Pandas: Filtering records by multiple condition, Comparison, Arithmetic, Boolean Operators in a given dataframe

Last update on April 04 2025 13:43:32 (UTC/GMT +8 hours)

14. Multi-Type High Consumption Filter

Write a Pandas program to find out the records where consumption of beverages per person average >=4 and Beverage Types is Beer, Wine, Spirits from 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: average consumption of \nbeverages per person >=4 and Beverage Types is Beer:")
print(w_a_con[(w_a_con['Display Value'] >= 4) & ((w_a_con['Beverage Types'] == 'Beer') | (w_a_con['Beverage Types'] == 'Wine')| (w_a_con['Beverage Types'] == 'Spirits'))].head(10))

Sample Output:

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: average consumption of 
beverages per person >=4 and Beverage Types is Beer:
    Year WHO region         Country Beverage Types  Display Value
3   1986   Americas        Colombia           Beer           4.27
21  1989   Americas      Costa Rica        Spirits           4.51
41  1986     Europe  Czech Republic           Beer           6.82
57  1989     Europe         Croatia           Wine           5.10
91  1989     Europe        Bulgaria           Beer           4.43
96  1985     Europe      Luxembourg           Wine           7.38

Click to download world_alcohol.csv

For more Practice: Solve these Related Problems:

• Write a Pandas program to filter records where 'Display Value' is at least 4 and 'Beverage Types' is one of ['Beer', 'Wine', 'Spirits'], then list the countries with highest consumption.
• Write a Pandas program to extract rows with consumption >= 4 for multiple beverage types and compute the average per type.
• Write a Pandas program to select records meeting the consumption criteria for Beer, Wine, and Spirits, and then sort by 'Beverage Types'.
• Write a Pandas program to filter the dataset for multiple beverage types with consumption above 4 and then pivot the data by year.

Python Code Editor:

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