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.

import pandas as pd

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

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

pandas.Categorical(values, categories, ordered)

import pandas as pd

cat = pd.Categorical([''a'', ''b'', ''c'', ''a'', ''b'', ''c''])
print cat

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

import pandas as pd

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

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

import pandas as pd

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

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

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()

       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

import pandas as pd
import numpy as np

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

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

import pandas as pd
import numpy as np

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

False

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

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

import pandas as pd

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

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

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")

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]

import 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

0  False
1  False
2  True
dtype: bool