Pandas: Find and drop the missing values
4. Missing Value Handling
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
For more Practice: Solve these Related Problems:
- Write a Pandas program to locate all missing values in the dataset and then drop the rows containing any NaNs.
- Write a Pandas program to identify columns with missing data and then fill those NaNs with the column mean before dropping remaining rows.
- Write a Pandas program to detect missing values and remove rows that have more than one NaN entry.
- Write a Pandas program to filter out rows with missing values in a specific column and then verify the absence of NaNs.
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Duplicate Removal in 'WHO region'.
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