Handling missing data using numpy

AayushKotwani3 · AayushKotwani3 · commit 97cb6e06505b · 2025-04-30T04:53:12.000+05:30
diff --git a/Handling Missing Values/README.md b/Handling Missing Values/README.md
@@ -0,0 +1,54 @@
+# NumPy: Handling NaN and Infinity
+
+This section of the repository demonstrates how to detect and replace special numerical values such as **NaN (Not a Number)** and **Infinity** using NumPy.
+
+---
+
+## 📌 Detecting NaN (Not a Number) values
+```python
+import numpy as np
+
+arr = np.array([1, 2, np.nan, 5, 6, np.nan, 33])
+print(np.isnan(arr))
+# Output: [False False  True False False  True False]
+```
+Use `np.isnan()` to return a boolean array indicating the positions of `NaN` values.
+
+---
+
+## 📌 Replacing NaN values
+```python
+cleaned_arr = np.nan_to_num(arr, nan=4)
+print(cleaned_arr)
+# Output: [ 1.  2.  4.  5.  6.  4. 33.]
+```
+Use `np.nan_to_num(array, nan=value)` to replace all NaNs with a specific value (default is 0).
+
+---
+
+## 📌 Detecting Infinity values
+```python
+arrinf = np.array([1, 2, np.inf, 5, 6, -np.inf, 33])
+print(np.isinf(arrinf))
+# Output: [False False  True False False  True False]
+```
+`np.isinf()` returns a boolean array where `True` represents either `+inf` or `-inf`.
+
+---
+
+## 📌 Replacing Infinity values
+```python
+replaced_inf = np.nan_to_num(arrinf, posinf=10, neginf=-10)
+print(replaced_inf)
+# Output: [  1.   2.  10.   5.   6. -10.  33.]
+```
+Use `np.nan_to_num()` to handle infinite values as well using `posinf` and `neginf` parameters.
+
+---
+
+## ✅ Summary
+- `np.isnan()` → Detects NaN
+- `np.nan_to_num()` → Replaces NaN or infinite values with specified defaults
+- `np.isinf()` → Detects infinity (`+inf`, `-inf`)
+
+These utilities are essential for data cleaning and preparing numerical data for machine learning or statistical analysis.
diff --git a/Handling Missing Values/nan.py b/Handling Missing Values/nan.py
@@ -0,0 +1,28 @@
+'''
+Finding Nan values
+'''
+import numpy as np
+arr=np.array([1,2,np.nan,5,6,np.nan,33])
+
+print(np.isnan(arr))
+
+'''
+Nan to num
+np.nan_to_num(array,nan=value) default is 0
+'''
+
+cleaned_arr=np.nan_to_num(arr,nan=4)
+print(cleaned_arr)
+
+'''
+Identifying infinity values
+use np.isinf()
+'''
+arrinf=np.array([1,2,np.inf,5,6,-np.inf,33])
+print(np.isinf(arrinf))
+
+'''
+replacing infinite values 
+'''
+
+print(np.nan_to_num(arrinf,posinf=10,neginf=-10))
