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# packages in environment at /home/ericdill/miniconda/envs/rapids-nightly:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
arrow-cpp 0.14.1 py37h5ac5442_4 conda-forge
backcall 0.1.0 py_0 conda-forge
bokeh 1.3.4 py37_0 conda-forge
boost-cpp 1.70.0 h8e57a91_2 conda-forge
brotli 1.0.7 he1b5a44_1000 conda-forge
bzip2 1.0.8 h516909a_1 conda-forge
c-ares 1.15.0 h516909a_1001 conda-forge
ca-certificates 2019.9.11 hecc5488_0 conda-forge
cairo 1.16.0 hfb77d84_1002 conda-forge
certifi 2019.9.11 py37_0 conda-forge
click 7.0 py_0 conda-forge
cloudpickle 1.2.2 py_0 conda-forge
cudatoolkit 10.0.130 0 nvidia
cudf 0.11.0a191018 py37_1340 rapidsai-nightly
cugraph 0.11.0a1191015 py37_0 rapidsai-nightly
cuml 0.11.0a1191018 cuda10.0_py37_23 rapidsai-nightly
cython 0.29.13 py37he1b5a44_0 conda-forge
cytoolz 0.10.0 py37h516909a_0 conda-forge
dask 2.6.0 py_0 conda-forge
dask-core 2.6.0 py_0 conda-forge
dask-cuda 0.11.0a0+16.gac37fed pypi_0 pypi
dask-cudf 0.11.0a191018 py37_1340 rapidsai-nightly
decorator 4.4.0 py_0 conda-forge
distributed 2.6.0 py_0 conda-forge
dlpack 0.2 he1b5a44_1 conda-forge
double-conversion 3.1.5 he1b5a44_1 conda-forge
expat 2.2.5 he1b5a44_1004 conda-forge
fastavro 0.22.5 py37h516909a_0 conda-forge
fontconfig 2.13.1 h86ecdb6_1001 conda-forge
freetype 2.10.0 he983fc9_1 conda-forge
fribidi 1.0.5 h516909a_1002 conda-forge
fsspec 0.5.2 py_0 conda-forge
gettext 0.19.8.1 hc5be6a0_1002 conda-forge
gflags 2.2.2 he1b5a44_1001 conda-forge
glib 2.58.3 h6f030ca_1002 conda-forge
glog 0.4.0 he1b5a44_1 conda-forge
graphite2 1.3.13 hf484d3e_1000 conda-forge
graphviz 2.40.1 h0f2764d_1 conda-forge
grpc-cpp 1.23.0 h18db393_0 conda-forge
harfbuzz 2.4.0 h9f30f68_3 conda-forge
heapdict 1.0.1 py_0 conda-forge
icu 64.2 he1b5a44_1 conda-forge
ipykernel 5.1.2 py37h5ca1d4c_0 conda-forge
ipython 7.8.0 py37h5ca1d4c_0 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
jedi 0.15.1 py37_0 conda-forge
jinja2 2.10.3 py_0 conda-forge
jpeg 9c h14c3975_1001 conda-forge
jupyter_client 5.3.3 py37_1 conda-forge
jupyter_core 4.5.0 py_0 conda-forge
kernda 0.3.0 py_1 conda-forge
libblas 3.8.0 14_openblas conda-forge
libcblas 3.8.0 14_openblas conda-forge
libcudf 0.11.0a191018 cuda10.0_1340 rapidsai-nightly
libcugraph 0.11.0a1191015 cuda10.0_0 rapidsai-nightly
libcuml 0.11.0a1191018 cuda10.0_23 rapidsai-nightly
libcumlprims 0.11.0a191018 cuda10.0_17 rapidsai-nightly
libevent 2.1.10 h72c5cf5_0 conda-forge
libffi 3.2.1 he1b5a44_1006 conda-forge
libgcc-ng 9.1.0 hdf63c60_0
libgfortran-ng 7.3.0 hdf63c60_2 conda-forge
libiconv 1.15 h516909a_1005 conda-forge
liblapack 3.8.0 14_openblas conda-forge
libnvstrings 0.11.0a191018 cuda10.0_1340 rapidsai-nightly
libopenblas 0.3.7 h6e990d7_2 conda-forge
libpng 1.6.37 hed695b0_0 conda-forge
libprotobuf 3.8.0 h8b12597_0 conda-forge
librmm 0.11.0a191018 cuda10.0_118 rapidsai-nightly
libsodium 1.0.17 h516909a_0 conda-forge
libstdcxx-ng 9.1.0 hdf63c60_0
libtiff 4.0.10 h57b8799_1003 conda-forge
libtool 2.4.6 h14c3975_1002 conda-forge
libuuid 2.32.1 h14c3975_1000 conda-forge
libxcb 1.13 h14c3975_1002 conda-forge
libxml2 2.9.9 hee79883_5 conda-forge
llvmlite 0.30.0 py37hf484d3e_0 numba
locket 0.2.0 py_2 conda-forge
lz4-c 1.8.3 he1b5a44_1001 conda-forge
markupsafe 1.1.1 py37h14c3975_0 conda-forge
msgpack-python 0.6.2 py37hc9558a2_0 conda-forge
nccl 2.4.6.1 cuda10.0_0 nvidia
ncurses 6.1 hf484d3e_1002 conda-forge
numba 0.46.0 np116py37hf484d3e_0 numba
numpy 1.16.4 py37h95a1406_0 conda-forge
nvstrings 0.11.0a191018 py37_1340 rapidsai-nightly
olefile 0.46 py_0 conda-forge
openssl 1.1.1c h516909a_0 conda-forge
packaging 19.2 py_0 conda-forge
pandas 0.24.2 py37hb3f55d8_0 conda-forge
pango 1.42.4 ha030887_1 conda-forge
parquet-cpp 1.5.1 2 conda-forge
parso 0.5.1 py_0 conda-forge
partd 1.0.0 py_0 conda-forge
pcre 8.43 he1b5a44_0 conda-forge
pexpect 4.7.0 py37_0 conda-forge
pickleshare 0.7.5 py37_1000 conda-forge
pillow 6.2.0 py37h6b7be26_0 conda-forge
pip 19.3.1 py37_0 conda-forge
pixman 0.38.0 h516909a_1003 conda-forge
prompt_toolkit 2.0.10 py_0 conda-forge
psutil 5.6.3 py37h516909a_0 conda-forge
pthread-stubs 0.4 h14c3975_1001 conda-forge
ptyprocess 0.6.0 py_1001 conda-forge
pyarrow 0.14.1 py37h8b68381_2 conda-forge
pygments 2.4.2 py_0 conda-forge
pynvml 8.0.3 py_0 conda-forge
pyparsing 2.4.2 py_0 conda-forge
python 3.7.3 h33d41f4_1 conda-forge
python-dateutil 2.8.0 py_0 conda-forge
python-graphviz 0.13 py_0 conda-forge
python-snappy 0.5.4 py37hee44bf9_0 conda-forge
pytz 2019.3 py_0 conda-forge
pyyaml 5.1.2 py37h516909a_0 conda-forge
pyzmq 18.1.0 py37h1768529_0 conda-forge
re2 2019.09.01 he1b5a44_0 conda-forge
readline 8.0 hf8c457e_0 conda-forge
rmm 0.11.0a191018 py37_118 rapidsai-nightly
setuptools 41.4.0 py37_0 conda-forge
six 1.12.0 py37_1000 conda-forge
snappy 1.1.7 he1b5a44_1002 conda-forge
sortedcontainers 2.1.0 py_0 conda-forge
sqlite 3.30.1 hcee41ef_0 conda-forge
tblib 1.4.0 py_0 conda-forge
thrift-cpp 0.12.0 hf3afdfd_1004 conda-forge
tk 8.6.9 hed695b0_1003 conda-forge
toolz 0.10.0 py_0 conda-forge
tornado 6.0.3 py37h516909a_0 conda-forge
traitlets 4.3.3 py37_0 conda-forge
uriparser 0.9.3 he1b5a44_1 conda-forge
wcwidth 0.1.7 py_1 conda-forge
wheel 0.33.6 py37_0 conda-forge
xorg-kbproto 1.0.7 h14c3975_1002 conda-forge
xorg-libice 1.0.10 h516909a_0 conda-forge
xorg-libsm 1.2.3 h84519dc_1000 conda-forge
xorg-libx11 1.6.9 h516909a_0 conda-forge
xorg-libxau 1.0.9 h14c3975_0 conda-forge
xorg-libxdmcp 1.1.3 h516909a_0 conda-forge
xorg-libxext 1.3.4 h516909a_0 conda-forge
xorg-libxpm 3.5.12 h516909a_1002 conda-forge
xorg-libxrender 0.9.10 h516909a_1002 conda-forge
xorg-libxt 1.1.5 h516909a_1003 conda-forge
xorg-renderproto 0.11.1 h14c3975_1002 conda-forge
xorg-xextproto 7.3.0 h14c3975_1002 conda-forge
xorg-xproto 7.0.31 h14c3975_1007 conda-forge
xz 5.2.4 h14c3975_1001 conda-forge
yaml 0.1.7 h14c3975_1001 conda-forge
zeromq 4.3.2 he1b5a44_2 conda-forge
zict 1.0.0 py_0 conda-forge
zlib 1.2.11 h516909a_1006 conda-forge
zstd 1.4.0 h3b9ef0a_0 conda-forge
<details><summary>Click here to see environment details</summary><pre>
**git***
commit cebbec2c4df9f4aa824aa6473e7db8547b99582f (HEAD, origin/master, origin/HEAD, master)
Author: Eric Dill <thedizzle@gmail.com>
Date: Fri Oct 18 07:32:24 2019 -0400
Better definition of rapids env
**git submodules***
***OS Information***
CentOS Linux release 7.6.1810 (Core)
NAME="CentOS Linux"
VERSION="7 (Core)"
ID="centos"
ID_LIKE="rhel fedora"
VERSION_ID="7"
PRETTY_NAME="CentOS Linux 7 (Core)"
ANSI_COLOR="0;31"
CPE_NAME="cpe:/o:centos:centos:7"
HOME_URL="https://www.centos.org/"
BUG_REPORT_URL="https://bugs.centos.org/"
CENTOS_MANTISBT_PROJECT="CentOS-7"
CENTOS_MANTISBT_PROJECT_VERSION="7"
REDHAT_SUPPORT_PRODUCT="centos"
REDHAT_SUPPORT_PRODUCT_VERSION="7"
CentOS Linux release 7.6.1810 (Core)
CentOS Linux release 7.6.1810 (Core)
Linux desktop-qepl07h.lan 3.10.0-957.27.2.el7.x86_64 #1 SMP Mon Jul 29 17:46:05 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
***GPU Information***
Fri Oct 18 09:13:43 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.48 Driver Version: 410.48 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 2070 On | 00000000:1F:00.0 On | N/A |
| 0% 32C P8 11W / 175W | 1250MiB / 7949MiB | 10% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 2980 G /usr/bin/X 237MiB |
| 0 6718 C ...iniconda/envs/rapids-nightly/bin/python 327MiB |
| 0 7194 G ...uest-channel-token=10447837260775572939 113MiB |
| 0 16765 G ...quest-channel-token=2750878945691515965 101MiB |
| 0 17413 C ...iniconda/envs/rapids-nightly/bin/python 231MiB |
| 0 17525 C ...iniconda/envs/rapids-nightly/bin/python 235MiB |
+-----------------------------------------------------------------------------+
***CPU***
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 12
On-line CPU(s) list: 0-11
Thread(s) per core: 2
Core(s) per socket: 6
Socket(s): 1
NUMA node(s): 1
Vendor ID: AuthenticAMD
CPU family: 23
Model: 8
Model name: AMD Ryzen 5 2600 Six-Core Processor
Stepping: 2
CPU MHz: 1550.000
CPU max MHz: 3400.0000
CPU min MHz: 1550.0000
BogoMIPS: 6799.09
Virtualization: AMD-V
L1d cache: 32K
L1i cache: 64K
L2 cache: 512K
L3 cache: 8192K
NUMA node0 CPU(s): 0-11
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc art rep_good nopl nonstop_tsc extd_apicid aperfmperf eagerfpu pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_l2 cpb hw_pstate sme retpoline_amd ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca
***CMake***
which: no cmake in (/home/ericdill/miniconda/envs/rapids-nightly/bin:/home/ericdill/miniconda/bin:/home/ericdill/miniconda/bin:/home/ericdill/miniconda/bin:/home/ericdill/miniconda/condabin:/home/ericdill/miniconda/bin:/home/ericdill/bin:/usr/local/bin:/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin:/home/ericdill/bin:/var/lib/snapd/snap/bin:/home/ericdill/autobin:/home/ericdill/.screenlayout)
***g++***
/usr/bin/g++
g++ (GCC) 4.8.5 20150623 (Red Hat 4.8.5-39)
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
***nvcc***
/usr/bin/nvcc
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2015 NVIDIA Corporation
Built on Tue_Aug_11_14:27:32_CDT_2015
Cuda compilation tools, release 7.5, V7.5.17
***Python***
/home/ericdill/miniconda/envs/rapids-nightly/bin/python
Python 3.7.3
***Environment Variables***
PATH : /home/ericdill/miniconda/envs/rapids-nightly/bin:/home/ericdill/miniconda/bin:/home/ericdill/miniconda/bin:/home/ericdill/miniconda/bin:/home/ericdill/miniconda/condabin:/home/ericdill/miniconda/bin:/home/ericdill/bin:/usr/local/bin:/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin:/home/ericdill/bin:/var/lib/snapd/snap/bin:/home/ericdill/autobin:/home/ericdill/.screenlayout
LD_LIBRARY_PATH :
NUMBAPRO_NVVM :
NUMBAPRO_LIBDEVICE :
CONDA_PREFIX : /home/ericdill/miniconda/envs/rapids-nightly
PYTHON_PATH :
***conda packages***
/home/ericdill/miniconda/bin/conda
# packages in environment at /home/ericdill/miniconda/envs/rapids-nightly:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
arrow-cpp 0.14.1 py37h5ac5442_4 conda-forge
backcall 0.1.0 py_0 conda-forge
bokeh 1.3.4 py37_0 conda-forge
boost-cpp 1.70.0 h8e57a91_2 conda-forge
brotli 1.0.7 he1b5a44_1000 conda-forge
bzip2 1.0.8 h516909a_1 conda-forge
c-ares 1.15.0 h516909a_1001 conda-forge
ca-certificates 2019.9.11 hecc5488_0 conda-forge
cairo 1.16.0 hfb77d84_1002 conda-forge
certifi 2019.9.11 py37_0 conda-forge
click 7.0 py_0 conda-forge
cloudpickle 1.2.2 py_0 conda-forge
cudatoolkit 10.0.130 0 nvidia
cudf 0.11.0a191018 py37_1340 rapidsai-nightly
cugraph 0.11.0a1191015 py37_0 rapidsai-nightly
cuml 0.11.0a1191018 cuda10.0_py37_23 rapidsai-nightly
cython 0.29.13 py37he1b5a44_0 conda-forge
cytoolz 0.10.0 py37h516909a_0 conda-forge
dask 2.6.0 py_0 conda-forge
dask-core 2.6.0 py_0 conda-forge
dask-cuda 0.11.0a0+16.gac37fed pypi_0 pypi
dask-cudf 0.11.0a191018 py37_1340 rapidsai-nightly
decorator 4.4.0 py_0 conda-forge
distributed 2.6.0 py_0 conda-forge
dlpack 0.2 he1b5a44_1 conda-forge
double-conversion 3.1.5 he1b5a44_1 conda-forge
expat 2.2.5 he1b5a44_1004 conda-forge
fastavro 0.22.5 py37h516909a_0 conda-forge
fontconfig 2.13.1 h86ecdb6_1001 conda-forge
freetype 2.10.0 he983fc9_1 conda-forge
fribidi 1.0.5 h516909a_1002 conda-forge
fsspec 0.5.2 py_0 conda-forge
gettext 0.19.8.1 hc5be6a0_1002 conda-forge
gflags 2.2.2 he1b5a44_1001 conda-forge
glib 2.58.3 h6f030ca_1002 conda-forge
glog 0.4.0 he1b5a44_1 conda-forge
graphite2 1.3.13 hf484d3e_1000 conda-forge
graphviz 2.40.1 h0f2764d_1 conda-forge
grpc-cpp 1.23.0 h18db393_0 conda-forge
harfbuzz 2.4.0 h9f30f68_3 conda-forge
heapdict 1.0.1 py_0 conda-forge
icu 64.2 he1b5a44_1 conda-forge
ipykernel 5.1.2 py37h5ca1d4c_0 conda-forge
ipython 7.8.0 py37h5ca1d4c_0 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
jedi 0.15.1 py37_0 conda-forge
jinja2 2.10.3 py_0 conda-forge
jpeg 9c h14c3975_1001 conda-forge
jupyter_client 5.3.3 py37_1 conda-forge
jupyter_core 4.5.0 py_0 conda-forge
kernda 0.3.0 py_1 conda-forge
libblas 3.8.0 14_openblas conda-forge
libcblas 3.8.0 14_openblas conda-forge
libcudf 0.11.0a191018 cuda10.0_1340 rapidsai-nightly
libcugraph 0.11.0a1191015 cuda10.0_0 rapidsai-nightly
libcuml 0.11.0a1191018 cuda10.0_23 rapidsai-nightly
libcumlprims 0.11.0a191018 cuda10.0_17 rapidsai-nightly
libevent 2.1.10 h72c5cf5_0 conda-forge
libffi 3.2.1 he1b5a44_1006 conda-forge
libgcc-ng 9.1.0 hdf63c60_0
libgfortran-ng 7.3.0 hdf63c60_2 conda-forge
libiconv 1.15 h516909a_1005 conda-forge
liblapack 3.8.0 14_openblas conda-forge
libnvstrings 0.11.0a191018 cuda10.0_1340 rapidsai-nightly
libopenblas 0.3.7 h6e990d7_2 conda-forge
libpng 1.6.37 hed695b0_0 conda-forge
libprotobuf 3.8.0 h8b12597_0 conda-forge
librmm 0.11.0a191018 cuda10.0_118 rapidsai-nightly
libsodium 1.0.17 h516909a_0 conda-forge
libstdcxx-ng 9.1.0 hdf63c60_0
libtiff 4.0.10 h57b8799_1003 conda-forge
libtool 2.4.6 h14c3975_1002 conda-forge
libuuid 2.32.1 h14c3975_1000 conda-forge
libxcb 1.13 h14c3975_1002 conda-forge
libxml2 2.9.9 hee79883_5 conda-forge
llvmlite 0.30.0 py37hf484d3e_0 numba
locket 0.2.0 py_2 conda-forge
lz4-c 1.8.3 he1b5a44_1001 conda-forge
markupsafe 1.1.1 py37h14c3975_0 conda-forge
msgpack-python 0.6.2 py37hc9558a2_0 conda-forge
nccl 2.4.6.1 cuda10.0_0 nvidia
ncurses 6.1 hf484d3e_1002 conda-forge
numba 0.46.0 np116py37hf484d3e_0 numba
numpy 1.16.4 py37h95a1406_0 conda-forge
nvstrings 0.11.0a191018 py37_1340 rapidsai-nightly
olefile 0.46 py_0 conda-forge
openssl 1.1.1c h516909a_0 conda-forge
packaging 19.2 py_0 conda-forge
pandas 0.24.2 py37hb3f55d8_0 conda-forge
pango 1.42.4 ha030887_1 conda-forge
parquet-cpp 1.5.1 2 conda-forge
parso 0.5.1 py_0 conda-forge
partd 1.0.0 py_0 conda-forge
pcre 8.43 he1b5a44_0 conda-forge
pexpect 4.7.0 py37_0 conda-forge
pickleshare 0.7.5 py37_1000 conda-forge
pillow 6.2.0 py37h6b7be26_0 conda-forge
pip 19.3.1 py37_0 conda-forge
pixman 0.38.0 h516909a_1003 conda-forge
prompt_toolkit 2.0.10 py_0 conda-forge
psutil 5.6.3 py37h516909a_0 conda-forge
pthread-stubs 0.4 h14c3975_1001 conda-forge
ptyprocess 0.6.0 py_1001 conda-forge
pyarrow 0.14.1 py37h8b68381_2 conda-forge
pygments 2.4.2 py_0 conda-forge
pynvml 8.0.3 py_0 conda-forge
pyparsing 2.4.2 py_0 conda-forge
python 3.7.3 h33d41f4_1 conda-forge
python-dateutil 2.8.0 py_0 conda-forge
python-graphviz 0.13 py_0 conda-forge
python-snappy 0.5.4 py37hee44bf9_0 conda-forge
pytz 2019.3 py_0 conda-forge
pyyaml 5.1.2 py37h516909a_0 conda-forge
pyzmq 18.1.0 py37h1768529_0 conda-forge
re2 2019.09.01 he1b5a44_0 conda-forge
readline 8.0 hf8c457e_0 conda-forge
rmm 0.11.0a191018 py37_118 rapidsai-nightly
setuptools 41.4.0 py37_0 conda-forge
six 1.12.0 py37_1000 conda-forge
snappy 1.1.7 he1b5a44_1002 conda-forge
sortedcontainers 2.1.0 py_0 conda-forge
sqlite 3.30.1 hcee41ef_0 conda-forge
tblib 1.4.0 py_0 conda-forge
thrift-cpp 0.12.0 hf3afdfd_1004 conda-forge
tk 8.6.9 hed695b0_1003 conda-forge
toolz 0.10.0 py_0 conda-forge
tornado 6.0.3 py37h516909a_0 conda-forge
traitlets 4.3.3 py37_0 conda-forge
uriparser 0.9.3 he1b5a44_1 conda-forge
wcwidth 0.1.7 py_1 conda-forge
wheel 0.33.6 py37_0 conda-forge
xorg-kbproto 1.0.7 h14c3975_1002 conda-forge
xorg-libice 1.0.10 h516909a_0 conda-forge
xorg-libsm 1.2.3 h84519dc_1000 conda-forge
xorg-libx11 1.6.9 h516909a_0 conda-forge
xorg-libxau 1.0.9 h14c3975_0 conda-forge
xorg-libxdmcp 1.1.3 h516909a_0 conda-forge
xorg-libxext 1.3.4 h516909a_0 conda-forge
xorg-libxpm 3.5.12 h516909a_1002 conda-forge
xorg-libxrender 0.9.10 h516909a_1002 conda-forge
xorg-libxt 1.1.5 h516909a_1003 conda-forge
xorg-renderproto 0.11.1 h14c3975_1002 conda-forge
xorg-xextproto 7.3.0 h14c3975_1002 conda-forge
xorg-xproto 7.0.31 h14c3975_1007 conda-forge
xz 5.2.4 h14c3975_1001 conda-forge
yaml 0.1.7 h14c3975_1001 conda-forge
zeromq 4.3.2 he1b5a44_2 conda-forge
zict 1.0.0 py_0 conda-forge
zlib 1.2.11 h516909a_1006 conda-forge
zstd 1.4.0 h3b9ef0a_0 conda-forge
</pre></details>
{
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"cell_type": "code",
"execution_count": 1,
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"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"output_file = 'test.parquet'"
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"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
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" val\n",
"2019-09-01 1"
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"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"orig_df = pd.DataFrame({'val': 1}, index=[pd.datetime(2019, 9, 1)])\n",
"orig_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"df = orig_df.copy()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"val int64\n",
"dtype: object"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
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{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"df['datetime'] = df.index"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"val int64\n",
"datetime datetime64[ns]\n",
"dtype: object"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"orig_df.to_parquet(output_file)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"import dask.dataframe as dd"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"ddf = dd.read_parquet(output_file)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ericdill/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/dask/dataframe/core.py:5738: UserWarning: Insufficient elements for `head`. 5 elements requested, only 1 elements available. Try passing larger `npartitions` to `head`.\n",
" warnings.warn(msg.format(n, len(r)))\n"
]
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"text/plain": [
" val\n",
"0 1"
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"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ddf.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"ddf['timestamp'] = ddf.index"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ericdill/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/dask/dataframe/core.py:5738: UserWarning: Insufficient elements for `head`. 5 elements requested, only 1 elements available. Try passing larger `npartitions` to `head`.\n",
" warnings.warn(msg.format(n, len(r)))\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <th></th>\n",
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" <th>timestamp</th>\n",
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"text/plain": [
" val timestamp\n",
"0 1 0"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ddf.head()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"import dask_cudf"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"cdf = dask_cudf.read_parquet(output_file)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ericdill/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/dask/dataframe/core.py:5738: UserWarning: Insufficient elements for `head`. 5 elements requested, only 1 elements available. Try passing larger `npartitions` to `head`.\n",
" warnings.warn(msg.format(n, len(r)))\n"
]
},
{
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>val</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-09-01</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" val\n",
"2019-09-01 1"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "from_pandas only accepts Pandas Dataframes, Series, and MultiIndex objects. Got <class 'pandas.core.indexes.datetimes.DatetimeIndex'>",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-50-73c5fc005ca5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'timestamp'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/dask/dataframe/core.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 3276\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3277\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3278\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3280\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/dask/dataframe/core.py\u001b[0m in \u001b[0;36massign\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 3524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3525\u001b[0m \u001b[0;31m# Figure out columns of the output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3526\u001b[0;31m \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_meta_nonempty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0m_extract_meta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnonempty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3527\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0melemwise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethods\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mpairs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/dask/dataframe/core.py\u001b[0m in \u001b[0;36m_extract_meta\u001b[0;34m(x, nonempty)\u001b[0m\n\u001b[1;32m 4707\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4708\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4709\u001b[0;31m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extract_meta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnonempty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4710\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4711\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDelayed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/dask/dataframe/core.py\u001b[0m in \u001b[0;36m_extract_meta\u001b[0;34m(x, nonempty)\u001b[0m\n\u001b[1;32m 4699\u001b[0m \"\"\"\n\u001b[1;32m 4700\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mScalar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Frame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4701\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_meta_nonempty\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnonempty\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_meta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4702\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4703\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_extract_meta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnonempty\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_x\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/dask/dataframe/core.py\u001b[0m in \u001b[0;36m_meta_nonempty\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_meta_nonempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[0;34m\"\"\" A non-empty version of `_meta` with fake data.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 333\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmeta_nonempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_meta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/dask/utils.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m 503\u001b[0m \"\"\"\n\u001b[1;32m 504\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/dask_cudf/backends.py\u001b[0m in \u001b[0;36mmeta_nonempty_cudf\u001b[0;34m(x, index)\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmeta_nonempty_cudf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeta_nonempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pandas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# TODO: add iloc[:5]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcudf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pandas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda/envs/rapids-nightly/lib/python3.7/site-packages/cudf/core/dataframe.py\u001b[0m in \u001b[0;36mfrom_pandas\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m 3972\u001b[0m \u001b[0;34m\"from_pandas only accepts Pandas Dataframes, Series, and \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3973\u001b[0m \u001b[0;34m\"MultiIndex objects. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3974\u001b[0;31m \u001b[0;34m\"Got %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3975\u001b[0m )\n\u001b[1;32m 3976\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: from_pandas only accepts Pandas Dataframes, Series, and MultiIndex objects. Got <class 'pandas.core.indexes.datetimes.DatetimeIndex'>"
]
}
],
"source": [
"cdf['timestamp'] = cdf.index"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"cdf = cdf.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>val</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
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"</div>"
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"text/plain": [
" index val\n",
"0 2019-09-01 1"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"import cudf"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"cdf2 = cudf.read_parquet(output_file)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>val</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-09-01</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" val\n",
"2019-09-01 1"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf2.head()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"cdf2['timestamp'] = cdf2.index"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>val</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-09-01</th>\n",
" <td>1</td>\n",
" <td>2019-09-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" val timestamp\n",
"2019-09-01 1 2019-09-01"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf2.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Rapids Nightly [py37]",
"language": "python",
"name": "rapids-nightly"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
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"nbformat": 4,
"nbformat_minor": 4
}
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