将Request中的JSON数据转换成Pandas DataFrame

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.

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

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]

Answer 3

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 4

这篇文章拯救了我从 API 到 Pandas——使用 Python 获取 JSON 数据

Answer 5

You may also want pd.json_normalize<\/code><\/a> for when your data isn't exactly the way from_dict() expects.

data = [
    {
        "id": 1,
        "name": "Cole Volk",
        "fitness": {"height": 130, "weight": 60},
    },
    {"name": "Mark Reg", "fitness": {"height": 130, "weight": 60}},
    {
        "id": 2,
        "name": "Faye Raker",
        "fitness": {"height": 130, "weight": 60},
    },
]
pd.json_normalize(data, max_level=1)
    id        name  fitness.height  fitness.weight
0  1.0   Cole Volk             130              60
1  NaN    Mark Reg             130              60
2  2.0  Faye Raker             130              60