Pandas: Select all rows which not appears in a list
18. Exclusion Filtering
Write a Pandas program to filter those records which not appears in a given list 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("\nSelect all rows which not appears in a given list:")
who_region = ["Africa", "Eastern Mediterranean", "Europe"]
flt_wine = ~new_w_a_con["WHO region"].isin(who_region)
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]
Select all rows which not appears in a given list:
Year WHO region ... Beverage Types Display Value
0 1986 Western Pacific ... Wine 0.00
1 1986 Americas ... Other 0.50
3 1986 Americas ... Beer 4.27
4 1987 Americas ... Beer 1.98
5 1987 Americas ... Other 0.00
8 1986 Americas ... Spirits 1.55
11 1989 Americas ... Beer 0.62
12 1985 Western Pacific ... Beer 0.00
14 1985 Western Pacific ... Spirits 0.05
16 1984 Americas ... Wine 0.06
20 1986 South-East Asia ... Wine 0.00
21 1989 Americas ... Spirits 4.51
28 1987 Western Pacific ... Beer 0.11
31 1986 Western Pacific ... Wine 0.00
35 1985 Americas ... Spirits 2.24
43 1984 Western Pacific ... Wine 0.03
46 1987 Americas ... Spirits 2.26
47 1986 Americas ... Other 0.04
48 1987 Americas ... Beer 0.70
54 1984 Americas ... Spirits 1.81
55 1989 Americas ... Wine 0.04
56 1987 Western Pacific ... Wine 0.00
61 1984 Western Pacific ... Spirits 0.08
62 1987 Americas ... Other 0.00
64 1989 Americas ... Beer 1.26
74 1986 Americas ... Spirits 2.06
78 1989 Americas ... Other 0.00
84 1986 South-East Asia ... Other 0.00
86 1986 Americas ... Wine 1.83
97 1984 South-East Asia ... Wine 0.00
99 1985 South-East Asia ... Wine 0.00
[31 rows x 5 columns]
Click to download world_alcohol.csv
For more Practice: Solve these Related Problems:
- Write a Pandas program to filter out records where the 'Country' column is present in a specified exclusion list.
- Write a Pandas program to remove rows with 'WHO region' matching a given list of values using boolean indexing.
- Write a Pandas program to exclude records with certain values in the 'Beverage Types' column and then verify the remaining rows.
- Write a Pandas program to filter the dataset to only include rows where the 'Country' does not appear in a predefined list.
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Consumption Range Filtering.
Python Code Editor:
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