pandas.DataFrame.loc#
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.
- 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’ .
Warning Note that contrary to usual python slices, both the start and the stop are included
If any items are not found.
If an indexed key is passed and its index is unalignable to the frame index.
Access a single value for a row/column label pair.
Access group of rows and columns by integer position(s).
Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
Access group of values using labels.
Getting values
>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]], . index=['cobra', 'viper', 'sidewinder'], . columns=['max_speed', 'shield']) >>> df max_speed shield cobra 1 2 viper 4 5 sidewinder 7 8
Single label. Note this returns the row as a Series.
>>> df.loc['viper'] max_speed 4 shield 5 Name: viper, dtype: int64
List of labels. Note using [[]] returns a DataFrame.
>>> df.loc[['viper', 'sidewinder']] max_speed shield viper 4 5 sidewinder 7 8
Single label for row and column
Slice with labels for row and single label for column. As mentioned above, note that both the start and stop of the slice are included.
>>> df.loc['cobra':'viper', 'max_speed'] cobra 1 viper 4 Name: max_speed, dtype: int64
Boolean list with the same length as the row axis
>>> df.loc[[False, False, True]] max_speed shield sidewinder 7 8
>>> df.loc[pd.Series([False, True, False], . index=['viper', 'sidewinder', 'cobra'])] max_speed shield sidewinder 7 8
Index (same behavior as df.reindex )
>>> df.loc[pd.Index(["cobra", "viper"], name="foo")] max_speed shield foo cobra 1 2 viper 4 5
Conditional that returns a boolean Series
>>> df.loc[df['shield'] > 6] max_speed shield sidewinder 7 8
Conditional that returns a boolean Series with column labels specified
>>> df.loc[df['shield'] > 6, ['max_speed']] max_speed sidewinder 7
Callable that returns a boolean Series
>>> df.loc[lambda df: df['shield'] == 8] max_speed shield sidewinder 7 8
Setting values
Set value for all items matching the list of labels
>>> df.loc[['viper', 'sidewinder'], ['shield']] = 50 >>> df max_speed shield cobra 1 2 viper 4 50 sidewinder 7 50
Set value for an entire row
>>> df.loc['cobra'] = 10 >>> df max_speed shield cobra 10 10 viper 4 50 sidewinder 7 50
Set value for an entire column
>>> df.loc[:, 'max_speed'] = 30 >>> df max_speed shield cobra 30 10 viper 30 50 sidewinder 30 50
Set value for rows matching callable condition
>>> df.loc[df['shield'] > 35] = 0 >>> df max_speed shield cobra 30 10 viper 0 0 sidewinder 0 0
Getting values on a DataFrame with an index that has integer labels
Another example using integers for the index
>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]], . index=[7, 8, 9], columns=['max_speed', 'shield']) >>> df max_speed shield 7 1 2 8 4 5 9 7 8
Slice with integer labels for rows. As mentioned above, note that both the start and stop of the slice are included.
>>> df.loc[7:9] max_speed shield 7 1 2 8 4 5 9 7 8
Getting values with a MultiIndex
A number of examples using a DataFrame with a MultiIndex
>>> tuples = [ . ('cobra', 'mark i'), ('cobra', 'mark ii'), . ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'), . ('viper', 'mark ii'), ('viper', 'mark iii') . ] >>> index = pd.MultiIndex.from_tuples(tuples) >>> values = [[12, 2], [0, 4], [10, 20], . [1, 4], [7, 1], [16, 36]] >>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index) >>> df max_speed shield cobra mark i 12 2 mark ii 0 4 sidewinder mark i 10 20 mark ii 1 4 viper mark ii 7 1 mark iii 16 36
Single label. Note this returns a DataFrame with a single index.
>>> df.loc['cobra'] max_speed shield mark i 12 2 mark ii 0 4
Single index tuple. Note this returns a Series.
>>> df.loc[('cobra', 'mark ii')] max_speed 0 shield 4 Name: (cobra, mark ii), dtype: int64
Single label for row and column. Similar to passing in a tuple, this returns a Series.
>>> df.loc['cobra', 'mark i'] max_speed 12 shield 2 Name: (cobra, mark i), dtype: int64
Single tuple. Note using [[]] returns a DataFrame.
>>> df.loc[[('cobra', 'mark ii')]] max_speed shield cobra mark ii 0 4
Single tuple for the index with a single label for the column
>>> df.loc[('cobra', 'mark i'), 'shield'] 2
Slice from index tuple to single label
>>> df.loc[('cobra', 'mark i'):'viper'] max_speed shield cobra mark i 12 2 mark ii 0 4 sidewinder mark i 10 20 mark ii 1 4 viper mark ii 7 1 mark iii 16 36
Slice from index tuple to index tuple
>>> df.loc[('cobra', 'mark i'):('viper', 'mark ii')] max_speed shield cobra mark i 12 2 mark ii 0 4 sidewinder mark i 10 20 mark ii 1 4 viper mark ii 7 1
Please see the user guide for more details and explanations of advanced indexing.
pandas.DataFrame.loc#
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.
- 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’ .
Warning Note that contrary to usual python slices, both the start and the stop are included
If any items are not found.
If an indexed key is passed and its index is unalignable to the frame index.
Access a single value for a row/column label pair.
Access group of rows and columns by integer position(s).
Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
Access group of values using labels.
Getting values
>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]], . index=['cobra', 'viper', 'sidewinder'], . columns=['max_speed', 'shield']) >>> df max_speed shield cobra 1 2 viper 4 5 sidewinder 7 8
Single label. Note this returns the row as a Series.
>>> df.loc['viper'] max_speed 4 shield 5 Name: viper, dtype: int64
List of labels. Note using [[]] returns a DataFrame.
>>> df.loc[['viper', 'sidewinder']] max_speed shield viper 4 5 sidewinder 7 8
Single label for row and column
Slice with labels for row and single label for column. As mentioned above, note that both the start and stop of the slice are included.
>>> df.loc['cobra':'viper', 'max_speed'] cobra 1 viper 4 Name: max_speed, dtype: int64
Boolean list with the same length as the row axis
>>> df.loc[[False, False, True]] max_speed shield sidewinder 7 8
>>> df.loc[pd.Series([False, True, False], . index=['viper', 'sidewinder', 'cobra'])] max_speed shield sidewinder 7 8
Index (same behavior as df.reindex )
>>> df.loc[pd.Index(["cobra", "viper"], name="foo")] max_speed shield foo cobra 1 2 viper 4 5
Conditional that returns a boolean Series
>>> df.loc[df['shield'] > 6] max_speed shield sidewinder 7 8
Conditional that returns a boolean Series with column labels specified
>>> df.loc[df['shield'] > 6, ['max_speed']] max_speed sidewinder 7
Multiple conditional using & that returns a boolean Series
>>> df.loc[(df['max_speed'] > 1) & (df['shield'] 8)] max_speed shield viper 4 5
Multiple conditional using | that returns a boolean Series
>>> df.loc[(df['max_speed'] > 4) | (df['shield'] 5)] max_speed shield cobra 1 2 sidewinder 7 8
Please ensure that each condition is wrapped in parentheses () . See the user guide for more details and explanations of Boolean indexing.
If you find yourself using 3 or more conditionals in .loc[] , consider using advanced indexing .
See below for using .loc[] on MultiIndex DataFrames.
Callable that returns a boolean Series
>>> df.loc[lambda df: df['shield'] == 8] max_speed shield sidewinder 7 8
Setting values
Set value for all items matching the list of labels
>>> df.loc[['viper', 'sidewinder'], ['shield']] = 50 >>> df max_speed shield cobra 1 2 viper 4 50 sidewinder 7 50
Set value for an entire row
>>> df.loc['cobra'] = 10 >>> df max_speed shield cobra 10 10 viper 4 50 sidewinder 7 50
Set value for an entire column
>>> df.loc[:, 'max_speed'] = 30 >>> df max_speed shield cobra 30 10 viper 30 50 sidewinder 30 50
Set value for rows matching callable condition
>>> df.loc[df['shield'] > 35] = 0 >>> df max_speed shield cobra 30 10 viper 0 0 sidewinder 0 0
Add value matching location
>>> df.loc["viper", "shield"] += 5 >>> df max_speed shield cobra 30 10 viper 0 5 sidewinder 0 0
Setting using a Series or a DataFrame sets the values matching the index labels, not the index positions.
>>> shuffled_df = df.loc[["viper", "cobra", "sidewinder"]] >>> df.loc[:] += shuffled_df >>> df max_speed shield cobra 60 20 viper 0 10 sidewinder 0 0
Getting values on a DataFrame with an index that has integer labels
Another example using integers for the index
>>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]], . index=[7, 8, 9], columns=['max_speed', 'shield']) >>> df max_speed shield 7 1 2 8 4 5 9 7 8
Slice with integer labels for rows. As mentioned above, note that both the start and stop of the slice are included.
>>> df.loc[7:9] max_speed shield 7 1 2 8 4 5 9 7 8
Getting values with a MultiIndex
A number of examples using a DataFrame with a MultiIndex
>>> tuples = [ . ('cobra', 'mark i'), ('cobra', 'mark ii'), . ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'), . ('viper', 'mark ii'), ('viper', 'mark iii') . ] >>> index = pd.MultiIndex.from_tuples(tuples) >>> values = [[12, 2], [0, 4], [10, 20], . [1, 4], [7, 1], [16, 36]] >>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index) >>> df max_speed shield cobra mark i 12 2 mark ii 0 4 sidewinder mark i 10 20 mark ii 1 4 viper mark ii 7 1 mark iii 16 36
Single label. Note this returns a DataFrame with a single index.
>>> df.loc['cobra'] max_speed shield mark i 12 2 mark ii 0 4
Single index tuple. Note this returns a Series.
>>> df.loc[('cobra', 'mark ii')] max_speed 0 shield 4 Name: (cobra, mark ii), dtype: int64
Single label for row and column. Similar to passing in a tuple, this returns a Series.
>>> df.loc['cobra', 'mark i'] max_speed 12 shield 2 Name: (cobra, mark i), dtype: int64
Single tuple. Note using [[]] returns a DataFrame.
>>> df.loc[[('cobra', 'mark ii')]] max_speed shield cobra mark ii 0 4
Single tuple for the index with a single label for the column
>>> df.loc[('cobra', 'mark i'), 'shield'] 2
Slice from index tuple to single label
>>> df.loc[('cobra', 'mark i'):'viper'] max_speed shield cobra mark i 12 2 mark ii 0 4 sidewinder mark i 10 20 mark ii 1 4 viper mark ii 7 1 mark iii 16 36
Slice from index tuple to index tuple
>>> df.loc[('cobra', 'mark i'):('viper', 'mark ii')] max_speed shield cobra mark i 12 2 mark ii 0 4 sidewinder mark i 10 20 mark ii 1 4 viper mark ii 7 1
Please see the user guide for more details and explanations of advanced indexing.