csrsen

# generate a 10MB file named `testfile` filled with random stuff
dd if=/dev/urandom of=testfile bs=1000000 count=10

csrsen

import numpy as np
import matplotlib.pyplot as plt 
import pandas as pd
from sklearn.linear_model import LinearRegression

data = pd.read_csv('data.csv')
X = data.iloc[:, 0].values.reshape(-1, 1)  # values converts it into a numpy array
Y = data.iloc[:, 1].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column
linear_regressor = LinearRegression()
linear_regressor.fit(X, Y)  # perform linear regression

csrsen

Shortcut	Description
Ctrl-P or up-arrow	Search backward in command history for commands starting with currently-entered text
Ctrl-N or down-arrow	Search forward in command history for commands starting with currently-entered text
Ctrl-R	Readline-style reverse history search (partial matching)
Ctrl-Shift-V	Paste text from clipboard
Ctrl-C	Interrupt currently-executing code
Ctrl-A	Move cursor to beginning of line
Ctrl-E	Move cursor to end of line
Ctrl-K	Delete text from cursor until end of line

csrsen

|iPython System Commands| |:---|:---| |!cmd |Execute cmd in the system shell | |output = !cmd args |Run cmd and store the stdout in output | |%alias alias_name cmd |Define an alias for a system (shell) command| |%bookmark |Utilize IPython’s directory bookmarking system | |%cd directory |Change system working directory to passed directory | |%pwd |Return the current system working directory | |%pushd directory |Place current directory on stack and change to target directory | |%popd |Change to directory popped off the top of the stack |

csrsen

Function	Description
unique(x)	Compute the sorted, unique elements in x
intersect1d(x, y)	Compute the sorted, common elements in x and y
union1d(x, y)	Compute the sorted union of elements
in1d(x, y)	Compute a boolean array indicating whether each element of x is contained in y
setdiff1d(x, y)	Set difference, elements in x that are not in y
setxor1d(x, y)	Set symmetric differences; elements that are in either of the arrays, but not both

csrsen

Function	Description
abs, fabs	Compute the absolute value element-wise for integer, floating point, or complex values.
Use fabs as a faster alternative for non-complex-valued data
sqrt	Compute the square root of each element. Equivalent to arr ** 0.5
square	Compute the square of each element. Equivalent to arr ** 2
exp	Compute the exponent ex of each element
log, log10, log2, log1p	Natural logarithm (base e), log base 10, log base 2, and log(1 + x), respectively
sign	Compute the sign of each element: 1 (positive), 0 (zero), or -1 (negative)
ceil	Compute the ceiling of each element, i.e. the smallest integer greater than or equal to each element

csrsen

from geopy import GoogleV3
place = "221b Baker Street, London"
location = GoogleV3().geocode(place)
print(location.address)
print(location.location)

csrsen

import glob
import os
import cv2
import concurrent.futures


def load_and_resize(image_filename):
  ### Read in the image data
  img = cv2.imread(image_filename)
  

csrsen

# HWES example
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from random import random
# contrived dataset
data = [x + random() for x in range(1, 100)]
# fit model
model = ExponentialSmoothing(data)
model_fit = model.fit()
# make prediction
yhat = model_fit.predict(len(data), len(data))

csrsen

# SES example
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
from random import random
# contrived dataset
data = [x + random() for x in range(1, 100)]
# fit model
model = SimpleExpSmoothing(data)
model_fit = model.fit()
# make prediction
yhat = model_fit.predict(len(data), len(data))