NumPy Exercises, Practice, Solution
NumPy
NumPy is a Python package providing fast, flexible, and expressive data structures designed to make working with 'relationa' or 'labeled' data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.
The best way we learn anything is by practice and exercise questions. Here you have the opportunity to practice the NumPy concepts by solving the exercises starting from basic to more complex exercises. A sample solution is provided for each exercise. It is recommended to do these exercises by yourself first before checking the solution.
Hope, these exercises help you to improve your NumPy coding skills. Currently, following sections are available, we are working hard to add more exercises .... Happy Coding!
List of NumPy Exercises:
- NumPy Basic [ 59 exercises with solution ]
- NumPy arrays [ 205 exercises with solution ]
- NumPy Linear Algebra [ 19 exercises with solution ]
- NumPy Random [ 17 exercises with solution ]
- NumPy Sorting and Searching [ 9 exercises with solution ]
- NumPy Mathematics [ 41 exercises with solution ]
- NumPy Statistics [ 14 exercises with solution ]
- NumPy DateTime [ 7 exercises with solution ]
- NumPy String [ 22 exercises with solution ]
- Advanced NumPy [ 15 exercises with solution ]
- More to come
NumPy Basics
| Operator | Description |
|---|---|
| np.array([1,2,3]) | 1d array |
| np.array([(1,2,3),(4,5,6)]) | 2d array |
| np.arange(start,stop,step) | range array |
Placeholders
| Operator | Description |
|---|---|
| np.linspace(0,2,9) | Add evenly spaced values btw interval to array of length |
| np.zeros((1,2)) | Create and array filled with zeros |
| np.ones((1,2)) | Creates an array filled with ones |
| np.random.random((5,5)) | Creates random array |
| np.empty((2,2)) | Creates an empty array |
Array
| Syntax | Description |
|---|---|
| array.shape | Dimensions (Rows,Columns) |
| len(array) | Length of Array |
| array.ndim | Number of Array Dimensions |
| array.dtype | Data Type |
| array.astype(type) | Converts to Data Type |
| type(array) | Type of Array |
Copying/Sorting
| Operators | Description |
|---|---|
| np.copy(array) | Creates copy of array |
| other = array.copy() | Creates deep copy of array |
| array.sort() | Sorts an array |
| array.sort(axis=0) | Sorts axis of array |
Array Manipulation
Adding or Removing Elements
| Operator | Description |
|---|---|
| np.append(a,b) | Append items to array |
| np.insert(array, 1, 2, axis) | Insert items into array at axis 0 or 1 |
| np.resize((2,4)) | Resize array to shape(2,4) |
| np.delete(array,1,axis) | Deletes items from array |
Combining Arrays
| Operator | Description |
|---|---|
| np.concatenate((a,b),axis=0) | Concatenates 2 arrays, adds to end |
| np.vstack((a,b)) | Stack array row-wise |
| np.hstack((a,b)) | Stack array column wise |
Splitting Arrays
| Operator | Description |
|---|---|
| numpy.split() | Split an array into multiple sub-arrays. |
| np.array_split(array, 3) | Split an array in sub-arrays of (nearly) identical size |
| numpy.hsplit(array, 3) | Split the array horizontally at 3rd index |
More
| Operator | Description |
|---|---|
| other = ndarray.flatten() | Flattens a 2d array to 1d |
| array = np.transpose(other) array.T |
Transpose array |
| inverse = np.linalg.inv(matrix) | Inverse of a given matrix |
Mathematics
Operations
| Operator | Description |
|---|---|
| np.add(x,y) x + y |
Addition |
| np.substract(x,y) x - y |
Subtraction |
| np.divide(x,y) x / y |
Division |
| np.multiply(x,y) x @ y |
Multiplication |
| np.sqrt(x) | Square Root |
| np.sin(x) | Element-wise sine |
| np.cos(x) | Element-wise cosine |
| np.log(x) | Element-wise natural log |
| np.dot(x,y) | Dot product |
| np.roots([1,0,-4]) | Roots of a given polynomial coefficients |
Comparison
| Operator | Description |
|---|---|
| == | Equal |
| != | Not equal |
| < | Smaller than |
| > | Greater than |
| <= | Smaller than or equal |
| >= | Greater than or equal |
| np.array_equal(x,y) | Array-wise comparison |
Basic Statistics
| Operator | Description |
|---|---|
| np.mean(array) | Mean |
| np.median(array) | Median |
| array.corrcoef() | Correlation Coefficient |
| np.std(array) | Standard Deviation |
More
| Operator | Description |
|---|---|
| array.sum() | Array-wise sum |
| array.min() | Array-wise minimum value |
| array.max(axis=0) | Maximum value of specified axis |
| array.cumsum(axis=0) | Cumulative sum of specified axis |
Slicing and Subsetting
| Operator | Description |
|---|---|
| array[i] | 1d array at index i |
| array[i,j] | 2d array at index[i][j] |
| array[i<4] | Boolean Indexing, see Tricks |
| array[0:3] | Select items of index 0, 1 and 2 |
| array[0:2,1] | Select items of rows 0 and 1 at column 1 |
| array[:1] | Select items of row 0 (equals array[0:1, :]) |
| array[1:2, :] | Select items of row 1 |
| [comment]: <> ( | array[1,...] |
| array[ : :-1] | Reverses array |
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Python: Tips of the Day
Executes the provided function once for each list element:
Example:
def tips_for_each(itr, fn):
for el in itr:
fn(el)
tips_for_each([3, 6, 9], print)
Output:
3 6 9
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