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]
The isin returns a boolean Series, so to select rows whose value is not in some_values, negate the boolean Series using ~:
df = df.loc[~df['column_name'].isin(some_values)] # .loc is not in-place replacement
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
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]
The isin returns a boolean Series, so to select rows whose value is not in some_values, negate the boolean Series using ~:
df = df.loc[~df['column_name'].isin(some_values)] # .loc is not in-place replacement
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
There are several ways to select rows from a Pandas dataframe:
- Boolean indexing (
df[df['col'] == value] ) - Positional indexing (
df.iloc[...]) - Label indexing (
df.xs(...)) 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 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})
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 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
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 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
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 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
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 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
My preference is to use the Boolean mask
Actual improvements can be made by modifying how we create our Boolean mask.
mask alternative 1
Use 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 2
We 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 E
0 0 2 7 3 8
1 7 0 6 8 6
2 0 2 0 4 9
3 7 3 2 4 3
4 3 6 7 7 4
5 5 3 7 5 9
6 8 7 6 4 7
7 6 2 6 6 5
8 2 8 7 5 8
9 4 7 6 1 5
%%timeit
mask = d1['A'].values == 7
d1[mask]
179 µs ± 8.73 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
Versus
%%timeit
mask = d1['A'].values == 7
pd.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 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
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 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
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 30000
mask_standard 2.156872 1.850663 2.034149 2.166312 2.164541 3.090372 2.981326 3.131151
mask_standard_loc 1.879035 1.782366 1.988823 2.338112 2.361391 3.036131 2.998112 2.990103
mask_with_values 1.010166 1.000000 1.005113 1.026363 1.028698 1.293741 1.007824 1.016919
mask_with_values_loc 1.196843 1.300228 1.000000 1.000000 1.038989 1.219233 1.037020 1.000000
query 4.997304 4.765554 5.934096 4.500559 2.997924 2.397013 1.680447 1.398190
xs_label 4.124597 4.272363 5.596152 4.295331 4.676591 5.710680 6.032809 8.950255
mask_with_isin 1.674055 1.679935 1.847972 1.724183 1.345111 1.405231 1.253554 1.264760
mask_with_in1d 1.000000 1.083807