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

Pandas Filter: Exercise-11 with Solution

Write a Pandas program to find out the alcohol consumption details 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 in 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'))].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 in the year ‘1986’ or ‘1989’ where  WHO region is ‘Americas’  or 'Europe':
    Year WHO region      ...      Beverage Types Display Value
11  1989   Americas      ...                Beer          0.62
21  1989   Americas      ...             Spirits          4.51
26  1985     Europe      ...                Wine          1.36
35  1985   Americas      ...             Spirits          2.24
44  1985     Europe      ...               Other           NaN
50  1985     Europe      ...               Other          0.30
55  1989   Americas      ...                Wine          0.04
57  1989     Europe      ...                Wine          5.10
64  1989   Americas      ...                Beer          1.26
78  1989   Americas      ...               Other          0.00

[10 rows x 5 columns]

Click to download world_alcohol.csv

Python Code Editor:

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