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Getting Unique values from a column in Pandas dataframe
  • Last Updated : 15 Jan, 2019

Let’s see how can we retrieve the unique values from pandas dataframe.

Let’s create a dataframe from CSV file. We are using the past data of GDP from different countries. You can get the dataset from here.




# import pandas as pd
import pandas as pd
  
gapminder_csv_url ='http://bit.ly/2cLzoxH'
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
  
record.head()

Method #1: Select the continent column from the record and apply the unique function to get the values as we want.




# import pandas as pd
import pandas as pd
  
gapminder_csv_url ='http://bit.ly/2cLzoxH'
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
  
print(record['continent'].unique())

Output:



['Asia' 'Europe' 'Africa' 'Americas' 'Oceania']

Method #2: Select unique values from the countrycolumn.




# import pandas as pd
import pandas as pd
  
gapminder_csv_url ='http://bit.ly/2cLzoxH'
  
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
  
print(record.country.unique())

Output:

['Afghanistan' 'Albania' 'Algeria' 'Angola' 'Argentina' 'Australia'
 'Austria' 'Bahrain' 'Bangladesh' 'Belgium' 'Benin' 'Bolivia'
 'Bosnia and Herzegovina' 'Botswana' 'Brazil' 'Bulgaria' 'Burkina Faso'
 'Burundi' 'Cambodia' 'Cameroon' 'Canada' 'Central African Republic'
 'Chad' 'Chile' 'China' 'Colombia' 'Comoros' 'Congo Dem. Rep.'
 'Congo Rep.' 'Costa Rica' "Cote d'Ivoire" 'Croatia' 'Cuba'
 'Czech Republic' 'Denmark' 'Djibouti' 'Dominican Republic' 'Ecuador'
 'Egypt' 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Ethiopia' 'Finland'
 'France' 'Gabon' 'Gambia' 'Germany' 'Ghana' 'Greece' 'Guatemala' 'Guinea'
 'Guinea-Bissau' 'Haiti' 'Honduras' 'Hong Kong China' 'Hungary' 'Iceland'
 'India' 'Indonesia' 'Iran' 'Iraq' 'Ireland' 'Israel' 'Italy' 'Jamaica'
 'Japan' 'Jordan' 'Kenya' 'Korea Dem. Rep.' 'Korea Rep.' 'Kuwait'
 'Lebanon' 'Lesotho' 'Liberia' 'Libya' 'Madagascar' 'Malawi' 'Malaysia'
 'Mali' 'Mauritania' 'Mauritius' 'Mexico' 'Mongolia' 'Montenegro'
 'Morocco' 'Mozambique' 'Myanmar' 'Namibia' 'Nepal' 'Netherlands'
 'New Zealand' 'Nicaragua' 'Niger' 'Nigeria' 'Norway' 'Oman' 'Pakistan'
 'Panama' 'Paraguay' 'Peru' 'Philippines' 'Poland' 'Portugal'
 'Puerto Rico' 'Reunion' 'Romania' 'Rwanda' 'Sao Tome and Principe'
 'Saudi Arabia' 'Senegal' 'Serbia' 'Sierra Leone' 'Singapore'
 'Slovak Republic' 'Slovenia' 'Somalia' 'South Africa' 'Spain' 'Sri Lanka'
 'Sudan' 'Swaziland' 'Sweden' 'Switzerland' 'Syria' 'Taiwan' 'Tanzania'
 'Thailand' 'Togo' 'Trinidad and Tobago' 'Tunisia' 'Turkey' 'Uganda'
 'United Kingdom' 'United States' 'Uruguay' 'Venezuela' 'Vietnam'
 'West Bank and Gaza' 'Yemen Rep.' 'Zambia' 'Zimbabwe']

Method #3:

In this method you can see that we use the dataframe inside the unique function as parameter although we select the same column as above so we get the same output.




# Write Python3 code here
# import pandas as pd
import pandas as pd
  
gapminder_csv_url ='http://bit.ly/2cLzoxH'
  
# load the data with pd.read_csv
record = pd.read_csv(gapminder_csv_url)
  
print(pd.unique(record['continent']))

Output:

['Asia' 'Europe' 'Africa' 'Americas' 'Oceania']

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