d3.unconf example gist. Fork it here.
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"""making a dataframe""" | |
df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB')) | |
"""quick way to create an interesting data frame to try things out""" | |
df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd']) | |
"""convert a dictionary into a DataFrame""" | |
"""make the keys into columns""" | |
df = pd.DataFrame(dic, index=[0]) |
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import sys, os | |
from cv import * | |
from cv2 import * | |
import glob | |
def detect_face(image): | |
"""Converts an image to grayscale and prints the ratio of face to image if _one_ face is found""" | |
grayscale = CreateImage((image.width, image.height), 8, 1) | |
CvtColor(image, grayscale, CV_BGR2GRAY) |
from numpy import dot,array,empty_like
from matplotlib.path import Path
def make_path(x1,y1,x2,y2):
return Path([[x1,y1],[x1,y2],[x2,y2],[x2,y1]])
def perp( a ) :
b = empty_like(a)
b[0] = -a[1]
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#!/usr/bin/env python | |
# shell commands being automated. | |
# w | |
# aws ec2 stop-instances --instance-id INSTANCE_ID | |
# az vm deallocate --name NAME --resource-group RESOURCE_GROUP | |
""" | |
The script is the easy part, installing it into the unfriendly(imo) cron system and |
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#!/usr/bin/env python | |
# shell commands being automated. | |
# w | |
# aws ec2 stop-instances --instance-id INSTANCE_ID | |
""" | |
The script is the easy part, installing it into the unfriendly(imo) cron system and | |
making all the permissions and paths are set correctly is another issue altogether. |
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import json | |
import subprocess | |
"""EDIT FNAME TO UPDATE WITH YOUR LOCAL FILE PATH""" | |
FNAME = "/PATH/To/SUBLIME/SFTP/FILE/sftp-config.json" | |
def update_json_file(given_ip, filename): | |
jsonfile = open(filename, "r") # open the json file for reading | |
data = json.load(jsonfile) # read the json into the buffer |
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# command to install this library. | |
# conda install pandas-datareader | |
# example 1 | |
import pandas_datareader as pdr | |
tsla = pdr.DataReader('tsla', data_source='google', start='2017-1-1') | |
# example 2 | |
# calculates the trailing stop order price, can be used for weight loss like time series as well. |
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# generate private key | |
openssl genrsa -out private_key.pem 4096 | |
# extract public key | |
openssl rsa -in private_key.pem -pubout > public_key.pub | |
# sign using private key | |
openssl dgst -sign private_key.pem -keyform PEM -sha256 -out VGG_ILSVRC_16_layers.caffemodel.sign -binary VGG_ILSVRC_16_layers.caffemodel | |
# extract using public key |
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import pandas as pd | |
import numpy as np | |
def style_dataframe(df, maxvals=None): | |
result = df.style | |
result.set_properties(**{'text-align': 'center'}) | |
result.set_properties(**{'font-family': 'monospace'}) | |
result.set_properties(**{'font-weight': 'bold'}) | |
result.set_table_styles([ |
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