jennyonjourney

import numpy as np
import pandas as pd
train = pd.read_csv("data/train (bike).csv")
print(train.shape)
train.head()
train = pd.read_csv("data/train (bike).csv", parse_dates=["datetime"])
print(train.shape)

## Data processing
train["year"] = train["datetime"].dt.year

jennyonjourney

# 10.2 Write a program to read through the mbox-short.txt and figure out the distribution by hour of the day for each of the messages. You can pull the hour out from the 'From ' line by finding the time and then splitting the string a second time using a colon.
#From stephen.marquard@uct.ac.za Sat Jan  5 09:14:16 2008
# Once you have accumulated the counts for each hour, print out the counts, sorted by hour as shown below.

name = input("Enter file:")
if len(name) < 1:
    name = "mbox-short.txt"
handle = open(name)

counts = dict()

jennyonjourney

import tensorflow as tf
import numpy as np
tf.set_random_seed(777)  # for reproducibility
learning_rate = 0.0001
xy = np.loadtxt('multiTimeline_bitcoin_2_normalized.csv', delimiter=',', dtype=np.float32)
X_data = xy[:, 0:-1]
def MinMaxScaler(x_data):
    numerator = x_data - np.min(x_data, 0)
    denominator = np.max(x_data, 0) - np.min(x_data, 0)
    return numerator / (denominator + 1e-10)

jennyonjourney

import tensorflow as tf
import numpy as np
tf.set_random_seed(777)  # for reproducibility

# Predicting animal type based on various features
xy = np.loadtxt('ted_main_ranking_viewer.csv', delimiter=',', dtype=np.float32)
X_data = xy[:, 0:-1]
def MinMaxScaler(x_data):
    numerator = x_data - np.min(x_data, 0)
    denominator = np.max(x_data, 0) - np.min(x_data, 0)

jennyonjourney

import urllib.request as ur
import json

page_name='MBC'

base="https://graph.facebook.com/v2.8/"

page_id='240263402699918'

from_date='2018-01-01'

jennyonjourney

# developers/facebook.com 으로 접속
import urllib.request as ur
import json

page_name='jtbcnews'

base = "https://graph.facebook.com/v2.8"
app_id = "200920440387013"
app_secret = "daccef14d5cd41c0e95060d65e66c41d"
access_token = app_id+"|"+app_secret

jennyonjourney

import requests
from lxml.html import parse
from io import StringIO

def rowData(rr, kind):
    cols = rr.findall('.//'+kind)
    res = [vv.text_content().replace("\t","").replace("\n","") for vv in cols]
    return res[:-1]

def rowWrite(rr):

jennyonjourney

import requests
from lxml.html import parse
from io import StringIO

url='http://finance.naver.com/sise/sise_market_sum.nhn'
text = requests.get(url)

ppp = parse(StringIO(text.text))
doc = ppp.getroot()
tables = doc.findall('.//table')

jennyonjourney

largest = None
smallest = None

while True:
    inp = raw_input("Enter a number: ")
    if inp == "done" : break
    try:
        num = float(inp)
    except:
        print ("Invalid input")

jennyonjourney

def order(ih,name='아시아노'):
    dd = {'아메리카노': 2000,
          '아프리카노': 3000,
          '아시아노': 3500}
    print(name, ih, dd[name])
order('아이스','아메리카노')
order('핫','아프리카노')
order('아이스')

print('----실습----')