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

Last update on December 21 2024 07:52:58 (UTC/GMT +8 hours)

Write a Pandas program to find out the alcohol consumption details in the year '1987' or '1989' 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 where year is 1987 or 1989:")
print((w_a_con[(w_a_con['Year']==1987) | (w_a_con['Year']==1989)]).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 where year is 1987 or 1989:
    Year       WHO region      ...      Beverage Types Display Value
4   1987         Americas      ...                Beer          1.98
5   1987         Americas      ...               Other          0.00
6   1987           Africa      ...                Wine          0.13
10  1987           Africa      ...                Wine          0.20
11  1989         Americas      ...                Beer          0.62
15  1987           Africa      ...                Wine          0.07
17  1989           Africa      ...                Beer          2.23
21  1989         Americas      ...             Spirits          4.51
28  1987  Western Pacific      ...                Beer          0.11
32  1989           Africa      ...                Beer          1.60

[10 rows x 5 columns]

Click to download world_alcohol.csv

Python Code Editor:

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