Another option with DataFrame.where():

df[['C1', 'V1']].where(df.Cond == "X", df[['C2', 'V2']].values)

#  C1   V1
#0  1    2
#1  7    8
#2  9   10

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

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

You can chain conditions by & for bitwise AND and filter by DataFrame.loc rows by conditions (boolean indexing) and columns by columns names in list:

mask = (ClaimsData["AccidentState"]=="AZ") & (ClaimsData["Coverage"]=="Liability")
df = ClaimsData.loc[mask, ["PolicyNumber","Coverage"]]

Like @Sandeep Kadapa pointed in comments there is alternative with DataFrame.query:

q = "AccidentState == 'AZ' & Coverage == 'Liability'"
df = ClaimsData.query(q)[["PolicyNumber","Coverage"]]

You can use all with boolean indexing:

print ((df == 'something1').all(1))
0     True
1    False
2     True
3    False
4    False
dtype: bool

print (df[(df == 'something1').all(1)])
         col1        col2
0  something1  something1
2  something1  something1

EDIT:

If need select only some columns you can use isin with boolean indexing for selecting desired columns and then use subset - df[cols]:

print (df)
         col1        col2 col3
0  something1  something1    a
1  something2  something3    s
2  something1  something1    r
3  something2  something3    a
4  something1  something2    a

cols = df.columns[df.columns.isin(['col1','col2'])]
print (cols)
Index(['col1', 'col2'], dtype='object')

print (df[(df[cols] == 'something1').all(1)])
         col1        col2 col3
0  something1  something1    a
2  something1  something1    r

You can pass list to DataFrame.loc:

df1 = df.loc[df['Student_id']=='1000102', ['Date', 'Subject']]
print (df1)
          Date    Subject
9   11/25/2020    Physics
10  11/25/2020       Math
11  12/15/2020  Chemistry

If need unique values add DataFrame.drop_duplicates:

df2 = df.loc[df['Student_id']=='1000102', ['Date', 'Subject']].drop_duplicates()
print (df2)
          Date    Subject
9   11/25/2020    Physics
10  11/25/2020       Math
11  12/15/2020  Chemistry

If need Series.unique for each column separately:

df3 = df.loc[df['Student_id']=='1000102', ['Date', 'Subject']].apply(lambda x: x.unique())
print (df3)
Date         [11/25/2020, 12/15/2020]
Subject    [Physics, Math, Chemistry]
dtype: object

I think below should do it, but its elegance is up for debate.

new_df = old_df[((old_df['C1'] > 0) & (old_df['C1'] < 20)) & ((old_df['C2'] > 0) & (old_df['C2'] < 20)) & ((old_df['C3'] > 0) & (old_df['C3'] < 20))]