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

Pandas Filter: Exercise-6 with Solution

Write a Pandas program to find out the alcohol consumption of a given year 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 1985:")
print(w_a_con[w_a_con['Year']==1985].head(10))
print("\nThe world alcohol consumption details in the year 1989:")
print(w_a_con[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 in the year 1985:
    Year       WHO region      ...      Beverage Types Display Value
2   1985           Africa      ...                Wine          1.62
7   1985           Africa      ...             Spirits          0.39
12  1985  Western Pacific      ...                Beer          0.00
14  1985  Western Pacific      ...             Spirits          0.05
24  1985           Africa      ...               Other           NaN
26  1985           Europe      ...                Wine          1.36
33  1985           Africa      ...               Other          0.00
35  1985         Americas      ...             Spirits          2.24
44  1985           Europe      ...               Other           NaN
50  1985           Europe      ...               Other          0.30

[10 rows x 5 columns]

The world alcohol consumption details in the year 1989:
    Year             WHO region      ...      Beverage Types Display Value
11  1989               Americas      ...                Beer          0.62
17  1989                 Africa      ...                Beer          2.23
21  1989               Americas      ...             Spirits          4.51
32  1989                 Africa      ...                Beer          1.60
45  1989                 Africa      ...                Beer          0.19
55  1989               Americas      ...                Wine          0.04
57  1989                 Europe      ...                Wine          5.10
59  1989  Eastern Mediterranean      ...               Other          0.00
64  1989               Americas      ...                Beer          1.26
65  1989  Eastern Mediterranean      ...                Beer          0.00

[10 rows x 5 columns]

Click to download world_alcohol.csv

Python Code Editor:


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Previous:Write a Pandas program to remove the duplicates from 'WHO region' column of World alcohol consumption dataset.
Next: Write a Pandas program to find out the alcohol consumption details in the year '1987' or '1989' from the world alcohol consumption dataset.

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Python: Tips of the Day

Merging two dicts in Python 3.5+ with a single expression

Example:

# How to merge two dictionaries
# in Python 3.5+

x = {'p': 1, 'q': 3}
y = {'q': 5, 'r': 8}

z = {**x, **y}

z
{'r': 4, 'p': 1, 'q': 3}

z = dict(x, **y)
print(z)

Output:

{'p': 1, 'q': 5, 'r': 8}