![Category 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]](https://image.slidesharecdn.com/assignment3-240718093659-15e3cf07/85/Data-Preprocessing-Perform-categorization-of-data-3-320.jpg)
![Using pd.Categorical
• Syntax:
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]](https://image.slidesharecdn.com/assignment3-240718093659-15e3cf07/85/Data-Preprocessing-Perform-categorization-of-data-4-320.jpg)
![import pandas as pd
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]](https://image.slidesharecdn.com/assignment3-240718093659-15e3cf07/85/Data-Preprocessing-Perform-categorization-of-data-5-320.jpg)
![.describe() command on the
categorical data
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](https://image.slidesharecdn.com/assignment3-240718093659-15e3cf07/85/Data-Preprocessing-Perform-categorization-of-data-6-320.jpg)
Data Preprocessing:Perform categorization of data
- 1. Assignment 3 Perform the Categorization of dataset
- 2. • 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.
- 3. Category 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]
- 4. Using pd.Categorical • Syntax: 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]
- 5. import pandas as pd 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]
- 6. .describe() command on the categorical data 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