read_json ignores dictionary as dtype

Code Sample, a copy-pastable example if possible

dtypes = {
    'created': 'int64',
    'eventType' : 'category',
    'severity' : 'category'
    }

df = pd.read_json('dataset.json', lines=True, dtype=dtypes)
df.info()

Results into:

created          int64
eventType        object
severity         object

Using .astype() instead converts types correctly:

df.astype(dtypes).info()
created          int64
eventType        category
severity         category

Problem description

Should take take appropriate data type during DataFrame loading from disc.

Output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.5.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.3
numpy            : 1.17.4
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 41.2.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : 0.4.0
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.11.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.16.0
pytables         : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None

[dtrizna]

Try it out with following json file:

> type test.json
{"created": 1585669938386, "eventType": "TEST", "severity": "INFO"}
{"created": 1585669938387, "eventType": "TEST2", "severity": "INFO"}

In case of using these dtype key word argment during read_json - pandas just ignores this setting (note data types are “object”, not “category” as specified in dtypes dictonary.

>>> import pandas as pd
>>> dtypes = {
...     'created': 'int64',
...     'eventType' : 'category',
...     'severity' : 'category'
...     }
>>> a = pd.read_json('test.json', lines=True, dtype=dtypes)
>>> a.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 3 columns):
created      2 non-null int64
eventType    2 non-null object
severity     2 non-null object
dtypes: int64(1), object(2)
memory usage: 176.0+ bytes

If we use same dtypes dictionary on DataFrame’s astype method - setting is applied (note correct data types):

>>> a.astype(dtypes).info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 3 columns):
created      2 non-null int64
eventType    2 non-null category
severity     2 non-null category
dtypes: category(2), int64(1)
memory usage: 332.0 bytes

This raises problems with large datasets, when reading data with correct types decrease usage of RAM drasticly.

[jake9wi]

Has there been any updates to this. I am experiencing this issue with pandas 1.2.4.