# How NumPy Filter 2D Array by Condition in Python [6 Methods]

Do you want to filter a 2D array in Python? In this Python blog, I will tell you different ways NumPy filter 2D array by condition in Python using some examples.

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.

## NumPy Filter 2D Array by Condition in Python

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

1. Using Boolean Indexing
2. Using np.where
3. Combining Conditions
4. Using numpy.any() method
5. Using numpy.all() method
6. Using np.asarray() method

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

### 1. NumPy filter 2D array in Python using boolean indexing

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.

### 2. Filter 2D array Python using np.where() function

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.

### 3. Python filter 2D array using combining conditions

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.

### 4. NumPy filter array by condition using numpy.any() method

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.

### 5. NumPy filter using np.all() function

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.

### 6. Filter 2D NumPy array using np.asarray() function

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.

## Conclusion

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.

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