python pandas.DataFrame.loc函数使用详解
官方函数
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，但是也可以使用布尔值
- Allowed inputs are: # 可以接受单个的label，多个label的列表，多个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). #这里的5不是数值指定的位置，而是label值
- A list or array of labels, e.g. [‘a", ‘b", ‘c"].
slice object with labels, e.g. ‘a":"f".
Warning: #如果使用多个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].
实例详解
一、选择数值
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. 单个 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 同时选定行和列
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. 同时选定多个行和单个列，注意的是通过列表选定多个row label 时，首位均是选定的。
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 布尔列表选择row label
布尔值列表是根据某个位置的True or False 来选定，如果某个位置的布尔值是True，则选定该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 条件布尔值
df.loc[df["shield"] > 6]
Out[34]:
max_speed shield
sidewinder 7 8
7、Conditional that returns a boolean Series with column labels specified 条件布尔值和具体某列的数据
df.loc[df["shield"] > 6, ["max_speed"]]
Out[35]:
max_speed
sidewinder 7
8、Callable that returns a boolean Series 通过函数得到布尔结果选定数据
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、赋值
1、Set value for all items matching the list of labels 根据某列表选定的row 及某列 column 赋值
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 将某行row的数据全部赋值
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 将某列的数据完全赋值
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 条件选定rows赋值
df.loc[df["shield"] > 35] = 0
df
Out[52]:
max_speed shield
cobra 30 10
viper 0 0
sidewinder 0 0
三、行索引是数值
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
通过 行 rows的切片的方式取多个：
df.loc[7:9] Out[55]: max_speed shield 7 1 2 8 4 5 9 7 8
四、多维索引
1、生成多维索引
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. 传入的就是最外层的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.传入的是索引元组，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.如果传入的是row和column，和传入tuple是类似的，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.传入一个数组，返回一个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 获取某个colum的某row的数据，需要左边传入多维索引的tuple，然后再传入column
df.loc[("cobra", "mark i"), "shield"]
Out[62]: 2
7、传入多维索引和单个索引的切片：
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