syagev
commented
Nov 8, 2020
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pydata
Here are 83 public repositories matching this topic...
Extract data from a wide range of Internet sources into a pandas DataFrame.
python
html
finance
data
pydata
pandas
fred
dataset
data-analysis
stock-data
financial-data
economic-data
fama-french
econdb
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Nov 18, 2020 - Python
seanlaw
commented
Oct 29, 2020
jrbourbeau
commented
Apr 9, 2020
In our API docs we currently use
.. autosummary::
Client
Client.call_stack
Client.cancel
...
To generate a table of Client methods at the top of the page. Later on we use
.. autoclass:: Client
:members:
to display the docstrings for all the public methods on Client (here an example for
evan-anderson-ts
commented
Oct 6, 2020
Instructions for how to install pyjanitor via pipenv
Some folks might use pipenv for environment management. The recent update requires a prerelease dependency (black, as menti
PyData, The Complete Works of
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Dec 27, 2016
lr4d
commented
Oct 8, 2020
Problem description
Our dask update graphs are not properly optimized.
We ussually use dask.dataframe optimization and set ave_width=repartition_ratio for kartothek.io.dask.dataframe.update_dataset_from_ddf graphs. We should return an optimized graph from update_dataset_from_ddf to make our users' life simple.
We already have code that does this, whoever picks this up can ping me
Notebooks for the Seattle PyData 2017 talk on Scattertext
visualization
nlp
natural-language-processing
word2vec
pydata
political-science
gender
political-parties
computational-social-science
text-visualization
text-as-data
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Jan 12, 2018 - HTML
Machine learning with scikit-learn tutorial at PyData Chicago 2016
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Oct 18, 2016 - Jupyter Notebook
randyzwitch
commented
Mar 28, 2019
In trying to write tests for #189, I'm finding very difficult to add columns to existing tests, as in some cases like the all_types table, the table is defined in a separate file than the tests and multiple tests try to write to the same table.
Additionally, our test suite doesn't prove that the data that are uploaded are the same as the data downloaded for all types.
We should consider m
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vtreat is a data frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. Distributed under a BSD-3-Clause license.
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Oct 17, 2020 - Python
Repo for my talk at the PyData Berlin 2017 conference
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Jul 30, 2017 - Jupyter Notebook
Introduction to Machine Learning with Time Series at PyData Festival Amsterdam 2020
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Nov 5, 2020 - Jupyter Notebook
Companion repo to the GitBook, notes on Data Science topics
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Nov 11, 2020 - Jupyter Notebook
Social network analyses code examples for PyCon 2019 talk
python
social-networks
social-network
network
pydata
network-theory
jupyter-notebook
pagerank
game-of-thrones
pandas
networkx
graph-theory
pycon
social-network-analysis
betweenness-centrality
influence-maximization
eurovision
information-flow
information-diffusion
pycon2019
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Jun 4, 2020 - Jupyter Notebook
Slides and notebooks for my tutorial at PyData London 2018
tutorial
deep-learning
optimization
pydata
linear-programming
survival-analysis
gradient-descent
quadratic-programming
convex-optimization
elastic-net
stochastic-gradient-descent
maximum-likelihood-estimation
quantile-regression
multilayer-perceptron
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Jul 2, 2018 - Jupyter Notebook
This is the code and presentation for my PyData2017 talk "Reverse Image Search Using Out-of-the-box Machine Learning Libraries
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Aug 14, 2018 - HTML
tomwhite
commented
Sep 16, 2020
The assert_array_shape call is used a lot in REGENIE, and serves both as a runtime check and as documentation for the reader. We should sprinkle it liberally through other functions.
@matthewbrems and I presented "Recreating, Understanding, and Visualizing FiveThirtyEight's Elections Forecast" at PyData DC 2018
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Nov 16, 2018 - Jupyter Notebook
Pydata 2017 workshop: build a clickbait detector with python
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Sep 14, 2017 - Jupyter Notebook
Material for working alongside my workshop session at PyData Berlin 2018
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Sep 24, 2018 - Shell
Implementation of Neural Image Captioning model using Keras with Theano backend
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Sep 2, 2017 - Jupyter Notebook
An example of how the LIME algorithm can be used to provide real-world insight into the decision processes of a 'black-box' machine learning algorithm - in this case a Radom Forest regressor.
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Feb 19, 2019 - Jupyter Notebook
Battle-hardened advice on efficient data loading for deep learning on videos.
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Jul 9, 2018 - Python
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