Pandas: Filter by values of specific Columns using Boolean OR , AND, OR Logic in a given dataframe

Pandas Filter: Exercise-12 with Solution

Write a Pandas program to find out the 'WHO region, 'Country', 'Beverage Types' in the year '1986' or '1989' where WHO region is 'Americas' or 'Europe' from the world alcohol consumption dataset.

Test Data:

   Year       WHO region                Country Beverage Types  Display Value
0  1986  Western Pacific               Viet Nam           Wine           0.00
1  1986         Americas                Uruguay          Other           0.50
2  1985           Africa           Cte d'Ivoire           Wine           1.62
3  1986         Americas               Colombia           Beer           4.27
4  1987         Americas  Saint Kitts and Nevis           Beer           1.98   

Sample Solution:

Python Code :

import pandas as pd
# World alcohol consumption data
w_a_con = pd.read_csv('world_alcohol.csv')
print("World alcohol consumption sample data:")
print(w_a_con.head())
print("\nThe world alcohol consumption details ('WHO region','Country','Beverage Types') \nin the year ‘1986’ or ‘1989’ where  WHO region is ‘Americas’  or 'Europe':")
print(w_a_con[((w_a_con['Year']==1985) | (w_a_con['Year']==1989)) & ((w_a_con['WHO region']=='Americas') | (w_a_con['WHO region']=='Europe'))][['WHO region','Country','Beverage Types']].head(10))

Sample Output:

World alcohol consumption sample data:
   Year       WHO region      ...      Beverage Types Display Value
0  1986  Western Pacific      ...                Wine          0.00
1  1986         Americas      ...               Other          0.50
2  1985           Africa      ...                Wine          1.62
3  1986         Americas      ...                Beer          4.27
4  1987         Americas      ...                Beer          1.98

[5 rows x 5 columns]

The world alcohol consumption details ('WHO region','Country','Beverage Types') 
in the year ‘1986’ or ‘1989’ where  WHO region is ‘Americas’  or 'Europe':
   WHO region      ...       Beverage Types
11   Americas      ...                 Beer
21   Americas      ...              Spirits
26     Europe      ...                 Wine
35   Americas      ...              Spirits
44     Europe      ...                Other
50     Europe      ...                Other
55   Americas      ...                 Wine
57     Europe      ...                 Wine
64   Americas      ...                 Beer
78   Americas      ...                Other

[10 rows x 3 columns]

Click to download world_alcohol.csv

Python Code Editor:

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