How to extract a specific data using python requests module

Question

I'm trying to extract the market close for the past 5 days of a certain stock in order to input their values into a function. So far I've got:

import requests
stock_url = "https://query1.finance.yahoo.com/v7/finance/download/BYND"
stock = 'BYND'
params = {
    'range':'6d',
    'interval': '1d',
    'events': 'history'
}
data = requests.get(stock_url.format(stock), params)
print(data.text)

And what I get is:

Date,Open,High,Low,Close,Adj Close,Volume
2021-05-26,124.000000,127.879997,121.269997,126.739998,126.739998,5569000
2021-05-27,134.389999,146.800003,133.539993,142.610001,142.610001,21735600
2021-05-28,149.979996,154.399994,143.000000,145.419998,145.419998,16985000
2021-06-01,142.899994,143.360001,132.130005,135.190002,135.190002,8631500
2021-06-02,134.593994,151.360001,134.593994,149.740005,149.740005,9488200
2021-06-03,145.000000,149.639999,140.110001,143.059998,143.059998,4840900

I'd like to only get the Close column or be able to manipulate (ex: print) specific cells but have no idea how to do so.

Answer 1

Converting this data to Pandas Dataframe will work here:

import pandas as pd

data = """
Date,Open,High,Low,Close,Adj Close,Volume
2021-05-26,124.000000,127.879997,121.269997,126.739998,126.739998,5569000
2021-05-27,134.389999,146.800003,133.539993,142.610001,142.610001,21735600
2021-05-28,149.979996,154.399994,143.000000,145.419998,145.419998,16985000
2021-06-01,142.899994,143.360001,132.130005,135.190002,135.190002,8631500
2021-06-02,134.593994,151.360001,134.593994,149.740005,149.740005,9488200
2021-06-03,145.000000,149.639999,140.110001,143.059998,143.059998,4840900
"""
print("*"*50)
print("Data: ")
print(data)
df = pd.DataFrame([x.split(',') for x in data.split('\n')])
print("*"*50)
print("Pandas Dataframe: ")
print(df)
print("*"*50)
print("Particular Column: ")
print(df[4])

Output:

**************************************************
Data: 

Date,Open,High,Low,Close,Adj Close,Volume
2021-05-26,124.000000,127.879997,121.269997,126.739998,126.739998,5569000
2021-05-27,134.389999,146.800003,133.539993,142.610001,142.610001,21735600
2021-05-28,149.979996,154.399994,143.000000,145.419998,145.419998,16985000
2021-06-01,142.899994,143.360001,132.130005,135.190002,135.190002,8631500
2021-06-02,134.593994,151.360001,134.593994,149.740005,149.740005,9488200
2021-06-03,145.000000,149.639999,140.110001,143.059998,143.059998,4840900

**************************************************
Pandas Dataframe: 
0           1           2  ...           4           5         6
0                    None        None  ...        None        None      None
1        Date        Open        High  ...       Close   Adj Close    Volume
2  2021-05-26  124.000000  127.879997  ...  126.739998  126.739998   5569000
3  2021-05-27  134.389999  146.800003  ...  142.610001  142.610001  21735600
4  2021-05-28  149.979996  154.399994  ...  145.419998  145.419998  16985000
5  2021-06-01  142.899994  143.360001  ...  135.190002  135.190002   8631500
6  2021-06-02  134.593994  151.360001  ...  149.740005  149.740005   9488200
7  2021-06-03  145.000000  149.639999  ...  143.059998  143.059998   4840900
8                    None        None  ...        None        None      None

[9 rows x 7 columns]
**************************************************
Particular Column: 
0          None
1         Close
2    126.739998
3    142.610001
4    145.419998
5    135.190002
6    149.740005
7    143.059998
8          None
Name: 4, dtype: object
> 

Explore more on the pandas framework for various operations that you can perform on dataframe and extract your required data out of it in the way you wanted.