如何根据Pandas中某列的值从DataFrame中选择行?
在SQL中,我会使用:
SELECT *
FROM table
WHERE column_name = some_value
如何根据Pandas中某列的值从DataFrame中选择行?
在SQL中,我会使用:
SELECT *
FROM table
WHERE column_name = some_value
当前回答
在Pandas的更新版本中,受文档启发(查看数据):
df[df["colume_name"] == some_value] #Scalar, True/False..
df[df["colume_name"] == "some_value"] #String
通过将子句放在括号()中,并用&和|(和/或)组合来组合多个条件。这样地:
df[(df["colume_name"] == "some_value1") & (pd[pd["colume_name"] == "some_value2"])]
其他过滤器
pandas.notna(df["colume_name"]) == True # Not NaN
df['colume_name'].str.contains("text") # Search for "text"
df['colume_name'].str.lower().str.contains("text") # Search for "text", after converting to lowercase
其他回答
您也可以使用.apply:
df.apply(lambda row: row[df['B'].isin(['one','three'])])
它实际上按行工作(即,将函数应用于每一行)。
输出为
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
结果与@unsubu提到的使用相同
df[[df['B'].isin(['one','three'])]]
使用numpy.where可以获得更快的结果。
例如,使用unubtu的设置-
In [76]: df.iloc[np.where(df.A.values=='foo')]
Out[76]:
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
时间比较:
In [68]: %timeit df.iloc[np.where(df.A.values=='foo')] # fastest
1000 loops, best of 3: 380 µs per loop
In [69]: %timeit df.loc[df['A'] == 'foo']
1000 loops, best of 3: 745 µs per loop
In [71]: %timeit df.loc[df['A'].isin(['foo'])]
1000 loops, best of 3: 562 µs per loop
In [72]: %timeit df[df.A=='foo']
1000 loops, best of 3: 796 µs per loop
In [74]: %timeit df.query('(A=="foo")') # slowest
1000 loops, best of 3: 1.71 ms per loop
对于Pandas中给定值的多个列中仅选择特定列:
select col_name1, col_name2 from table where column_name = some_value.
选项位置:
df.loc[df['column_name'] == some_value, [col_name1, col_name2]]
或查询:
df.query('column_name == some_value')[[col_name1, col_name2]]
您可以在函数中使用loc(方括号):
# Series
s = pd.Series([1, 2, 3, 4])
s.loc[lambda x: x > 1]
# s[lambda x: x > 1]
输出:
1 2
2 3
3 4
dtype: int64
or
# DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [10, 20, 30]})
df.loc[lambda x: x['A'] > 1]
# df[lambda x: x['A'] > 1]
输出:
A B
1 2 20
2 3 30
在Pandas的更新版本中,受文档启发(查看数据):
df[df["colume_name"] == some_value] #Scalar, True/False..
df[df["colume_name"] == "some_value"] #String
通过将子句放在括号()中,并用&和|(和/或)组合来组合多个条件。这样地:
df[(df["colume_name"] == "some_value1") & (pd[pd["colume_name"] == "some_value2"])]
其他过滤器
pandas.notna(df["colume_name"]) == True # Not NaN
df['colume_name'].str.contains("text") # Search for "text"
df['colume_name'].str.lower().str.contains("text") # Search for "text", after converting to lowercase