NumPy for Data Science ๐ ๐ฅ
A Mega Thread ๐งต๐
A Mega Thread ๐งต๐
NumPy stands for Numerical Python that is one of the most fundamental libraries in Python. You must have a solid grasp of NumPy to be a better Data Scientist.
So, let's get started!
So, let's get started!
๐ Creating Arrays
๐ถ Basic ndarrays
๐น np.array()
- Create an array.
- You can also specify the data type.
๐ถ Basic ndarrays
๐น np.array()
- Create an array.
- You can also specify the data type.
๐ถ Evenly Spaced ndarrays
๐น np.arange(start, end, step)
- Create an array between two numbers(start and end) with a given step size.
- Interval: [start, end) --> Excludes last number
๐น np.arange(start, end, step)
- Create an array between two numbers(start and end) with a given step size.
- Interval: [start, end) --> Excludes last number
๐น np.linspace(start, end, num)
- Create an array of total num elements between two numbers(start and end).
- Interval: [start, end] --> Includes last number
- Create an array of total num elements between two numbers(start and end).
- Interval: [start, end] --> Includes last number
๐ถ Miscellaneous
๐น np.zeros(shape)
- Create an array of all 0s.
๐น np.ones(shape)
- Create an array of all 1s.
๐น np.eye(shape)
- Create an Identity matrix.
๐น np.random.rand(shape)
- Create an array with random values.
๐น np.zeros(shape)
- Create an array of all 0s.
๐น np.ones(shape)
- Create an array of all 1s.
๐น np.eye(shape)
- Create an Identity matrix.
๐น np.random.rand(shape)
- Create an array with random values.
๐ Reshaping Arrays
๐น .shape
- Return shape of an array.
๐น .ndim
- Return dimension of an array. (1, 2, 3, etc.)
๐น .size
- Return the size of an array.
- Size is the total number of elements in an array.
Note: The above three are attributes and not the methods.
๐น .shape
- Return shape of an array.
๐น .ndim
- Return dimension of an array. (1, 2, 3, etc.)
๐น .size
- Return the size of an array.
- Size is the total number of elements in an array.
Note: The above three are attributes and not the methods.
๐น np.reshape(a, new_shape)
- Changes the shape of an array without changing the data.
- Changes the shape of an array without changing the data.
๐ Flattening Arrays
๐น np.flatten()
- Flattens an array (Convert Multidimensional arrays into single-dimensional)
๐น np.ravel()
- Flattens an array (Convert Multidimensional arrays into single-dimensional)
๐น np.flatten()
- Flattens an array (Convert Multidimensional arrays into single-dimensional)
๐น np.ravel()
- Flattens an array (Convert Multidimensional arrays into single-dimensional)
๐ถ Difference between flatten() and ravel()
- flatten() returns a copy of the original array and ravel() returns a reference to the original array. Any changes made to the array returned from ravel() will be reflected in the original array but this will not be true for flatten()
- flatten() returns a copy of the original array and ravel() returns a reference to the original array. Any changes made to the array returned from ravel() will be reflected in the original array but this will not be true for flatten()
๐ Transposing Arrays
๐น .T
- Return transposed array (Swaps rows and columns).
- It also changes the shape.
๐น .T
- Return transposed array (Swaps rows and columns).
- It also changes the shape.
๐ Squeezing and Expanding
๐น np.expand_dims()
- Add a new axis to an array along the specified axis.
๐น np.expand_dims()
- Add a new axis to an array along the specified axis.
๐น np.squeeze()
- Remove axis of length one from an array.
- Remove axis of length one from an array.
๐ Slicing and Indexing
๐ถ 1D arrays
- Work in a pretty similar fashion to the list indexing and slicing.
๐ถ 1D arrays
- Work in a pretty similar fashion to the list indexing and slicing.
๐ถ 2D arrays
- It is an array of arrays.
- It is an array of arrays.
๐ถ 3D arrays
- It can be thought of as an n 2-dimensional array.
- It can be thought of as an n 2-dimensional array.
๐ Stacking and Concatenating
๐น np.vstack()
- Combines arrays vertically and thus increases the total rows.
๐น np.hstack()
- Combines arrays horizontally and thus increases the total columns.
๐น np.vstack()
- Combines arrays vertically and thus increases the total rows.
๐น np.hstack()
- Combines arrays horizontally and thus increases the total columns.
๐น np.concatenate()
- Concatenate arrays along the specified axis.
- Concatenate arrays along the specified axis.
๐ Broadcasting
- It lets you perform arithmetics operations between arrays of different sizes or between an array and a scaler number! woohoo!
- Hypothetically, it stretches a smaller array to match the shape of a larger array.
- It lets you perform arithmetics operations between arrays of different sizes or between an array and a scaler number! woohoo!
- Hypothetically, it stretches a smaller array to match the shape of a larger array.
๐ Maths with NumPy
๐ถ Basic Arithmetic Operations
- With the power of Broadcasting, you can perform all the basic arithmetic operations as shown below.
๐ถ Basic Arithmetic Operations
- With the power of Broadcasting, you can perform all the basic arithmetic operations as shown below.
๐ถ Statistical
๐น np.mean(a, axis)
- Return the mean of an array along the specified axis.
๐น np.median(a, axis)
- Return the median of an array along the specified axis.
๐น np.std(a, axis)
- Return the standard deviation of an array along the specified axis.
๐น np.mean(a, axis)
- Return the mean of an array along the specified axis.
๐น np.median(a, axis)
- Return the median of an array along the specified axis.
๐น np.std(a, axis)
- Return the standard deviation of an array along the specified axis.
๐น np.min(a, axis)
- Return the minimum value of an array along the specified axis.
๐น np.max(a, axis)
- Return the maximum value of an array along the specified axis.
- Return the minimum value of an array along the specified axis.
๐น np.max(a, axis)
- Return the maximum value of an array along the specified axis.
๐น np.argmin(a, axis)
- Return an index of the minimum value of an array along the specified axis.
๐น np.argmax(a, axis)
- Return an index of the maximum value of an array along the specified axis.
- Return an index of the minimum value of an array along the specified axis.
๐น np.argmax(a, axis)
- Return an index of the maximum value of an array along the specified axis.
Did I miss something important?
Let me know in the comments section below ๐
Let me know in the comments section below ๐
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