In Numpy we can calculate the Median using two main functions:
numpy.median(): This function returns the median of a given array.
numpy.nanmedian(): This function is similar to numpy.median() but is designed to handle arrays with missing values.
Example: Using "numpy.median()"
As we have already mentioned above, "numpy.median()" is used to calculate the median of an array.
import numpy as np
# Creating an array
data = np.array([10, 2, 5, 8, 7, 3, 12])
# Calculating the median
median_value = np.median(data)
print("Median:", median_value)
In above example, we created an array called data, and then we calculated its median using np.median() function
Output:
Median: 7.0
Example: Using "numpy.nanmedian()"
If our dataset contains missing values (NaN), we can use the numpy.nanmedian() function to calculate median.
import numpy as np
# Creating an array with NaN values
data_with_nan = np.array([10, 2, 5, np.nan, 7, 3, 12])
# Calculate the median, ignoring NaN values
median_nan = np.nanmedian(data_with_nan)
print("Median:", median_nan)
In above example, NumPy automatically ignores the NaN values and computes the median for the remaining values.
Output:
Median: 6.0
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