**The np.count() function in Python is a tool used for counting occurrences of a specific substring within each element of an array. Part of the NumPy library, this function works efficiently on arrays, including multi-dimensional ones, to find and count instances of a given word or character.**

The **np.char.count() function in Python** is part of the NumPy library, which is widely used for numerical computations. This function is specifically used for performing vectorized string operations for arrays of dtype **numpy.str_** or **numpy.unicode_**.

It counts the number of occurrences of a substring in each element of an array.

The **np.count()** function in Python syntax is as follows:

`np.char.count(arr, sub, start=0, end=None)`

Here,

arr | This is the array in which the function searches for the substring in Python. |

sub | The substring to search for within each element of arr in Python. |

start | The starting position in each element of arr in Python from which the search begins. The default is 0. |

end | The ending position in each element of arr in Python up to which the search is performed. None means the search continues until the end of each element. The default is None. |

The np.count() function in Python returns an array of integers, with each element representing the count of the substring sub in the corresponding element of the input array **arr**.

Let’s see some examples where we can learn the use of the np.count() function in Python.

In this example, the **np.count() function in Python** counts occurrences of the substring ‘hello’ in each element of the array arr.

```
import numpy as np
arr = np.array(['apple pie', 'baseball game', 'American dream', 'statue of liberty'])
sub = 'American'
result = np.char.count(arr, sub)
print(result)
```

**Output:**

`[0 0 1 0]`

The output from running the code in PyCharm is visually represented in the screenshot below.

Here, the np.count() function in Python searches for the substring between two positions in each element of the array.

```
import numpy as np
arr = np.array(['Alabama', 'Alaska', 'California', 'Arizona'])
sub = 'a'
result = np.char.count(arr, sub, start=1, end=4)
print(result)
```

**Output:**

`[1 1 1 0]`

Displayed below is a screenshot capturing the outcome of the code execution in the PyCharm editor.

Here, we have to count the occurrence of the value in a 2D array in Python.

```
import numpy as np
arr = np.array([['freedom of speech', 'freedom of expression'],
['freedom fighters', 'land of the free']])
sub = 'freedom'
result = np.char.count(arr, sub)
print(result)
```

**Output:**

```
[[1 1]
[1 0]]
```

The following screenshot illustrates the results obtained from executing the code in the PyCharm editor.

let’s incorporate the start and end parameters in examples with 2D arrays using the np.char.count() function.

```
import numpy as np
arr = np.array([['liberty and justice', 'pursuit of liberty'],
['statue of liberty', 'liberty bell']])
sub = 'liberty'
start, end = 0, 10
result = np.char.count(arr, sub, start=start, end=end)
print(result)
```

**Output:**

```
[[1 0]
[0 1]]
```

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

In this article, I have explained what the **np.count() function** in Python is in detail, how we can apply it, what the parameters required, and the return value. Also, different use cases in Python illustrate the **np.char.count()** function.

You may also like to read:

]]>**To flatten a list of lists in Python, various methods can be employed such as list comprehension for concise one-liners, traditional for loops for straightforward iteration, the sum() function for easy concatenation, recursion for handling nested lists, the reduce() function from functools for cumulative concatenation, and itertools.chain() for efficiently linking iterables.**

There are six different methods to flatten a list of lists in Python:

- Using list comprehension
- Using nested for loop
- Using Recursion
- Using sum()
- Using lambda and reduce()
- Using itertools.chain()

Let’s see them one by one using some illustrative examples:

**List comprehension** is a concise way to create lists. This method uses a concise syntax to iterate over each sublist and then each element within the sublist, creating a flat list in a single line of code in Python. For example:

```
landmarks = [["Statue of Liberty", "NY"], ["Golden Gate Bridge", "CA"], ["Grand Canyon", "AZ"]]
flattened_list = [item for sublist in landmarks for item in sublist]
print(flattened_list)
```

**Output:**

`['Statue of Liberty', 'NY', 'Golden Gate Bridge', 'CA', 'Grand Canyon', 'AZ']`

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

A **for loop** iterates over each sublist and then each item within those sublists, appending each element to a new flat list in Python. For instance:

```
landmarks = [["Statue of Liberty", "NY"], ["Golden Gate Bridge", "CA"], ["Grand Canyon", "AZ"]]
flattened_list = []
for sublist in landmarks:
for item in sublist:
flattened_list.append(item)
print(flattened_list)
```

**Output:**

`['Statue of Liberty', 'NY', 'Golden Gate Bridge', 'CA', 'Grand Canyon', 'AZ']`

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

**Recursion** is a method where the function calls itself. It’s useful for nested lists of unknown depth. For example:

```
def flatten_list(nested_list):
if not nested_list:
return nested_list
if isinstance(nested_list[0], list):
return flatten_list(nested_list[0]) + flatten_list(nested_list[1:])
return nested_list[:1] + flatten_list(nested_list[1:])
landmarks = [["Statue of Liberty", ["NY"]], ["Golden Gate", ["CA"]], ["Grand Canyon", ["AZ"]]]
flattened_list = flatten_list(landmarks)
print(flattened_list)
```

**Output:**

`['Statue of Liberty', 'NY', 'Golden Gate', 'CA', 'Grand Canyon', 'AZ']`

After executing the code in Pycharm, one can see the output in the below screenshot.

The **sum() function** in Python can concatenate all the sublists into a single flat list starting from an empty list. For instance:

```
landmarks = [["Statue of Liberty", "NY"], ["Golden Gate Bridge", "CA"], ["Grand Canyon", "AZ"]]
flattened_list = sum(landmarks, [])
print(flattened_list)
```

**Output:**

`['Statue of Liberty', 'NY', 'Golden Gate Bridge', 'CA', 'Grand Canyon', 'AZ']`

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

The **reduce() function **from the **functools module** and a **lambda function** to cumulatively concatenate sublists into a single list. For example:

```
from functools import reduce
landmarks = [["Statue of Liberty", "NY"], ["Golden Gate Bridge", "CA"], ["Grand Canyon", "AZ"]]
flattened_list = reduce(lambda x, y: x + y, landmarks)
print(flattened_list)
```

**Output:**

`['Statue of Liberty', 'NY', 'Golden Gate Bridge', 'CA', 'Grand Canyon', 'AZ']`

After executing the code in Pycharm, one can see the output in the below screenshot.

The **chain() function **from the **itertools module**, is specifically designed to iterate through a series of iterables, effectively chaining them into a single iterable in Python.

```
from itertools import chain
landmarks = [["Statue of Liberty", "NY"], ["Golden Gate Bridge", "CA"], ["Grand Canyon", "AZ"]]
flattened_list = list(chain(*landmarks))
print(flattened_list)
```

**Output:**

`['Statue of Liberty', 'NY', 'Golden Gate Bridge', 'CA', 'Grand Canyon', 'AZ']`

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

There are many ways to **flatten a list of lists in Python**, each with its own advantages. We can use quick and easy methods like **list comprehension** or **for loops**, or more advanced techniques like **recursion**, **sum()**, **lambda with reduce()**, and **itertools.chain()**. Each method works well in different situations, giving us the flexibility to choose the best one for our needs.

You may also like to read:

]]>**To copy elements from one list to another in Python, several methods are available: use copy() for a shallow copy, list slicing (original_list[:]) for a quick duplicate, list() function for an easy copy, copy.copy() for a generic shallow copy, copy.deepcopy() for a deep copy of nested structures, or the * operator ([*original_list]) for a concise and readable approach.**

There are six different methods to copy elements from one list to another in Python:

- Using copy() function
- Using List slicing
- list() constructor
- copy.copy() method
- copy.deepcopy() method
- * (unpacking) operator

Let’s see them one by one using some demonstrative examples:

The **copy() method** is the most straightforward way to copy elements from one list to another in Python. It creates a shallow copy of the list.

**Note:** This method only copies the list itself, not the objects referenced by the list. If the original list contains objects and one of the objects is modified, the change will be reflected in both lists.

```
original_cities = ["New York", "Los Angeles", "Chicago"]
itinerary_cities = original_cities.copy()
print(itinerary_cities)
```

**Output:**

`['New York', 'Los Angeles', 'Chicago']`

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

List slicing in Python can also be used for copying lists. By slicing the entire list, we create a new list. Like the copy() method, **list slicing** creates a shallow copy.

```
presidents = ["Washington", "Lincoln", "Jefferson"]
backup_presidents = presidents[:]
print(backup_presidents)
```

**Output:**

`['Washington', 'Lincoln', 'Jefferson']`

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

Another way to copy a list is by using the built-in **list() function**, which creates a new list based on the elements of the existing one. This method, too, produces a shallow copy of the original list.

```
fast_food_chains = ["McDonald's", "Burger King", "Taco Bell"]
study_list = list(fast_food_chains)
print(study_list)
```

**Output:**

`["McDonald's", 'Burger King', 'Taco Bell']`

After executing the code in Pycharm, one can see the output in the below screenshot.

Python has a copy module that provides a **copy() function**. This method is particularly useful when dealing with complex data structures. The **copy.copy()** creates a shallow copy of the list.

```
import copy
nba_teams = ["Lakers", "Bulls", "Celtics"]
analysis_teams = copy.copy(nba_teams)
print(analysis_teams)
```

**Output:**

`['Lakers', 'Bulls', 'Celtics']`

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

The **deepcopy() method** from the copy module creates a deep copy of the list, meaning that it also copies the inner objects. Modifications to objects in the original list will not affect the copied list.

```
import copy
states_cities = [["California", ["Los Angeles", "San Francisco"]], ["Texas", ["Houston", "Dallas"]]]
copied_states_cities = copy.deepcopy(states_cities)
print(copied_states_cities)
```

**Output:**

`[['California', ['Los Angeles', 'San Francisco']], ['Texas', ['Houston', 'Dallas']]]`

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

The **unpacking operator *** can be used to copy lists. This method is similar to list slicing and is useful in contexts where we need to unpack the elements of the list. This approach also creates a shallow copy of the list.

```
us_holidays = ["Independence Day", "Thanksgiving", "Christmas"]
calendar_holidays = [*us_holidays]
print(calendar_holidays)
```

**Output:**

`['Independence Day', 'Thanksgiving', 'Christmas']`

After executing the code in Pycharm, one can see the output in the below screenshot.

Here, I have explained how to **copy elements from one list to another in Python** using six different methods **copy() function, list slicing method, list() constructor, copy.copy() function, copy.deepcopy() function, and * operator** with some demonstrative examples.

The selection of the method depends upon the requirement of the problem.

You may also like to read:

]]>**To remove multiple elements from a list in Python, we can use several methods like list comprehension for filtering elements, the remove() method for specific values, the filter() function to create a new list based on a condition, the del keyword for removing elements by index, and list slicing for selectively keeping elements.**

There are five different methods present to remove multiple elements from a list in Python:

- List comprehension
- remove() function
- filter() function
- del keyword
- List slicing

Let’s see them one by one using some illustrative examples:

**List comprehension** is a concise way to create lists. It’s especially useful for filtering elements out of a list, which means to remove multiple elements from a list in Python. For instance:

```
states = ["California", "Texas", "Florida", "New York", "Illinois"]
states_to_remove = ["Texas", "Florida"]
new_states = [state for state in states if state not in states_to_remove]
print(new_states)
```

**Output:**

`['California', 'New York', 'Illinois']`

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

The **remove() method** is used to remove the first occurrence of an element in the list in Python. If we need to remove multiple occurrences, we will have to use a loop in Python. For example:

```
cities = ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"]
cities_to_remove = ["Houston", "Phoenix"]
for city in cities_to_remove:
if city in cities:
cities.remove(city)
print(cities)
```

**Output:**

`['New York', 'Los Angeles', 'Chicago']`

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

The **filter() function** constructs an iterator from elements of an iterable for which a function returns **True** and, hence remove multiple elements from a list in Python. For instance:

```
landmarks = ["Statue of Liberty", "Grand Canyon", "Yellowstone", "Mount Rushmore"]
landmarks_to_remove = ["Grand Canyon", "Mount Rushmore"]
new_landmarks = list(filter(lambda x: x not in landmarks_to_remove, landmarks))
print(new_landmarks)
```

**Output:**

`['Statue of Liberty', 'Yellowstone']`

After executing the code in Pycharm, one can see the output in the below screenshot.

The **del keyword** in Python is used to delete objects. When it comes to lists, we can use del with slicing to remove multiple elements from a list in Python. For example:

```
universities = ["MIT", "Stanford", "Harvard", "Caltech", "Princeton"]
del universities[1:4:2]
print(universities)
```

**Output:**

`['MIT', 'Harvard', 'Princeton']`

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

**List slicing** can be useful for removing a sequence of elements from a list based on their indices. This method is ideal when we know the positions of the elements to be removed. For example:

```
novels = ["To Kill a Mockingbird", "The Great Gatsby", "Moby-Dick", "The Catcher in the Rye", "Beloved"]
new_novels = novels[:1] + novels[3:]
print(new_novels)
```

**Output:**

`['To Kill a Mockingbird', 'The Catcher in the Rye', 'Beloved']`

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

Here, we have learned different ways to **remove multiple elements from a list in Python**, like using **list comprehension**, **remove()**, **filter()**, **del keyword**, and **slicing**. Each method has its own use, helping you to easily and effectively handle different situations when working with lists. Knowing these methods is very useful for writing better and more efficient Python code.

You may also like to read:

]]>**To use np.argsort in descending order in Python, first apply np.argsort to our array, and then either invert the result using array slicing ([::-1]) or negate the array before sorting. For inverting, sort the array using np.argsort and then reverse the resulting indices. For negating, apply np.argsort to the negated array (-arr) to get the indices in descending order.**

NumPy **np.argsort function in Python** is an efficient way to obtain the indices of an array that result in a sorted order. This is particularly useful in situations where we want to sort an array but also need to track the original positions of elements.

**Syntax:**

```
numpy.argsort(arr, axis=-1, kind=None, order=None)
```

Here,

arr | Python array need to be sorted. |

axis | The axis along which to sort. If -1, the Python array is flattened before sorting. |

kind | Specifies the sorting algorithm (‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’). |

order | If arr is an array in Python of structured data types, this argument specifies the field to compare. |

**Return value:**

When we apply **np.argsort()** in Python to an array, it computes the indices of the array’s elements in sorted order. However, it’s important to note that it does not change the original array.

Instead, it provides a way to access the elements in the order that would sort the array in Python.

By default, the **np.argsort()** function in Python, sorts in ascending order. The first index in the returned array refers to the smallest item, the second index to the second smallest item, and so on.

**Example:** Let’s consider an example where we have an array of numbers and we want to sort it in ascending order using **np.argsort()** in Python.

This function doesn’t directly sort the array; instead, it returns the indices of the array in the order they would be if the array were sorted

```
import numpy as np
arr = np.array([10, 5, 8, 1, 7])
sorted_indices = np.argsort(arr)
print("Indices that would sort the array:", sorted_indices)
sorted_arr = arr[sorted_indices]
print("Sorted array:", sorted_arr)
```

**Output:**

```
Indices that would sort the array: [3 1 4 2 0]
Sorted array: [ 1 5 7 8 10]
```

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

While np.argsort in Python naturally sorts in ascending order, there are two ways to use np.argsort in Descending order in Python:

- Inverting the Result.
- Negating the Array

Let’s see them one by one with the example:

After applying np.argsort, invert the order of the result. This can be done by using array slicing. For instance:

```
import numpy as np
arr = np.array([3, 1, 4, 1, 5, 9, 2])
ascending_order_indices = np.argsort(arr)
descending_order_indices = ascending_order_indices[::-1]
arr_descending_order = arr[descending_order_indices]
print(arr_descending_order)
```

**Output:** Here, [::-1] reverses the array in Python, turning the ascending order into descending order.

`[9 5 4 3 2 1 1]`

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

Another approach is to negate the array values if the array consists of numeric data in Python. This way, the largest negative value (which is the smallest positive value) gets sorted first. This way we use np.argsort in Descending order in Python For example:

```
import numpy as np
arr = np.array([3, 1, 4, 1, 5, 9, 2])
descending_order_indices = np.argsort(-arr)
arr_descending_order = arr[descending_order_indices]
print(arr_descending_order)
```

**Output:** By negating arr, **np.argsort in Python** sorts as if it were sorted in descending order.

`[9 5 4 3 2 1 1]`

After executing the code in Pycharm, one can see the output in the below screenshot.

In this article, I have explained what the **np.argsort() function** is, and how to use **np.argsort in descending order in Python** with inverting the result and negating the array with examples. These techniques offer a straightforward and versatile approach to descending order sorting, extending the utility of **np.argsort** beyond its default ascending order functionality.

You may also like to read:

]]>**To filter a 2D NumPy array by condition in Python, you can use techniques like boolean indexing for direct element-wise selection, np.where() to locate elements, and combine conditions using logical operators. Additionally, np.all() and np.any() are useful for row-wise or column-wise filtering based on uniform or any-true conditions, respectively. For more complex criteria, np.asarray() with a mean() can convert data structures and apply aggregate condition checks.**

Filtering involves selecting elements from an array that meet certain criteria. Let’s see some common methods to filter a 2D array in NumPy:

- Using Boolean Indexing
- Using np.where
- Combining Conditions
- Using numpy.any() method
- Using numpy.all() method
- Using np.asarray() method

Let’s see them one by one using some illustrative examples:

The **Boolean indexing** in NumPy allows us to filter an array using a boolean expression. Here’s an example:

```
import numpy as np
stock_prices = np.random.uniform(45, 100, size=(30, 5))
days_with_low_prices = stock_prices[stock_prices < 50]
print("Days with stock prices below $50:\n", days_with_low_prices)
```

**Output:**

```
Days with stock prices below $50:
[46.07200099 49.96345168 47.27621964 48.67303959 49.542139 46.54178479
49.45113595 47.25793938 47.46435936 48.81178511 45.55335432 45.29821599
49.06888685]
```

The output from running the code in PyCharm is visually represented in the screenshot below.

The **np.where()** function is a versatile way in NumPy filter 2D array by condition in Python. It returns the indices of elements that satisfy the given condition.

```
import numpy as np
covid_cases = np.random.randint(800, 1500, size=(4, 3))
hotspots = np.where(covid_cases > 1000)
print("Weeks and states with more than 1000 cases:\n", hotspots)
```

**Output:**

```
Weeks and states with more than 1000 cases:
(array([0, 0, 1, 1, 2], dtype=int64), array([0, 1, 1, 2, 0], dtype=int64))
```

Displayed below is a screenshot capturing the outcome of the code execution in the PyCharm editor.

We can combine **multiple conditions** using logical operators like **& (and) and | (or)** in NumPy filter 2D array by condition in Python.

```
import numpy as np
developer_data = np.random.randint(400, 800, size=(10, 1))
manager_data = np.random.randint(20, 50, size=(10, 1))
combined_data = np.hstack((developer_data, manager_data))
high_ratio_company = combined_data[(combined_data[:, 0] > 500) & (combined_data[:, 1] < 30)]
print("companies with high developer-to-manager ratio:\n", high_ratio_company)
```

**Output:**

```
companies with high developer-to-manager ratio:
[[784 28]
[582 21]
[761 27]]
```

The following screenshot illustrates the results obtained from executing the code in the PyCharm editor.

The **np.any()** method tests whether any array element along a given axis evaluates to True. It’s often used in filtering to check if any element in an array or sub-array satisfies a condition in Python.

```
import numpy as np
temperature_data = np.array([
[90, 85, 92, 88],
[95, 102, 99, 101],
[88, 91, 87, 89],
[101, 98, 95, 102]
])
heatwave_weeks = temperature_data[np.any(temperature_data > 100, axis=1)]
print("Heatwave Weeks:\n", heatwave_weeks)
```

**Output:**

```
Heatwave Weeks:
[[ 95 102 99 101]
[101 98 95 102]]
```

After executing the code in Pycharm, one can see the output in the below screenshot.

The **np.all() function** in Python checks if all elements along a specified axis satisfy a condition. It is useful for filtering out complete rows or columns based on a uniform condition.

```
import numpy as np
aqi_data = np.array([
[45, 40, 50],
[55, 60, 52],
[42, 38, 47],
[30, 28, 35]
])
good_air_quality_days = aqi_data[np.all(aqi_data < 50, axis=1)]
print("Good Air Quality Days:\n", good_air_quality_days)
```

**Output:**

```
Good Air Quality Days:
[[42 38 47]
[30 28 35]]
```

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

The **np.asarray() function** in Python converts an input to an array. While it’s not a filtering function per se, it can be part of a filtering process, especially when dealing with data that might not initially be in array form.

```
import numpy as np
Match_scores_list = [
[1050, 1100, 1020],
[1250, 1300, 1280],
[1150, 1200, 1180],
]
Match_scores = np.asarray(Match_scores_list)
high_scoring_company = Match_scores[np.mean(Match_scores, axis=1) > 1200]
print("High Scoring company:\n", high_scoring_company)
```

**Output:**

```
High Scoring company:
[[1250 1300 1280]]
```

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

Learning a variety of methods like **boolean indexing**, **conditional operators**, **np.where()**, **np.all()**, **np.any()**, and **np.asarray() **with **mean() functions** in **NumPy filter 2D array by condition in Python**. These functions are helpful for sorting through data, allowing us to pick out exactly what we need based on specific conditions.

You may also like to read:

]]>**To understand the np.diff() function in Python, it’s essential to recognize that it calculates the difference between successive elements of an array. By adjusting its parameters, users can specify the number of times this operation is performed (n), the axis along which the differences are calculated (axis), and optionally prepend or append values.**

The **np.diff() function in Python** NumPy library calculates the discrete difference between consecutive elements of an array. An input array computes the output as **a[i+1] – a[i]** for each element **i**, where **i** ranges over the array’s length minus one.

Here’s a brief overview of how **np.diff() function in Python** works:

The basic syntax of the **np.diff() function in Python** is as follows:

`numpy.diff(arr, n=1, axis=-1, prepend=<no value>, append=<no value>)`

Here,

arr | Input array in Python. The differences are calculated along this array. |

n | (optional) The number of times to perform the differentiation in Python. The default is 1. |

axis | (optional) The axis along which the difference is taken in the Python array. By default, it is the last axis. |

prepend | (optional) Value to prepend to a along the specified axis before performing the difference in Python. |

append | (optional) Value to append to a along the specified axis before performing the difference in Python. |

The **np.diff() function in Python** returns a new array of the same type as a, except along the specified axis where the dimension is smaller by n units. The returned array holds the calculated differences.

Let’s see different use cases for the **np.diff() function in Python**:

Calculates the difference between each pair of consecutive elements in a one-dimensional array in Python. For example:

```
import numpy as np
temperatures = np.array([58, 60, 62, 65, 63, 66, 68])
temperature_change = np.diff(temperatures)
print(temperature_change)
```

**Output:**

`[ 2 2 3 -2 3 2]`

The output from running the code in PyCharm is visually represented in the screenshot below.

Computes the second-order difference, which is the difference of the consecutive differences in the array in Python.

```
import numpy as np
stock_prices = np.array([120, 125, 123, 130, 128])
price_change = np.diff(stock_prices, n=2)
print(price_change)
```

**Output:**

`[-7 9 -9]`

Displayed below is a screenshot capturing the outcome of the code execution in the PyCharm editor.

Finds the difference along the default last axis (axis=-1) in a multidimensional array in Python, effectively performing the operation on each sub-array.

```
import numpy as np
rainfall = np.array([[0.1, 0.2, 0.0, 0.3],
[0.3, 0.4, 0.5, 0.2],
[0.0, 0.0, 0.1, 0.2]])
daily_change = np.diff(rainfall)
print(daily_change)
```

**Output:**

```
[[ 0.1 -0.2 0.3]
[ 0.1 0.1 -0.3]
[ 0. 0.1 0.1]]
```

The following screenshot illustrates the results obtained from executing the code in the PyCharm editor.

Computes the difference between elements along the vertical axis (row-wise) in a multidimensional Python array.

```
import numpy as np
rainfall = np.array([[0.1, 0.2, 0.0, 0.3],
[0.3, 0.4, 0.5, 0.2],
[0.0, 0.0, 0.1, 0.2]])
city_change = np.diff(rainfall, axis=0)
print(city_change)
```

**Output:**

```
[[ 0.2 0.2 0.5 -0.1]
[-0.3 -0.4 -0.4 0. ]]
```

After executing the code in Pycharm, one can see the output in the below screenshot.

Calculates the difference between elements along the horizontal axis (column-wise) in a multidimensional array in Python.

```
import numpy as np
rainfall = np.array([[0.1, 0.2, 0.0, 0.3],
[0.3, 0.4, 0.5, 0.2],
[0.0, 0.0, 0.1, 0.2]])
city_change = np.diff(rainfall, axis=1)
print(city_change)
```

**Output:**

```
[[ 0.1 -0.2 0.3]
[ 0.1 0.1 -0.3]
[ 0. 0.1 0.1]]
```

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

Understanding The **np.diff() function in Python**, that computes differences. And its flexibility with parameters like **n** and **axis** makes it an essential function for data analysis, signal processing, and scientific computing.

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]]>**To perform NumPy matrix multiplication in Python, we can use the np.dot() function for dot product or standard matrix multiplication, utilize the @ operator introduced in Python 3.5 for a more intuitive syntax, or opt for np.matmul() for stricter handling of matrix shapes. Additionally, a nested for loop approach can be implemented for a manual method of matrix multiplication.**

Before performing matrix multiplication, we need to create matrices. Here’s how we can create a matrix in NumPy.

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

These lines create two 2×2 matrices, A and B.

There are multiple ways to perform NumPy Matrix Multiplication in Python:

- np.dot()
- @ Operator (Python 3.5+)
- np.matmul()
- nested for loop

Let’s see them one by one with some examples:

The **np.dot() function** in Python is used to perform the **dot product** of two arrays. For 2-D vectors, it’s equivalent to NumPy matrix multiplication in Python. For example:

```
import numpy as np
gdp_growth_2019 = np.array([[2.5, 3.0], [1.5, 2.5]])
gdp_growth_2020 = np.array([[3.0, 2.0], [3.5, 2.5]])
total_growth = np.dot(gdp_growth_2019, gdp_growth_2020)
print("After multiplying we get:\n", total_growth)
```

**Output:**

```
After multiplying we get:
[[18. 12.5 ]
[13.25 9.25]]
```

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

Python 3.5 introduced a dedicated matrix multiplication **operator @** which can be used with NumPy arrays. Let’s see how to use the @ operator in NumPy matrix multiplication in Python:

```
import numpy as np
east_coast_sales = np.array([[50, 60], [30, 40]])
west_coast_sales = np.array([[55, 45], [35, 40]])
total_sales = east_coast_sales @ west_coast_sales
print("After multiplying we get:\n", total_sales)
```

**Output:**

```
After multiplying we get:
[[4850 4650]
[3050 2950]]
```

The **matmul() function in Python** is another way to perform matrix multiplication. It’s similar to dot for two-dimensional arrays, but it’s preferred for NumPy matrix multiplication in Python.

```
import numpy as np
income_nyc_la = np.array([[80, 90], [70, 75]])
spending_nyc_la = np.array([[60, 55], [65, 50]])
income_spending_product = np.matmul(income_nyc_la, spending_nyc_la)
print("After multiplying we get:\n", income_spending_product)
```

**Output:**

```
After multiplying we get:
[[10650 8900]
[ 9075 7600]]
```

Displayed below is a screenshot capturing the outcome of the code execution in the PyCharm editor.

Matrix multiplication can be performed manually using **nested for loops** in Python. This method iterates through rows of the first matrix and columns of the second matrix. Multiply corresponding elements and sum them up to get each element of the result matrix.

**For example**: Let’s take two matrices and try to find the multiplication by for loop in Python.

```
import numpy as np
A = np.array([[300, 200, 250],
[150, 100, 120]])
B = np.array([[1.1, 1.2],
[1.0, 1.1],
[1.05, 1.15]])
rows_A, cols_A = len(A), len(A[0])
rows_B, cols_B = len(B), len(B[0])
result = [[0 for _ in range(cols_B)] for _ in range(rows_A)]
for i in range(rows_A):
for j in range(cols_B):
for k in range(cols_A):
result[i][j] += (A[i][k]) * (B[k][j])
for r in result:
print(r)
```

**Output:**

```
[792.5, 867.5]
[391.0, 428.0]
```

The output from running the code in PyCharm is visually represented in the screenshot below.

In this article, I have explained how to do **NumPy matrix multiplication in Python** with four different methods like **np.dot() function**, the **@ operator**, the **np.matmul() function**, and **nested for loop** using demonstrative examples. The choice depends on the requirements of the program.

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]]>**To replace values in a NumPy array by index in Python, use simple indexing for single values (e.g., array[0] = new_value), slicing for multiple values (array[start:end] = new_values_array), boolean indexing for condition-based replacement (array[array > threshold] = new_value), and fancy indexing to change specific positions (array[[index1, index2]] = [new_value1, new_value2]).**

To replace values in NumPy array by index in Python is a fundamental operation in data manipulation and analysis. Here, are the four different functions and methods available in NumPy:

- Simple Value Replacement
- Replacing Multiple Values
- Boolean Indexing
- Fancy Indexing

Let’s see them one by one using some examples:

To replace a value in NumPy array by index in Python, assign a new value to the desired index. For instance:

```
import numpy as np
temperatures = np.array([58, 66, 52, 69, 77])
temperatures[0] = 59
print("Updated Temperatures:", temperatures)
```

**Output:**

`Updated Temperatures: [59 66 52 69 77]`

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

To replace multiple values, we can use slicing in Python. Slices include the start index and exclude the end index. For instance:

```
import numpy as np
sunny_days = np.array([152, 259, 245, 233, 294])
sunny_days[1:3] = [260, 250]
print("Updated Sunny Days:", sunny_days)
```

**Output:**

`Updated Sunny Days: [152 260 250 233 294]`

A screenshot is mentioned below, after implementing the code in the Pycharm editor.

With boolean indexing, we can replace values in NumPy array by index in Python that meet a certain condition. For example:

```
import numpy as np
sales = np.array([45, 55, 40, 50, 60])
sales[sales < 50] = -1
print("Updated Sales Data:", sales)
```

**Output:**

`Updated Sales Data: [-1 55 -1 50 60]`

After executing the code in Pycharm, one can see the output in the below screenshot.

Fancy indexing involves passing an array of indices to access multiple elements to replace values in NumPy array by index in Python. For example:

```
import numpy as np
populations = np.array([120, 85, 95, 110, 100])
populations[[0, 4]] = populations[[4, 0]]
print("Updated Populations:", populations)
```

**Output:**

`Updated Populations: [100 85 95 110 120]`

Let’s see a situation where we have to replace 0 in the array by 1 through Python:

```
import numpy as np
species_presence = np.array([1, 0, 1, 0, 0])
species_presence[species_presence == 0] = 1
print("Updated Species Presence:", species_presence)
```

**Output:**

`Updated Species Presence: [1 1 1 1 1]`

Displayed below is a screenshot capturing the outcome of the code execution in the PyCharm editor.

This article explains how to **replace values in NumPy array by index in Python** using four different ways such as **simple indexing**, **multiple values at a time**, **boolean indexing**, and **fancy indexing** with illustrative examples. I have also explained how in NumPy array, **replace 0 with 1.**

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]]>**To understand np.add.at() in Python, it is a specialized NumPy function for unbuffered, in-place addition at specific array indices, including handling repeated indices. Like, np.add.at(arr, [index1, index2], [value1, value2]) adds values at the specified indices of arr. Meanwhile, np.add.reduce() is used for reducing an array’s dimensions by summing its elements along a given axis, such as np.add.reduce(arr, axis=0) for column-wise summation in a 2D array.**

The **np.add.at() function in Python** is a specialized method offered by NumPy. This is used to perform element-wise operations on arrays. The **add.at()** method provides a way to perform unbuffered in-place addition on an array at specified indices.

The basic syntax of **np.add.at() function** in Python is as follows:

`numpy.add.at(arr, indices, values)`

Here,

arr | The array in Python to which values will be added. This array is modified in place. |

indices | The locations in the Python array where values should be added. This can be a single integer, a list of integers, or a tuple of integer arrays. |

values | The values are to be added at the specified indices in the array in Python. |

The key feature of **np.add.at() function in Python** is its ability to perform in-place, unbuffered addition at specified indices. This means that if an index is repeated in indices, the corresponding value in values is added multiple times.

Let’s see some use cases of the np.add.at() function in Python:

Here, we will try to do simple addition with all the parameters of the np.add.at() function in Python.:

```
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
np.add.at(arr, [0, 2, 4], 10)
print(arr)
```

**Output:**

`[11 2 13 4 15]`

Here, we will try to add arrays with repeated indices in Python.

```
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
np.add.at(arr, [0, 0, 2, 2], [1, 2, 3, 4])
print(arr)
```

**Output:**

`[ 4 2 10 4 5]`

The output from running the code in PyCharm is visually represented in the screenshot below.

The numpy.add.reduce() function in Python applies the add operation repeatedly to the elements of an array, effectively reducing the array’s dimension by one.

**Syntax:**

`numpy.add.reduce(array, axis=0, dtype=None, out=None, keepdims=False, initial)`

**For example:**

```
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
total_sum = np.add.reduce(arr)
print("Total Sum:", total_sum)
sum_along_axis0 = np.add.reduce(arr, axis=0)
print("Sum along axis 0:", sum_along_axis0)
sum_along_axis1 = np.add.reduce(arr, axis=1)
print("Sum along axis 1:", sum_along_axis1)
```

**Output:**

```
Total Sum: [5 7 9]
Sum along axis 0: [5 7 9]
Sum along axis 1: [ 6 15]
```

Displayed below is a screenshot capturing the outcome of the code execution in the PyCharm editor.

To summarize, the **np.add.at() function in Python** is used for unbuffered in-place addition at specified indices of an array, useful for handling repeated indices and accumulating values. And also the **np.add.reduce() function**, applies addition across an array’s elements to reduce its dimension, ideal for summing values along a specified axis.

Both functions are integral for efficient numerical operations in Python.

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]]>