Pandas: Filtering rows based on row number

Last update on December 21 2024 07:52:53 (UTC/GMT +8 hours)

Write a Pandas program to filter rows based on row numbers ended with 0, like 0, 10, 20, 30 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
w_a_con = pd.read_csv('world_alcohol.csv')
print("World alcohol consumption sample data:")
print(w_a_con.head())
print("\nFilter rows based on row numbers ended with 0, like 0, 10, 20, 30:")
print(w_a_con.filter(regex='0$', axis=0))

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 rows based on row numbers ended with 0, like 0, 10, 20, 30:
    Year             WHO region      ...      Beverage Types Display Value
0   1986        Western Pacific      ...                Wine          0.00
10  1987                 Africa      ...                Wine          0.20
20  1986        South-East Asia      ...                Wine          0.00
30  1986                 Africa      ...               Other          4.48
40  1987                 Europe      ...             Spirits          1.90
50  1985                 Europe      ...               Other          0.30
60  1987  Eastern Mediterranean      ...               Other          0.00
70  1986                 Africa      ...             Spirits          1.02
80  1985                 Africa      ...               Other          0.84
90  1989                 Africa      ...                Wine          0.01

[10 rows x 5 columns]

Click to download world_alcohol.csv

Python Code Editor:

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