[英]How to filter pandas dataframe rows based on dictionary keys and values?
I have a dataframe and a dictionary in Python as shown below and I need to filter the dataframe based on the dictionary.我在 Python 中有一个数据框和一个字典,如下所示,我需要根据字典过滤数据框。 As you see, the keys and values of the dictionary are two columns of the dataframe.
如您所见,字典的键和值是数据框的两列。 I want to have a subset of dataframe which contains the keys and values of dictionary plus other columns.
我想要一个数据框的子集,其中包含字典的键和值以及其他列。
df : df:
Customer_ID![]() |
Category![]() |
Type![]() |
Delivery![]() |
---|---|---|---|
40275 ![]() |
Book![]() |
Buy![]() |
True![]() |
40275 ![]() |
Software![]() |
Sell![]() |
False![]() |
40275 ![]() |
Video Game![]() |
Sell![]() |
False![]() |
40275 ![]() |
Cell Phone![]() |
Sell![]() |
False![]() |
39900 ![]() |
CD/DVD ![]() |
Sell![]() |
True![]() |
39900 ![]() |
Book![]() |
Buy![]() |
True![]() |
39900 ![]() |
Software![]() |
Sell![]() |
True![]() |
35886 ![]() |
Cell Phone![]() |
Sell![]() |
False![]() |
35886 ![]() |
Video Game![]() |
Buy![]() |
False![]() |
35886 ![]() |
CD/DVD ![]() |
Sell![]() |
False![]() |
35886 ![]() |
Software![]() |
Sell![]() |
False![]() |
40350 ![]() |
Software![]() |
Sell![]() |
True![]() |
28129 ![]() |
Software![]() |
Buy![]() |
False![]() |
And dictionary is:字典是:
d = {
40275: ['Book','Software'],
39900: ['Book'],
35886: ['Software'],
40350: ['Software'],
28129: ['Software']
}
And I need the following dataframe:我需要以下数据框:
Customer_ID![]() |
Category![]() |
Type![]() |
Delivery![]() |
---|---|---|---|
40275 ![]() |
Book![]() |
Buy![]() |
True![]() |
40275 ![]() |
Software![]() |
Sell![]() |
False![]() |
39900 ![]() |
Book![]() |
Buy![]() |
True![]() |
35886 ![]() |
Software![]() |
Sell![]() |
False![]() |
40350 ![]() |
Software![]() |
Sell![]() |
True![]() |
28129 ![]() |
Software![]() |
Buy![]() |
False![]() |
We can set_index
to the Customer_ID
and Category
columns then build a list of tuples from the dictionary d
and reindex
the DataFrame to include only the rows which match the list of tuples, then reset_index
to restore the columns:我们可以
set_index
到Customer_ID
和Category
列,然后从字典d
构建元组列表并reindex
DataFrame 以仅包含与元组列表匹配的行,然后reset_index
恢复列:
new_df = df.set_index(['Customer_ID', 'Category']).reindex(
[(k, v) for k, lst in d.items() for v in lst]
).reset_index()
new_df
: new_df
:
Customer_ID Category Type Delivery
0 40275 Book Buy True
1 40275 Software Sell False
2 39900 Book Buy True
3 35886 Software Sell False
4 40350 Software Sell True
5 28129 Software Buy False
*Note this only works if the MultiIndex is unique (like the shown example). *请注意,这只适用于 MultiIndex 是唯一的(如所示示例)。 It will also add rows if the dictionary does not represent a subset of the DataFrame's MultiIndex (which may or may not be the desired behaviour).
如果字典不代表 DataFrame 的 MultiIndex 的子集(这可能是也可能不是所需的行为),它也会添加行。
Setup:设置:
import pandas as pd
d = {
40275: ['Book', 'Software'],
39900: ['Book'],
35886: ['Software'],
40350: ['Software'],
28129: ['Software']
}
df = pd.DataFrame({
'Customer_ID': [40275, 40275, 40275, 40275, 39900, 39900, 39900, 35886,
35886, 35886, 35886, 40350, 28129],
'Category': ['Book', 'Software', 'Video Game', 'Cell Phone', 'CD/DVD',
'Book', 'Software', 'Cell Phone', 'Video Game', 'CD/DVD',
'Software', 'Software', 'Software'],
'Type': ['Buy', 'Sell', 'Sell', 'Sell', 'Sell', 'Buy', 'Sell', 'Sell',
'Buy', 'Sell', 'Sell', 'Sell', 'Buy'],
'Delivery': [True, False, False, False, True, True, True, False, False,
False, False, True, False]
})
You can use df.merge
with df.append
:您可以将
df.merge
与df.append
df.merge
使用:
In [444]: df1 = pd.DataFrame.from_dict(d, orient='index', columns=['Cat1', 'Cat2']).reset_index()
In [449]: res = df.merge(df1[['index', 'Cat1']], left_on=['Customer_ID', 'Category'], right_on=['index', 'Cat1']).drop(['index', 'Cat1'], 1)
In [462]: res = res.append(df.merge(df1[['index', 'Cat2']], left_on=['Customer_ID', 'Category'], right_on=['index', 'Cat2']).drop(['index', 'Cat2'], 1)).sort_values('Customer_ID', ascending=False)
In [463]: res
Out[463]:
Customer_ID Category Type Delivery
3 40350 Software Sell True
0 40275 Book Buy True
0 40275 Software Sell False
1 39900 Book Buy True
2 35886 Software Sell False
4 28129 Software Buy False
Flatten the dictionary and create a new dataframe, then inner merge df
with the new dataframe展平字典并创建一个新的数据帧,然后将
df
与新的数据帧进行内部合并
df.merge(pd.DataFrame([{'Customer_ID': k, 'Category': i}
for k, v in d.items() for i in v]))
Customer_ID Category Type Delivery
0 40275 Book Buy True
1 40275 Software Sell False
2 39900 Book Buy True
3 35886 Software Sell False
4 40350 Software Sell True
5 28129 Software Buy False
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