Pandas Series property: loc
Access a group of rows and columns in Pandas
The loc property is used to access a group of rows and columns by label(s) or a boolean array.
.loc[] is primarily label based, but may also be used with a boolean array.
Allowed inputs are:
- A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
- A list or array of labels, e.g. ['a', 'b', 'c'].
- A slice object with labels, e.g. 'a':'f'.
- A boolean array of the same length as the axis being sliced, e.g. [True, False, True].
- A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above)
Syntax:
Series.loc
Raises: KeyError
when any items are not found
Example - Getting values:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df
Output:
max_speed shield
cobra 2 3
viper 6 5
sidewinder 9 8
Example - Single label. Note this returns the row as a Series:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc['viper']
Output:
max_speed 6 shield 5 Name: viper, dtype: int64
Example - List of labels. Note using [[]] returns a DataFrame:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc[['viper', 'sidewinder']]
Output:
max_speed shield
viper 6 5
sidewinder 9 8
Example - Single label for row and column:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc['cobra', 'shield']
Output:
3
Slice with labels for row and single label for column. As mentioned above, note that both the start and stop of the slice<br. are included.
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc['cobra':'viper', 'max_speed']
Output:
cobra 2 viper 6 Name: max_speed, dtype: int64
Example - Boolean list with the same length as the row axis:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc[[False, False, True]]
Output:
max_speed shield
sidewinder 9 8
Example - Conditional that returns a boolean Series:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc[df['shield'] > 6]
Output:
max_speed shield
sidewinder 9 8
Example - Conditional that returns a boolean Series with column labels specified:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc[df['shield'] > 6, ['max_speed']]
Output:
max_speed
sidewinder 9
Example - Callable that returns a boolean Series:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc[lambda df: df['shield'] == 8]
Output:
max_speed shield
sidewinder 9 8
Example - Setting values:
Set value for all items matching the list of labels
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc[['viper', 'sidewinder'], ['shield']] = 50
df
Output:
max_speed shield
cobra 2 3
viper 6 50
sidewinder 9 50
Example - Set value for an entire row:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc[['viper', 'sidewinder'], ['shield']] = 50
df.loc['cobra'] = 10
df
Output:
max_speed shield
cobra 10 10
viper 6 50
sidewinder 9 50
Example - Set value for an entire column:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc[['viper', 'sidewinder'], ['shield']] = 50
df.loc[:, 'max_speed'] = 40
df
Output:
max_speed shield
cobra 40 10
viper 40 50
sidewinder 40 50
Example - Set value for rows matching callable condition:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=['cobra', 'viper', 'sidewinder'],
columns=['max_speed', 'shield'])
df.loc[['viper', 'sidewinder'], ['shield']] = 50
df.loc[df['shield'] > 25] = 0
df
Output:
max_speed shield
cobra 40 10
viper 0 0
sidewinder 0 0
Example - Getting values on a DataFrame with an index that has integer labels:
Another example using integers for the index
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=[3, 4, 5], columns=['max_speed', 'shield'])
df
Output:
max_speed shield
3 2 3
4 6 5
5 9 8
Slice with integer labels for rows. As mentioned above, note that both the start and stop of the slice are included.
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([[2, 3], [6, 5], [9, 8]],
index=[3, 4, 5], columns=['max_speed', 'shield'])
df.loc[3:5]
Output:
max_speed shield
3 2 3
4 6 5
5 9 8
Example - Getting values with a MultiIndex:
A number of examples using a DataFrame with a MultiIndex
Python-Pandas Code:
import numpy as np
import pandas as pd
tuples = [
('cobra', 's1'), ('cobra', 's2'),
('sidewinder', 's1'), ('sidewinder', 's2'),
('viper', 's2'), ('viper', 's3')
]
index = pd.MultiIndex.from_tuples(tuples)
values = [[6, 2], [0, 4], [20, 30],
[1, 4], [5, 1], [36, 56]]
df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
df
Output:
max_speed shield
cobra s1 6 2
s2 0 4
sidewinder s1 20 30
s2 1 4
viper s2 5 1
s3 36 56
Example - Single label. Note this returns a DataFrame with a single index:
Python-Pandas Code:
import numpy as np
import pandas as pd
tuples = [
('cobra', 's1'), ('cobra', 's2'),
('sidewinder', 's1'), ('sidewinder', 's2'),
('viper', 's2'), ('viper', 's3')
]
index = pd.MultiIndex.from_tuples(tuples)
values = [[6, 2], [0, 4], [20, 30],
[1, 4], [5, 1], [36, 56]]
df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
df.loc['cobra']
Output:
max_speed shield
s1 6 2
s2 0 4
Example - Single index tuple. Note this returns a Series:
Python-Pandas Code:
import numpy as np
import pandas as pd
tuples = [
('cobra', 's1'), ('cobra', 's2'),
('sidewinder', 's1'), ('sidewinder', 's2'),
('viper', 's2'), ('viper', 's3')
]
index = pd.MultiIndex.from_tuples(tuples)
values = [[6, 2], [0, 4], [20, 30],
[1, 4], [5, 1], [36, 56]]
df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
df.loc[('cobra', 's2')]
Output:
max_speed 0 shield 4 Name: (cobra, s2), dtype: int64
Example - Single label for row and column. Similar to passing in a tuple, this returns a Series:
Python-Pandas Code:
import numpy as np
import pandas as pd
tuples = [
('cobra', 's1'), ('cobra', 's2'),
('sidewinder', 's1'), ('sidewinder', 's2'),
('viper', 's2'), ('viper', 's3')
]
index = pd.MultiIndex.from_tuples(tuples)
values = [[6, 2], [0, 4], [20, 30],
[1, 4], [5, 1], [36, 56]]
df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
df.loc['cobra', 's1']
Output:
max_speed 6 shield 2 Name: (cobra, s1), dtype: int64
Example - Single tuple. Note using [[]] returns a DataFrame:
Python-Pandas Code:
import numpy as np
import pandas as pd
tuples = [
('cobra', 's1'), ('cobra', 's2'),
('sidewinder', 's1'), ('sidewinder', 's2'),
('viper', 's2'), ('viper', 's3')
]
index = pd.MultiIndex.from_tuples(tuples)
values = [[6, 2], [0, 4], [20, 30],
[1, 4], [5, 1], [36, 56]]
df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
df.loc[[('cobra', 's2')]]
Output:
max_speed shield
cobra s2 0 4
Example - Single tuple for the index with a single label for the column:
Python-Pandas Code:
import numpy as np
import pandas as pd
tuples = [
('cobra', 's1'), ('cobra', 's2'),
('sidewinder', 's1'), ('sidewinder', 's2'),
('viper', 's2'), ('viper', 's3')
]
index = pd.MultiIndex.from_tuples(tuples)
values = [[6, 2], [0, 4], [20, 30],
[1, 4], [5, 1], [36, 56]]
df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
df.loc[('cobra', 's1'), 'shield']
Output:
2
Example - Slice from index tuple to single label:
Python-Pandas Code:
import numpy as np
import pandas as pd
tuples = [
('cobra', 's1'), ('cobra', 's2'),
('sidewinder', 's1'), ('sidewinder', 's2'),
('viper', 's2'), ('viper', 's3')
]
index = pd.MultiIndex.from_tuples(tuples)
values = [[6, 2], [0, 4], [20, 30],
[1, 4], [5, 1], [36, 56]]
df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
df.loc[('cobra', 's1'):'viper']
Output:
max_speed shield
cobra s1 6 2
s2 0 4
sidewinder s1 20 30
s2 1 4
viper s2 5 1
s3 36 56
Example - Slice from index tuple to index tuple:
Python-Pandas Code:
import numpy as np
import pandas as pd
tuples = [
('cobra', 's1'), ('cobra', 's2'),
('sidewinder', 's1'), ('sidewinder', 's2'),
('viper', 's2'), ('viper', 's3')
]
index = pd.MultiIndex.from_tuples(tuples)
values = [[6, 2], [0, 4], [20, 30],
[1, 4], [5, 1], [36, 56]]
df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
df.loc[('cobra', 's1'):('viper', 's2')]
Output:
max_speed shield
cobra s1 6 2
s2 0 4
sidewinder s1 20 30
s2 1 4
viper s2 5 1
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