BUG: json_normalize() does not parse floats in scientific notation

[KaiRoesner]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

import json
import pandas as pd

body = '''[
{ "c1": 1, "c2": "1.01" },
{ "c1": 2, "c2": "1E-3" },
{ "c1": 3, "c2": "3.03" }
]'''

df1 = pd.read_json(body)
df2 = pd.json_normalize(json.loads(body))
print(df1, "\n\n", df1.dtypes)
print("\n\n", df2, "\n\n", df2.dtypes)

Problem description

When creating a dataframe from json string input via json.loads() and pd.json_normalize() float values in scientific notation (e.g. "1E-3") are not parsed as floats. When using pd.read_json() they are parsed correctly.

Output:

    c1     c2
0   1  1.010
1   2  0.001
2   3  3.030 

 c1      int64
c2    float64
dtype: object

    c1    c2
0   1  1.01
1   2  1E-3
2   3  3.03 

 c1     int64
c2    object
dtype: object

Expected Output

    c1     c2
0   1  1.010
1   2  0.001
2   3  3.030 

 c1      int64
c2    float64
dtype: object

    c1    c2
0   1  1.010
1   2  0.001
2   3  3.030

 c1      int64
c2    float64
dtype: object

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 67a3d42
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1019-gcp
Version : #19-Ubuntu SMP Tue Jun 23 15:46:40 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None

[jreback]

json_normalize doesn’t parse anything
this is all json.loads which makes a dictionary

[jreback]

to get correct dtypes use read_json

[KaiRoesner]

to get correct dtypes use read_json

I can't use read_json because I need to flatten nested columns.

[jreback]

to get correct dtypes use read_json

I can't use read_json because I need to flatten nested columns.

then u will need to some work to preprocess - try using to_numeric

[KaiRoesner]

json_normalize doesn’t parse anything
this is all json.loads which makes a dictionary

Hm. But json.loads() doesn't parse either if the floats are represented as strings in the json string. I tried using the parse_float kwarg but it doesn't even get called for these columns. I thought pandas.read_json() and pandas.json_normalize() should behave in the same way in this respect.