将值连接到Pandas系列中

Question

我收到API请求的以下回复：

    <movies>
    <movie>
    <rating>5</rating>
    <name>star wars</name>
    </movie>
    <movie>
    <rating>8</rating>
    <name>jurassic park</name>
    </movie>
    </movies>

是否有办法获取这些信息，获得评级和名称值，并将其存储在熊猫系列中

最终结果如下所示：

      Movie Rating  
     5 - star Wars  
 8 - Jurassic park    

您会注意到，我已经获取了在响应中找到的每个值，并将它们添加到“一”列中。例如，我想把5个连接“-”和《星球大战》加在一起

Answer 1

这就是你要找的吗？我已经在代码中一步一步地解释了。有一部分我不知道怎么做，但我研究并弄明白了

import pandas as pd
import numpy as np
df = pd.DataFrame({'Data' : ['<movies>','<movie>','<rating>5</rating>',
                             '<name>star wars</name>', '</movie>', 
                             '<rating>8</rating>', '<name>jurassic park</name>', 
                             '</movie>', '</movies>']})
#Filter for the relevant rows of data based upon the logic of the pattern. I have also 
#done an optional reset of the index.
df = df.loc[df['Data'].str.contains('>.*<', regex=True)].reset_index(drop=True)
#For the rows we just filtered for, get rid of the irrelevant data with some regex 
#string manipulation
df['Data'] = df['Data'].str.findall('>.*<').str[0].replace(['>','<'], '', regex=True)
#Use join with shift and add_suffix CREDIT to @joelostblom:
#https://stackoverflow.com/questions/47450259/merge-row-with-next-row-in-dataframe- 
#pandas
df = df.add_suffix('1').join(df.shift(-1).add_suffix('2'))
#Filter for numeric rows only
df = df.loc[df['Data1'].str.isnumeric() == True]
#Combine Columns with desired format
df['Movie Rating'] = df['Data1'] + ' - ' + df['Data2']
#Filter for only relevant column and print dataframe
df = df[['Movie Rating']]
print(df)