官方函數

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", (note that 5 is interpreted as a label of the index, and never as an integer position along the index). #這里的5不是數值指定的位置，而是label值

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

slice object with labels, e.g. ‘a":"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據

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

Out[35]:

max_speed

sidewinder 7

8、Callable that returns a boolean Series 通過函數得到布爾結果選定數據

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的數據全部賦值

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 將某列的數據完全賦值

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的數據，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

到此這篇關于python pandas.DataFrame.loc函數使用詳解的文章就介紹到這了,更多相關pandas.DataFrame.loc函數內容請搜索云海天教程以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持云海天教程！

總結

以上是生活随笔為你收集整理的python dataframe loc函数_python pandas.DataFrame.loc函数使用详解的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。