diff --git a/contrib/numpy/statistical-operations-on-arrays.md b/contrib/numpy/statistical-operations-on-arrays.md
index 8b13789..06fbae2 100644
--- a/contrib/numpy/statistical-operations-on-arrays.md
+++ b/contrib/numpy/statistical-operations-on-arrays.md
@@ -1 +1,154 @@
+# Statistical Operations on Arrays
 
+Statistics involves collecting data, analyzing it, and drawing conclusions from the gathered information.
+
+NumPy provides powerful statistical functions to perform efficient data analysis on arrays, including `minimum`, `maximum`, `mean`, `median`, `variance`, `standard deviation`, and more.
+
+## Minimum
+
+In NumPy, the minimum value of an array is the smallest element present.
+
+The smallest element of an array is calculated using the `np.min()` function.
+
+**Code**
+```python
+import numpy as np
+array = np.array([100,20,300,400])
+#Calculating the minimum
+result = np.min(array)
+print("Minimum :", result)
+```
+
+**Output**
+```
+Minimum : 20
+```
+
+## Maximum
+
+In NumPy, the maximum value of an array is the largest element present.
+
+The largest element of an array is calculated using the `np.max()` function.
+
+**Code**
+```python
+import numpy as np
+array = np.array([100,20,300,400])
+#Calculating the maximum
+result = np.max(array)
+print("Maximum :", result)
+```
+
+**Output**
+```
+Maximum : 400
+```
+
+## Mean
+
+The mean value of a NumPy array is the average of all its elements.
+
+It is calculated by summing all the elements and then dividing by the total number of elements.
+
+The mean of an array is calculated using the `np.mean()` function.
+
+**Code**
+```python
+import numpy as np
+array = np.array([10,20,30,40])
+#Calculating the mean
+result = np.mean(array)
+print("Mean :", result)
+```
+
+**Output**
+```
+Mean : 25.0
+```
+
+## Median
+
+The median value of a NumPy array is the middle value in a sorted array.
+
+It separates the higher half of the data from the lower half.
+
+The median of an array is calculated using the `np.median()` function.
+
+It is important to note that:
+
+- If the number of elements is `odd`, the median is the middle element.
+- If the number of elements is `even`, the median is the average of the two middle elements.
+
+**Code**
+```python
+import numpy as np
+#The number of elements is odd
+array = np.array([5,6,7,8,9])
+#Calculating the median
+result = np.median(array)
+print("Median :", result)
+```
+
+**Output**
+```
+Median : 7.0
+```
+
+**Code**
+```python
+import numpy as np
+#The number of elements is even
+array = np.array([1,2,3,4,5,6])
+#Calculating the median
+result = np.median(array)
+print("Median :", result)
+```
+
+**Output**
+```
+Median : 3.5
+```
+
+## Variance
+
+Variance in a NumPy array measures the spread or dispersion of data points.
+
+Calculated as the average of the squared differences from the mean.
+
+The variance of an array is calculated using the `np.var()` function.
+
+**Code**
+```python
+import numpy as np
+array = np.array([10,70,80,50,30])
+#Calculating the variance
+result = np.var(array)
+print("Variance :", result)
+```
+
+**Output**
+```
+Variance : 656.0
+```
+
+## Standard Deviation
+
+The standard deviation of a NumPy array measures the amount of variation or dispersion of the elements in the array.
+
+It is calculated as the square root of the average of the squared differences from the mean, providing insight into how spread out the values are around the mean.
+
+The standard deviation of an array is calculated using the `np.std()` function.
+
+**Code**
+```python
+import numpy as np
+array = np.array([25,30,40,55,75,100])
+#Calculating the standard deviation
+result = np.std(array)
+print("Standard Deviation :", result)
+```
+
+**Output**
+```
+Standard Deviation : 26.365486699260625
+```