Python：在列表中查找相似字符串的算法
[英]Python: Algorithm to find similar strings in a list
问题描述
I am not able to structure my ideas with this thing.
我无法用这个东西来构建我的想法。 I hope you could help me.我希望你能帮助我。 I have a financial report like this:我有这样的财务报告：
CONSOLIDATED BALANCE SHEETS - USD ($) $ in Millions Sep. 28, 2019 Sep. 29, 2018
0 Current assets: NaN NaN
1 Cash and cash equivalents 48844 25913
2 Marketable securities 51713 40388
3 Accounts receivable, net 22926 23186
4 Inventories 4106 3956
5 Vendor non-trade receivables 22878 25809
6 Other current assets 12352 12087
7 Total current assets 162819 131339
8 Non-current assets: NaN NaN
9 Marketable securities 105341 170799
10 Property, plant and equipment, net 37378 41304
11 Other non-current assets 32978 22283
12 Total non-current assets 175697 234386
13 Total assets 338516 365725
14 Current liabilities: NaN NaN
15 Accounts payable 46236 55888
16 Other current liabilities 37720 33327
17 Deferred revenue 5522 5966
18 Commercial paper 5980 11964
19 Term debt 10260 8784
20 Total current liabilities 105718 115929
21 Non-current liabilities: NaN NaN
22 Term debt 91807 93735
23 Other non-current liabilities 50503 48914
24 Total non-current liabilities 142310 142649
25 Total liabilities 248028 258578
26 Commitments and contingencies
27 Shareholders’ equity: NaN NaN
28 Common stock and additional paid-in capital, $... 45174 40201
29 Retained earnings 45898 70400
30 Accumulated other comprehensive income/(loss) -584 -3454
31 Total shareholders’ equity 90488 107147
32 Total liabilities and shareholders’ equity 338516 365725
Which is a pandas Dataframe read it from excel.这是 pandas Dataframe 从 excel 读取的。 I want to - with some algorithm - get this output:我想 - 使用一些算法 - 得到这个 output：
CONSOLIDATED BALANCE SHEETS - USD ($) $ in Millions Sep. 28, 2019 Sep. 29, 2018
0 Cash and cash equivalents 48844 25913
1 Total current assets 162819 131339
2 Property, plant and equipment, net 37378 41304
3 Total non-current assets 175697 234386
4 Total assets 338516 365725
5 Accounts payable 46236 55888
6 Total current liabilities 105718 115929
Total debt 108047 114483
7 Total non-current liabilities 142310 142649
8 Total liabilities 248028 258578
9 Total shareholders’ equity 90488 107147
Basically the thing is, with a given key values, search in the first column of the DataFrame and return every matching row.基本上问题是，使用给定的键值，在 DataFrame 的第一列中搜索并返回每个匹配的行。 With only one dataframe is easy, because the key values are exactly the same as the values searched.只有一个 dataframe 很容易，因为键值与搜索的值完全相同。 But actually is not like this.但实际上并非如此。 I have thousands of reports in which the values searched are slightly different.我有数千份报告，其中搜索的值略有不同。 eg: key value = Cash , values in the df = Cash and Cash equivalents , key value = net sales , value in the df = net revenue What have I tried so far?例如：键值 = Cash ，df 中的值 = Cash and Cash equivalents ，键值 = net sales ，df 中的值 = net revenue到目前为止我尝试了什么？ I've tried fuzzywuzzy module but sometimes it doesn't work fine.我已经尝试过fuzzywuzzy模块，但有时它不能正常工作。 Any ideas?有任何想法吗？
1 个解决方案
解决方案1
1 2020-07-08 09:00:38
One way to deal with this kind of search is to add a classification name to make it easier to narrow down.处理这种搜索的一种方法是添加分类名称以使其更容易缩小范围。 If you want to know the total of current assets, you can extract 'Class 1' as current assets, 'flg' as total, and It's a good idea to use the You can also use str.contains() to perform fuzzy searches.如果你想知道当前资产的总和，你可以提取'Class 1'作为当前资产，'flg'作为总资产，并且使用str.contains()来进行模糊搜索是个好主意。 Note: Column names have been changed in the creation of the code.注意：列名在创建代码时已更改。
df.replace('NaN', np.NaN, inplace=True)
df.rename(columns={'CONSOLIDATED BALANCE SHEETS - USD ($) $ in Millions':'accounts','Sep. 28, 2019':'this_year','Sep. 29, 2018':'last_year'}, inplace=True)
df['NO'] = np.arange(len(df))
df['Class1'] = df['accounts'][df.isnull().any(axis=1)]
df['Class1'] = df['Class1'].fillna(method='ffill')
df['flg'] = np.where(df['accounts'].str.contains(r'^(Total)'), 'total', 'items')
df
| | accounts | this_year | last_year | NO | Class1 | flg |
|---:|:--------------------------------------------------|------------:|------------:|-----:|:------------------------------|:------|
| 0 | Current assets: | nan | nan | 0 | Current assets: | items |
| 1 | Cash and cash equivalents | 48844 | 25913 | 1 | Current assets: | items |
| 2 | Marketable securities | 51713 | 40388 | 2 | Current assets: | items |
| 3 | Accounts receivable, net | 22926 | 23186 | 3 | Current assets: | items |
| 4 | Inventories | 4106 | 3956 | 4 | Current assets: | items |
| 5 | Vendor non-trade receivables | 22878 | 25809 | 5 | Current assets: | items |
| 6 | Other current assets | 12352 | 12087 | 6 | Current assets: | items |
| 7 | Total current assets | 162819 | 131339 | 7 | Current assets: | total |
| 8 | Non-current assets: | nan | nan | 8 | Non-current assets: | items |
| 9 | Marketable securities | 105341 | 170799 | 9 | Non-current assets: | items |
| 10 | roperty, plant and equipment, net | 37378 | 41304 | 10 | Non-current assets: | items |
| 11 | Other non-current assets | 32978 | 22283 | 11 | Non-current assets: | items |
| 12 | Total non-current assets | 175697 | 234386 | 12 | Non-current assets: | total |
| 13 | Total assets | 338516 | 365725 | 13 | Non-current assets: | total |
| 14 | Current liabilities: | nan | nan | 14 | Current liabilities: | items |
| 15 | Accounts payable | 46236 | 55888 | 15 | Current liabilities: | items |
| 16 | Other current liabilities | 37720 | 33327 | 16 | Current liabilities: | items |
| 17 | Deferred revenue | 5522 | 5966 | 17 | Current liabilities: | items |
| 18 | Commercial paper | 5980 | 11964 | 18 | Current liabilities: | items |
| 19 | Term debt | 10260 | 8784 | 19 | Current liabilities: | items |
| 20 | Total current liabilities | 105718 | 115929 | 20 | Current liabilities: | total |
| 21 | Non-current liabilities: | nan | nan | 21 | Non-current liabilities: | items |
| 22 | Term debt | 91807 | 93735 | 22 | Non-current liabilities: | items |
| 23 | Other non-current liabilities | 50503 | 48914 | 23 | Non-current liabilities: | items |
| 24 | Total non-current liabilities | 142310 | 142649 | 24 | Non-current liabilities: | total |
| 25 | Total liabilities | 248028 | 258578 | 25 | Non-current liabilities: | total |
| 26 | Commitments and contingencies | nan | nan | 26 | Commitments and contingencies | items |
| 27 | Shareholders’ equity: | nan | nan | 27 | Shareholders’ equity: | items |
| 28 | Common stock and additional paid-in capital, $... | 45174 | 40201 | 28 | Shareholders’ equity: | items |
| 29 | Retained earnings | 45898 | 70400 | 29 | Shareholders’ equity: | items |
| 30 | Accumulated other comprehensive income/(loss) | -584 | -3454 | 30 | Shareholders’ equity: | items |
| 31 | Total shareholders’ equity | 90488 | 107147 | 31 | Shareholders’ equity: | total |
| 32 | Total liabilities and shareholders’ equity | 338516 | 365725 | 32 | Shareholders’ equity: | total |
EX: str.contains()例如： str.contains()
df[df['accounts'].str.contains('Accounts payable')]
accounts this_year last_year NO Class1 flg
15 Accounts payable 46236.0 55888.0 15 Current liabilities: items
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