这篇文章主要介绍了python pandas.DataFrame.loc函数使用详解,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧
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) value<strong style="color:transparent">来源gao@daima#com搞(%代@#码网</strong>s = [[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
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