选择列值为 column_name
的行时等于标量,some_value
, 我们使用 ==:
df.loc[df['column_name'] == some_value]
或使用 .query()
df.query('column_name == some_value')
具体例子:
import pandas as pd
import numpy as np
df = pd.DataFrame({'Col1': 'what are men to rocks and mountains'.split(),
'Col2': 'the curves of your lips rewrite history.'.split(),
'Col3': np.arange(7),
'Col4': np.arange(7) * 8})
print(df)
Col1 Col2 Col3 Col4
0 what the 0 0
1 are curves 1 8
2 men of 2 16
3 to your 3 24
4 rocks lips 4 32
5 and rewrite 5 40
6 mountains history 6 48
查询可以是
rocks_row = df.loc[df['Col1'] == "rocks"]
哪些输出
print(rocks_row)
Col1 Col2 Col3 Col4
4 rocks lips 4 32
我想通过一个值列表来查询一个数据框,它输出一个“正确查询”列表。
要执行的查询将在列表中,例如
list_match = ['men', 'curves', 'history']
这将输出满足此条件的所有行,即
matches = pd.concat([df1, df2, df3])
在哪里
df1 = df.loc[df['Col1'] == "men"]
df2 = df.loc[df['Col1'] == "curves"]
df3 = df.loc[df['Col1'] == "history"]
我的想法是创建一个接收
的函数output = []
def find_queries(dataframe, column, value, output):
for scalar in value:
query = dataframe.loc[dataframe[column] == scalar]]
output.append(query) # append all query results to a list
return pd.concat(output) # return concatenated list of dataframes
但是,这看起来异常缓慢,并且实际上并没有利用 pandas 数据结构。通过 Pandas 数据框传递查询列表的“标准”方法是什么?
编辑:这如何转化为 pandas 中“更复杂”的查询?例如where
使用 HDF5 文档?
df.to_hdf('test.h5','df',mode='w',format='table',data_columns=['A','B'])
pd.read_hdf('test.h5','df')
pd.read_hdf('test.h5','df',where='A=["foo","bar"] & B=1')
最佳答案
如果我正确理解了您的问题,您可以使用 bool 索引作为 @uhjish has already shown in his answer 来完成或使用 query()方法:
In [30]: search_list = ['rocks','mountains']
In [31]: df
Out[31]:
Col1 Col2 Col3 Col4
0 what the 0 0
1 are curves 1 8
2 men of 2 16
3 to your 3 24
4 rocks lips 4 32
5 and rewrite 5 40
6 mountains history. 6 48
.query()
方法:
In [32]: df.query('Col1 in @search_list and Col4 > 40')
Out[32]:
Col1 Col2 Col3 Col4
6 mountains history. 6 48
In [33]: df.query('Col1 in @search_list')
Out[33]:
Col1 Col2 Col3 Col4
4 rocks lips 4 32
6 mountains history. 6 48
使用 bool 索引:
In [34]: df.ix[df.Col1.isin(search_list) & (df.Col4 > 40)]
Out[34]:
Col1 Col2 Col3 Col4
6 mountains history. 6 48
In [35]: df.ix[df.Col1.isin(search_list)]
Out[35]:
Col1 Col2 Col3 Col4
4 rocks lips 4 32
6 mountains history. 6 48
更新:使用函数:
def find_queries(df, qry, debug=0, **parms):
if debug:
print('[DEBUG]: Query:\t' + qry.format(**parms))
return df.query(qry.format(**parms))
In [31]: find_queries(df, 'Col1 in {Col1} and Col4 > {Col4}', Col1='@search_list', Col4=40)
...:
Out[31]:
Col1 Col2 Col3 Col4
6 mountains history. 6 48
In [32]: find_queries(df, 'Col1 in {Col1} and Col4 > {Col4}', Col1='@search_list', Col4=10)
Out[32]:
Col1 Col2 Col3 Col4
4 rocks lips 4 32
6 mountains history. 6 48
包括调试信息(打印查询):
In [40]: find_queries(df, 'Col1 in {Col1} and Col4 > {Col4}', Col1='@search_list', Col4=10, debug=1)
[DEBUG]: Query: Col1 in @search_list and Col4 > 10
Out[40]:
Col1 Col2 Col3 Col4
4 rocks lips 4 32
6 mountains history. 6 48
关于python - 如何将查询列表传递给 pandas 数据框,并输出结果列表?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/39988589/