I was having a very hard time figuring out

fill = A.stack().mean()
A.add(B, fill_value=fill)

fill = 4.5. However I computed a value of 3.2 because I was taking the mean from the column of A not the DataFrame A.
This coming after the Indexing chapter where "explicit is better than implicit." I was thinking that this should be a little more explicit.

In ch2 Logistic example, when we assume our data follows logistic, why then use Bernoulli after that? In previous examples we only had an assumption of the data distribution and calculated the likelyhood form that. But here why has Bernoulli suddenly appeared when we already get a probability from logistic?

def challenger_joint_log_prob(D, temperature_, alpha, beta):
    """
    Joint log 

Considering the MNIST dataset, wich has 5923 instances of the 0 class in the training set, I'm alittle confused about the following code for detemining the relative errors of the SGD classification model:

row_sums = conf_mx.sum(axis=1, keepdims=True)
norm_conf_mx = conf_mx / row_sums

(https://github.com/ageron/handson-ml/blob/master/03_classification.ipynb // In: 67)

Since using `axi

It may be good to provide pure Python implementation of Gradient Descent (instead of SciPy one) for Logistic Regression just for the learning purposes.

Description

@wutaomsft suggestion:

it would be a good discussion point what is preferred way to make references in notebooks. I prefer not to have a"reference" section where references are separate from where they are referred. Instead, link the reference "in place". And then add a paragraph for "additional reading", which is more descr

Detail in matplotlib/matplotlib#13904 .

0. style was set

    from jupyterthemes import jtplot
    jtplot.style()
   

I used to set font by :

1. Find matplotlib config path : python -c 'import matplotlib; print(matplotlib.matplotlib_fname())'

2. add font to that file:

    font.family : YaHei Consolas Hybrid
    font.serif : YaHei Consola
   

Hi there,

While trying to configure JupyterHub locally, running in the terminal gives the following link:

"JupyterHub is now running at http://127.0.0.1:port/"

To which my browser responded "The connection was reset". I found that using HTTPS and ignoring the warnings solved this issue.

My specs are: Ubuntu 19.04, Firefox and I used openssl to get a key and certificate.
JupyterHub ve

Context

Currently, we have the tutorial-style nteract docs published at docs.nteract.io from the nteract/docs repo. Unfortunately, this separation creates a lot of friction as we work towards improving our tutorial-style docs. I believe that we should move our tutorial-style docs to the monorepo, especially since a bulk of the packages and apps that we ship are located there. I'm largely ins

Describe the bug
When running from pycharm the report is generated but an error is thrown
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.6/bin/pandas_profiling", line 10, in
sys.exit(main())
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas_profiling/controller/console.py", line 78, in main

🔬 This is an experiment in doing radically open research. I plan to post all my work on this openly as I do it, tracking it in this issue. I'd love for people to comment, or better yet collaborate! See more.

*Please be respectful of the fact that this is unpublished research and that people involved

The purpose of this issue is to document a few ways to build a documentation site. Our requirements are:

• The source documents can be edited as Jupyter notebooks
• Source files are text files (Markdown or others)
• Documentation site can show the result of the code being executed, including plots.

We should explore at least

• Jupyter Book (can we use it with Jupytext notebooks?)
• Sphinx

How to use Watcher / WatcherClient over tcp/ip network?

Watcher seems to ZMQ server, and WatcherClient is ZMQ Client, but there is no API/Interface to config server IP address.
Do I need to implement a class that inherits from WatcherClient?

In documentation about Quantization-Aware Training it is stated that "Note that the current implementation of QuantAwareTrainRangeLinearQuantizer supports training with single GPU only."
This is not correct since #284 - QuantAwareTrainRangeLinearQuantizer supports nn.DataParallel.

Coming from a Python-in-Jupyter background, I notice that Tab works as you would expect (reveals all available methods/attributes), but Shift+Tab (signature hinting) does not. I could not find documentation on this. Is Shift+Tab not supported, or is my setup is incorrect?

Example:

type Foo struct {
    name string
    description string
}
func (f Foo) Qux(s string) string {
   

Use this checklist to track logstash wiki and documentation

• Update HELK overview picture
  • remove alien vault integration
  • update with new indexes #256
• document catch all for windows
• document indexme catch all
• some general guideline on X amount of devices and or X amount of EPS and or X amount of storage - scenarios
• how to add custom configurati

lgo does not complete import paths now.

We want to complete import paths for these patterns:

import [cur]

import (
    [cur]

import "a/b[cur]

import "[cur]

Why the input is forward padded?
For e.g. 31, Aug 1986

Is there specific reason for that? What difference does it make to do padding before the input sequence instead of padding after input sequence. Please explain.

https://github.com/apachecn/ds-ipynb-zh

• 七、Pandas
  • 7.1 Pandas
  • 7.2 数据整理
  • 7.3 Pandas 数据操作
  • [7.4

Tried to see the nbviewer: Interactive plots with Plotly clicking in the link you offer ## https://nbviewer.jupyter.org/github/plotly/python-user-guide/blob/master/Index.ipynb

got:

404 : Not Found
You are requesting a page that does not exist!

The remote resource was not found.

Summary

Hello, I found the cursor in vim mode covers two letters' width, which is misleading.

How can I address that ?
Thank.

Environment

• Operating system : remote Ubuntu 16.04 server
• Web browser : local chrome 73.0.3683.103
• Version or revision of

When I cloned the repository on my local system, I am not able to follow the logical order of the notebooks. In other words on the Github Repository a logical order is specified against each topic as below:

1. Strings [notebook] [exercise]
2. Numbers [notebook] [exercise]
3. Conditionals [notebook] [exercise]
4. Lists [notebook] [exercise]
5. Dictionaries [notebook] [exercise]
6. For loo

The Notebook Documents section of https://jupyter.org/documentation would naturally include a link to nbdime (and nbval). A good way to advertise the tools!