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Pandas: Find all columns where all entries present, rows and columns has a NaN and drop rows with any NaNs

Pandas Filter: Exercise-25 with Solution

Write a Pandas program to filter all columns where all entries present, check which rows and columns has a NaN and finally drop rows with any NaNs 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("\nFind  all columns which all entries present:")
print(w_a_con.loc[:, w_a_con.notnull().all()])
print("\nRows and columns has a NaN:")
print(w_a_con.loc[:,w_a_con.isnull().any()])
print("\nDrop rows with any NaNs:")
print(w_a_con.dropna(how='any'))  

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]

Find  all columns which all entries present:
    Year      ...       Beverage Types
0   1986      ...                 Wine
1   1986      ...                Other
2   1985      ...                 Wine
3   1986      ...                 Beer
4   1987      ...                 Beer
5   1987      ...                Other
6   1987      ...                 Wine
7   1985      ...              Spirits
8   1986      ...              Spirits
9   1984      ...                Other
10  1987      ...                 Wine
11  1989      ...                 Beer
12  1985      ...                 Beer
13  1984      ...                Other
14  1985      ...              Spirits
15  1987      ...                 Wine
16  1984      ...                 Wine
17  1989      ...                 Beer
18  1984      ...              Spirits
19  1984      ...                 Beer
20  1986      ...                 Wine
21  1989      ...              Spirits
22  1984      ...              Spirits
23  1984      ...                 Beer
24  1985      ...                Other
25  1984      ...                Other
26  1985      ...                 Wine
27  1984      ...                 Beer
28  1987      ...                 Beer
29  1986      ...                Other
..   ...      ...                  ...
70  1986      ...              Spirits
71  1985      ...                Other
72  1987      ...                Other
73  1986      ...                Other
74  1986      ...              Spirits
75  1989      ...                Other
76  1985      ...                 Beer
77  1985      ...              Spirits
78  1989      ...                Other
79  1989      ...                Other
80  1985      ...                Other
81  1985      ...                 Wine
82  1987      ...              Spirits
83  1986      ...                Other
84  1986      ...                Other
85  1985      ...                 Wine
86  1986      ...                 Wine
87  1989      ...                 Wine
88  1987      ...                 Beer
89  1986      ...                 Wine
90  1989      ...                 Wine
91  1989      ...                 Beer
92  1986      ...              Spirits
93  1987      ...                Other
94  1985      ...              Spirits
95  1984      ...                Other
96  1985      ...                 Wine
97  1984      ...                 Wine
98  1984      ...                 Wine
99  1985      ...                 Wine

[100 rows x 4 columns]

Rows and columns has a NaN:
    Display Value
0            0.00
1            0.50
2            1.62
3            4.27
4            1.98
5            0.00
6            0.13
7            0.39
8            1.55
9            6.10
10           0.20
11           0.62
12           0.00
13           0.00
14           0.05
15           0.07
16           0.06
17           2.23
18           1.62
19           1.08
20           0.00
21           4.51
22           2.67
23           0.44
24            NaN
25           0.00
26           1.36
27           2.22
28           0.11
29            NaN
..            ...
70           1.02
71           0.57
72           0.00
73           0.01
74           2.06
75           0.00
76           0.02
77           0.01
78           0.00
79           2.09
80           0.84
81           2.54
82           2.25
83            NaN
84           0.00
85           0.01
86           1.83
87           0.01
88           0.42
89           0.70
90           0.01
91           4.43
92           0.00
93            NaN
94           3.06
95           0.00
96           7.38
97           0.00
98           0.00
99           0.00

[100 rows x 1 columns]

Drop rows with any NaNs:
    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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Previous: Write a Pandas program to find which years have all non-zero values and which years have any non-zero values.
Next: Write a Pandas program to filter all records starting from the 'Year' column, access every other column from world alcohol consumption dataset.

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