Create Unique value in Data

descriptive analytics

Introduction

count and find uniqueness in each columns

Dataset

Load dataset

we use simple and common titanic dataset from seaborn library.

df = sns.load_dataset("titanic")

	survived	pclass	sex	age	sibsp	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22	1	7.25	S	Third	man	True	nan	Southampton	no	False
1	1	1	female	38	1	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26	0	7.925	S	Third	woman	False	nan	Southampton	yes	True
3	1	1	female	35	1	53.1	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35	0	8.05	S	Third	man	True	nan	Southampton	no	True

The Code

By calling, describe.unique we can calculate and print all unique value in each columns with treshold value.

describe.unique(df,r=0.1)

This function requires the following parameters:

main_data (dataframe): Data Input
r (float): ratio unique value

The result

survived column has : 2 distinct values
[0 1]

pclass column has : 3 distinct values
[3 1 2]

sex column has : 2 distinct values
['male' 'female']

age column has : 89 distinct values
[22.   38.   26.   35.     nan 54.    2.   27.   14.    4.   58.   20.
 39.   55.   31.   34.   15.   28.    8.   19.   40.   66.   42.   21.
 18.    3.    7.   49.   29.   65.   28.5   5.   11.   45.   17.   32.
 16.   25.    0.83 30.   33.   23.   24.   46.   59.   71.   37.   47.
 14.5  70.5  32.5  12.    9.   36.5  51.   55.5  40.5  44.    1.   61.
 56.   50.   36.   45.5  20.5  62.   41.   52.   63.   23.5   0.92 43.
 60.   10.   64.   13.   48.    0.75 53.   57.   80.   70.   24.5   6.
  0.67 30.5   0.42 34.5  74.  ]

sibsp column has : 7 distinct values
[1 0 3 4 2 5 8]

parch column has : 7 distinct values
[0 1 2 5 3 4 6]

fare column has : 248 distinct values

embarked column has : 4 distinct values
['S' 'C' 'Q' nan]

class column has : 3 distinct values
['Third', 'First', 'Second']
Categories (3, object): ['Third', 'First', 'Second']

who column has : 3 distinct values
['man' 'woman' 'child']

adult_male column has : 2 distinct values
[ True False]

deck column has : 8 distinct values
[NaN, 'C', 'E', 'G', 'D', 'A', 'B', 'F']
Categories (7, object): ['C', 'E', 'G', 'D', 'A', 'B', 'F']

embark_town column has : 4 distinct values
['Southampton' 'Cherbourg' 'Queenstown' nan]

alive column has : 2 distinct values
['no' 'yes']

alone column has : 2 distinct values
[False  True]