import pandas as pd
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
'ID':[1,2,3,4,5,6],
'GAME':['CRICKET','TENNIS','CRICKET','HOCKEY','CRICKET','TENNIS'],
'CLASS1':['Four','Three','Three','Four','Five','Three']}
my_data = pd.DataFrame(data=my_dict)
print(my_data['CLASS1'].unique())
Output
['Four' 'Three' 'Five']
We will get unique games played by students.
print(my_data['GAME'].unique())
Output
['CRICKET' 'TENNIS' 'HOCKEY']
What are the number of unique data ( use nunique() ).
print(my_data['CLASS1'].nunique())
Output
3
We can use `unique()` to get distinct values across different columns. This is helpful when analyzing multiple categories.
import pandas as pd
my_data = pd.DataFrame({
'A': [1, 2, 2, 3, 4],
'B': [4, 4, 3, 3, 2]
})
unique_values = pd.concat([my_data['A'], my_data['B']]).unique()
print(unique_values)
[1 2 3 4]
When working with multiple datasets, you may want to compare unique values to identify overlaps or discrepancies.
import pandas as pd
df1 = pd.DataFrame({'City': ['NY', 'LA', 'SF', 'NY']})
df2 = pd.DataFrame({'City': ['NY', 'DC', 'SF', 'Chicago']})
unique_cities_df1 = df1['City'].unique()
unique_cities_df2 = df2['City'].unique()
common_cities = set(unique_cities_df1).intersection(unique_cities_df2)
print(common_cities)
{'NY', 'SF'}
Pandas can work more efficiently by converting columns to categorical types before using `unique()`, especially with repeated data.
my_data['Category'] = my_data['A'].astype('category')
unique_categories = my_data['Category'].unique()
print(unique_categories)
Alongside `unique()`, we can use `nunique()` to quickly count distinct entries in a column.
import pandas as pd
data = pd.Series(['apple', 'orange', 'apple', 'banana', 'orange'])
unique_count = data.nunique()
print(unique_count)
3
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