Jupyter Notebook displays Series VERY slowly

Code Sample

import pandas as pd
from IPython.display import display
df = pd.DataFrame(
    pd.np.random.random((10000000, 1)), 
    columns=['a'], 
    index=pd.date_range(start='2001-01-01', freq='min', periods=10000000)
)
display(df.a)

import pandas as pd
from IPython.display import display

# read some data for size 200,000 x 4

display(data.loc[:, ['reading']])  #25ms
display(data.reading.to_frame())  #25ms
display(data.reading) #3.53s
s = data.reading
display(s) #3.32s possibly cached
print(data.reading) #9ms
print(data.loc[:, ['reading']]) # 15ms

Problem description

In the Jupyter notebook, displaying a pd.Series is VERY slow. It is displays quicker when kept as a pd.DataFrame but is of course more verbose.

Expected Output

A pd.Series display/print out at reasonable speeds.

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None python: 3.5.4.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None

pandas: 0.21.0 pytest: 3.2.1 pip: 9.0.1 setuptools: 36.2.4 Cython: 0.26 numpy: 1.13.3 scipy: 0.19.1 pyarrow: None xarray: None IPython: 5.3.0 sphinx: 1.6.3 patsy: 0.4.1 dateutil: 2.6.1 pytz: 2017.3 blosc: None bottleneck: 1.2.1 tables: 3.4.2 numexpr: 2.6.2 feather: None matplotlib: 2.0.0 openpyxl: 2.5.0a2 xlrd: 1.0.0 xlwt: 1.2.0 xlsxwriter: 0.9.8 lxml: 3.8.0 bs4: 4.6.0 html5lib: 0.9999999 sqlalchemy: 1.1.13 pymysql: 0.7.9.None psycopg2: None jinja2: 2.9.6 s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: 0.4.0

[Strateus]

I have same issue. How to reproduce:

import pandas as pd
from IPython.display import display
df = pd.DataFrame(
    pd.np.random.random((10000000, 1)), 
    columns=['a'], 
    index=pd.date_range(start='2001-01-01', freq='min', periods=10000000)
)
display(df.a)

if you just run display(df) - it works well.

[JoshuaC3]

I no longer get this issue. I have updated a lot of things a lot of times so couldn’t pin it to any one thing. Thanks 😃