1. 选取等于某个特定值的行记录,用 ==
df.loc[df['column_name'] == some_value]
2. 选取等于某些的数值的行记录,用 isin
df.loc[df['column_name'].isin(some_values)]
3. 选取不等于某个特定值的行记录,用 !=
df.loc[df['column_name'] != some_value]
4. 选取不符合某些特定值的行记录,用~isin
df.loc[~df['column_name'].isin(some_values)
5. 多种条件的选取合并,用 &
df.loc[(df['column'] == some_value) & df['other_column'].isin(some_values)]
样例 Example
输入:
import numpy as np
import pandas as pd
df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
'B': 'one one two three two two one three'.split(),
'C': np.arange(8), 'D': np.arange(8) * 2})
A B C D
0 foo one 0 0
1 bar one 1 2
2 foo two 2 4
3 bar three 3 6
4 foo two 4 8
5 bar two 5 10
6 foo one 6 12
7 foo three 7 14
生成数据的Python代码 :
1. 输出某个特定值。
print(df.loc[df['A'] == 'foo'])
A B C D
0 foo one 0 0
2 foo two 2 4
4 foo two 4 8
6 foo one 6 12
7 foo three 7 14
2. 输出某些特定值。
print(df.loc[df['B'].isin(['one','three'])])
A B C D
0 foo one 0 0
1 bar one 1 2
3 bar three 3 6
6 foo one 6 12
7 foo three 7 14
3. 把某一列设为索引,然后输出某个特定值。
df = df.set_index(['B'])
print(df.loc['one'])
A C D
B
one foo 0 0
one bar 1 2
one foo 6 12