pmlandwehr

def get_token_count_tfidf_df(texts, tokenizer=None, preprocessor=None, analyzer='word', ngram_range=(1,1)):
    """
    Take a list of texts, preprocess and tokenize them, and returns the counts and TF-IDF values for each feature.
    :param list|pd.Series texts: collection of texts
    :param tokenizer: tokenizer for the vectorizers. By default tries to load the punkt sentence tokenizer from NLTK.
    :param preprocessor: Preprocessor for texts. By default converts numbers to "<NUM>"
    :return pandas.DataFrame: DataFrame of sorted features, counts, and TF-IDFs.
    """
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.feature_extraction.text import TfidfVectorizer

pmlandwehr

/*
Placeholder for custom user CSS
mainly to be overridden in profile/static/custom/custom.css
This will always be an empty file in IPython
*/

@import url('http://fonts.googleapis.com/css?family=Crimson+Text');
@import url('http://fonts.googleapis.com/css?family=Kameron');
@import url('http://fonts.googleapis.com/css?family=Lato:200');
@import url('http://fonts.googleapis.com/css?family=Lato:300');

pmlandwehr

function string-pad --description 'Pad strings to a particular length using a character'

    set -l options
    set -a options "h/help"
    set -a options "l/left"
    set -a options "r/right"
    set -a options "m/max=!_validate_int"
    set -a options "n/count=!_validate_int"
    set -a options "c/char="
    argparse --name="string-pad" $options -- $argv

pmlandwehr

function string-pad --description 'Pad strings to a particular length using a character'

    set -l options
    set -a options "h/help"
    set -a options "l/left"
    set -a options "r/right"
    set -a options "m/max=!_validate_int"
    set -a options "n/count=!_validate_int"
    set -a options "c/char="
    argparse --name="string-pad" $options -- $argv

pmlandwehr

{% set name = "package_name" %}
{% set version = "insert_real_version" %}
{% set bundle = "tar.gz" %}
{% set hash_type = "sha256" %}
{% set hash = "insert-real-hash" %}

package:
  name: {{ name|lower }}
  version: {{ version }}

pmlandwehr

import csv
import ujson as json
import gzip
import sys
from tqdm import tqdm


def validate_to_set(x):
    if x is None:
        return set()

pmlandwehr

mkdir data_work

docker run -d -p 8888:8888 \
-e GEN_CERT=yes \
-v data_work:/home/jovyan/work \
jupyter/datascience-notebook start-notebook.sh \
--NotebookApp.password='sha1:74ba40f8a388:c913541b7ee99d15d5ed31d4226bf7838f83a50e'  # put hashed password here 

pmlandwehr

#!/usr/bin/env bash

# Install any necessary packages with apt-get
sudo apt-get install -y curl wget

# Get installer
curl https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
--output miniconda_installer.sh

# Run installer

pmlandwehr

from bs4 import BeautifulSoup
import numpy as np
import pandas
import requests
from tqdm import tqdm

TOTAL_CF_REPO_PAGES = 45

package_tags = list(chain.from_iterable([
    BeautifulSoup(requests.get('https://anaconda.org/conda-forge/repo',

pmlandwehr

def mefi_strptime(x):
    from datetime import datetime
    try:
        return datetime.strptime(x, '%b %d %Y %I:%M:%S:%f%p')
    except ValueError:
        return datetime(1900, 1, 1, 0, 0, 0)


def int_with_filled_nans(x):
    import numpy as np