python-3.x - 如何从python中的pandas数据框中的列中提取关键字(字符串)
问题描述
我有一个数据框df
,它看起来像这样:
id Type agent_id created_at
0 44525 Stunning 6 bedroom villa in New Delhi 184 2018-03-09
1 44859 Villa for sale in Amritsar 182 2017-02-19
2 45465 House in Faridabad 154 2017-04-17
3 50685 5 Hectre land near New Delhi 113 2017-09-01
4 130728 Duplex in Mumbai 157 2017-02-07
5 130856 Large plot with fantastic views in Mumbai 137 2018-01-16
6 130857 Modern Design Penthouse in Bangalore 199 2017-03-24
我有这个表格数据,我试图通过从列中提取关键字来清理这些数据,从而创建一个带有新列的新数据框。
Apartment = ['apartment', 'penthouse', 'duplex']
House = ['house', 'villa', 'country estate']
Plot = ['plot', 'land']
Location = ['New Delhi','Mumbai','Bangalore','Amritsar']
因此,所需的数据框应如下所示:
id Type Location agent_id created_at
0 44525 House New Delhi 184 2018-03-09
1 44859 House Amritsar 182 2017-02-19
2 45465 House Faridabad 154 2017-04-17
3 50685 Plot New Delhi 113 2017-09-01
4 130728 Apartment Mumbai 157 2017-02-07
5 130856 Plot Mumbai 137 2018-01-16
6 130857 Apartment Bangalore 199 2017-03-24
所以直到现在我已经尝试过这个:
import pandas as pd
df = pd.read_csv('test_data.csv')
#i can extract these keywords one by one by using for loops but how
#can i do this work in pandas with minimum possible line of code.
for index, values in df.type.iteritems():
for i in Apartment:
if i in values:
print(i)
df_new = pd. Dataframe(df['id'])
有人可以告诉我如何解决这个问题吗?
解决方案
首先通过with for regex创建Location
列:str.extract
|
OR
pat = '|'.join(r"\b{}\b".format(x) for x in Location)
df['Location'] = df['Type'].str.extract('('+ pat + ')', expand=False)
然后从另一个 s 创建字典list
,用值交换键,并在循环中通过掩码str.contains
和参数设置值case=False
:
d = {'Apartment' : Apartment,
'House' : House,
'Plot' : Plot}
d1 = {k: oldk for oldk, oldv in d.items() for k in oldv}
for k, v in d1.items():
df.loc[df['Type'].str.contains(k, case=False), 'Type'] = v
print (df)
id Type agent_id created_at Location
0 44525 House 184 2018-03-09 New Delhi
1 44859 House 182 2017-02-19 Amritsar
2 45465 House 154 2017-04-17 NaN
3 50685 Plot 113 2017-09-01 New Delhi
4 130728 Apartment 157 2017-02-07 Mumbai
5 130856 Plot 137 2018-01-16 Mumbai
6 130857 Apartment 199 2017-03-24 Bangalore
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