Scales in Data Science

Scales in Data Science¶

• Ratio Scale
  • Units are equally spaced
  • Mathematical operations such as +,-,*,/ are all valid
  • Example : Height and Weight
• Interval Scale
  • Units are equally spaced, but there is no true zero value
  • For example, in temperature values, zero doesn't indicate that there is an absence of temperature.
• Ordinal Scale
  • The order of the scale is important. It's not evenly spaced scale.
  • Example : Grades such as A, A-, A+.
• Nominal Scale(Categorical)
  • It's very common in data science. These are the categories of data.
  • The order of the data is not important.
  • For example : Sports team

In [1]:

import pandas as pd
import numpy as np

In [2]:

student = ["alex","bob","cynthia","daniel","evans"]
tshirt = ["L","XL","S","M","L"]

In [3]:

df = pd.DataFrame(data = tshirt, index=student)

In [4]:

df = df.rename(columns={0:"tshirt"})

In [5]:

df

Out[5]:

	tshirt
alex	L
bob	XL
cynthia	S
daniel	M
evans	L

Nominal Scale (Setting type as category)¶

In [6]:

df["tshirt"].astype("category")

Out[6]:

alex        L
bob        XL
cynthia     S
daniel      M
evans       L
Name: tshirt, dtype: category
Categories (4, object): [L, M, S, XL]

Ordinal scale (ordered = True)¶

In [7]:

df = df["tshirt"].astype("category", categories = ["S","M","L","XL"],ordered = True)

In [8]:

df

Out[8]:

alex        L
bob        XL
cynthia     S
daniel      M
evans       L
Name: tshirt, dtype: category
Categories (4, object): [S < M < L < XL]

In [9]:

df.loc[["alex"]] < df.loc[["daniel"]]

Out[9]:

alex    False
Name: tshirt, dtype: bool

In [10]:

df.loc["alex"]

Out[10]:

'L'

In [11]:

df.loc["daniel"]

Out[11]:

'M'

In [12]:

df >="S" 

Out[12]:

alex       True
bob        True
cynthia    True
daniel     True
evans      True
Name: tshirt, dtype: bool

cut function¶

In [13]:

s = pd.Series([9,8,10,1,2,3,6,7,4,5])


pd.cut(s, 3)

Out[13]:

0       (7, 10]
1       (7, 10]
2       (7, 10]
3    (0.991, 4]
4    (0.991, 4]
5    (0.991, 4]
6        (4, 7]
7        (4, 7]
8    (0.991, 4]
9        (4, 7]
dtype: category
Categories (3, object): [(0.991, 4] < (4, 7] < (7, 10]]

In [14]:

s

Out[14]:

0     9
1     8
2    10
3     1
4     2
5     3
6     6
7     7
8     4
9     5
dtype: int64

In [15]:

# You can also add labels for the sizes [Small < Medium < Large].
pd.cut(s, 3, labels=['Small', 'Medium', 'Large'])

Out[15]:

0     Large
1     Large
2     Large
3     Small
4     Small
5     Small
6    Medium
7    Medium
8     Small
9    Medium
dtype: category
Categories (3, object): [Small < Medium < Large]

In [ ]: