[英]How to replace string values in pandas dataframe to integers?
I have a Pandas DataFrame that contains several string values. 我有一个包含几个字符串值的Pandas DataFrame。 I want to replace them with integer values in order to calculate similarities. 我想用整数值替换它们以计算相似性。 For example: 例如:
stores[['CNPJ_Store_Code','region','total_facings']].head()
Out[24]:
CNPJ_Store_Code region total_facings
1 93209765046613 Geo RS/SC 1.471690
16 93209765046290 Geo RS/SC 1.385636
19 93209765044084 Geo PR/SPI 0.217054
21 93209765044831 Geo RS/SC 0.804633
23 93209765045218 Geo PR/SPI 0.708165
and I want to replace region == 'Geo RS/SC' ==> 1, region == 'Geo PR/SPI'==> 2 etc. 我想替换region =='Geo RS / SC'==> 1,region =='Geo PR / SPI'==> 2等
Clarification: I want to do the replacement automatically, without creating a dictionary first, since I don't know in advance what my regions will be. 澄清:我想先自动更换,而不先创建字典,因为我事先并不知道我的地区会是什么。 Any ideas? 有任何想法吗? I am trying to use DictVectorizer, with no success. 我试图使用DictVectorizer,但没有成功。
I'm sure there's a way to do it in intelligent way, but I just can't find it. 我确信有一种方法可以通过智能方式实现,但我找不到它。
Anyone familiar with a solution? 有人熟悉解决方案吗?
You can use the .apply()
function and a dictionary to map all known string values to their corresponding integer values: 您可以使用.apply()
函数和字典将所有已知字符串值映射到其对应的整数值:
region_dictionary = {'Geo RS/SC': 1, 'Geo PR/SPI' : 2, .... }
stores['region'] = stores['region'].apply(lambda x: region_dictionary[x])
It looks to me like you really would like panda categories 它看起来像你真的想要熊猫类别
http://pandas-docs.github.io/pandas-docs-travis/categorical.html http://pandas-docs.github.io/pandas-docs-travis/categorical.html
I think you just need to change the dtype of your text column to "category" and you are done. 我认为你只需要将文本列的dtype更改为“category”即可。
stores['region'] = stores["region"].astype('category')
You can do: 你可以做:
df = pd.read_csv(filename, index_col = 0) # Assuming it's a csv file.
def region_to_numeric(a):
if a == 'Geo RS/SC':
return 1
if a == 'Geo PR/SPI':
return 2
df['region_num'] = df['region'].apply(region_to_numeric)
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