Pandas: Find and drop the missing values
Pandas Filter: Exercise-4 with Solution
Write a Pandas program to find and drop the missing values 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("\nMissing values:")
print(w_a_con.isnull())
print("\nDropping the missing values:")
print(w_a_con.dropna())
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] Missing values: Year WHO region Country Beverage Types Display Value 0 False False False False False 1 False False False False False 2 False False False False False 3 False False False False False 4 False False False False False 5 False False False False False 6 False False False False False 7 False False False False False 8 False False False False False 9 False False False False False 10 False False False False False 11 False False False False False 12 False False False False False 13 False False False False False 14 False False False False False 15 False False False False False 16 False False False False False 17 False False False False False 18 False False False False False 19 False False False False False 20 False False False False False 21 False False False False False 22 False False False False False 23 False False False False False 24 False False False False True 25 False False False False False 26 False False False False False 27 False False False False False 28 False False False False False 29 False False False False True .. ... ... ... ... ... 70 False False False False False 71 False False False False False 72 False False False False False 73 False False False False False 74 False False False False False 75 False False False False False 76 False False False False False 77 False False False False False 78 False False False False False 79 False False False False False 80 False False False False False 81 False False False False False 82 False False False False False 83 False False False False True 84 False False False False False 85 False False False False False 86 False False False False False 87 False False False False False 88 False False False False False 89 False False False False False 90 False False False False False 91 False False False False False 92 False False False False False 93 False False False False True 94 False False False False False 95 False False False False False 96 False False False False False 97 False False False False False 98 False False False False False 99 False False False False False [100 rows x 5 columns] Dropping the missing values: 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 1987 Americas ... Other 0.00 6 1987 Africa ... Wine 0.13 7 1985 Africa ... Spirits 0.39 8 1986 Americas ... Spirits 1.55 9 1984 Africa ... Other 6.10 10 1987 Africa ... Wine 0.20 11 1989 Americas ... Beer 0.62 12 1985 Western Pacific ... Beer 0.00 13 1984 Eastern Mediterranean ... Other 0.00 14 1985 Western Pacific ... Spirits 0.05 15 1987 Africa ... Wine 0.07 16 1984 Americas ... Wine 0.06 17 1989 Africa ... Beer 2.23 18 1984 Europe ... Spirits 1.62 19 1984 Africa ... Beer 1.08 20 1986 South-East Asia ... Wine 0.00 21 1989 Americas ... Spirits 4.51 22 1984 Europe ... Spirits 2.67 23 1984 Europe ... Beer 0.44 25 1984 Eastern Mediterranean ... Other 0.00 26 1985 Europe ... Wine 1.36 27 1984 Eastern Mediterranean ... Beer 2.22 28 1987 Western Pacific ... Beer 0.11 30 1986 Africa ... Other 4.48 31 1986 Western Pacific ... Wine 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 74 1986 Americas ... Spirits 2.06 75 1989 Eastern Mediterranean ... Other 0.00 76 1985 Africa ... Beer 0.02 77 1985 Africa ... Spirits 0.01 78 1989 Americas ... Other 0.00 79 1989 Europe ... Other 2.09 80 1985 Africa ... Other 0.84 81 1985 Europe ... Wine 2.54 82 1987 Europe ... Spirits 2.25 84 1986 South-East Asia ... Other 0.00 85 1985 Africa ... Wine 0.01 86 1986 Americas ... Wine 1.83 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 94 1985 Europe ... Spirits 3.06 95 1984 Africa ... Other 0.00 96 1985 Europe ... Wine 7.38 97 1984 South-East Asia ... Wine 0.00 98 1984 Africa ... Wine 0.00 99 1985 South-East Asia ... Wine 0.00 [95 rows x 5 columns]
Click to download world_alcohol.csv
Python Code Editor:
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