[FIXED] How to convert values from two different lists into a single dictionary?

Issue

I am scraping financial summary from https://www.investing.com/equities/nvidia-corp-financial-summary. Code:

To get the ratio descriptions:

for element in soup.find_all('span', attrs={'class': 'float_lang_base_1'}):
    print(element)

The code will result in:

<span class="float_lang_base_1">Gross margin</span>
<span class="float_lang_base_1">Operating margin</span>
<span class="float_lang_base_1">Net Profit margin</span>
<span class="float_lang_base_1">Return on Investment</span>
<span class="float_lang_base_1">Quick Ratio</span>
<span class="float_lang_base_1">Current Ratio</span>
<span class="float_lang_base_1">LT Debt to Equity</span>
<span class="float_lang_base_1">Total Debt to Equity</span>
<span class="float_lang_base_1">Cash Flow/Share</span>
<span class="float_lang_base_1">Revenue/Share</span>
<span class="float_lang_base_1">Operating Cash Flow</span>

To get the values for each of ratio above:

for element in soup.find_all('span', attrs={'class': 'float_lang_base_2 text_align_lang_base_2 dirLtr bold'}):
    a = element.get_text()

results in:

 60.45%
 31.47%
 26.03%
 22.86%
 2.95
 3.62
 -
 49.02%
 -
 -
 16.77%

Now, I need to match the two, so that it will be a key value pair that can be transformed into a dataframe.

Gross margin : 60.45%
Operating margin: 31.47%
Net Profit margin: 26.03% 
...

Solution

You can find main div tag which has both the values and iterate over that to identify other properties using class and append to dict1

dict1={}
for element in soup.find_all('div', attrs={'class': 'infoLine'}):
    name=element.find("span",class_="float_lang_base_1").get_text()
    value=element.find("span",class_="float_lang_base_2").get_text()
    dict1[name]=value

Here you can use pandas to create df and transform dict1 to table form data

import pandas as pd
df=pd.DataFrame(dict1.items(),columns=['A','B'])
df

Output:

       A                  B
0   Gross margin        60.45%
1   Operating margin    31.47%
.....

Answered By - Bhavya Parikh