Question

我有一个数据框df，我想导出到json输出。我还需要对两列进行过滤，比如列a和列b。默认情况下，我需要导出整个数据框，但我还想将值作为可选变量传递到列a和列b，以仅导出某些数据。例如，当col a =“yes”和col b =“red”时，将其导出。我试过这个：df.to_json但我想知道如何过滤。我怎样才能实现这一目标？我是熊猫和蟒蛇的新手，请提供更详细的解释。感谢任何帮助，非常感谢你！

Answer 1

我认为您需要boolean indexing &（and），但还需要检查是否需要所有值。解决方案是添加其他条件并使用|（or）进行链接。

df = pd.DataFrame({'col a':['yes','no','yes', 'yes'],
                   'col b':['red','green','orange','red']})
print (df)
  col a   col b
0   yes     red
1    no   green
2   yes  orange
3   yes     red

def filtering(a='ALL',b='ALL'):

    m1 = df['col a'] == a
    m2 = df['col b'] == b
    m3 = a == 'ALL'
    m4 = b == 'ALL'
    return df[(m1|m3) & (m2|m4)].to_json() 

print (filtering())
{"col a":{"0":"yes","1":"no","2":"yes","3":"yes"},
"col b":{"0":"red","1":"green","2":"orange","3":"red"}}

print (filtering('yes','red'))
{"col a":{"0":"yes","3":"yes"},"col b":{"0":"red","3":"red"}}

编辑：

按值列表进行过滤是类似的解决方案，只有条件发生了变化 - 需要isin和in（将ALL更改为某些通用值永远不会出现在数据中）：

def filtering(a=['ALL'],b=['ALL']):

    m1 = df['col a'].isin(a)
    m2 = df['col b'].isin(b)
    m3 = 'ALL' in a
    m4 = 'ALL' in b
    return df[(m1|m3) & (m2|m4)].to_json() 
print (filtering())
{"col a":{"0":"yes","1":"no","2":"yes","3":"yes"},
"col b":{"0":"red","1":"green","2":"orange","3":"red"}}

print (filtering(['yes'],['red', 'orange']))
{"col a":{"0":"yes","2":"yes","3":"yes"},
"col b":{"0":"red","2":"orange","3":"red"}}

使用两个过滤器将数据框导出到json

1 个答案: