Pandas: Filter by values using Boolean OR, AND, OR Logic in a given dataframe
11. Filtering by Year and Multiple Regions
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
For more Practice: Solve these Related Problems:
- Write a Pandas program to filter records for 1986 or 1989 where 'WHO region' is either 'Americas' or 'Europe', then list the unique countries.
- Write a Pandas program to extract data for specified years with regions in ['Americas', 'Europe'] and compute average 'Display Value' for each region.
- Write a Pandas program to select records for 1986/1989 with regions 'Americas' or 'Europe', and then count the number of records per region.
- Write a Pandas program to filter the dataset for the specified years and regions, and then sort the data by 'Beverage Types'.
Python Code Editor:
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