官方函數(shù)

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.

# 可以使用label值，但是也可以使用布爾值

Allowed inputs are: # 可以接受單個的label，多個label的列表，多個label的切片

A single label, e.g. 5 or ‘a(chǎn)", (note that 5 is interpreted as a label of the index, and never as an integer position along the index). #這里的5不是數(shù)值指定的位置，而是label值

A list or array of labels, e.g. [‘a(chǎn)", ‘b", ‘c"].

slice object with labels, e.g. ‘a(chǎn)":"f".

Warning: #如果使用多個label的切片，那么切片的起始位置都是包含的

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].

實例詳解

一、選擇數(shù)值

1、生成df

df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],

... index=["cobra", "viper", "sidewinder"],

... columns=["max_speed", "shield"])

df

Out[15]:

max_speed shield

cobra 1 2

viper 4 5

sidewinder 7 8

2、Single label. 單個 row_label 返回的Series

df.loc["viper"]

Out[17]:

max_speed 4

shield 5

Name: viper, dtype: int64

2、List of labels. 列表 row_label 返回的DataFrame

df.loc[["cobra","viper"]]

Out[20]:

max_speed shield

cobra 1 2

viper 4 5

3、Single label for row and column 同時選定行和列

df.loc["cobra", "shield"]

Out[24]: 2

4、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. 同時選定多個行和單個列，注意的是通過列表選定多個row label 時，首位均是選定的。

df.loc["cobra":"viper", "max_speed"]

Out[25]:

cobra 1

viper 4

Name: max_speed, dtype: int64

5、Boolean list with the same length as the row axis 布爾列表選擇row label

布爾值列表是根據(jù)某個位置的True or False 來選定，如果某個位置的布爾值是True，則選定該row

df

Out[30]:

max_speed shield

cobra 1 2

viper 4 5

sidewinder 7 8

df.loc[[True]]

Out[31]:

max_speed shield

cobra 1 2

df.loc[[True,False]]

Out[32]:

max_speed shield

cobra 1 2

df.loc[[True,False,True]]

Out[33]:

max_speed shield

cobra 1 2

sidewinder 7 8

6、Conditional that returns a boolean Series 條件布爾值

df.loc[df["shield"] > 6]

Out[34]:

max_speed shield

sidewinder 7 8

7、Conditional that returns a boolean Series with column labels specified 條件布爾值和具體某列的數(shù)據(jù)

df.loc[df["shield"] > 6, ["max_speed"]]

Out[35]:

max_speed

sidewinder 7

8、Callable that returns a boolean Series 通過函數(shù)得到布爾結(jié)果選定數(shù)據(jù)

df

Out[37]:

max_speed shield

cobra 1 2

viper 4 5

sidewinder 7 8

df.loc[lambda df: df["shield"] == 8]

Out[38]:

max_speed shield

sidewinder 7 8

二、賦值

1、Set value for all items matching the list of labels 根據(jù)某列表選定的row 及某列 column 賦值

df.loc[["viper", "sidewinder"], ["shield"]] = 50

df

Out[43]:

max_speed shield

cobra 1 2

viper 4 50

sidewinder 7 50

2、Set value for an entire row 將某行row的數(shù)據(jù)全部賦值

df.loc["cobra"] =10

df

Out[48]:

max_speed shield

cobra 10 10

viper 4 50

sidewinder 7 50

3、Set value for an entire column 將某列的數(shù)據(jù)完全賦值

df.loc[:, "max_speed"] = 30

df

Out[50]:

max_speed shield

cobra 30 10

viper 30 50

sidewinder 30 50

4、Set value for rows matching callable condition 條件選定rows賦值

df.loc[df["shield"] > 35] = 0

df

Out[52]:

max_speed shield

cobra 30 10

viper 0 0

sidewinder 0 0

三、行索引是數(shù)值

df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],

... index=[7, 8, 9], columns=["max_speed", "shield"])

df

Out[54]:

max_speed shield

7 1 2

8 4 5

9 7 8

通過 行 rows的切片的方式取多個：

df.loc[7:9]

Out[55]:

max_speed shield

7 1 2

8 4 5

9 7 8

四、多維索引

1、生成多維索引

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

Out[57]:

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

2、Single label. 傳入的就是最外層的row label，返回DataFrame

df.loc["cobra"]

Out[58]:

max_speed shield

mark i 12 2

mark ii 0 4

3、Single index tuple.傳入的是索引元組，返回Series

df.loc[("cobra", "mark ii")]

Out[59]:

max_speed 0

shield 4

Name: (cobra, mark ii), dtype: int64

4、Single label for row and column.如果傳入的是row和column，和傳入tuple是類似的，返回Series

df.loc["cobra", "mark i"]

Out[60]:

max_speed 12

shield 2

Name: (cobra, mark i), dtype: int64

5、Single tuple. Note using [[ ]] returns a DataFrame.傳入一個數(shù)組，返回一個DataFrame

df.loc[[("cobra", "mark ii")]]

Out[61]:

max_speed shield

cobra mark ii 0 4

6、Single tuple for the index with a single label for the column 獲取某個colum的某row的數(shù)據(jù)，需要左邊傳入多維索引的tuple，然后再傳入column

df.loc[("cobra", "mark i"), "shield"]

Out[62]: 2

7、傳入多維索引和單個索引的切片：

df.loc[("cobra", "mark i"):"viper"]

Out[63]:

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

df.loc[("cobra", "mark i"):"sidewinder"]

Out[64]:

max_speed shield

cobra mark i 12 2

mark ii 0 4

sidewinder mark i 10 20

mark ii 1 4

df.loc[("cobra", "mark i"):("sidewinder","mark i")]

Out[65]:

max_speed shield

cobra mark i 12 2

mark ii 0 4

sidewinder mark i 10 20

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