Pandas: Filter all records starting from the 2nd row, access every 5th row from a given dataframe
27. Strided Row Selection
Write a Pandas program to filter all records starting from the 2nd row, access every 5th row 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("\nStarting from the 2nd row, access every 5th row:")
print(w_a_con.iloc[1::5].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]
Starting from the 2nd row, access every 5th row:
Year WHO region ... Beverage Types Display Value
1 1986 Americas ... Other 0.50
6 1987 Africa ... Wine 0.13
11 1989 Americas ... Beer 0.62
16 1984 Americas ... Wine 0.06
21 1989 Americas ... Spirits 4.51
26 1985 Europe ... Wine 1.36
31 1986 Western Pacific ... Wine 0.00
36 1987 Eastern Mediterranean ... Beer 0.07
41 1986 Europe ... Beer 6.82
46 1987 Americas ... Spirits 2.26
[10 rows x 5 columns]
Click to download world_alcohol.csv
For more Practice: Solve these Related Problems:
- Write a Pandas program to select rows starting from the second row and then every 5th row using iloc slicing.
- Write a Pandas program to extract every 5th row from the dataset starting at index 1 and then reset the index of the subset.
- Write a Pandas program to filter rows by skipping four rows at a time beginning from the 2nd row and then verify the selected indices.
- Write a Pandas program to select every 5th row starting from the 2nd row and then compare the number of extracted rows with the expected count.
Python Code Editor:
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