For quick analysis of a pandas dataframe I recommend pandas_profiling. You can see an example below.
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I came across pandas_profiling elsewhere, haven’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 I’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