Pandas: Filter by values of specific Columns using Boolean OR , AND, OR Logic in a given dataframe
12. Select Specific Columns with Filtering
Write a Pandas program to find out the 'WHO region, 'Country', 'Beverage Types' 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 ('WHO region','Country','Beverage Types') \nin 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'))][['WHO region','Country','Beverage Types']].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 ('WHO region','Country','Beverage Types')
in the year ‘1986’ or ‘1989’ where WHO region is ‘Americas’ or 'Europe':
WHO region ... Beverage Types
11 Americas ... Beer
21 Americas ... Spirits
26 Europe ... Wine
35 Americas ... Spirits
44 Europe ... Other
50 Europe ... Other
55 Americas ... Wine
57 Europe ... Wine
64 Americas ... Beer
78 Americas ... Other
[10 rows x 3 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 with regions 'Americas' or 'Europe' and then select only the columns 'WHO region', 'Country', and 'Beverage Types'.
- Write a Pandas program to extract a subset of columns for specified years and regions, and then drop duplicate rows based on these columns.
- Write a Pandas program to select the desired columns for given filtering conditions and then rename the columns to lowercase.
- Write a Pandas program to filter the dataset by year and region, then output only the chosen columns and sort them by 'Country'.
Go to:
PREV : Filtering by Year and Multiple Regions.
NEXT :
High Consumption and Beer Filter.
Python Code Editor:
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