pandas.loc/iloc/ix的区别
pandas.loc/iloc/ix的区别
loc[]：Access a group of rows and columns by label(s) or a boolean array.
通过标签或布尔数组访问DataF对象的行或列
允许的输入（输入指的是df.loc[]中[]内部的部分）包括：
-
单个标签，如"5"或"a"，都是字符串，”5“也不代表数字
-
标签列表list或标签数组array，如[‘a’,‘b’,‘c’]
-
标签切片，如’a’:‘c’，与序列切片如0:2不同，后者不包含index=2的元素，前者包含结束标签’c’所在的行。
-
布尔类型数组作为标签，例如[True, False, True]等价于[‘a’,‘c’]
-
一个带有一个参数（调用Series，DataFrame或Panel）的可调用
callable函数，它返回索引的有效输出（上面列出的那些）callable 参见 https://www.aliyun.com/jiaocheng/462985.html?spm=5176.100033.2.15.6cba1f38Y84nok
例子：
import pandas as pd
data=[[1,2,3,4],[5,6,7,8],[9,10,11,12]]
print(type(data))
index=['a','b','c']
columns=['d','e','f','g']
df=pd.DataFrame(data=data,index=index,columns=columns)
df
d e f g
a 1 2 3 4
b 5 6 7 8
c 9 10 11 12
df.loc['a'] #将'a'行作为Series返回
type(df.loc['a']) #pandas.core.series.Series
df.loc[['a']] #将'a'行作为DataFrame返回
type(df.loc[['a']]) #pandas.core.frame.DataFrame
#综上，[]返回Series,[[]]返回DataFrame
df.loc['a':'c'] #将'a'到'c'行作为DataFrame返回
type(df.loc['a':'c']) #pandas.core.frame.DataFrame
df.loc['a','f']#返回'a'行，'f'列的元素
df.loc[：,'f']#将'f'列作为Series返回
df.loc[['a','c'],['d','f']]#将'a'、'c'行'd'、'f'列作为DataFrame返回
df.loc[:,'d':'f']#将所有行，'d'到'f'列作为DataFrame返回
#综上，切片不用[]包围，标签列表或数组需要[]包围
df['g']#将第'g'列以Series返回，与df.loc[:,'g']等价
df['g'].equals(df.loc[:,'g'])#True
df.loc[df['g'] > 6]#将所有第'g'列大于6的行以DataFrame返回
#说明条件表达式和切片类似，将其作为loc[]的输入，返回DataFrame
df.loc[df['g']>6,['g']]
df.loc[df['g']>6,'g']#分别返回DataFrame和Series
df.loc[lambda df:df['g']==8]#DataFrame
#赋值和取值一样
df.loc[['a','c'],['d','f']]=10#将'a'、'c'行'd'、'f'列的所有元素赋值为10
#多索引（MultiIndex）的DataFrame，官方文档的例子
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]]
dfm = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
dfm
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
dfm.loc['cobra']#返回DataFrame
dfm.loc[('cobra', 'mark ii')]#Series
dfm.loc['cobra', 'mark i']#Series
dfm.loc['cobra', 'mark i'].equals(dfm.loc[('cobra', 'mark i')])#True
dfm.loc[[('cobra', 'mark ii')]]#DataFrame
dfm.loc[('cobra', 'mark i'), 'shield']#single tuple
dfm.loc[('cobra', 'mark i'):'viper']#DataFrame
dfm.loc['cobra':'viper']#DataFrame
dfm.loc[('cobra', 'mark i'):'viper'].equals(dfm.loc['cobra':'viper']) #True
dfm.loc[('cobra', 'mark i'):('viper', 'mark ii')]#DataFrame
iloc[]:
通过整数索引（0到length-1）或布尔数组来访问DataFrame对象。把DataFrame对象看做一个二维数组来访问。
例如，
dfm.iloc[0]#Series
dfm.iloc[0:1]#DataFrame
dfm.iloc[0:3,0:1]#DataFrame
ix[]: pandas 0.20.0以后，已经不用（deprecated）了。
原始文档
Type: property
String form: <property object at 0x0000021B3606B728>
Docstring:
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'``.
.. warning:: Note that contrary to usual python slices, **both** the
start and the stop are included
- 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, DataFrame
or Panel) and that returns valid output for indexing (one of the above)
See more at :ref:`Selection by Label <indexing.label>`
See Also
--------
DataFrame.at : Access a single value for a row/column label pair
DataFrame.iloc : Access group of rows and columns by integer position(s)
DataFrame.xs : Returns a cross-section (row(s) or column(s)) from the
Series/DataFrame.
Series.loc : Access group of values using labels
Examples
--------
**Getting values**
>>> dfm = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
... index=['cobra', 'viper', 'sidewinder'],
... columns=['max_speed', 'shield'])
>>> dfm
max_speed shield
cobra 1 2
viper 4 5
sidewinder 7 8
Single label. Note this returns the row as a Series.
>>> dfm.loc['viper']
max_speed 4
shield 5
Name: viper, dtype: int64
List of labels. Note using ``[[]]`` returns a DataFrame.
>>> dfm.loc[['viper', 'sidewinder']]
max_speed shield
viper 4 5
sidewinder 7 8
Single label for row and column
>>> df.loc['cobra', 'shield']
2
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
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
Raises
------
KeyError:
when any items are not found
Type: property
String form: <property object at 0x0000021B3606B688>
Docstring:
Purely integer-location based indexing for selection by position.
``.iloc[]`` is primarily integer position based (from ``0`` to
``length-1`` of the axis), but may also be used with a boolean
array.
Allowed inputs are:
- An integer, e.g. ``5``.
- A list or array of integers, e.g. ``[4, 3, 0]``.
- A slice object with ints, e.g. ``1:7``.
- A boolean array.
- A ``callable`` function with one argument (the calling Series, DataFrame
or Panel) and that returns valid output for indexing (one of the above)
``.iloc`` will raise ``IndexError`` if a requested indexer is
out-of-bounds, except *slice* indexers which allow out-of-bounds
indexing (this conforms with python/numpy *slice* semantics).
See more at :ref:`Selection by Position <indexing.integer>`