To select rows whose column value equals a scalar, `some_value`, use `==`:

``df.loc[df['column_name'] == some_value]``

To select rows whose column value is in an iterable, `some_values`, use `isin`:

``df.loc[df['column_name'].isin(some_values)]``

Combine multiple conditions with `&`:

``df.loc[(df['column_name'] >= A) & (df['column_name'] <= B)]``

Note the parentheses. Due to Python's operator precedence rules, `&` binds more tightly than `<=` and `>=`. Thus, the parentheses in the last example are necessary. Without the parentheses

``df['column_name'] >= A & df['column_name'] <= B``

is parsed as

``df['column_name'] >= (A & df['column_name']) <= B``

which results in a Truth value of a Series is ambiguous error.

To select rows whose column value does not equal `some_value`, use `!=`:

``df.loc[df['column_name'] != some_value]``

`isin` returns a boolean Series, so to select rows whose value is not in `some_values`, negate the boolean Series using `~`:

``df.loc[~df['column_name'].isin(some_values)]``

For example,

``````import pandas as pd
import numpy as np
df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
'B': 'one one two three two two one three'.split(),
'C': np.arange(8), 'D': np.arange(8) * 2})
print(df)
#      A      B  C   D
# 0  foo    one  0   0
# 1  bar    one  1   2
# 2  foo    two  2   4
# 3  bar  three  3   6
# 4  foo    two  4   8
# 5  bar    two  5  10
# 6  foo    one  6  12
# 7  foo  three  7  14

print(df.loc[df['A'] == 'foo'])``````

yields

``````A      B  C   D
0  foo    one  0   0
2  foo    two  2   4
4  foo    two  4   8
6  foo    one  6  12
7  foo  three  7  14``````

If you have multiple values you want to include, put them in a list (or more generally, any iterable) and use `isin`:

``print(df.loc[df['B'].isin(['one','three'])])``

yields

``````A      B  C   D
0  foo    one  0   0
1  bar    one  1   2
3  bar  three  3   6
6  foo    one  6  12
7  foo  three  7  14``````

Note, however, that if you wish to do this many times, it is more efficient to make an index first, and then use `df.loc`:

``````df = df.set_index(['B'])
print(df.loc['one'])``````

yields

``````A  C   D
B
one  foo  0   0
one  bar  1   2
one  foo  6  12``````

or, to include multiple values from the index use `df.index.isin`:

``df.loc[df.index.isin(['one','two'])]``

yields

``````A  C   D
B
one  foo  0   0
one  bar  1   2
two  foo  2   4
two  foo  4   8
two  bar  5  10
one  foo  6  12``````