Boolean indexing<\/a> – guide you on how to index an array using another array of boolean values.<\/li>
- View vs. copy<\/a> – show you the difference between a view & copy of an array and how to use the copy() method to make a copy of an array.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 4. Aggregate functions #<\/a><\/h2>\n\n\n\n- sum()<\/a>– return the sum of all elements<\/li>
- mean()<\/a> – return the average of all elements in an array.<\/li>
- var() <\/a>– return the variance of all elements in an array.<\/li>
- std()<\/a> – calculate the standard deviation of elements of an array.<\/li>
- prod()<\/a> – return the product of all elements.<\/li>
- amin()<\/a> – return the minimum value in an array.<\/li>
- amax()<\/a> – return the maximum value in an array.<\/li>
- all()<\/a> – return
True<\/code> if all elements in an array evaluate to True<\/code>.<\/li>- any()<\/a> – return True if any of the elements in an array is nonzero.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 5. Array operations #<\/a><\/h2>\n\n\n\n- reshape()<\/a> – give an array a new shape while keeping the same elements.<\/li>
- transpose()<\/a> – return a view of an array with axes transposed.<\/li>
- sort()<\/a> – return a sorted copy of an array.<\/li>
- flatten()<\/a> – return a copy of an array collapsed into one dimension.<\/li>
- ravel()<\/a> – return a contiguous flattened array.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 6. Arithmetic operations #<\/a><\/h2>\n\n\n\n- add()<\/a> – return the sum of two equal-sized arrays.<\/li>
- subtract()<\/a> – return the difference between two equal-sized arrays.<\/li>
- multiply()<\/a> – return the product of two equal-sized arrays.<\/li>
- divide()<\/a> – return the quotient of two equal-sized arrays.<\/li>
- Broadcasting<\/a> – show you how NumPy uses broadcasting to perform arithmetic operations on arrays with different shapes.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 7. Joining & splitting arrays #<\/a><\/h2>\n\n\n\n- concatenate()<\/a> – join two or more arrays along an existing axis.<\/li>
- stack()<\/a> – join two or more arrays along a new axis.<\/li>
- vstack()<\/a> – join two or more arrays vertically.<\/li>
- hstack()<\/a> – join two or more arrays horizontally.<\/li>
- split()<\/a> – split an array into subarrays.<\/li><\/ul>\n<\/div><\/div>\n\n\t
\n\t\tWas this tutorial helpful ?<\/div>\n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t Yes <\/span>\n\t\t\t<\/button>\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t No <\/span>\n\t\t\t<\/button>\n\t\t<\/div>\n\t<\/header>\n\n\t\n\t\t\n\t\t\t<\/div>\n\t\t\t
\nSection 3. Array indexing & slicing #<\/a><\/h2>\n\n\n\n
Section 3. Array indexing & slicing #<\/a><\/h2>\n\n\n\n- Indexing<\/a> – learn how to select elements using indexing.<\/li>
- Slicing<\/a> – show you how to use slices to extract elements from an array.<\/li>
- Fancy indexing<\/a> – learn how to index a numpy array using another numpy array.<\/li>
- Boolean indexing<\/a> – guide you on how to index an array using another array of boolean values.<\/li>
- View vs. copy<\/a> – show you the difference between a view & copy of an array and how to use the copy() method to make a copy of an array.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 4. Aggregate functions #<\/a><\/h2>\n\n\n\n- sum()<\/a>– return the sum of all elements<\/li>
- mean()<\/a> – return the average of all elements in an array.<\/li>
- var() <\/a>– return the variance of all elements in an array.<\/li>
- std()<\/a> – calculate the standard deviation of elements of an array.<\/li>
- prod()<\/a> – return the product of all elements.<\/li>
- amin()<\/a> – return the minimum value in an array.<\/li>
- amax()<\/a> – return the maximum value in an array.<\/li>
- all()<\/a> – return
True<\/code> if all elements in an array evaluate to True<\/code>.<\/li>- any()<\/a> – return True if any of the elements in an array is nonzero.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 5. Array operations #<\/a><\/h2>\n\n\n\n- reshape()<\/a> – give an array a new shape while keeping the same elements.<\/li>
- transpose()<\/a> – return a view of an array with axes transposed.<\/li>
- sort()<\/a> – return a sorted copy of an array.<\/li>
- flatten()<\/a> – return a copy of an array collapsed into one dimension.<\/li>
- ravel()<\/a> – return a contiguous flattened array.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 6. Arithmetic operations #<\/a><\/h2>\n\n\n\n- add()<\/a> – return the sum of two equal-sized arrays.<\/li>
- subtract()<\/a> – return the difference between two equal-sized arrays.<\/li>
- multiply()<\/a> – return the product of two equal-sized arrays.<\/li>
- divide()<\/a> – return the quotient of two equal-sized arrays.<\/li>
- Broadcasting<\/a> – show you how NumPy uses broadcasting to perform arithmetic operations on arrays with different shapes.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 7. Joining & splitting arrays #<\/a><\/h2>\n\n\n\n- concatenate()<\/a> – join two or more arrays along an existing axis.<\/li>
- stack()<\/a> – join two or more arrays along a new axis.<\/li>
- vstack()<\/a> – join two or more arrays vertically.<\/li>
- hstack()<\/a> – join two or more arrays horizontally.<\/li>
- split()<\/a> – split an array into subarrays.<\/li><\/ul>\n<\/div><\/div>\n\n\t
\n\t\tWas this tutorial helpful ?<\/div>\n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t Yes <\/span>\n\t\t\t<\/button>\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t No <\/span>\n\t\t\t<\/button>\n\t\t<\/div>\n\t<\/header>\n\n\t\n\t\t\n\t\t\t<\/div>\n\t\t\t
\nSection 4. Aggregate functions #<\/a><\/h2>\n\n\n\n
Section 4. Aggregate functions #<\/a><\/h2>\n\n\n\n- sum()<\/a>– return the sum of all elements<\/li>
- mean()<\/a> – return the average of all elements in an array.<\/li>
- var() <\/a>– return the variance of all elements in an array.<\/li>
- std()<\/a> – calculate the standard deviation of elements of an array.<\/li>
- prod()<\/a> – return the product of all elements.<\/li>
- amin()<\/a> – return the minimum value in an array.<\/li>
- amax()<\/a> – return the maximum value in an array.<\/li>
- all()<\/a> – return
True<\/code> if all elements in an array evaluate to True<\/code>.<\/li>- any()<\/a> – return True if any of the elements in an array is nonzero.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 5. Array operations #<\/a><\/h2>\n\n\n\n- reshape()<\/a> – give an array a new shape while keeping the same elements.<\/li>
- transpose()<\/a> – return a view of an array with axes transposed.<\/li>
- sort()<\/a> – return a sorted copy of an array.<\/li>
- flatten()<\/a> – return a copy of an array collapsed into one dimension.<\/li>
- ravel()<\/a> – return a contiguous flattened array.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 6. Arithmetic operations #<\/a><\/h2>\n\n\n\n- add()<\/a> – return the sum of two equal-sized arrays.<\/li>
- subtract()<\/a> – return the difference between two equal-sized arrays.<\/li>
- multiply()<\/a> – return the product of two equal-sized arrays.<\/li>
- divide()<\/a> – return the quotient of two equal-sized arrays.<\/li>
- Broadcasting<\/a> – show you how NumPy uses broadcasting to perform arithmetic operations on arrays with different shapes.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 7. Joining & splitting arrays #<\/a><\/h2>\n\n\n\n- concatenate()<\/a> – join two or more arrays along an existing axis.<\/li>
- stack()<\/a> – join two or more arrays along a new axis.<\/li>
- vstack()<\/a> – join two or more arrays vertically.<\/li>
- hstack()<\/a> – join two or more arrays horizontally.<\/li>
- split()<\/a> – split an array into subarrays.<\/li><\/ul>\n<\/div><\/div>\n\n\t
\n\t\tWas this tutorial helpful ?<\/div>\n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t Yes <\/span>\n\t\t\t<\/button>\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t No <\/span>\n\t\t\t<\/button>\n\t\t<\/div>\n\t<\/header>\n\n\t\n\t\t\n\t\t\t<\/div>\n\t\t\t
True<\/code> if all elements in an array evaluate to True<\/code>.<\/li>- any()<\/a> – return True if any of the elements in an array is nonzero.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 5. Array operations #<\/a><\/h2>\n\n\n\n- reshape()<\/a> – give an array a new shape while keeping the same elements.<\/li>
- transpose()<\/a> – return a view of an array with axes transposed.<\/li>
- sort()<\/a> – return a sorted copy of an array.<\/li>
- flatten()<\/a> – return a copy of an array collapsed into one dimension.<\/li>
- ravel()<\/a> – return a contiguous flattened array.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 6. Arithmetic operations #<\/a><\/h2>\n\n\n\n- add()<\/a> – return the sum of two equal-sized arrays.<\/li>
- subtract()<\/a> – return the difference between two equal-sized arrays.<\/li>
- multiply()<\/a> – return the product of two equal-sized arrays.<\/li>
- divide()<\/a> – return the quotient of two equal-sized arrays.<\/li>
- Broadcasting<\/a> – show you how NumPy uses broadcasting to perform arithmetic operations on arrays with different shapes.<\/li><\/ul>\n<\/div><\/div>\n\n\n\n\n
Section 7. Joining & splitting arrays #<\/a><\/h2>\n\n\n\n- concatenate()<\/a> – join two or more arrays along an existing axis.<\/li>
- stack()<\/a> – join two or more arrays along a new axis.<\/li>
- vstack()<\/a> – join two or more arrays vertically.<\/li>
- hstack()<\/a> – join two or more arrays horizontally.<\/li>
- split()<\/a> – split an array into subarrays.<\/li><\/ul>\n<\/div><\/div>\n\n\t
\n\t\tWas this tutorial helpful ?<\/div>\n\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t Yes <\/span>\n\t\t\t<\/button>\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t No <\/span>\n\t\t\t<\/button>\n\t\t<\/div>\n\t<\/header>\n\n\t\n\t\t\n\t\t\t<\/div>\n\t\t\t