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 pdimport numpy as npdf = 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  14print(df.loc[df['A'] == 'foo'])

yields

     A      B  C   D0  foo    one  0   02  foo    two  2   44  foo    two  4   86  foo    one  6  127  foo  three  7  14

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

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

yields

     A      B  C   D0  foo    one  0   01  bar    one  1   23  bar  three  3   66  foo    one  6  127  foo  three  7  14

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

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

yields

       A  C   DB              one  foo  0   0one  bar  1   2one  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   DB              one  foo  0   0one  bar  1   2two  foo  2   4two  foo  4   8two  bar  5  10one  foo  6  12

There are several ways to select rows from a Pandas dataframe:

1. Boolean indexing (df[df['col'] == value] )
2. Positional indexing (df.iloc[...])
3. Label indexing (df.xs(...))
4. df.query(...) API

Below I show you examples of each, with advice when to use certain techniques. Assume our criterion is column 'A' == 'foo'

(Note on performance: For each base type, we can keep things simple by using the Pandas API or we can venture outside the API, usually into NumPy, and speed things up.)

Setup

The first thing we'll need is to identify a condition that will act as our criterion for selecting rows. We'll start with the OP's case column_name == some_value, and include some other common use cases.

Borrowing from @unutbu:

import pandas as pd, numpy as npdf = 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})

1. Boolean indexing

... Boolean indexing requires finding the true value of each row's 'A' column being equal to 'foo', then using those truth values to identify which rows to keep. Typically, we'd name this series, an array of truth values, mask. We'll do so here as well.

mask = df['A'] == 'foo'

We can then use this mask to slice or index the data frame

df[mask]     A      B  C   D0  foo    one  0   02  foo    two  2   44  foo    two  4   86  foo    one  6  127  foo  three  7  14

This is one of the simplest ways to accomplish this task and if performance or intuitiveness isn't an issue, this should be your chosen method. However, if performance is a concern, then you might want to consider an alternative way of creating the mask.

2. Positional indexing

Positional indexing (df.iloc[...]) has its use cases, but this isn't one of them. In order to identify where to slice, we first need to perform the same boolean analysis we did above. This leaves us performing one extra step to accomplish the same task.

mask = df['A'] == 'foo'pos = np.flatnonzero(mask)df.iloc[pos]     A      B  C   D0  foo    one  0   02  foo    two  2   44  foo    two  4   86  foo    one  6  127  foo  three  7  14

3. Label indexing

Label indexing can be very handy, but in this case, we are again doing more work for no benefit

df.set_index('A', append=True, drop=False).xs('foo', level=1)     A      B  C   D0  foo    one  0   02  foo    two  2   44  foo    two  4   86  foo    one  6  127  foo  three  7  14

4. df.query() API

pd.DataFrame.query is a very elegant/intuitive way to perform this task, but is often slower. However, if you pay attention to the timings below, for large data, the query is very efficient. More so than the standard approach and of similar magnitude as my best suggestion.

df.query('A == "foo"')     A      B  C   D0  foo    one  0   02  foo    two  2   44  foo    two  4   86  foo    one  6  127  foo  three  7  14

My preference is to use the Boolean mask

Actual improvements can be made by modifying how we create our Boolean mask.

mask alternative 1Use the underlying NumPy array and forgo the overhead of creating another pd.Series

mask = df['A'].values == 'foo'

I'll show more complete time tests at the end, but just take a look at the performance gains we get using the sample data frame. First, we look at the difference in creating the mask

%timeit mask = df['A'].values == 'foo'%timeit mask = df['A'] == 'foo'5.84 µs ± 195 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)166 µs ± 4.45 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Evaluating the mask with the NumPy array is ~ 30 times faster. This is partly due to NumPy evaluation often being faster. It is also partly due to the lack of overhead necessary to build an index and a corresponding pd.Series object.

Next, we'll look at the timing for slicing with one mask versus the other.

mask = df['A'].values == 'foo'%timeit df[mask]mask = df['A'] == 'foo'%timeit df[mask]219 µs ± 12.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)239 µs ± 7.03 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

The performance gains aren't as pronounced. We'll see if this holds up over more robust testing.

mask alternative 2We could have reconstructed the data frame as well. There is a big caveat when reconstructing a dataframe—you must take care of the dtypes when doing so!

Instead of df[mask] we will do this

pd.DataFrame(df.values[mask], df.index[mask], df.columns).astype(df.dtypes)

If the data frame is of mixed type, which our example is, then when we get df.values the resulting array is of dtype object and consequently, all columns of the new data frame will be of dtype object. Thus requiring the astype(df.dtypes) and killing any potential performance gains.

%timeit df[m]%timeit pd.DataFrame(df.values[mask], df.index[mask], df.columns).astype(df.dtypes)216 µs ± 10.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)1.43 ms ± 39.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

However, if the data frame is not of mixed type, this is a very useful way to do it.

Given

np.random.seed([3,1415])d1 = pd.DataFrame(np.random.randint(10, size=(10, 5)), columns=list('ABCDE'))d1   A  B  C  D  E0  0  2  7  3  81  7  0  6  8  62  0  2  0  4  93  7  3  2  4  34  3  6  7  7  45  5  3  7  5  96  8  7  6  4  77  6  2  6  6  58  2  8  7  5  89  4  7  6  1  5

%%timeitmask = d1['A'].values == 7d1[mask]179 µs ± 8.73 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Versus

%%timeitmask = d1['A'].values == 7pd.DataFrame(d1.values[mask], d1.index[mask], d1.columns)87 µs ± 5.12 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

We cut the time in half.

mask alternative 3

@unutbu also shows us how to use pd.Series.isin to account for each element of df['A'] being in a set of values. This evaluates to the same thing if our set of values is a set of one value, namely 'foo'. But it also generalizes to include larger sets of values if needed. Turns out, this is still pretty fast even though it is a more general solution. The only real loss is in intuitiveness for those not familiar with the concept.

mask = df['A'].isin(['foo'])df[mask]     A      B  C   D0  foo    one  0   02  foo    two  2   44  foo    two  4   86  foo    one  6  127  foo  three  7  14

However, as before, we can utilize NumPy to improve performance while sacrificing virtually nothing. We'll use np.in1d

mask = np.in1d(df['A'].values, ['foo'])df[mask]     A      B  C   D0  foo    one  0   02  foo    two  2   44  foo    two  4   86  foo    one  6  127  foo  three  7  14

Timing

I'll include other concepts mentioned in other posts as well for reference.

Code Below

Each column in this table represents a different length data frame over which we test each function. Each column shows relative time taken, with the fastest function given a base index of 1.0.

res.div(res.min())                         10        30        100       300       1000      3000      10000     30000mask_standard         2.156872  1.850663  2.034149  2.166312  2.164541  3.090372  2.981326  3.131151mask_standard_loc     1.879035  1.782366  1.988823  2.338112  2.361391  3.036131  2.998112  2.990103mask_with_values      1.010166  1.000000  1.005113  1.026363  1.028698  1.293741  1.007824  1.016919mask_with_values_loc  1.196843  1.300228  1.000000  1.000000  1.038989  1.219233  1.037020  1.000000query                 4.997304  4.765554  5.934096  4.500559  2.997924  2.397013  1.680447  1.398190xs_label              4.124597  4.272363  5.596152  4.295331  4.676591  5.710680  6.032809  8.950255mask_with_isin        1.674055  1.679935  1.847972  1.724183  1.345111  1.405231  1.253554  1.264760mask_with_in1d        1.000000  1.083807  1.220493  1.101929  1.000000  1.000000  1.000000  1.144175

You'll notice that the fastest times seem to be shared between mask_with_values and mask_with_in1d.

res.T.plot(loglog=True)

Functions

def mask_standard(df):    mask = df['A'] == 'foo'    return df[mask]def mask_standard_loc(df):    mask = df['A'] == 'foo'    return df.loc[mask]def mask_with_values(df):    mask = df['A'].values == 'foo'    return df[mask]def mask_with_values_loc(df):    mask = df['A'].values == 'foo'    return df.loc[mask]def query(df):    return df.query('A == "foo"')def xs_label(df):    return df.set_index('A', append=True, drop=False).xs('foo', level=-1)def mask_with_isin(df):    mask = df['A'].isin(['foo'])    return df[mask]def mask_with_in1d(df):    mask = np.in1d(df['A'].values, ['foo'])    return df[mask]

Testing

res = pd.DataFrame(    index=[        'mask_standard', 'mask_standard_loc', 'mask_with_values', 'mask_with_values_loc',        'query', 'xs_label', 'mask_with_isin', 'mask_with_in1d'    ],    columns=[10, 30, 100, 300, 1000, 3000, 10000, 30000],    dtype=float)for j in res.columns:    d = pd.concat([df] * j, ignore_index=True)    for i in res.index:a        stmt = '{}(d)'.format(i)        setp = 'from __main__ import d, {}'.format(i)        res.at[i, j] = timeit(stmt, setp, number=50)

Special Timing

Looking at the special case when we have a single non-object dtype for the entire data frame.

Code Below

spec.div(spec.min())                     10        30        100       300       1000      3000      10000     30000mask_with_values  1.009030  1.000000  1.194276  1.000000  1.236892  1.095343  1.000000  1.000000mask_with_in1d    1.104638  1.094524  1.156930  1.072094  1.000000  1.000000  1.040043  1.027100reconstruct       1.000000  1.142838  1.000000  1.355440  1.650270  2.222181  2.294913  3.406735

Turns out, reconstruction isn't worth it past a few hundred rows.

spec.T.plot(loglog=True)

Functions

np.random.seed([3,1415])d1 = pd.DataFrame(np.random.randint(10, size=(10, 5)), columns=list('ABCDE'))def mask_with_values(df):    mask = df['A'].values == 'foo'    return df[mask]def mask_with_in1d(df):    mask = np.in1d(df['A'].values, ['foo'])    return df[mask]def reconstruct(df):    v = df.values    mask = np.in1d(df['A'].values, ['foo'])    return pd.DataFrame(v[mask], df.index[mask], df.columns)spec = pd.DataFrame(    index=['mask_with_values', 'mask_with_in1d', 'reconstruct'],    columns=[10, 30, 100, 300, 1000, 3000, 10000, 30000],    dtype=float)

Testing

for j in spec.columns:    d = pd.concat([df] * j, ignore_index=True)    for i in spec.index:        stmt = '{}(d)'.format(i)        setp = 'from __main__ import d, {}'.format(i)        spec.at[i, j] = timeit(stmt, setp, number=50)

tl;dr

The Pandas equivalent to

select * from table where column_name = some_value

is

table[table.column_name == some_value]

Multiple conditions:

table[(table.column_name == some_value) | (table.column_name2 == some_value2)]

or

table.query('column_name == some_value | column_name2 == some_value2')

Code example

import pandas as pd# Create data setd = {'foo':[100, 111, 222],     'bar':[333, 444, 555]}df = pd.DataFrame(d)# Full dataframe:df# Shows:#    bar   foo# 0  333   100# 1  444   111# 2  555   222# Output only the row(s) in df where foo is 222:df[df.foo == 222]# Shows:#    bar  foo# 2  555  222

In the above code it is the line df[df.foo == 222] that gives the rows based on the column value, 222 in this case.

Multiple conditions are also possible:

df[(df.foo == 222) | (df.bar == 444)]#    bar  foo# 1  444  111# 2  555  222

But at that point I would recommend using the query function, since it's less verbose and yields the same result:

df.query('foo == 222 | bar == 444')