# Python Pandas - Categorical Data

Often in real-time, data includes the text columns, which are repetitive. Features like gender, country, and codes are always repetitive. These are the examples for categorical data.

Categorical variables can take on only a limited, and usually fixed number of possible values. Besides the fixed length, categorical data might have an order but cannot perform numerical operation. Categorical are a Pandas data type.

The categorical data type is useful in the following cases −

A string variable consisting of only a few different values. Converting such a string variable to a categorical variable will save some memory.

The lexical order of a variable is not the same as the logical order (“one”, “two”, “three”). By converting to a categorical and specifying an order on the categories, sorting and min/max will use the logical order instead of the lexical order.

As a signal to other python libraries that this column should be treated as a categorical variable (e.g. to use suitable statistical methods or plot types).

## Object Creation

Categorical object can be created in multiple ways. The different ways have been described below −

### category

By specifying the dtype as "category" in pandas object creation.

Live Demoimport pandas as pd s = pd.Series(["a","b","c","a"], dtype="category") print s

Its **output** is as follows −

0 a 1 b 2 c 3 a dtype: category Categories (3, object): [a, b, c]

The number of elements passed to the series object is four, but the categories are only three. Observe the same in the output Categories.

### pd.Categorical

Using the standard pandas Categorical constructor, we can create a category object.

pandas.Categorical(values, categories, ordered)

Let’s take an example −

Live Demoimport pandas as pd cat = pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c']) print cat

Its **output** is as follows −

[a, b, c, a, b, c] Categories (3, object): [a, b, c]

Let’s have another example −

Live Demoimport pandas as pd cat = cat=pd.Categorical(['a','b','c','a','b','c','d'], ['c', 'b', 'a']) print cat

Its **output** is as follows −

[a, b, c, a, b, c, NaN] Categories (3, object): [c, b, a]

Here, the second argument signifies the categories. Thus, any value which is not present in the categories will be treated as **NaN**.

Now, take a look at the following example −

Live Demoimport pandas as pd cat = cat=pd.Categorical(['a','b','c','a','b','c','d'], ['c', 'b', 'a'],ordered=True) print cat

Its **output** is as follows −

[a, b, c, a, b, c, NaN] Categories (3, object): [c < b < a]

Logically, the order means that, **a** is greater than **b** and **b** is greater than **c**.

### Description

Using the **.describe()** command on the categorical data, we get similar output to a **Series** or **DataFrame** of the **type** string.

import pandas as pd import numpy as np cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"]) df = pd.DataFrame({"cat":cat, "s":["a", "c", "c", np.nan]}) print df.describe() print df["cat"].describe()

Its **output** is as follows −

cat s count 3 3 unique 2 2 top c c freq 2 2 count 3 unique 2 top c freq 2 Name: cat, dtype: object

### Get the Properties of the Category

**obj.cat.categories** command is used to get the **categories of the object**.

import pandas as pd import numpy as np s = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"]) print s.categories

Its **output** is as follows −

Index([u'b', u'a', u'c'], dtype='object')

**obj.ordered** command is used to get the order of the object.

import pandas as pd import numpy as np cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"]) print cat.ordered

Its **output** is as follows −

False

The function returned **false** because we haven't specified any order.

### Renaming Categories

Renaming categories is done by assigning new values to the **series.cat.categories**series.cat.categories property.

import pandas as pd s = pd.Series(["a","b","c","a"], dtype="category") s.cat.categories = ["Group %s" % g for g in s.cat.categories] print s.cat.categories

Its **output** is as follows −

Index([u'Group a', u'Group b', u'Group c'], dtype='object')

Initial categories **[a,b,c]** are updated by the **s.cat.categories** property of the object.

### Appending New Categories

Using the Categorical.add.categories() method, new categories can be appended.

Live Demoimport pandas as pd s = pd.Series(["a","b","c","a"], dtype="category") s = s.cat.add_categories([4]) print s.cat.categories

Its **output** is as follows −

Index([u'a', u'b', u'c', 4], dtype='object')

### Removing Categories

Using the **Categorical.remove_categories()** method, unwanted categories can be removed.

import pandas as pd s = pd.Series(["a","b","c","a"], dtype="category") print ("Original object:") print s print ("After removal:") print s.cat.remove_categories("a")

Its **output** is as follows −

Original object: 0 a 1 b 2 c 3 a dtype: category Categories (3, object): [a, b, c] After removal: 0 NaN 1 b 2 c 3 NaN dtype: category Categories (2, object): [b, c]

### Comparison of Categorical Data

Comparing categorical data with other objects is possible in three cases −

comparing equality (== and !=) to a list-like object (list, Series, array, ...) of the same length as the categorical data.

all comparisons (==, !=, >, >=, <, and <=) of categorical data to another categorical Series, when ordered==True and the categories are the same.

all comparisons of a categorical data to a scalar.

Take a look at the following example −

Live Demoimport pandas as pd cat = pd.Series([1,2,3]).astype("category", categories=[1,2,3], ordered=True) cat1 = pd.Series([2,2,2]).astype("category", categories=[1,2,3], ordered=True) print cat>cat1

Its **output** is as follows −

0 False 1 False 2 True dtype: bool