Pandas: Match if a given column has a particular sub string in a given dataframe
Pandas Filter: Exercise-16 with Solution
Write a Pandas program to filter those records where WHO region contains "Ea" substring 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())
# Remove NA / NaN values
new_w_a_con = w_a_con.dropna()
print("\nMatch if a given column has a particular sub string:")
print(new_w_a_con[new_w_a_con["WHO region"].str.contains("Ea")])
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] Match if a given column has a particular sub string: Year WHO region ... Beverage Types Display Value 13 1984 Eastern Mediterranean ... Other 0.00 20 1986 South-East Asia ... Wine 0.00 25 1984 Eastern Mediterranean ... Other 0.00 27 1984 Eastern Mediterranean ... Beer 2.22 36 1987 Eastern Mediterranean ... Beer 0.07 38 1987 Eastern Mediterranean ... Other 0.00 52 1986 Eastern Mediterranean ... Wine 0.00 53 1984 Eastern Mediterranean ... Beer 0.00 58 1984 Eastern Mediterranean ... Spirits 0.00 59 1989 Eastern Mediterranean ... Other 0.00 60 1987 Eastern Mediterranean ... Other 0.00 63 1985 Eastern Mediterranean ... Other 0.00 65 1989 Eastern Mediterranean ... Beer 0.00 66 1987 Eastern Mediterranean ... Wine 0.01 73 1986 Eastern Mediterranean ... Other 0.01 75 1989 Eastern Mediterranean ... Other 0.00 84 1986 South-East Asia ... Other 0.00 87 1989 Eastern Mediterranean ... Wine 0.01 88 1987 Eastern Mediterranean ... Beer 0.42 89 1986 Eastern Mediterranean ... Wine 0.70 97 1984 South-East Asia ... Wine 0.00 99 1985 South-East Asia ... Wine 0.00 [22 rows x 5 columns]
Click to download world_alcohol.csv
Python Code Editor:
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Next: Write a Pandas program to filter those records where WHO region matches with multiple values (Africa, Eastern Mediterranean, Europe) from world alcohol consumption dataset.What is the difficulty level of this exercise?
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