Pandas: Filter by matching multiple values in a given dataframe
17. Filtering by Multiple WHO Regions
Write a Pandas program to filter those records where WHO region matches with multiple values (Africa, Eastern Mediterranean, Europe) 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
new_w_a_con = pd.read_csv('world_alcohol.csv')
print("World alcohol consumption sample data:")
print(new_w_a_con.head())
print("\nFilter by matching multiple values in a given dataframe:")
flt_wine = new_w_a_con["WHO region"].isin(["Africa", "Eastern Mediterranean", "Europe"])
print(new_w_a_con[flt_wine])
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]
Filter by matching multiple values in a given dataframe:
Year WHO region ... Beverage Types Display Value
2 1985 Africa ... Wine 1.62
6 1987 Africa ... Wine 0.13
7 1985 Africa ... Spirits 0.39
9 1984 Africa ... Other 6.10
10 1987 Africa ... Wine 0.20
13 1984 Eastern Mediterranean ... Other 0.00
15 1987 Africa ... Wine 0.07
17 1989 Africa ... Beer 2.23
18 1984 Europe ... Spirits 1.62
19 1984 Africa ... Beer 1.08
22 1984 Europe ... Spirits 2.67
23 1984 Europe ... Beer 0.44
24 1985 Africa ... Other NaN
25 1984 Eastern Mediterranean ... Other 0.00
26 1985 Europe ... Wine 1.36
27 1984 Eastern Mediterranean ... Beer 2.22
29 1986 Europe ... Other NaN
30 1986 Africa ... Other 4.48
32 1989 Africa ... Beer 1.60
33 1985 Africa ... Other 0.00
34 1986 Europe ... Wine 0.80
36 1987 Eastern Mediterranean ... Beer 0.07
37 1986 Europe ... Beer 3.04
38 1987 Eastern Mediterranean ... Other 0.00
39 1987 Africa ... Spirits 0.01
40 1987 Europe ... Spirits 1.90
41 1986 Europe ... Beer 6.82
42 1984 Europe ... Spirits 3.06
44 1985 Europe ... Other NaN
45 1989 Africa ... Beer 0.19
.. ... ... ... ... ...
63 1985 Eastern Mediterranean ... Other 0.00
65 1989 Eastern Mediterranean ... Beer 0.00
66 1987 Eastern Mediterranean ... Wine 0.01
67 1989 Africa ... Beer 0.00
68 1989 Africa ... Beer 0.12
69 1986 Africa ... Spirits 0.42
70 1986 Africa ... Spirits 1.02
71 1985 Africa ... Other 0.57
72 1987 Africa ... Other 0.00
73 1986 Eastern Mediterranean ... Other 0.01
75 1989 Eastern Mediterranean ... Other 0.00
76 1985 Africa ... Beer 0.02
77 1985 Africa ... Spirits 0.01
79 1989 Europe ... Other 2.09
80 1985 Africa ... Other 0.84
81 1985 Europe ... Wine 2.54
82 1987 Europe ... Spirits 2.25
83 1986 Europe ... Other NaN
85 1985 Africa ... Wine 0.01
87 1989 Eastern Mediterranean ... Wine 0.01
88 1987 Eastern Mediterranean ... Beer 0.42
89 1986 Eastern Mediterranean ... Wine 0.70
90 1989 Africa ... Wine 0.01
91 1989 Europe ... Beer 4.43
92 1986 Africa ... Spirits 0.00
93 1987 Africa ... Other NaN
94 1985 Europe ... Spirits 3.06
95 1984 Africa ... Other 0.00
96 1985 Europe ... Wine 7.38
98 1984 Africa ... Wine 0.00
[69 rows x 5 columns]
Click to download world_alcohol.csv
For more Practice: Solve these Related Problems:
- Write a Pandas program to filter rows where 'WHO region' is in the list ['Africa', 'Eastern Mediterranean', 'Europe'] and then display the unique 'Country' names.
- Write a Pandas program to extract records with 'WHO region' matching any of the specified values and then compute the total 'Display Value' per region.
- Write a Pandas program to select rows based on a multi-value condition for 'WHO region' and then sort the resulting DataFrame by 'Year'.
- Write a Pandas program to filter the dataset for rows where 'WHO region' equals any value from a given list and then group by 'Beverage Types'.
Python Code Editor:
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