[英]How to Export 2D Table in a csv file using PyCharm
I have a xml file: 'product.xml', here is an example of the sample file:我有一个 xml 文件:'product.xml',这是示例文件的示例:
<?xml version="1.0"?>
<Rowset>
<ROW>
<Product_ID>32</Product_ID>
<Company_ID>2</Company_ID>
<User_ID>90</User_ID>
<Product_Type>1</Product_Type>
<Application_ID>BBC#:1010</Application_ID>
</ROW>
<ROW>
<Product_ID>22</Product_ID>
<Company_ID>4</Company_ID>
<User_ID>190</User_ID>
<Product_Type>2</Product_Type>
<Application_ID>NBA#:1111</Application_ID>
</ROW>
<ROW>
<Product_ID>63</Product_ID>
<Company_ID>4</Company_ID>
<User_ID>99</User_ID>
<Product_Type>1</Product_Type>
<Application_ID>BBC#:1212</Application_ID>
</ROW>
<ROW>
<Product_ID>22</Product_ID>
<Company_ID>2</Company_ID>
<User_ID>65</User_ID>
<Product_Type>2</Product_Type>
<Application_ID>NBA#:2210</Application_ID>
</ROW>
This is my code:这是我的代码:
import xml.etree.cElementTree as ET
tree = ET.parse('product.xml')
root = tree.getroot()
for rows in root:
for attr in rows:
if (attr.tag=='User_ID'):
print('User_ID: ' + attr.text)
if (attr.tag=='Application_ID'):
print('Application_ID: ' + attr.text)
Output for this is:输出为:
User_ID: 90
Application_ID: BBC#:1010
User_ID: 190
Application_ID: NBA#:1111
User_ID: 99
Application_ID: BBC#:1212
I am wondering how can I generate a 2D table with Pandas Data frame, using 'Application_ID' and 'User_ID' as ROW Headers and their data as columns, like:我想知道如何使用 Pandas 数据框生成二维表,使用“Application_ID”和“User_ID”作为 ROW 标题,并将它们的数据用作列,例如:
Application_ID User_ID
BBC#:1010 90
NBA#:1111 190
BBC#:1212 99
And export these 2D Table results into a csv file to save them, Thank you.并将这些二维表结果导出到 csv 文件中进行保存,谢谢。
Try:尝试:
def parse_row(row):
ret = {'User_ID':np.nan, 'Application_ID':np.nan}
for attr in row:
if attr.tag in ret: ret[attr.tag] = attr.text
return ret
out = pd.DataFrame([parse_row(r) for r in root])
Output:输出:
User_ID Application_ID
0 90 BBC#:1010
1 190 NBA#:1111
2 99 BBC#:1212
3 65 NBA#:2210
Pandas is able to read most file types into DataFrames. Pandas 能够将大多数文件类型读入 DataFrames。
### This line would get you all of your columns
df = pd.read_xml('product.xml')
### Drop (remove) unwanted columns
df.drop(['Product_ID', 'Company_ID', 'Product_Type'], axis=1, inplace=True)
### Export to csv
df.to_csv('outputfile.csv')
Something like the below像下面这样的东西
import xml.etree.ElementTree as ET
import pandas as pd
xml = '''<?xml version="1.0"?>
<Rowset>
<ROW>
<Product_ID>32</Product_ID>
<Company_ID>2</Company_ID>
<User_ID>90</User_ID>
<Product_Type>1</Product_Type>
<Application_ID>BBC#:1010</Application_ID>
</ROW>
<ROW>
<Product_ID>22</Product_ID>
<Company_ID>4</Company_ID>
<User_ID>190</User_ID>
<Product_Type>2</Product_Type>
<Application_ID>NBA#:1111</Application_ID>
</ROW>
<ROW>
<Product_ID>63</Product_ID>
<Company_ID>4</Company_ID>
<User_ID>99</User_ID>
<Product_Type>1</Product_Type>
<Application_ID>BBC#:1212</Application_ID>
</ROW>
<ROW>
<Product_ID>22</Product_ID>
<Company_ID>2</Company_ID>
<User_ID>65</User_ID>
<Product_Type>2</Product_Type>
<Application_ID>NBA#:2210</Application_ID>
</ROW>
</Rowset>
'''
FIELDS = ['Application_ID','User_ID']
data = []
root = ET.fromstring(xml)
for row in root.findall('.//ROW'):
data.append([row.find(f).text for f in FIELDS])
df = pd.DataFrame(data,columns=FIELDS)
print(df)
output输出
Application_ID User_ID
0 BBC#:1010 90
1 NBA#:1111 190
2 BBC#:1212 99
3 NBA#:2210 65
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