[英]Filter a pandas dataframe using values from a dict
I need to filter a data frame with a dict, constructed with the key being the column name and the value being the value that I want to filter:我需要使用 dict 过滤数据框,其中键是列名,值是我要过滤的值:
filter_v = {'A':1, 'B':0, 'C':'This is right'}
# this would be the normal approach
df[(df['A'] == 1) & (df['B'] ==0)& (df['C'] == 'This is right')]
But I want to do something on the lines但我想做点事情就行了
for column, value in filter_v.items():
df[df[column] == value]
but this will filter the data frame several times, one value at a time, and not apply all filters at the same time.但这将多次过滤数据框,一次一个值,而不是同时应用所有过滤器。 Is there a way to do it programmatically?
有没有办法以编程方式做到这一点?
EDIT: an example:编辑:一个例子:
df1 = pd.DataFrame({'A':[1,0,1,1, np.nan], 'B':[1,1,1,0,1], 'C':['right','right','wrong','right', 'right'],'D':[1,2,2,3,4]})
filter_v = {'A':1, 'B':0, 'C':'right'}
df1.loc[df1[filter_v.keys()].isin(filter_v.values()).all(axis=1), :]
gives给
A B C D
0 1 1 right 1
1 0 1 right 2
3 1 0 right 3
but the expected result was但预期的结果是
A B C D
3 1 0 right 3
only the last one should be selected.只应选择最后一个。
IIUC, you should be able to do something like this: IIUC,你应该能够做这样的事情:
>>> df1.loc[(df1[list(filter_v)] == pd.Series(filter_v)).all(axis=1)]
A B C D
3 1 0 right 3
This works by making a Series to compare against:这通过制作一个系列来比较:
>>> pd.Series(filter_v)
A 1
B 0
C right
dtype: object
Selecting the corresponding part of df1
:选择
df1
的对应部分:
>>> df1[list(filter_v)]
A C B
0 1 right 1
1 0 right 1
2 1 wrong 1
3 1 right 0
4 NaN right 1
Finding where they match:找到他们匹配的地方:
>>> df1[list(filter_v)] == pd.Series(filter_v)
A B C
0 True False True
1 False False True
2 True False False
3 True True True
4 False False True
Finding where they all match:找到它们都匹配的地方:
>>> (df1[list(filter_v)] == pd.Series(filter_v)).all(axis=1)
0 False
1 False
2 False
3 True
4 False
dtype: bool
And finally using this to index into df1:最后使用它来索引 df1:
>>> df1.loc[(df1[list(filter_v)] == pd.Series(filter_v)).all(axis=1)]
A B C D
3 1 0 right 3
Here is a way to do it:这是一种方法:
df.loc[df[filter_v.keys()].isin(filter_v.values()).all(axis=1), :]
UPDATE:更新:
With values being the same across columns you could then do something like this:随着列之间的值相同,您可以执行以下操作:
# Create your filtering function:
def filter_dict(df, dic):
return df[df[dic.keys()].apply(
lambda x: x.equals(pd.Series(dic.values(), index=x.index, name=x.name)), asix=1)]
# Use it on your DataFrame:
filter_dict(df1, filter_v)
Which yields:其中产生:
A B C D
3 1 0 right 3
If it something that you do frequently you could go as far as to patch DataFrame for an easy access to this filter:如果这是您经常做的事情,您可以尽可能修补 DataFrame 以便轻松访问此过滤器:
pd.DataFrame.filter_dict_ = filter_dict
And then use this filter like this:然后像这样使用这个过滤器:
df1.filter_dict_(filter_v)
Which would yield the same result.这将产生相同的结果。
BUT , it is not the right way to do it, clearly.但是,这显然不是正确的方法。 I would use DSM's approach.
我会使用 DSM 的方法。
For python2, that's OK in @primer's answer.对于python2,@primer 的回答没问题。 But, you should be careful in Python3 because of dict_keys .
但是,由于dict_keys ,您应该在 Python3 中小心。 For instance,
例如,
>> df.loc[df[filter_v.keys()].isin(filter_v.values()).all(axis=1), :]
>> TypeError: unhashable type: 'dict_keys'
The correct way to Python3: Python3的正确方法:
df.loc[df[list(filter_v.keys())].isin(list(filter_v.values())).all(axis=1), :]
Abstraction of the above for case of passing array of filter values rather than single value (analogous to pandas.core.series.Series.isin()).对于传递过滤器值数组而不是单个值的情况的上述抽象(类似于 pandas.core.series.Series.isin())。 Using the same example:
使用相同的示例:
df1 = pd.DataFrame({'A':[1,0,1,1, np.nan], 'B':[1,1,1,0,1], 'C':['right','right','wrong','right', 'right'],'D':[1,2,2,3,4]})
filter_v = {'A':[1], 'B':[1,0], 'C':['right']}
##Start with array of all True
ind = [True] * len(df1)
##Loop through filters, updating index
for col, vals in filter_v.items():
ind = ind & (df1[col].isin(vals))
##Return filtered dataframe
df1[ind]
##Returns
A B C D
0 1.0 1 right 1
3 1.0 0 right 3
Here's another way:这是另一种方式:
filterSeries = pd.Series(np.ones(df.shape[0],dtype=bool))
for column, value in filter_v.items():
filterSeries = ((df[column] == value) & filterSeries)
This gives:这给出:
>>> df[filterSeries]
A B C D
3 1 0 right 3
要跟进 DSM 的回答,您还可以使用any()
将您的查询转换为 OR 操作(而不是 AND):
df1.loc[(df1[list(filter_v)] == pd.Series(filter_v)).any(axis=1)]
You can also create a query您还可以创建查询
query_string = ' and '.join(
[f'({key} == "{val}")' if type(val) == str else f'({key} == {val})' for key, val in filter_v.items()]
)
df1.query(query_string)
Combining previous answers, here's a function you can feed to df1.loc
.结合以前的答案,这是一个 function 您可以提供给
df1.loc
。 Allows for AND/OR (using how='all'
/ 'any'
), plus it allows comparisons other than ==
using the op
keyword, if desired.允许 AND/OR(使用
how='all'
/ 'any'
),如果需要,它还允许使用op
关键字进行==
以外的比较。
import operator
def quick_mask(df, filters, how='all', op=operator.eq) -> pd.Series:
if how == 'all':
comb = pd.Series.all
elif how == 'any':
comb = pd.Series.any
return comb(op(df[[*filters]], pd.Series(filters)), axis=1)
# Usage
df1.loc[quick_mask(df1, filter_v)]
I had an issue due to my dictionary having multiple values for the same key.由于我的字典对同一个键有多个值,我遇到了问题。
I was able to change DSM's query to:我能够将 DSM 的查询更改为:
df1.loc[df1[list(filter_v)].isin(filter_v).all(axis=1), :]
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