The unique function#

Finding the number of unique countries/states/cities in the Superstore dataset.

Importing libraries and packages#

# Mathematical operations and data manipulation
import pandas as pd

Set paths#

# Path to datasets directory
data_path = "./datasets"
# Path to assets directory (for saving results to)
assets_path = "./assets"

Loading dataset#

dataset = pd.read_csv(f"{data_path}/cleaned_mpi_disagg_by_groups.csv")

Wrangling#

dataset.head()

	Country	Type of survey	Survey year	Ethnic/racial/caste group	MPI: Value for the country	MPI: Value for the group	Headcount (%)	Number of multidimensionally poor people by group (thousands)	Intensity of deprivation (%)	Health (%)	...	Cooking fuel (%)	Sanitation (%)	Drinking water (%)	Electricity (%)	Housing (%)	Assets (%)	Population share by group (%)	Population size by group (thousands)	Population size (thousands)	Region
0	Bangladesh	MICS	2019	Bengali	0.104060	0.102702	24.384759	39284.990511	42.117223	17.441109	...	12.484664	8.274627	0.569494	2.308714	12.478603	8.562996	98.809242	161104.688057	163046.173	South Asia
1	Bangladesh	MICS	2019	Other	0.104060	0.216783	45.868093	890.521140	47.262356	10.881517	...	11.733451	10.198139	8.354676	8.593331	11.536150	10.738271	1.190756	1941.482818	163046.173	South Asia
2	Belize	MICS	2015/2016	Creole	0.017109	0.003768	1.051818	0.940881	35.820526	52.086931	...	1.126231	3.964365	1.126231	3.383591	6.162911	4.409921	22.916001	89.452839	390.351	Latin America and the Caribbean
3	Belize	MICS	2015/2016	Garifuna	0.017109	0.003887	1.097083	0.224891	35.433114	85.184902	...	2.963020	2.963020	0.000000	2.963020	2.963020	2.963020	5.251431	20.499014	390.351	Latin America and the Caribbean
4	Belize	MICS	2015/2016	Maya	0.017109	0.078922	18.631953	8.557940	42.358151	37.911840	...	11.931632	7.811719	2.319572	9.465594	11.165109	4.267081	11.766724	45.931523	390.351	Latin America and the Caribbean

5 rows × 26 columns

dataset["Country"].unique()

array(['Bangladesh', 'Belize', 'Bolivia, Plurinational State of',
       'Burkina Faso', 'Central African Republic', 'Chad', 'Colombia',
       "Cote d'Ivoire", 'Cuba', 'Ecuador', 'Gabon', 'Gambia', 'Georgia',
       'Ghana', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'India',
       'Kazakhstan', 'Kenya', 'Kyrgyzstan',
       "Lao People's Democratic Republic", 'Malawi', 'Mali',
       'Moldova, Republic of', 'Mongolia', 'Nigeria', 'North Macedonia',
       'Paraguay', 'Peru', 'Philippines', 'Senegal', 'Serbia',
       'Sierra Leone', 'Sri Lanka', 'Suriname', 'Togo',
       'Trinidad and Tobago', 'Uganda', 'Viet Nam'], dtype=object)

dataset["Country"].nunique()