Very likely, you're using the wrong type for the year. I imagine these are integers.

You should try:

df.loc[(df['Granularity'].isin(['Total', 'Urban'])) & df['Year'].eq(2017)]

output (for the Year 2018 as 2017 is missing from the provided data):

           Zone Granularity  Year      Value
20909  Zimbabwe       Total  2018  14438.802
20913  Zimbabwe       Urban  2018   5447.513

Sure! Setup:

>>> import pandas as pd
>>> from random import randint
>>> df = pd.DataFrame({'A': [randint(1, 9) for x in range(10)],
                   'B': [randint(1, 9)*10 for x in range(10)],
                   'C': [randint(1, 9)*100 for x in range(10)]})
>>> df
   A   B    C
0  9  40  300
1  9  70  700
2  5  70  900
3  8  80  900
4  7  50  200
5  9  30  900
6  2  80  700
7  2  80  400
8  5  80  300
9  7  70  800

We can apply column operations and get boolean Series objects:

>>> df["B"] > 50
0    False
1     True
2     True
3     True
4    False
5    False
6     True
7     True
8     True
9     True
Name: B
>>> (df["B"] > 50) & (df["C"] != 900)

or

>>> (df["B"] > 50) & ~(df["C"] == 900)
0    False
1    False
2     True
3     True
4    False
5    False
6    False
7    False
8    False
9    False

[Update, to switch to new-style .loc]:

And then we can use these to index into the object. For read access, you can chain indices:

>>> df["A"][(df["B"] > 50) & (df["C"] != 900)]
2    5
3    8
Name: A, dtype: int64

but you can get yourself into trouble because of the difference between a view and a copy doing this for write access. You can use .loc instead:

>>> df.loc[(df["B"] > 50) & (df["C"] != 900), "A"]
2    5
3    8
Name: A, dtype: int64
>>> df.loc[(df["B"] > 50) & (df["C"] != 900), "A"].values
array([5, 8], dtype=int64)
>>> df.loc[(df["B"] > 50) & (df["C"] != 900), "A"] *= 1000
>>> df
      A   B    C
0     9  40  300
1     9  70  700
2  5000  70  900
3  8000  80  900
4     7  50  200
5     9  30  900
6     2  80  700
7     2  80  400
8     5  80  300
9     7  70  800

For some dataframe X

   p A  p B  p C
0    0    0    0
1    0    0    0
2    0    0    1
3    0    0    0
4    0    0    0
5    0    0    0
6    1    0    0

If you can set up the names of the columns you want to test for in col_list

col_list = X.columns

You can then use np.any() to test with or between each:

X.loc[(X[col_list] == 1).any(axis=1)]

Which gives you:

   p A  p B  p C
2    0    0    1
6    1    0    0

Informed you don't need loc and will still get the same answer, credit to @MaartynFabre for the info

X[(X[col_list] == 1).any(axis=1)]

   p A  p B  p C
2    0    0    1
6    1    0    0