Convert JSON data from Request into Pandas DataFra

Question

I'm trying to scrape some data from a web page and put it into a pandas dataframe. I tried and read many things but I just cannot get what I want. And I want a dataframe with all the data in separate columns and rows. Below is my code.

import requests
import json
import pandas as pd
from pandas.io.json import json_normalize

r = requests.get('http://www.starcapital.de/test/Res_Stockmarketvaluation_FundamentalKZ_Tbl.php')

a = json.loads(r.text)

res = json_normalize(a)
##print(res)

df = pd.DataFrame(res)
print(df)

##df = pd.read_json(a)
##print(df)

pd.read_json(a) doesn't seem to work in any way. Could someone give it a try?

Thanks for all the help in advance.

Best regards, David

Answer 1

Or, more simply:

import requests
import pandas as pd

r = requests.get('http://www.starcapital.de/test/Res_Stockmarketvaluation_FundamentalKZ_Tbl.php')

j = r.json()

df = pd.DataFrame.from_dict(j)

Answer 2

And one step simpler than Justin's (already helpful) response...by putting .json() at the end of the r = requests.get line

import requests
import pandas as pd

r = requests.get('http://www.starcapital.de/test/Res_Stockmarketvaluation_FundamentalKZ_Tbl.php').json()

df = pd.DataFrame.from_dict(r)

Answer 3

you can do it this way:

import requests
import pandas as pd

r = requests.get('http://www.starcapital.de/test/Res_Stockmarketvaluation_FundamentalKZ_Tbl.php')

j = r.json()

df = pd.DataFrame([[d['v'] for d in x['c']] for x in j['rows']],
                  columns=[d['label'] for d in j['cols']])

Result:

In [217]: df
Out[217]:
                   Country  Weight  CAPE    PE    PC   PB   PS   DY  RS 26W  RS 52W  Score
0                   Russia     1.1   5.9   9.1   5.1  1.0  0.9  3.7    1.22    1.35    1.0
1                    China     1.1  12.8   7.2   4.5  0.9  0.6  4.2    1.05    1.13    2.0
2                    Italy     1.0  12.7  31.5   5.7  1.2  0.6  3.3    1.13    1.11    3.0
3                  Austria     0.2  14.3  21.7   7.3  1.1  0.7  2.5    1.10    1.15    4.0
4                   Norway     0.4  12.8  32.4   7.4  1.6  1.2  4.0    1.10    1.17    5.0
5                  Hungary     0.0  12.5  49.8   7.5  1.4  0.7  2.3    1.12    1.19    6.0
6                    Spain     1.2  11.7  24.7   7.0  1.4  1.2  3.7    1.08    1.11    7.0
7                    Czech     0.0   8.9  13.6   6.1  1.3  1.0  6.7    1.03    1.05    8.0
8                   Brazil     1.3   9.8  42.1   7.4  1.6  1.2  3.0    1.06    1.24    9.0
9                 Portugal     0.1  11.3  29.0   4.8  1.5  0.7  3.9    1.05    1.06   10.0
..                     ...     ...   ...   ...   ...  ...  ...  ...     ...     ...    ...
42        EMERGING MARKETS    13.5  14.0  16.0   8.8  1.6  1.3  2.9    1.04    1.11    NaN
43        DEVELOPED EUROPE    22.4  16.6  26.5   9.9  1.8  1.1  3.2    1.06    1.08    NaN
44         EMERGING EUROPE     1.7   8.6  10.9   5.8  1.1  0.8  3.4    1.13    1.20    NaN
45        EMERGING AMERICA     3.0  15.2  30.1   9.4  1.9  1.2  2.4    1.03    1.11    NaN
46  DEVELOPED ASIA-PACIFIC    17.7   NaN  17.7   8.8  1.3  0.9  2.5    1.03    1.09    NaN
47   EMERGING ASIA-PACIFIC     6.9  14.9  15.1   9.1  1.8  1.4  2.7    1.01    1.08    NaN
48         EMERGING AFRICA     0.8   NaN  16.5  10.6  2.0  1.4  3.8    1.06    1.12    NaN
49             MIDDLE EAST     1.3   NaN  13.7  11.8  1.5  1.8  3.9    1.06    1.10    NaN
50                    BRIC     5.9  11.8  14.6   7.4  1.4  1.2  2.7    1.06    1.16    NaN
51     OTHER EMERGING MKT.     2.5   NaN  17.7  12.9  1.8  1.5  3.1    1.16    1.20    NaN

[52 rows x 11 columns]