15. Outlier in pandas

What is an Outlier?

An outlier is a data point that is significantly different from the rest of the values in a dataset. It either lies too high or too low compared to the majority of the observations.

In simple words:

• Outliers are values that don’t fit the general pattern of the data.

• They can occur due to data entry errors, measurement mistakes, or genuine rare events.

• Outliers can distort averages, affect model performance, and mislead analysis, so they need special attention.

Example Scenario

Suppose you are analyzing daily temperatures of a city, and most days fall between 20°C – 35°C. If one day suddenly shows a recorded temperature of 70°C, that value is an outlier.

Why Outliers Matter

• They affect mean, standard deviation, and visualisation shapes.

• They can indicate system errors, sensor malfunctions, or special cases.

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Simple Scenario

1

Salary example — baseline

salary1 = 100 200 500

2

Salary example — with a possible outlier

salary = 100 200 500 10000

In this scenario, the outlier is the person who has salary of 10000. We need to detect outlier(s) and remove them for some analysis.

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Measure of Central Tendency

The measure of central tendency finds the center or typical value in a set of data.

Three main types:

• Mean (Average) – Add up all numbers and divide by how many there are. Example: (2 + 4 + 6) / 3 = 4

• Median – The middle value when numbers are arranged in order. Example: In [3, 5, 8], the median is 5.

• Mode – The value that appears most often. Example: In [2, 3, 3, 5], the mode is 3.

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Measure of Spread

• Range = Max - Min Example: (10,20,30,40) → Range = 40 - 10 = 30

Example code (illustrative):

import numpy as np
score1 = [10,20,30,70]
score2 = [0,5,4,100]
range1 = max(score1) - min(score1)
range2 = max(score2) - min(score2)
range1, range2
# Out: (60, 100)

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Variance and Standard Deviation

• Variance: how spread out numbers are from the mean.

• Standard Deviation (STD): square root of variance. We generally prefer STD because its scale is the same as the original data and is easier to interpret.

Understanding STD1, STD2, STD3 (1, 2, 3 standard deviations) is helpful to classify data points as normal, unusual, or outliers.

Empirical Rule (68-95-99.7):

• STD1: Mean ± 1 STD → ~68% of data (very normal)

• STD2: Mean ± 2 STD → ~95% of data (less common)

• STD3: Mean ± 3 STD → ~99.7% of data (rare)

• Beyond STD3 → Outliers (~0.3% of data)

Example (Mean = 50, STD = 10):

• STD1 range: 40 to 60

• STD2 range: 30 to 70

• STD3 range: 20 to 80

• Outliers: values < 20 or > 80

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Normal Distribution

A Way to Find Outliers (worked example with code)

The examples below use a dataset loaded from "Adidas US Sales Datasets.xlsx". The column total is computed as Price per Unit * Units Sold.

Step-by-step process (mean/std and IQR approaches):

1

Load data and compute total

import pandas as pd
import numpy as np

df = pd.read_excel('Adidas US Sales Datasets.xlsx')
df['total'] = df['Price per Unit'] * df['Units Sold']
df.shape
# Out: (9648, 10)

Example of first rows (truncated):

Retailer	Invoice Date	Region	State	City	Product	Price per Unit	Units Sold	Sales Method	total
Foot Locker	2020-01-01	Northeast	New York	New York	Men's Street Footwear	50.0	1200	In-store	60000.0
...	...	...	...	...	...	...	...	...	...

2

Method 1 — Mean and Standard Deviation (STD) approach

Compute mean and std:

mean = df['total'].mean()
std = df['total'].std()
mean, std
# Example Out:
# mean = 12455.083955223881
# std  = 12716.3921114572

1-STD range:

std1 = (mean - std, mean + std)
# Example Out: (-261.3081562333191, 25171.476066681083)

3-STD range:

lv, uv = mean - 3 * std, mean + 3 * std
# Example Out: (-25694.092379147718, 50604.260289595484)

Filter rows within ±3 STD:

odf = df.loc[df['total'].between(lv, uv)]
odf.shape
# Example Out: (9442, 10)
odf['total'].mean()
# Example Out: 11414.382546070749

Note: mean/std is sensitive to extreme outliers — high values inflate the mean and std.

3

Percentiles and Quartiles (concept)

• A percentile shows the percentage of observations below a value. Example: 50th percentile = median.

• Quartiles: 25% (Q1), 50% (Q2), 75% (Q3). Most data often lies between Q1 and Q3.

Example usage:

x = [10,20,30,40,50,60,70,80,90]
np.percentile(x, 50)   # median
np.percentile(x, [25,75])
# Out: array([30., 70.])

4

Method 2 — IQR (Interquartile Range) method

Compute Q1 and Q3, then IQR and fences:

q1, q3 = np.percentile(df['total'], [25, 75])
iqr = q3 - q1
lv = q1 - 1.5 * iqr
uv = q3 + 1.5 * iqr
q1, q3, iqr, lv, uv
# Example Out:
# q1 = 4065.25
# q3 = 15864.5
# iqr = 11799.25
# lv = -13633.625
# uv = 33563.375

Filter using IQR fences:

odf = df.loc[df['total'].between(lv, uv)]
odf.shape
# Example Out: (8849, 10)
odf['total'].mean()
# Example Out: 9446.502429653068

Notes:

• IQR is robust to outliers and often preferred to find outliers in skewed data.

• Observations outside [Q1 - 1.5IQR, Q3 + 1.5IQR] are commonly considered outliers.

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Box plot illustration

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Box plot IQR example (small numeric example)

Data:

x = [10,20,30,40,50,60,70,80,90]

Find Q1 and Q3:

q1,q3 = np.percentile(x,[25,75])
# q1 = 30.0, q3 = 70.0

IQR:

iqr = q3 - q1  # 40.0

Fences:

lv = q1 - 1.5 * iqr   # -30.0
uv = q3 + 1.5 * iqr   # 130.0

Note: Not every dataset will contain outliers; fences indicate where outliers would lie.

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Summary: Which method to pick?

• Use IQR when you want a robust method less affected by extreme values (good for skewed distributions).