A useful tool for fast data exploration

For quick analysis of a pandas dataframe I recommend pandas_profiling. You can see an example below.


I came across pandas_profiling elsewhere, havenā€™t tried it. conda wants to downgrade a lot of packages.
has anyone used it in parallel with fastai? any tips? any conflicts? (if it matters Iā€™m on windows)

The following NEW packages will be INSTALLED:

pandas-profiling: 1.4.0-0               conda-forge

The following packages will be UPDATED:

ca-certificates:  2017.08.26-h94faf87_0             --> 2018.1.18-0            conda-forge
certifi:          2018.1.18-py36_0                  --> 2018.1.18-py36_0       conda-forge
cymem:            1.31.2-py36h51d26f2_0             --> 1.31.2-py36_vc14_0     conda-forge [vc14]
openssl:          1.0.2n-h74b6da3_0                 --> 1.0.2n-vc14_0          conda-forge [vc14]
preshed:          1.0.0-py36h065ec1e_0              --> 1.0.0-py36_vc14_0      conda-forge [vc14]

The following packages will be DOWNGRADED:

bzip2:            1.0.6-haa5b126_2                  --> 1.0.6-vc14_1           conda-forge [vc14]
expat:            2.2.5-hcc4222d_0                  --> 2.1.0-vc14_2           conda-forge [vc14]
freetype:         2.8-h51f8f2c_1                    --> 2.6.3-vc14_1           conda-forge [vc14]
hdf5:             1.10.1-h98b8871_1                 --> 1.8.17-vc14_8          conda-forge [vc14]
icu:              58.2-ha66f8fd_1                   --> 58.1-vc14_0            conda-forge [vc14]
jpeg:             9b-hb83a4c4_2                     --> 9b-vc14_1              conda-forge [vc14]
libiconv:         1.15-h1df5818_7                   --> 1.14-vc14_3            conda-forge [vc14]
libpng:           1.6.34-h79bbb47_0                 --> 1.6.24-vc14_0          conda-forge [vc14]
libtiff:          4.0.9-h0f13578_0                  --> 4.0.6-vc14_6           conda-forge [vc14]
libxml2:          2.9.7-h79bbb47_0                  --> 2.9.3-vc14_9           conda-forge [vc14]
matplotlib:       2.1.2-py36h016c42a_0              --> 2.0.0-np111py36_0      conda-forge
murmurhash:       0.28.0-py36h866ba4d_0             --> 0.26.4-py36_vc14_0     conda-forge [vc14]
numpy:            1.14.0-py36h4a99626_1             --> 1.11.3-py36hb60be0b_3
pillow:           5.0.0-py36h0738816_0              --> 4.0.0-py36_1           conda-forge
pyqt:             5.6.0-py36hb5ed885_5              --> 4.11.4-py36_2          conda-forge
pytables:         3.4.2-py36h71138e3_2              --> 3.4.2-np111py36_0      conda-forge
qt:               5.6.2-vc14h6f8c307_12             --> 4.8.7-4                conda-forge
sip:              4.18.1-py36h9c25514_2             --> 4.18-py36_1            conda-forge
spacy:            2.0.5-py36h6538335_0              --> 1.9.0-np111py36_vc14_1 conda-forge [vc14]
sqlite:           3.22.0-h9d3ae62_0                 --> 3.16.2-vc14_0          conda-forge [vc14]
thinc:            6.10.1-py36h58cf350_0             --> 6.5.2-np111py36_vc14_0 conda-forge [vc14]
tk:               8.6.7-hcb92d03_3                  --> 8.5.19-vc14_0          conda-forge [vc14]
yaml:             0.1.7-hc54c509_2                  --> 0.1.7-vc14_0           conda-forge [vc14]
zlib:             1.2.11-h8395fce_2                 --> 1.2.11-vc14_0          conda-forge [vc14]

Proceed ([y]/n)? n