Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀

Last update: Jan 01, 2023

Overview

What is Vaex?

Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).

Installing

With pip:

$ pip install vaex

Or conda:

$ conda install -c conda-forge vaex

For more details, see the documentation

Key features

Instant opening of Huge data files (memory mapping)

HDF5 and Apache Arrow supported.

Read the documentation on how to efficiently convert your data from CSV files, Pandas DataFrames, or other sources.

Lazy streaming from S3 supported in combination with memory mapping.

Expression system

Don't waste memory or time with feature engineering, we (lazily) transform your data when needed.

Out-of-core DataFrame

Filtering and evaluating expressions will not waste memory by making copies; the data is kept untouched on disk, and will be streamed only when needed. Delay the time before you need a cluster.

Fast groupby / aggregations

Vaex implements parallelized, highly performant groupby operations, especially when using categories (>1 billion/second).

Fast and efficient join

Vaex doesn't copy/materialize the 'right' table when joining, saving gigabytes of memory. With subsecond joining on a billion rows, it's pretty fast!

More features

Remote DataFrames (documentation coming soon)
Integration into Jupyter and Voila for interactive notebooks and dashboards
Machine Learning without (explicit) pipelines

Learn more about Vaex

Articles
Follow our tutorials
Watch our more recent talks:
- PyData London 2019
- SciPy 2019
Contact us for data science solutions, training, or enterprise support at https://vaex.io/

Comments

When opening a bigger than memory CSV, using chunksize and converting to HDF5, the memory is still fully used until the machine hangs and the kernel dies.

Following the recommendation here: https://vaex.readthedocs.io/en/latest/faq.html

I have been able to verify that the small files (from each chunk) are being generated.

However, although the data is being chunked and the small files are created, the memory usage increases until all memory is used, and the machine hangs until the kernel dies.

When reading the dataset using the chunksize argument, into pandas.read_csv(), the memory is kept as low as the chunksize. Perhaps the memory leak is happening in the process of conversion to hdf5?

To reproduce the issue, just try to read a bigger than memory csv file with this code (as recommended on the faq link):

df = vaex.from_csv('./my_data/my_big_file.csv', convert=True, chunk_size=5_000_000)

opened by nf78 42
List of Numpy functions that can be used in expressions

I am trying to create a virtual column that is the one shifted value of another column. I know there is a shift() method on the dataframe class, but it appears that creates a whole new copy of the column. I tried to create an expression using a Numpy function, i.e., np.roll, that could then be used to form a virtual column, but it appears it isn't compatible with vaex expressions. Specifically, I did the following:

import vaex osc_data = vaex.open('.../data.hdf5') # '...' is the path to data.hdf5 import numpy as np osc_data['sx'] = np.roll(osc_data.x, -1)

This threw an error:

TypeError Traceback (most recent call last) /var/folders/l5/wjz73b69427bh_9x0954j0dr0000gn/T/ipykernel_6758/539048344.py in 1 import numpy as np ----> 2 osc_data['sx'] = np.roll(osc_data.x, -1) 3 # print(type(osc_data['sx'])) 4 # print(type(osc_data['x'])) 5 # print(osc_data[['x', 'sx']])

/usr/local/lib/python3.9/site-packages/vaex/dataframe.py in setitem(self, name, value) 4969 if isinstance(name, six.string_types): 4970 if isinstance(value, supported_column_types): -> 4971 self.add_column(name, value) 4972 else: 4973 self.add_virtual_column(name, value)

/usr/local/lib/python3.9/site-packages/vaex/dataframe.py in add_column(self, name, f_or_array, dtype) 3261 self._length_original = _len(data) 3262 self._index_end = self._length_unfiltered -> 3263 if _len(ar) != self.length_original(): 3264 if self.filtered: 3265 # give a better warning to avoid confusion

/usr/local/lib/python3.9/site-packages/vaex/dataframe.py in _len(o) 65 66 def _len(o): ---> 67 return o.len() 68 69

TypeError: len() of unsized object

My guess is np.roll is not one of the Numpy functions supported in vaex expressions. However, I could find not list of Numpy functions that are valid in vaex expressions. This means those of us coding with vaex have to use hit or miss guessing which functions are compatible and which are not. This is extremely frustrating.

As a side question, is there any other way of creating an expression that shifts a column without copying the column's data?

opened by dnessett 35
Virtual Column coordinates __xxx_matrix not found / Stride != 8
A strange bug appears when I add a virtual column like

cat.add_virtual_columns_eq2gal('RAout', 'DECout')

I can evaluate the column but when I try to select upon it I get a strange message:

error name '__celestial_eq2gal_matrix' is not defined
opened by mfouesneau 31
Add basic support for accessing struct arrays
This PR continues work described in #1423. It adds support to access individual fields of arrow based struct arrays and wraps them in vaex expressions.

The following additions are made:

Adds StructOperations namespace to Expression.

Adds Expression.struct.get_field and Expression.struct.project.

Adds Expression.struct.field_names and Expression.struct.field_types

Adds shorthand notation to use struct_get_field and struct_project via Expression.__getitem__.

Still to do and to be discussed:

[ ] Add examples to documentation.

[ ] Add changes to changelog.

Please feel free to modify the function names. I'm not 100% sure about the API.
opened by mansenfranzen 29
[BUG-REPORT] - `to_numpy` corrupts numpy array
Thank you for reaching out and helping us improve Vaex!

Before you submit a new Issue, please read through the documentation. Also, make sure you search through the Open and Closed Issues - your problem may already be discussed or addressed.

Description Please provide a clear and concise description of the problem. This should contain all the steps needed to reproduce the problem. A minimal code example that exposes the problem is very appreciated.

When you have a column whose values are a pyarrow Arrays, and you try to convert that to numpy, it corrupts the numpy array and it cannot be worked with directly.

NUM_SAMPLES = 100 WIDTH = 20 vals = [pa.array([random() for _ in range(WIDTH)]) for _ in range(NUM_SAMPLES)] ids = list(range(NUM_SAMPLES)) data = { 'vals': vals, 'id': ids } df = vaex.from_pandas(pd.DataFrame(data)) bad = df['vals'].to_numpy() # np.array of type Object, cannot become a (NUM_SAMPLES,WIDTH) matrix good = np.array(vals) # (NUM_SAMPLES,WIDTH) matrix

In the example above, good is a numpy matrix that you'd expect. But bad is a numpy array of pyarrow arrays. Numpy, for some reason, cannot convert it to a numpy matrix, it thinks the object is of unequal length. The only way to do it is either to

Cast it to a list, then back to numpy

Convert each pa.array to a numpy array first, then call the np.array constructor

Both of those are very slow. Do you have a suggestion for a way to get around this? Thanks!

Software information

Vaex version (import vaex; vaex.__version__):

{'vaex': '4.5.0', 'vaex-core': '4.5.1', 'vaex-viz': '0.5.0', 'vaex-hdf5': '0.10.0', 'vaex-server': '0.6.1', 'vaex-astro': '0.9.0', 'vaex-jupyter': '0.6.0', 'vaex-ml': '0.14.0'}

Vaex was installed via: pip / conda-forge / from source : pip

OS: MacOS big sur

Additional information Please state any supplementary information or provide additional context for the problem (e.g. screenshots, data, etc..).
opened by Ben-Epstein 26

[BUG-REPORT] pip install is broken

there are many other closed issues about this but it still pops up for me. If I do pip install vaex-core vaex-hdf5 and then try to import vaex it it cant find ANY of the submodules. I have also tried the more general pip install vaex with same results.

I have tried everything, reinstalling multiple times, cleared pip cache etc. It just never works somehow. It used to work for me but I am on a new virtual machine and now I cant install anymore.

I am running python3.9 on latest centos. installed packages:

Package         Version
--------------- -----------
aplus           0.11.0
blake3          0.1.8
certifi         2021.5.30
chardet         4.0.0
cloudpickle     1.6.0
dask            2021.6.2
frozendict      2.0.3
fsspec          2021.6.1
future          0.18.2
h5py            3.3.0
idna            2.10
locket          0.2.1
nest-asyncio    1.5.1
numpy           1.21.0
pandas          1.2.5
partd           1.2.0
pip             21.1.2
progressbar2    3.53.1
pyarrow         4.0.1
python-dateutil 2.8.1
python-utils    2.5.6
pytz            2021.1
PyYAML          5.4.1
requests        2.25.1
setuptools      57.0.0
six             1.16.0
tabulate        0.8.9
toolz           0.11.1
urllib3         1.26.6
vaex-core       4.3.0.post1
vaex-hdf5       0.8.0
wheel           0.36.2

when I try to open a hdf5 file I get:

ModuleNotFoundError: No module named 'vaex.hdf5'

very frustrating to not get it to work :( anybody any ideas?

opened by FrankBoermanTenneT 25

Problems with vaex to support Python3

Once we installed vaex using conda the original Python3 was replaced by Python2 when call ipython. In principle vaex supports Python3, how to avoid this?

opened by fprada 25

[FEATURE-REQUEST] Create OHLC from tick data

Description Trying to create OHLC (Open High Low Close) data from price tick data. Can sort of create Open, High, Low, but Open is wrong and times have some offset. Not sure about close.

import pandas as pd
import vaex
import numpy as np
import time
dates = pd.date_range("01-01-2019", "14-04-2020", freq="60s")
num = len(dates)
vdf = vaex.from_arrays(ts=pd.to_datetime(dates), x=np.random.randint(1, 1000, num))
print(vdf.head(17))
print()

# Desired output
print(vdf.to_pandas_df().resample('15Min', on='ts')['x'].ohlc())
print()

# Create Open High Low (Bit off)
vdf2 = vdf.groupby(by=vaex.BinnerTime(vdf.ts, resolution='m', every=15), agg={'O': vaex.agg.first('x', 'ts'), 'H': vaex.agg.max('x'), 'L': vaex.agg.min('x')})
print(vdf2)
print()

# Create Close?
vdf3 = vdf.groupby(by=vaex.BinnerTime(vdf.ts, resolution='m', every=15), agg={'C': vaex.agg.first('x', '-ts')})
print(vdf3)

Output:

#    ts                             x
0    2019-01-01 00:00:00.000000000  5
1    2019-01-01 00:01:00.000000000  20
2    2019-01-01 00:02:00.000000000  690
3    2019-01-01 00:03:00.000000000  434
4    2019-01-01 00:04:00.000000000  686
...  ...                            ...
12   2019-01-01 00:12:00.000000000  182
13   2019-01-01 00:13:00.000000000  530
14   2019-01-01 00:14:00.000000000  659
15   2019-01-01 00:15:00.000000000  929
16   2019-01-01 00:16:00.000000000  734

                     open  high  low  close
ts                                         
2019-01-01 00:00:00     5   894    5    659
2019-01-01 00:15:00   929   929  217    611
2019-01-01 00:30:00   424   966   41    228
2019-01-01 00:45:00    19   977   19     42
2019-01-01 01:00:00   137   989   96    686
...                   ...   ...  ...    ...
2020-04-13 23:00:00   756   994   99    204
2020-04-13 23:15:00   510   847    3      3
2020-04-13 23:30:00   128   898   62    501
2020-04-13 23:45:00   920   937   54    626
2020-04-14 00:00:00   694   694  694    694

[45025 rows x 4 columns]

#       ts                O    H    L
0       2018-12-31 23:59  690  894  5
1       2019-01-01 00:14  929  929  217
2       2019-01-01 00:29  938  966  41
3       2019-01-01 00:44  904  977  19
4       2019-01-01 00:59  96   989  42
...     ...               ...  ...  ...
45,020  2020-04-13 22:59  440  994  3
45,021  2020-04-13 23:14  204  847  117
45,022  2020-04-13 23:29  263  898  3
45,023  2020-04-13 23:44  54   937  54
45,024  2020-04-13 23:59  626  694  626

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
KeyError: "Unknown variables or column: '-ts'"

During handling of the above exception, another exception occurred:

UFuncTypeError: ufunc 'negative' did not contain a loop with signature matching types dtype('<M8[ns]') -> dtype('<M8[ns]')

During handling of the above exception, another exception occurred:

...
KeyError: "Unknown variables or column: '-ts'"

During handling of the above exception, another exception occurred:
...
UFuncTypeError: ufunc 'negative' did not contain a loop with signature matching types dtype('<M8[ns]') -> dtype('<M8[ns]')

Additional Context

Trying to emulate Pandas OHLC

opened by Penacillin 19

[BUG-REPORT] problem using data from parquet/pyarrow in correlation function
Dear Vaex developers,

Description I would like to use the Vaex correlation function to calculate the correlation coefficient between two columns of a dataframe (one type int64, the other type str). I am loading the dataframe from a parquet file generated using the pyarrow engine. It seems that correlation may not work correctly with data in this format.

Here is a reproducer:

#use pandas to create and save dataframe as parquet data = [[0,'hello'], [1,'world'], [3,'this'], [5,'example'], [0,'hello']] pdf = pd.DataFrame(data, columns=['a', 'b'], dtype=int) pdf.to_parquet('test', engine='pyarrow') #now load into vaex vdf = vaex.open('test') #try correlation without .values out = vdf.correlation(vdf.a, vdf.b)

This fails with a traceback that ends in ValueError: could not convert string to float: 'hello'.

I thought I might need to explictly request .values:

#try correlation with .values out = vdf.correlation(vdf.a.values, vdf.b.values)

This fails with a traceback that ends in

ValueError: <pyarrow.lib.Int64Array object at 0x2aaae733da60> [ 0, 1, 3, 5, 0 ] is not of string or Expression type, but <class 'pyarrow.lib.Int64Array'>

Software information

Vaex version:

{'vaex-core': '4.1.0', 'vaex-viz': '0.5.0', 'vaex-hdf5': '0.7.0', 'vaex-server': '0.4.0', 'vaex-astro': '0.8.0', 'vaex-jupyter': '0.6.0', 'vaex-ml': '0.11.1'}

Vaex was installed via: conda-forge

OS: Ubuntu 20.04 (in Docker)

Apologies if I am using Vaex or this function incorrectly.

Thank you very much, Laurie
opened by lastephey 19
AttributeError: module 'vaex' has no attribute 'from_csv'
I have attempted to utilize vaex, but have been unsuccessful. Reason unknown. I am using the most recent version of anaconda 3. I just uninstalled and reinstalled the most recent version as I was experiencing this same problem and figured a fresh install may help to clear the issue. The same error is appearing. I have copied and pasted the console here.

Python 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] Type "copyright", "credits" or "license" for more information.

IPython 7.19.0 -- An enhanced Interactive Python.

conda install -c conda-forge vaex Collecting package metadata (current_repodata.json): ...working... done Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve. Solving environment: ...working... failed with repodata from current_repodata.json, will retry with next repodata source. Collecting package metadata (repodata.json): ...working... done Solving environment: ...working... done

Package Plan

environment location: C:\Users\sutei\anaconda3

added / updated specs: - vaex

The following packages will be downloaded:

package | build ---------------------------|----------------- abseil-cpp-20200225.2 | ha925a31_2 1.9 MB conda-forge aplus-0.11.0 | py_1 6 KB conda-forge arrow-cpp-2.0.0 |py38h647f3f1_6_cpu 13.4 MB conda-forge aws-c-common-0.4.59 | h8ffe710_1 151 KB conda-forge aws-c-event-stream-0.1.6 | hb4e73fc_6 26 KB conda-forge aws-checksums-0.1.10 | h6f0a1a5_0 51 KB conda-forge aws-sdk-cpp-1.8.70 | he2782d2_1 2.9 MB conda-forge boto3-1.17.19 | pyhd8ed1ab_0 70 KB conda-forge botocore-1.20.19 | pyhd8ed1ab_0 4.5 MB conda-forge bqplot-0.12.20 | pyhd8ed1ab_0 1.0 MB conda-forge branca-0.4.2 | pyhd8ed1ab_0 26 KB conda-forge brotli-1.0.9 | h0e60522_4 882 KB conda-forge c-ares-1.17.1 | h8ffe710_0 109 KB conda-forge cachetools-4.2.1 | pyhd8ed1ab_0 13 KB conda-forge conda-4.9.2 | py38haa244fe_0 3.1 MB conda-forge geos-3.9.1 | h39d44d4_2 1.1 MB conda-forge gflags-2.2.2 | ha925a31_1004 80 KB conda-forge glog-0.4.0 | h0174b99_3 83 KB conda-forge grpc-cpp-1.33.2 | h59b151f_1 14.0 MB conda-forge ipydatawidgets-4.2.0 | pyhd3deb0d_0 171 KB conda-forge ipyleaflet-0.13.4 | pyhd3deb0d_0 4.3 MB conda-forge ipympl-0.5.8 | pyh9f0ad1d_0 506 KB conda-forge ipyvolume-0.6.0a6 | pyh9f0ad1d_0 5.1 MB conda-forge ipyvue-1.5.0 | pyhd3deb0d_0 2.2 MB conda-forge ipyvuetify-1.6.2 | pyh44b312d_0 7.7 MB conda-forge ipywebrtc-0.5.0 | py38h32f6830_1 877 KB conda-forge jmespath-0.10.0 | pyh9f0ad1d_0 21 KB conda-forge

Note: you may need to restart the kernel to use updated packages.

libprotobuf-3.13.0.1 | h200bbdf_0 2.3 MB conda-forge libthrift-0.13.0 | hdfef310_6 1.6 MB conda-forge libutf8proc-2.6.1 | hcb41399_0 98 KB conda-forge openssl-1.1.1h | he774522_0 5.8 MB conda-forge parquet-cpp-1.5.1 | 2 3 KB conda-forge pcre-8.44 | ha925a31_0 498 KB conda-forge progressbar2-3.53.1 | pyh9f0ad1d_0 25 KB conda-forge pyarrow-2.0.0 |py38ha37a76c_6_cpu 2.0 MB conda-forge python-utils-2.5.5 | pyh44b312d_0 15 KB conda-forge python_abi-3.8 | 1_cp38 4 KB conda-forge pythreejs-2.3.0 | pyhd8ed1ab_0 2.6 MB conda-forge re2-2020.11.01 | h0e60522_0 468 KB conda-forge s3fs-0.2.2 | py_0 20 KB conda-forge s3transfer-0.3.4 | pyhd8ed1ab_0 51 KB conda-forge shapely-1.7.1 | py38h2426642_4 420 KB conda-forge snappy-1.1.8 | ha925a31_3 50 KB conda-forge tabulate-0.8.9 | pyhd8ed1ab_0 26 KB conda-forge traittypes-0.2.1 | pyh9f0ad1d_2 10 KB conda-forge vaex-3.0.0 | pyh9f0ad1d_0 9 KB conda-forge vaex-arrow-0.5.1 | pyh9f0ad1d_0 10 KB conda-forge vaex-astro-0.7.0 | pyh9f0ad1d_0 13 KB conda-forge vaex-core-2.0.3 | py38h4c96930_1 1.5 MB conda-forge vaex-hdf5-0.6.0 | pyh9f0ad1d_0 13 KB conda-forge vaex-jupyter-0.5.2 | pyh9f0ad1d_0 36 KB conda-forge vaex-ml-0.9.0 | pyh9f0ad1d_0 76 KB conda-forge vaex-server-0.3.1 | pyh9f0ad1d_0 14 KB conda-forge vaex-viz-0.4.0 | pyh9f0ad1d_0 18 KB conda-forge xarray-0.17.0 | pyhd8ed1ab_0 561 KB conda-forge ------------------------------------------------------------ Total: 82.3 MB

The following NEW packages will be INSTALLED:

abseil-cpp conda-forge/win-64::abseil-cpp-20200225.2-ha925a31_2 aplus conda-forge/noarch::aplus-0.11.0-py_1 arrow-cpp conda-forge/win-64::arrow-cpp-2.0.0-py38h647f3f1_6_cpu aws-c-common conda-forge/win-64::aws-c-common-0.4.59-h8ffe710_1 aws-c-event-stream conda-forge/win-64::aws-c-event-stream-0.1.6-hb4e73fc_6 aws-checksums conda-forge/win-64::aws-checksums-0.1.10-h6f0a1a5_0 aws-sdk-cpp conda-forge/win-64::aws-sdk-cpp-1.8.70-he2782d2_1 boto3 conda-forge/noarch::boto3-1.17.19-pyhd8ed1ab_0 botocore conda-forge/noarch::botocore-1.20.19-pyhd8ed1ab_0 bqplot conda-forge/noarch::bqplot-0.12.20-pyhd8ed1ab_0 branca conda-forge/noarch::branca-0.4.2-pyhd8ed1ab_0 brotli conda-forge/win-64::brotli-1.0.9-h0e60522_4 c-ares conda-forge/win-64::c-ares-1.17.1-h8ffe710_0 cachetools conda-forge/noarch::cachetools-4.2.1-pyhd8ed1ab_0 geos conda-forge/win-64::geos-3.9.1-h39d44d4_2 gflags conda-forge/win-64::gflags-2.2.2-ha925a31_1004 glog conda-forge/win-64::glog-0.4.0-h0174b99_3 grpc-cpp conda-forge/win-64::grpc-cpp-1.33.2-h59b151f_1 ipydatawidgets conda-forge/noarch::ipydatawidgets-4.2.0-pyhd3deb0d_0 ipyleaflet conda-forge/noarch::ipyleaflet-0.13.4-pyhd3deb0d_0 ipympl conda-forge/noarch::ipympl-0.5.8-pyh9f0ad1d_0 ipyvolume conda-forge/noarch::ipyvolume-0.6.0a6-pyh9f0ad1d_0 ipyvue conda-forge/noarch::ipyvue-1.5.0-pyhd3deb0d_0 ipyvuetify conda-forge/noarch::ipyvuetify-1.6.2-pyh44b312d_0 ipywebrtc conda-forge/win-64::ipywebrtc-0.5.0-py38h32f6830_1 jmespath conda-forge/noarch::jmespath-0.10.0-pyh9f0ad1d_0 libprotobuf conda-forge/win-64::libprotobuf-3.13.0.1-h200bbdf_0 libthrift conda-forge/win-64::libthrift-0.13.0-hdfef310_6 libutf8proc conda-forge/win-64::libutf8proc-2.6.1-hcb41399_0 parquet-cpp conda-forge/noarch::parquet-cpp-1.5.1-2 pcre conda-forge/win-64::pcre-8.44-ha925a31_0 progressbar2 conda-forge/noarch::progressbar2-3.53.1-pyh9f0ad1d_0 pyarrow conda-forge/win-64::pyarrow-2.0.0-py38ha37a76c_6_cpu python-utils conda-forge/noarch::python-utils-2.5.5-pyh44b312d_0 python_abi conda-forge/win-64::python_abi-3.8-1_cp38 pythreejs conda-forge/noarch::pythreejs-2.3.0-pyhd8ed1ab_0 re2 conda-forge/win-64::re2-2020.11.01-h0e60522_0 s3fs conda-forge/noarch::s3fs-0.2.2-py_0 s3transfer conda-forge/noarch::s3transfer-0.3.4-pyhd8ed1ab_0 shapely conda-forge/win-64::shapely-1.7.1-py38h2426642_4 snappy conda-forge/win-64::snappy-1.1.8-ha925a31_3 tabulate conda-forge/noarch::tabulate-0.8.9-pyhd8ed1ab_0 traittypes conda-forge/noarch::traittypes-0.2.1-pyh9f0ad1d_2 vaex conda-forge/noarch::vaex-3.0.0-pyh9f0ad1d_0 vaex-arrow conda-forge/noarch::vaex-arrow-0.5.1-pyh9f0ad1d_0 vaex-astro conda-forge/noarch::vaex-astro-0.7.0-pyh9f0ad1d_0 vaex-core conda-forge/win-64::vaex-core-2.0.3-py38h4c96930_1 vaex-hdf5 conda-forge/noarch::vaex-hdf5-0.6.0-pyh9f0ad1d_0 vaex-jupyter conda-forge/noarch::vaex-jupyter-0.5.2-pyh9f0ad1d_0 vaex-ml conda-forge/noarch::vaex-ml-0.9.0-pyh9f0ad1d_0 vaex-server conda-forge/noarch::vaex-server-0.3.1-pyh9f0ad1d_0 vaex-viz conda-forge/noarch::vaex-viz-0.4.0-pyh9f0ad1d_0 xarray conda-forge/noarch::xarray-0.17.0-pyhd8ed1ab_0

The following packages will be SUPERSEDED by a higher-priority channel:

conda pkgs/main::conda-4.9.2-py38haa95532_0 --> conda-forge::conda-4.9.2-py38haa244fe_0 openssl pkgs/main --> conda-forge

Downloading and Extracting Packages

vaex-core-2.0.3 | 1.5 MB | | 0% vaex-core-2.0.3 | 1.5 MB | 1 | 1% vaex-core-2.0.3 | 1.5 MB | ####9 | 49% vaex-core-2.0.3 | 1.5 MB | ########## | 100% vaex-core-2.0.3 | 1.5 MB | ########## | 100%

boto3-1.17.19 | 70 KB | | 0% boto3-1.17.19 | 70 KB | ########## | 100% boto3-1.17.19 | 70 KB | ########## | 100%

libprotobuf-3.13.0.1 | 2.3 MB | | 0% libprotobuf-3.13.0.1 | 2.3 MB | #######3 | 73% libprotobuf-3.13.0.1 | 2.3 MB | ########## | 100% libprotobuf-3.13.0.1 | 2.3 MB | ########## | 100%

grpc-cpp-1.33.2 | 14.0 MB | | 0% grpc-cpp-1.33.2 | 14.0 MB | | 0% grpc-cpp-1.33.2 | 14.0 MB | 2 | 2% grpc-cpp-1.33.2 | 14.0 MB | 8 | 9% grpc-cpp-1.33.2 | 14.0 MB | ##1 | 22% grpc-cpp-1.33.2 | 14.0 MB | ####1 | 41% grpc-cpp-1.33.2 | 14.0 MB | ###### | 60% grpc-cpp-1.33.2 | 14.0 MB | ######## | 80% grpc-cpp-1.33.2 | 14.0 MB | #########9 | 100% grpc-cpp-1.33.2 | 14.0 MB | ########## | 100%

pythreejs-2.3.0 | 2.6 MB | | 0% pythreejs-2.3.0 | 2.6 MB | ###### | 60% pythreejs-2.3.0 | 2.6 MB | ########## | 100% pythreejs-2.3.0 | 2.6 MB | ########## | 100%

vaex-hdf5-0.6.0 | 13 KB | | 0% vaex-hdf5-0.6.0 | 13 KB | ########## | 100%

openssl-1.1.1h | 5.8 MB | | 0% openssl-1.1.1h | 5.8 MB | 1 | 2% openssl-1.1.1h | 5.8 MB | ###8 | 38% openssl-1.1.1h | 5.8 MB | ########8 | 88% openssl-1.1.1h | 5.8 MB | ########## | 100%

ipyvue-1.5.0 | 2.2 MB | | 0% ipyvue-1.5.0 | 2.2 MB | #######1 | 72% ipyvue-1.5.0 | 2.2 MB | ########## | 100% ipyvue-1.5.0 | 2.2 MB | ########## | 100%

xarray-0.17.0 | 561 KB | | 0% xarray-0.17.0 | 561 KB | ########2 | 83% xarray-0.17.0 | 561 KB | ########## | 100%

conda-4.9.2 | 3.1 MB | | 0% conda-4.9.2 | 3.1 MB | #####3 | 54% conda-4.9.2 | 3.1 MB | ########## | 100% conda-4.9.2 | 3.1 MB | ########## | 100%

ipympl-0.5.8 | 506 KB | | 0% ipympl-0.5.8 | 506 KB | ########## | 100% ipympl-0.5.8 | 506 KB | ########## | 100%

libutf8proc-2.6.1 | 98 KB | | 0% libutf8proc-2.6.1 | 98 KB | ########## | 100%

aws-checksums-0.1.10 | 51 KB | | 0% aws-checksums-0.1.10 | 51 KB | ########## | 100%

ipyvolume-0.6.0a6 | 5.1 MB | | 0% ipyvolume-0.6.0a6 | 5.1 MB | ##9 | 29% ipyvolume-0.6.0a6 | 5.1 MB | ########1 | 82% ipyvolume-0.6.0a6 | 5.1 MB | ########## | 100%

snappy-1.1.8 | 50 KB | | 0% snappy-1.1.8 | 50 KB | ########## | 100%

parquet-cpp-1.5.1 | 3 KB | | 0% parquet-cpp-1.5.1 | 3 KB | ########## | 100%

brotli-1.0.9 | 882 KB | | 0% brotli-1.0.9 | 882 KB | ########## | 100% brotli-1.0.9 | 882 KB | ########## | 100%

arrow-cpp-2.0.0 | 13.4 MB | | 0% arrow-cpp-2.0.0 | 13.4 MB | | 0% arrow-cpp-2.0.0 | 13.4 MB | 8 | 9% arrow-cpp-2.0.0 | 13.4 MB | #8 | 19% arrow-cpp-2.0.0 | 13.4 MB | ###4 | 35% arrow-cpp-2.0.0 | 13.4 MB | ####5 | 45% arrow-cpp-2.0.0 | 13.4 MB | #####7 | 57% arrow-cpp-2.0.0 | 13.4 MB | ######7 | 68% arrow-cpp-2.0.0 | 13.4 MB | #######7 | 78% arrow-cpp-2.0.0 | 13.4 MB | ######### | 91% arrow-cpp-2.0.0 | 13.4 MB | ########## | 100%

branca-0.4.2 | 26 KB | | 0% branca-0.4.2 | 26 KB | ########## | 100%

ipyleaflet-0.13.4 | 4.3 MB | | 0% ipyleaflet-0.13.4 | 4.3 MB | 5 | 5% ipyleaflet-0.13.4 | 4.3 MB | ######7 | 68% ipyleaflet-0.13.4 | 4.3 MB | ########## | 100% ipyleaflet-0.13.4 | 4.3 MB | ########## | 100%

vaex-astro-0.7.0 | 13 KB | | 0% vaex-astro-0.7.0 | 13 KB | ########## | 100%

gflags-2.2.2 | 80 KB | | 0% gflags-2.2.2 | 80 KB | ########## | 100%

libthrift-0.13.0 | 1.6 MB | | 0% libthrift-0.13.0 | 1.6 MB | #####4 | 55% libthrift-0.13.0 | 1.6 MB | ########## | 100% libthrift-0.13.0 | 1.6 MB | ########## | 100%

abseil-cpp-20200225. | 1.9 MB | | 0% abseil-cpp-20200225. | 1.9 MB | | 1% abseil-cpp-20200225. | 1.9 MB | ########## | 100% abseil-cpp-20200225. | 1.9 MB | ########## | 100%

progressbar2-3.53.1 | 25 KB | | 0% progressbar2-3.53.1 | 25 KB | ########## | 100%

pcre-8.44 | 498 KB | | 0% pcre-8.44 | 498 KB | ########## | 100% pcre-8.44 | 498 KB | ########## | 100%

pyarrow-2.0.0 | 2.0 MB | | 0% pyarrow-2.0.0 | 2.0 MB | | 1% pyarrow-2.0.0 | 2.0 MB | #########2 | 93% pyarrow-2.0.0 | 2.0 MB | ########## | 100%

c-ares-1.17.1 | 109 KB | | 0% c-ares-1.17.1 | 109 KB | ########## | 100% c-ares-1.17.1 | 109 KB | ########## | 100%

shapely-1.7.1 | 420 KB | | 0% shapely-1.7.1 | 420 KB | ########## | 100% shapely-1.7.1 | 420 KB | ########## | 100%

aws-sdk-cpp-1.8.70 | 2.9 MB | | 0% aws-sdk-cpp-1.8.70 | 2.9 MB | ##6 | 27% aws-sdk-cpp-1.8.70 | 2.9 MB | ########6 | 87% aws-sdk-cpp-1.8.70 | 2.9 MB | ########## | 100%

vaex-server-0.3.1 | 14 KB | | 0% vaex-server-0.3.1 | 14 KB | ########## | 100%

python-utils-2.5.5 | 15 KB | | 0% python-utils-2.5.5 | 15 KB | ########## | 100%

vaex-viz-0.4.0 | 18 KB | | 0% vaex-viz-0.4.0 | 18 KB | ########## | 100%

s3fs-0.2.2 | 20 KB | | 0% s3fs-0.2.2 | 20 KB | ########## | 100%

ipyvuetify-1.6.2 | 7.7 MB | | 0% ipyvuetify-1.6.2 | 7.7 MB | 4 | 5% ipyvuetify-1.6.2 | 7.7 MB | #8 | 19% ipyvuetify-1.6.2 | 7.7 MB | ##3 | 24% ipyvuetify-1.6.2 | 7.7 MB | ####1 | 41% ipyvuetify-1.6.2 | 7.7 MB | #####5 | 55% ipyvuetify-1.6.2 | 7.7 MB | #######7 | 78% ipyvuetify-1.6.2 | 7.7 MB | #########6 | 96% ipyvuetify-1.6.2 | 7.7 MB | ########## | 100%

aws-c-common-0.4.59 | 151 KB | | 0% aws-c-common-0.4.59 | 151 KB | ########## | 100% aws-c-common-0.4.59 | 151 KB | ########## | 100%

geos-3.9.1 | 1.1 MB | | 0% geos-3.9.1 | 1.1 MB | ########2 | 82% geos-3.9.1 | 1.1 MB | ########## | 100%

s3transfer-0.3.4 | 51 KB | | 0% s3transfer-0.3.4 | 51 KB | ########## | 100%

vaex-jupyter-0.5.2 | 36 KB | | 0% vaex-jupyter-0.5.2 | 36 KB | ########## | 100%

glog-0.4.0 | 83 KB | | 0% glog-0.4.0 | 83 KB | ########## | 100%

jmespath-0.10.0 | 21 KB | | 0% jmespath-0.10.0 | 21 KB | ########## | 100%

vaex-ml-0.9.0 | 76 KB | | 0% vaex-ml-0.9.0 | 76 KB | ########## | 100%

aws-c-event-stream-0 | 26 KB | | 0% aws-c-event-stream-0 | 26 KB | ########## | 100%

ipydatawidgets-4.2.0 | 171 KB | | 0% ipydatawidgets-4.2.0 | 171 KB | ########## | 100% ipydatawidgets-4.2.0 | 171 KB | ########## | 100%

bqplot-0.12.20 | 1.0 MB | | 0% bqplot-0.12.20 | 1.0 MB | 1 | 2% bqplot-0.12.20 | 1.0 MB | ########## | 100% bqplot-0.12.20 | 1.0 MB | ########## | 100%

tabulate-0.8.9 | 26 KB | | 0% tabulate-0.8.9 | 26 KB | ########## | 100%

vaex-3.0.0 | 9 KB | | 0% vaex-3.0.0 | 9 KB | ########## | 100%

aplus-0.11.0 | 6 KB | | 0% aplus-0.11.0 | 6 KB | ########## | 100%

vaex-arrow-0.5.1 | 10 KB | | 0% vaex-arrow-0.5.1 | 10 KB | ########## | 100%

botocore-1.20.19 | 4.5 MB | | 0% botocore-1.20.19 | 4.5 MB | 9 | 9% botocore-1.20.19 | 4.5 MB | ##7 | 28% botocore-1.20.19 | 4.5 MB | #####1 | 52% botocore-1.20.19 | 4.5 MB | #######6 | 77% botocore-1.20.19 | 4.5 MB | ########## | 100% botocore-1.20.19 | 4.5 MB | ########## | 100%

python_abi-3.8 | 4 KB | | 0% python_abi-3.8 | 4 KB | ########## | 100%

cachetools-4.2.1 | 13 KB | | 0% cachetools-4.2.1 | 13 KB | ########## | 100%

traittypes-0.2.1 | 10 KB | | 0% traittypes-0.2.1 | 10 KB | ########## | 100%

re2-2020.11.01 | 468 KB | | 0% re2-2020.11.01 | 468 KB | ########## | 100% re2-2020.11.01 | 468 KB | ########## | 100%

ipywebrtc-0.5.0 | 877 KB | | 0% ipywebrtc-0.5.0 | 877 KB | ########7 | 88% ipywebrtc-0.5.0 | 877 KB | ########## | 100% Preparing transaction: ...working... done Verifying transaction: ...working... done Executing transaction: ...working... done

runfile('C:/Users/sutei/Desktop/CASEY/vaex2.py', wdir='C:/Users/sutei/Desktop/CASEY')

WARNING: This is not valid Python code. If you want to use IPython magics, flexible indentation, and prompt removal, please save this file with the .ipy extension. This will be an error in a future version of Spyder.

<class 'pandas.core.frame.DataFrame'> RangeIndex: 100000 entries, 0 to 99999 Data columns (total 10 columns):

Column Non-Null Count Dtype

0 c0 100000 non-null int32 1 c1 100000 non-null int32 2 c2 100000 non-null int32 3 c3 100000 non-null int32 4 c4 100000 non-null int32 5 c5 100000 non-null int32 6 c6 100000 non-null int32 7 c7 100000 non-null int32 8 c8 100000 non-null int32 9 c9 100000 non-null int32 dtypes: int32(10) memory usage: 3.8 MB Traceback (most recent call last):

File "C:\Users\sutei\Desktop\CASEY\vaex2.py", line 25, in vaex_df = vaex.from_csv(filepath, convert = True, chunk_size = 5_000_000)

AttributeError: module 'vaex' has no attribute 'from_csv'

Code: import vaex import pandas as pd import numpy as np

n_rows = 100000 n_cols = 10 df = pd.DataFrame(np.random.randint(0, 100, size=(n_rows, n_cols)), columns=['c%d' % i for i in range(n_cols)])

df.info(memory_usage='deep')

creating .csv files

filepath = 'main_dataset.csv' df.to_csv(filepath, index=False)

create hdf5 files

vaex_df = vaex.from_csv(filepath, convert = True, chunk_size = 5_000_000)

type(vaex_df)
opened by steveentrehub 19

csv file without header not supported

Hi, I am trying to load a CSV with no header using

df = vaex.open('data/star0000-1.csv',sep=",", header=None, error_bad_lines=False)

but I get

could not convert column 0, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 0, error: TypeError('getattr(): attribute name must be string')
could not convert column 1, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 1, error: TypeError('getattr(): attribute name must be string')
could not convert column 2, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 2, error: TypeError('getattr(): attribute name must be string')
could not convert column 3, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 3, error: TypeError('getattr(): attribute name must be string')
could not convert column 4, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 4, error: TypeError('getattr(): attribute name must be string')
could not convert column 5, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 5, error: TypeError('getattr(): attribute name must be string')
could not convert column 6, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 6, error: TypeError('getattr(): attribute name must be string')
could not convert column 7, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 7, error: TypeError('getattr(): attribute name must be string')
could not convert column 8, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 8, error: TypeError('getattr(): attribute name must be string')
could not convert column 9, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 9, error: TypeError('getattr(): attribute name must be string')
could not convert column 10, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 10, error: TypeError('getattr(): attribute name must be string')
could not convert column 11, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 11, error: TypeError('getattr(): attribute name must be string')

Also it takes like 20x the time to do nothing, any help?

enhancement help wanted priority: low good first issue

opened by argenisleon 19

[BUG-REPORT] `tolist` is much slower than `to_numpy().tolist()`
Thank you for reaching out and helping us improve Vaex!

Before you submit a new Issue, please read through the documentation. Also, make sure you search through the Open and Closed Issues - your problem may already be discussed or addressed.

Description Please provide a clear and concise description of the problem. This should contain all the steps needed to reproduce the problem. A minimal code example that exposes the problem is very appreciated.

Software information

Vaex version (import vaex; vaex.__version__): {'vaex-core': '4.16.0', 'vaex-hdf5': '0.12.2'}

Vaex was installed via: pip / conda-forge / from source

OS:

Additional information

import vaex df = vaex.example() df.export("file.arrow") df2 = vaex.open("file.arrow") # time this with vaex.cache.off(): df2.id.tolist() # vs this with vaex.cache.off(): df2.id.to_numpy.tolilst()
opened by Ben-Epstein 0

[BUG-REPORT] Can't do binary ops between expressions from copied DFs

Description

import vaex

df = vaex.from_arrays(x=[1, 2, 3])
df2 = df.copy()
df.x + df2.x

raises:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
Cell In[5], line 5
      3 df = vaex.from_arrays(x=[1, 2, 3])
      4 df2 = df.copy()
----> 5 df.x + df2.x

File ~/Library/Application Support/hatch/env/virtual/noatak-UM6-FHel/noatak/lib/python3.10/site-packages/vaex/expression.py:144, in Meta.__new__.<locals>.wrap.<locals>.f(a, b)
    142 else:
    143     if isinstance(b, Expression):
--> 144         assert b.ds == a.ds
    145         b = b.expression
    146     elif isinstance(b, (np.timedelta64)):

AssertionError:

I'm not sure if this is a bug. It seems to me as though it should be: Both df and df2 are from the same source of data, and that should be the important thing that should make this operation possible. But vaex complains that df and df2 aren't the exact same DataFrame. Am I missing something though?

Software information

Vaex version (import vaex; vaex.__version__): {'vaex': '4.16.0', 'vaex-core': '4.16.1', 'vaex-viz': '0.5.4', 'vaex-hdf5': '0.14.1', 'vaex-server': '0.8.1', 'vaex-astro': '0.9.3', 'vaex-jupyter': '0.8.1', 'vaex-ml': '0.18.1'}

opened by NickCrews 0

[BUG-REPORT] Problems unlinking shared memory of arrays after dataframe deletion
I'm trying to use vaex with numpy arrays that reference shared memory and experience problems when trying to unlink the shared memory. Here a minimal reproducing example:

import numpy as np from multiprocessing import shared_memory import time import vaex shm = shared_memory.SharedMemory(create=True, size=8) arr = np.frombuffer(shm.buf, dtype="uint8", count=8) df = vaex.from_dict(dict(x=arr)) del arr del df time.sleep(2) shm.close() shm.unlink()

Execution throws the following exception:

Traceback (most recent call last): File "<...>\memory_test.py", line 15, in <module> shm.close() File "<...>\.pyenv\pyenv-win\versions\3.9.10\lib\multiprocessing\shared_memory.py", line 227, in close self._mmap.close() BufferError: cannot close exported pointers exist

It works fine when not creating the dataframe object.

It seems like vaex is still keeping a reference to the array/shm block after deleting the dataframe object. Is that a bug or is there a recommended way to delete all references?

Software information

Vaex version (import vaex; vaex.__version__): {'vaex': '4.16.0', 'vaex-core': '4.16.1', 'vaex-viz': '0.5.4', 'vaex-hdf5': '0.14.1', 'vaex-server': '0.8.1', 'vaex-astro': '0.9.3', 'vaex-jupyter': '0.8.1', 'vaex-ml': '0.18.1'}

Vaex was installed via: pip

OS: Windows 10

Python: 3.9.10
opened by schwingkopf 0
Issue when trying to use interactive heatmap (df.widget.heatmap --> NameError: name 'observe' is not defined)
I was following your article (https://towardsdatascience.com/vaex-out-of-core-dataframes-for-python-and-fast-visualization-12c102db044a) and tried to reproduce it locally (on an Azure AML compute instance)

Whilst most things work as expected, creating an interactive heatmap fails. I am using the yellow taxi 2015 hdf5 file you provide in your datasets section. Invoking df_yt_2015.widget.heatmap(df_yt_2015.dropoff_longitude, df_yt_2015.dropoff_latitude, shape=400, f='log1p', controls_selection=True) fails with:

File /anaconda/envs/my_env/lib/python3.8/site-packages/vaex/jupyter/widgets.py:517, in ToolsToolbar() 513 @traitlets.default('template') 514 def _template(self): 515 return load_template('vue/tools-toolbar.vue') --> 517 @observe('z_normalize') 518 def _observe_normalize(self, change): 519 self.normalize = bool(self.z_normalize) NameError: name 'observe' is not defined

I didn't have time to dig further into it.
opened by meierale 0
Issue on page /faq.html

I am not able to install via pip install vaex

I install python version 3.11.1, error given below which I am getting when I try to install

Using cached numba-0.56.4.tar.gz (2.4 MB) Preparing metadata (setup.py) ... error error: subprocess-exited-with-error

× python setup.py egg_info did not run successfully. │ exit code: 1 ╰─> [8 lines of output] Traceback (most recent call last): File "", line 2, in File "", line 34, in File "C:\Users\HP\AppData\Local\Temp\pip-install-ksldzaij\numba_1712998ee6f0470e807e228c6b892e9b\setup.py", line 51, in _guard_py_ver() File "C:\Users\HP\AppData\Local\Temp\pip-install-ksldzaij\numba_1712998ee6f0470e807e228c6b892e9b\setup.py", line 48, in _guard_py_ver raise RuntimeError(msg.format(cur_py, min_py, max_py)) RuntimeError: Cannot install on Python version 3.11.1; only versions >=3.7,<3.11 are supported. [end of output]

note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed

× Encountered error while generating package metadata. ╰─> See above for output.

note: This is an issue with the package mentioned above, not pip. hint: See above for details.

opened by navikaran1 0
[BUG-REPORT] Runtime error occurs when joining dataframes

Hi Vaex Team,

Description When joining two dataframes, getting runtime error saying Oops, get an empty chunk, from 33112 to 33112, that should not happen. First dataframe df1 is filtered and aggregated. Second dataframe df2 is simply the source dataframe filtered. Size of both left and right dataframes is 33112. I am using c5.x large EC2 instance. Can you please confirm if it is a bug or I am missing any parameter to be passed ?

Software information

Vaex version 4.14.0 Vaex was installed via: pip OS: Linux Additional information Jupyter notebook and sample data attached

Additional information Error Log Attached

opened by vignesh-bungee 0

Releases(vaexpaper_v1)

vaexpaper_v1(Mar 29, 2018)

Source code(tar.gz)
Source code(zip)
1.0.0-beta.1(Jun 10, 2016)

Beta release
Source code(tar.gz)
Source code(zip)
vaex-1.0.0-beta.1-linux.tar.gz(199.30 MB)
vaex-1.0.0-beta.1-osx.zip(193.08 MB)
0.3.10(Feb 27, 2015)
Main changes

added layers: overplot different datasets

2^31 rows supported

undo/redo for navigation and selections

selections by logical expressions

exporting a matplotlib template and data

added MIT license

Source code(tar.gz)
Source code(zip)
vaex-0.3.10-linux.tar.gz(117.59 MB)
vaex-0.3.10-osx.zip(94.98 MB)
v0.2(Feb 25, 2015)

This is the first version using volume rendering for 3d views. Also attached is a larger dataset used in Helmi and de Zeeuw 2000.
Source code(tar.gz)
Source code(zip)
helmi-dezeeuw-2000-FeH.hdf5(251.77 MB)
vaex-0.2.1-linux.tar.gz(206.33 MB)
vaex-0.2.1-osx.zip(120.86 MB)

Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀

Related tags

Overview

What is Vaex?

Installing

Key features

Instant opening of Huge data files (memory mapping)

Expression system

Out-of-core DataFrame

Fast groupby / aggregations

Fast and efficient join

More features

Learn more about Vaex

Comments

Package Plan

Column Non-Null Count Dtype

creating .csv files

create hdf5 files

Releases(vaexpaper_v1)

vaexpaper_v1(Mar 29, 2018)

1.0.0-beta.1(Jun 10, 2016)

0.3.10(Feb 27, 2015)

v0.2(Feb 25, 2015)

Owner

vaex io

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Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀

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