 # IQR Method

In this method we will use the Inter Quartile Range(IQR) to detect outliers. IQR tells us the variation in the data set. Any value, which is beyond the range of -1.5 x IQR to 1.5 x IQR are treated as outliers

• Q1 represents the 1st quartile/25th percentile of the data.
• Q2 represents the 2nd quartile/median/50th percentile of the data.
• Q3 represents the 3rd quartile/75th percentile of the data.
• (Q1–1.5IQR) represent the smallest value in the data set and (Q3+1.5IQR) represent the largest value in the data set.
INSTRUCTIONS
• First, we will import `Numpy` as `np`

``````import numpy as <<your code goes here>>
``````
• Next, we will define the datapoints we had used previously

``````x = [5, 5, 5, -99, 5, 5, 5, 5, 5, 5, 88, 5, 5, 5]
``````
• Now, we will define a function `calculate_iqr` to detect the outliers using the IQR method

``````def <<your code goes here>>(data):
Q1 = np.percentile(data, 25, interpolation = 'midpoint')
Q2 = np.percentile(data, 50, interpolation = 'midpoint')
Q3 = np.percentile(data, 75, interpolation = 'midpoint')
IQR = Q3 - Q1
low_lim = Q1 - 1.5 * IQR
up_lim = Q3 + 1.5 * IQR
outlier =[]
for num in x:
if ((num> up_lim) or (num<low_lim)):
outlier.append(num)
print("Outliers:",outlier)
``````
• Finally, we will call the function using our datapoints

``````<<your code goes here>>(x)
``````

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