cmparlettpelleriti

library(tidyverse)
library(MASS)
library(patchwork)
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# generate data with given cor matrix
a <- 0.9
s1 <- matrix(c(1,a,
               a,1), ncol = 2)

lmcinnes

twiecki

import dask
import dask.array as da
import dask.dataframe as dd
import sparse

@dask.delayed(pure=True)
def corr_on_chunked(chunk1, chunk2, corr_thresh=0.9):
    return sparse.COO.from_numpy((np.dot(chunk1, chunk2.T) > corr_thresh))
        
def chunked_corr_sparse_dask(data, chunksize=5000, corr_thresh=0.9):

fperez

Polyglot Data Science with IPython & friends

Author: Fernando Pérez.

A demonstration of how to use Python, Julia, Fortran and R cooperatively to analyze data, in the same process.

This is supported by the IPython kernel and a few extensions that take advantage of IPython's magic system to provide low-level integration between Python and other languages.

See the companion notebook for data preparation and setup.

dgrtwo

library(tidyverse)

# Data is downloaded from here:
# https://www.kaggle.com/c/digit-recognizer
kaggle_data <- read_csv("~/Downloads/train.csv")

pixels_gathered <- kaggle_data %>%
  mutate(instance = row_number()) %>%
  gather(pixel, value, -label, -instance) %>%
  extract(pixel, "pixel", "(\\d+)", convert = TRUE)

brandon-rhodes

# Inspired by the following sentence that I ran across this morning:
#
#    "f_lineno is the current line number of the frame - writing to
#     this from within a trace function jumps to the given line
#     (only for the bottom-most frame). A debugger can implement a
#     Jump command (aka Set Next Statement) by writing to f_lineno."
#
#    https://docs.python.org/2/reference/datamodel.html
#
# There is an older implementation of a similar idea:

fperez

gizmaa

Plotting

Last Update: May 13, 2019
Offline Version

Contents

• Translating
• Important Note
Basic Plot

mblondel

"""Kernel K-means"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000