### 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]:

### Nominal Scale (Setting type as category)¶

In [6]:

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

Out[6]:

### Ordinal scale (ordered = True)¶

In [7]:

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

In [8]:

```
df
```

Out[8]:

In [9]:

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

Out[9]:

In [10]:

```
df.loc["alex"]
```

Out[10]:

In [11]:

```
df.loc["daniel"]
```

Out[11]:

In [12]:

```
df >="S"
```

Out[12]:

### cut function¶

In [13]:

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

Out[13]:

In [14]:

```
s
```

Out[14]:

In [15]:

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

Out[15]:

In [ ]:

```
```

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