Table of content

Environment
- 1.1. Command line
- 1.2. pip
- 1.3. pyenv
- 1.4. virtualenv
- 1.5. iPython Notebook
Module
File access
Database access
Plotting with matplotlib

1. Environment

1.1. Command line

To execute a python script:

$ python myscript.py

To open python command line:

$ python

1.2. pip

pip is a Python package manager.
pip is already installed for Python 2 >=2.7.9 or Python 3 >=3.4.
If your environment has no pip, you can install pip by the following steps:

$ wget https://bootstrap.pypa.io/get-pip.py
$ python get-pip.py

Reference: https://pip.pypa.io/en/stable/user_guide

1.3. pyenv

Manage multiple versions of Python like rbenv in Ruby
https://github.com/yyuu/pyenv

1.4. virtualenv

A tool to create multiple Python environments
Can use virtualenv directly, or use pyenv-virtualenv, a plugin of pyenv
virtualenv: https://pypi.python.org/pypi/virtualenv
pyenv-virtualenv: https://github.com/yyuu/pyenv-virtualenv

1.5. iPython Notebook

An interactive Python environment
Write & execute Python code, markdown, mathmatical notations in LaTex, plots & media.
Can be used for experiment, demo, presentation & communication
iPython Notebook is currently renamed to Jupyter Notebook
http://jupyter.org/

Install iPython Notebook with pip

$ pip install jupyter

Note: iPython works on Python2.7 or Python3.3 or above.

Start iPython Notebook

$ jupyter notebook

Using iPython Notebook

Browse file from directory tree
Open existing notebook
Create new notebook
Write python code in cell
Write markdown text in cell (normal text, Latex)
New cell above/below a current cell
Delete a cell

2. Module

Basic on module

In mymod.py:

def mysum(a, b):
    return a + b

In myscript.py:

import mymod
print(mymod.mysum(3, 4))

Execute myscript.py

> python myscript.py
7

Use alias to module:

import mymod as mm
print(mm.mysum(3, 4))
# mm works as alias to mymod

Functions or variables from modules can be accessed directly by:

from mymod import mysum
print(mysum(3, 4))

Import patterns

# Import a module and access the module's elements through it name
import modulename
modulename.func1(params)

# Use alias to a module
import modulename as alias
alias.func1(params)

# Import specific names from a module
from modulename import func1, func2
func1(params)
func2(params)

# Import specific names and use alias
from modulename import func1 as f
f(params)

# Import all names from a module except those beginning with an underscore (_)
from modulename import *
func1(params)
func2(params)

Global variable `name`

Within a module, global variable __name__ returns the module's name.
The script which is being execute has __main__ as its module name.

For example:

In mymod.py:

print(__name__)

In myscript.py:

import mymod

Execute myscripts.py

> python myscripts.py
mymod

Execute mymod.py

> python mymod.py
__main__

Module can be executed directly

We can use __name__ to distinguish a module is called directly or not.
In mymod.py

def mysum(a, b):
    return a + b
if __name__ == "__main__":
    print("Mymod is executed directly")

Module search path

Python search module from directories listed in sys.path

import sys
print(sys.path)

3. File access

Reading from file

Read all data from file

f = open('stock1.csv', 'r')
data = f.read()
f.close()
print(data)

sec_code,opn,high,low,now,time
6702,602,610,599,608,2016-01-04 00:00:00
6702,608,611,608,610,2016-01-04 00:10:00
6758,2958,3000,2940,2998,2016-01-04 00:00:00
6758,2998,3011,2992,3009,2016-01-04 00:10:00

Read one line

f = open('stock1.csv', 'r')
data = f.readline()
f.close()
print(data)

sec_code,opn,high,low,now,time

Read all lines

f = open('stock1.csv', 'r')
for line in f:
    print(line)
f.close()

sec_code,opn,high,low,now,time

6702,602,610,599,608,2016-01-04 00:00:00

6702,608,611,608,610,2016-01-04 00:10:00

6758,2958,3000,2940,2998,2016-01-04 00:00:00

6758,2998,3011,2992,3009,2016-01-04 00:10:00

Write to file

f = open('sample_out.csv', 'wb')
f.write('Programming with Python \n')
f.write('Write 2 numbers: %d %f' % (5, 7.95))
f.close()

Reading from CSV

import csv
linecount = 0
with open('stock1.csv', 'rb') as csvfile:
    stockreader = csv.reader(csvfile, delimiter=',')
    for row in stockreader:
        print(row)
        linecount += 1

print("%d lines" % linecount)

['sec_code', 'opn', 'high', 'low', 'now', 'time ']
['6702', '602', '610', '599', '608', '2016-01-04 00:00:00']
['6702', '608', '611', '608', '610', '2016-01-04 00:10:00']
['6758', '2958', '3000', '2940', '2998', '2016-01-04 00:00:00']
['6758', '2998', '3011', '2992', '3009', '2016-01-04 00:10:00']
5 lines

Writing to CSV

import csv
with open('sample_out.csv', 'wb') as csvfile:
    csvwriter = csv.writer(csvfile, delimiter=',', lineterminator="\n")
    csvwriter.writerow(['Product', 'Price', 'Amount'])
    csvwriter.writerow(['Table', 12000, 5])
    csvwriter.writerow(['Chair', 4700, 25])

Serialization with pickle

Writing data with pickle

import pickle
f = open('sample_out.pkl', 'wb')
mixed = ['abc', 1, 5, 7.39]
str = "String data in pickle"
pickle.dump(mixed, f)
pickle.dump(str, f)
f.close()

Reading data with pickle

f = open('sample_out.pkl', 'rb')
new_mixed = pickle.load(f)
new_str = pickle.load(f)
f.close()
print(new_mixed)
print(new_str)

['abc', 1, 5, 7.39]
String data in pickle

4. Database access with mysql-connector-python

Install mysql-connector-python package

Install manually

Download mysql-connector-python-2.1.3.zip from
- https://cdn.mysql.com/Downloads/Connector-Python/mysql-connector-python-2.1.3.tar.gz
Unzip the file
Change dir to mysql-connector-python-2.1.3
./setup.py install

Install through pip

$ echo https://cdn.mysql.com/Downloads/Connector-Python/mysql-connector-python-2.1.3.tar.gz >> requirements.txt
$ pip install -r requirements.txt
$ pip list | grep mysql

Connector/Python API Reference

https://dev.mysql.com/doc/connector-python/en/connector-python-reference.html

Connect to DB

import mysql.connector
from mysql.connector import errorcode

config = {
    'user': 'us',
    'password': 'pwd',
    'host': 'localhost',
    'database': 'stocks'
}

try:
    mysql_cnn = mysql.connector.connect(**config)
except mysql.connector.Error as err:
    if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:
        print("Something is wrong with your user name or password")
    elif err.errno == errorcode.ER_BAD_DB_ERROR:
        print("Database does not exist")
    else:
        print(err)

SELECT data

cursor = mysql_cnn.cursor(buffered=True)
sql = 'SELECT * FROM stocks_10minute WHERE sec_code = 9984'
cursor.execute(sql)

Get number of records

print('Row count: %d' % cursor.rowcount)

Row count: 608

Extract 1 record

rec = cursor.fetchone()
print(rec)

(26911001, 9984, 6050, 6081, 6030, 6046, datetime.datetime(2016, 1, 4, 0, 0))

Extract price & time columns and save into 2 lists

records = [rec for rec in cursor]
price = [rec[5] for rec in records] # now column
time = [rec[6] for rec in records] # time column
cursor.close()

True

UPDATE data

Get a cursor for updating

upd_cursor = mysql_cnn.cursor()

Execute UPDATE statement

id = 29269249
value = 5266
upd_sql = "UPDATE stocks_10minute SET now = %d WHERE id = %d" % (value, id)
upd_cursor.execute(upd_sql)

Commit the data to DB & close cursor

mysql_cnn.commit()
upd_cursor.close()

True

INSERT data

import datetime

Get a cursor for insertion

ins_cursor = mysql_cnn.cursor()

Execute INSERT statement

ins_sql = (
  "INSERT INTO stocks_10minute (sec_code, open, high, low, now, time) "
  "VALUES (%s, %s, %s, %s, %s, %s)"
)
data = (9984, 5245, 5288, 5243, 5265, datetime.datetime(2016, 2, 1, 0, 10))
ins_cursor.execute(ins_sql, data)

Get the new ID in case the id column is auto_increment

new_id = ins_cursor.lastrowid
new_id

29278575

Commit the data to DB & close cursor

mysql_cnn.commit()
ins_cursor.close()

True

DELETE data

Get a cursor for deletion

del_cursor = mysql_cnn.cursor()

Execute DELETE statement

del_sql = (
  "DELETE FROM stocks_10minute WHERE id = %d " % new_id
)
del_cursor.execute(del_sql)

Commit the data to DB & close cursor

mysql_cnn.commit()
del_cursor.close()

True

Disconnect

mysql_cnn.close()

5. Plotting with matplotlib

Introduction

matplotlib is a powerful library for 2D plotting in Python.

Install

$ pip install matplotlib

Basic

Import required libraries

import numpy as np
import matplotlib.pyplot as plt

Command to show plot directly on notebook

%matplotlib inline

We can draw a plot by simply passing arrays for X & Y into plot function
If only 1 array is passed to plot function, it will be considered as data for the Y-axis.
And the values for X-axis will be automatically generated from 0, spaced by 1.

plt.plot([2, 3, 4, 5])
plt.ylabel('some numbers')
plt.show()

If 2 arrays are passed to plot, they will be considered as data for X & Y-axis respectively

plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.xlabel('Some numbers')
plt.ylabel('Their squares')
plt.show()

The 3rd param in plot function is used to format the color & line type of the plot.
A format string may have 3 component: color, marker style & line style.
EX: 'ro--' indicates: RED color, circle marker & dashed line.
For more info on the format string: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot
'axis' command takes [xmin, xmax, ymin, ymax] to specify the viewport of the axes.

plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'r^--')
plt.xlabel('Some numbers')
plt.ylabel('Their squares')
plt.axis([0, 6, 0, 20])
plt.show()

'plot' function may receive data for multiple line. In this example, it draws 3 lines, with 3 params per each.

# Numpy's arange function receive the start, end & interval value to generate an array of evenly spaced values.
# Here, the values range from 0 -> 5, evenly spaced by 0.2
t = np.arange(0., 5., 0.2)

plt.plot(t, t, 'r--', t, t ** 2, 'bs', t, t ** 3, 'g^')
plt.show()

Controlling line properties

We can set properties for line through keyword arguments.

x = np.arange(0., 5., 0.2)
y = x ** 2
plt.plot(x, y, linewidth=4.0)
plt.show()

plot can return multiple Line2D objects, corresponding to the input params.
For complex property setting, we can receive Line2D object, and call methods to set properties for the lines.
List of properties & methods of Line2D object: http://matplotlib.org/api/lines_api.html#matplotlib.lines.Line2D
Basically, a property can be accessed through keyword args with the same name, or through methods with the following patterns:
- get_[property]
- set_[property]

line1, line2, line3 = plt.plot(x, x, x, x ** 2, x, x ** 3)
line1.set_color('red')
line2.set_color('green')
line3.set_color('blue')
line3.set_alpha(0.2)
plt.show()

To get a list of settable properties & gettable properties of Line2D object, call 'setp' & 'getp'

plt.setp(line1)

plt.getp(line1)

Working with multiple figures and axes

Use 'figure' function to set current figure. By default, figure(1) is automatically called behind the scene.
Use 'subplot' function to set the current plot & also define the grid of the current figure.
A figure is divided into grids of rows & columns.
Subplot receives 3 parameters: nrows, ncols, plot_number
plot_number ranges from 1 to nrows * ncols
- EX: Calling subplot(2, 1, 2) specifies that the figure has 2 rows, 1 cols, and select the lower plot as active.
If all of nrows, ncols, plot_number are less that 10, we can use only one 3-digit number to identify the grid system & the plot number.
- EX: Calling subplot(324) specifies that the figure has 3 rows, 2 cols & select the plot at (2, 1) as active.

def f(t):
    return np.exp(-t) * np.cos(2 * np.pi * t)

t1 = np.arange(0.0, 5.0, 0.1)
t2 = np.arange(0.0, 5.0, 0.02)

# Select the 1st figure
plt.figure(1)
plt.subplot(2, 1, 1)
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'r')

plt.subplot(2, 1, 2)
plt.plot(t2, np.cos(2 * np.pi * t2), 'r--')

# Select the 2nd figure
plt.figure(2)
# 3 rows, 2 cols & set the (2, 2) as active
plt.subplot(324)
plt.plot(t2, np.cos(2 * np.pi * t2), 'b-')

# 3 rows, 2 cols & set the (1, 1) as active
plt.subplot(321)
plt.plot(t2, np.cos(2 * np.pi * t2), 'g-')

plt.show()

Working with text

xlabel, ylabel, title are functions used to put text in specific positions.
text function is used to put text at any position.
The text function receives:
- x, y: as position of the text. These specify the values in X-axis & Y-axis
- s: the text content
- And other properties of text in form of keyword params. For more detail: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.text
For text content, we can write LaTex notation inside \$..\$

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)

n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ as normal distribution of $\mu=100,\ \sigma=15$')
plt.text(57, .025, r'$\mu=100,\ \sigma=15$', color='r')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()

Annotating text

Use 'annotate' function to put a text for explanation. 'annotate' receives the following params:
- s: the explanation text.
- xy: position to annotate. The position will be pointed by an arrow.
- xytext: position of the text.
- arrowprops: format of the arrow.
For more info: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate

ax = plt.subplot(111)
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2 * np.pi * t)
line, = plt.plot(t, s, lw=2)
plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5), arrowprops=dict(facecolor='k', shrink=0.05))
plt.ylim(-2, 2)
plt.show()

Working with legend

Use legend function to draw a legend for the axes.
Each line should be assigned a label, then call legend to draw a legend box.
Some important params of legend function:
- loc: location of the legend box in plot. 1: upper right, 2: upper left, 3: lower left, 4: lower right...
- title: title of the legend box
For more details: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.legend

x = np.array(range(100))
line1, line2 = plt.plot(x, x, 'r', x, x ** 2, 'b')
line1.set_label('$Y=x$')
line2.set_label('$Y=x^2$')
plt.legend(loc=2, title='Explanation')
# plt.legend((line1, line2), ('For $Y=x$', 'For $Y=x^2$'))  # Or use legend function to set labels for lines
plt.show()

Creating artists specifically for adding to the legend (aka. Proxy artists)

import matplotlib.patches as mpatches

red_patch = mpatches.Patch(color='r', label='The red data')
plt.legend(handles=[red_patch])

plt.show()

import matplotlib.lines as mlines

line1, = plt.plot([1,3,4,5], label='Real line', color='r')
blue_line = mlines.Line2D([], [], color='blue', marker='*', linestyle='',
                          markersize=15, label='Blue stars')
plt.legend(handles=[line1, blue_line])
plt.show()

Legend location

Use bbox_to_anchor property to adjust position of the legend box, for example, to put it outside the plot.

x = np.array(range(100))
line1, = plt.plot(x, x, 'r', label='$Y=x$')
line2, = plt.plot(x, x ** 2, 'g--', label='$Y=x^2$')
plt.legend([line1, line2], ['For $Y=x$', 'For $Y=x^2$'], loc=2, bbox_to_anchor=(1.05, 1), borderaxespad=0.0)
plt.show()

Multiple legends on the same Axes

line1, = plt.plot([1, 2, 3], label='Line1', linestyle='--')
line2, = plt.plot([3, 2, 1], label='Line2', linewidth=4)

# Create the legend for 1st line
first_legend = plt.legend(handles=[line1], loc=1)

# Add a new legend
ax = plt.gca().add_artist(first_legend)

# Create another legend for the 2nd line
plt.legend(handles=[line2], loc=2)

plt.show()

Financial charts

Candle stick chart

from matplotlib.dates import DateFormatter, WeekdayLocator,\
    DayLocator, MONDAY
from matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc


# (Year, month, day) tuples suffice as args for quotes_historical_yahoo
date1 = (2004, 2, 1)
date2 = (2004, 4, 12)


mondays = WeekdayLocator(MONDAY)        # major ticks on the mondays
alldays = DayLocator()              # minor ticks on the days
weekFormatter = DateFormatter('%b %d')  # e.g., Jan 12
dayFormatter = DateFormatter('%d')      # e.g., 12

quotes = quotes_historical_yahoo_ohlc('INTC', date1, date2)
if len(quotes) == 0:
    raise SystemExit

fig, ax = plt.subplots()
fig.set_size_inches(10, 5)
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
#ax.xaxis.set_minor_formatter(dayFormatter)

#plot_day_summary(ax, quotes, ticksize=3)
candlestick_ohlc(ax, quotes, width=0.6)

ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

plt.show()

(From http://matplotlib.org/examples/pylab_examples/finance_demo.html)

Stock prices with indicators

import datetime
import numpy as np
import matplotlib.colors as colors
import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager


startdate = datetime.date(2006, 1, 1)
today = enddate = datetime.date.today()
ticker = 'SPY'


fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)
# a numpy record array with fields: date, open, high, low, close, volume, adj_close)

r = mlab.csv2rec(fh)
fh.close()
r.sort()


def moving_average(x, n, type='simple'):
    """
    compute an n period moving average.

    type is 'simple' | 'exponential'

    """
    x = np.asarray(x)
    if type == 'simple':
        weights = np.ones(n)
    else:
        weights = np.exp(np.linspace(-1., 0., n))

    weights /= weights.sum()

    a = np.convolve(x, weights, mode='full')[:len(x)]
    a[:n] = a[n]
    return a


def relative_strength(prices, n=14):
    """
    compute the n period relative strength indicator
    http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex
    http://www.investopedia.com/terms/r/rsi.asp
    """

    deltas = np.diff(prices)
    seed = deltas[:n+1]
    up = seed[seed >= 0].sum()/n
    down = -seed[seed < 0].sum()/n
    rs = up/down
    rsi = np.zeros_like(prices)
    rsi[:n] = 100. - 100./(1. + rs)

    for i in range(n, len(prices)):
        delta = deltas[i - 1]  # cause the diff is 1 shorter

        if delta > 0:
            upval = delta
            downval = 0.
        else:
            upval = 0.
            downval = -delta

        up = (up*(n - 1) + upval)/n
        down = (down*(n - 1) + downval)/n

        rs = up/down
        rsi[i] = 100. - 100./(1. + rs)

    return rsi


def moving_average_convergence(x, nslow=26, nfast=12):
    """
    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
    return value is emaslow, emafast, macd which are len(x) arrays
    """
    emaslow = moving_average(x, nslow, type='exponential')
    emafast = moving_average(x, nfast, type='exponential')
    return emaslow, emafast, emafast - emaslow


plt.rc('axes', grid=True)
plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)

textsize = 9
left, width = 0.1, 0.8
rect1 = [left, 0.7, width, 0.2]
rect2 = [left, 0.3, width, 0.4]
rect3 = [left, 0.1, width, 0.2]


fig = plt.figure(figsize=(10, 5), facecolor='white')
axescolor = '#f6f6f6'  # the axes background color

ax1 = fig.add_axes(rect1, axisbg=axescolor)  # left, bottom, width, height
ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
ax2t = ax2.twinx()
ax3 = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)


# plot the relative strength indicator
prices = r.adj_close
rsi = relative_strength(prices)
fillcolor = 'darkgoldenrod'

ax1.plot(r.date, rsi, color=fillcolor)
ax1.axhline(70, color=fillcolor)
ax1.axhline(30, color=fillcolor)
ax1.fill_between(r.date, rsi, 70, where=(rsi >= 70), facecolor=fillcolor, edgecolor=fillcolor)
ax1.fill_between(r.date, rsi, 30, where=(rsi <= 30), facecolor=fillcolor, edgecolor=fillcolor)
ax1.text(0.6, 0.9, '>70 = overbought', va='top', transform=ax1.transAxes, fontsize=textsize)
ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes, fontsize=textsize)
ax1.set_ylim(0, 100)
ax1.set_yticks([30, 70])
ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes, fontsize=textsize)
ax1.set_title('%s daily' % ticker)

# plot the price and volume data
dx = r.adj_close - r.close
low = r.low + dx
high = r.high + dx

deltas = np.zeros_like(prices)
deltas[1:] = np.diff(prices)
up = deltas > 0
ax2.vlines(r.date[up], low[up], high[up], color='black', label='_nolegend_')
ax2.vlines(r.date[~up], low[~up], high[~up], color='black', label='_nolegend_')
ma20 = moving_average(prices, 20, type='simple')
ma200 = moving_average(prices, 200, type='simple')

linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')
linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')


last = r[-1]
s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (
    today.strftime('%d-%b-%Y'),
    last.open, last.high,
    last.low, last.close,
    last.volume*1e-6,
    last.close - last.open)
t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)

props = font_manager.FontProperties(size=10)
leg = ax2.legend(loc='center left', bbox_to_anchor=(1.05, 1.3), shadow=True, fancybox=True, prop=props)
leg.get_frame().set_alpha(0.5)


volume = (r.close*r.volume)/1e6  # dollar volume in millions
vmax = volume.max()
poly = ax2t.fill_between(r.date, volume, 0, label='Volume', facecolor=fillcolor, edgecolor=fillcolor)
ax2t.set_ylim(0, 5*vmax)
ax2t.set_yticks([])


# compute the MACD indicator
fillcolor = 'darkslategrey'
nslow = 26
nfast = 12
nema = 9
emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)
ema9 = moving_average(macd, nema, type='exponential')
ax3.plot(r.date, macd, color='black', lw=2)
ax3.plot(r.date, ema9, color='blue', lw=1)
ax3.fill_between(r.date, macd - ema9, 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)


ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)' % (nfast, nslow, nema), va='top',
         transform=ax3.transAxes, fontsize=textsize)

#ax3.set_yticks([])
# turn off upper axis tick labels, rotate the lower ones, etc
for ax in ax1, ax2, ax2t, ax3:
    if ax != ax3:
        for label in ax.get_xticklabels():
            label.set_visible(False)
    else:
        for label in ax.get_xticklabels():
            label.set_rotation(30)
            label.set_horizontalalignment('right')

    ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')


class MyLocator(mticker.MaxNLocator):
    def __init__(self, *args, **kwargs):
        mticker.MaxNLocator.__init__(self, *args, **kwargs)

    def __call__(self, *args, **kwargs):
        return mticker.MaxNLocator.__call__(self, *args, **kwargs)

# at most 5 ticks, pruning the upper and lower so they don't overlap
# with other ticks
#ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
#ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))

ax2.yaxis.set_major_locator(MyLocator(5, prune='both'))
ax3.yaxis.set_major_locator(MyLocator(5, prune='both'))

plt.show()

(From http://matplotlib.org/examples/pylab_examples/finance_work2.html)

XKCD-style sketch plots

Simply blocking our plotting code by with plt.xkcd(), we can draw plots in XKCD-style (comic-style).
XKCD-style sketch is based on XKCD by Randall Monroe: http://xkcd.com/
For more information on xkcd function, see: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xkcd

with plt.xkcd():
    line1, = plt.plot([1, 2, 3], label='Line1', linestyle='--')
    line2, = plt.plot([3, 2, 1], label='Line2', linewidth=4)

    # Create the legend for 1st line
    first_legend = plt.legend(handles=[line1], loc=1)

    # Add a new legend
    ax = plt.gca().add_artist(first_legend)

    # Create another legend for the 2nd line
    plt.legend(handles=[line2], loc=2)

    plt.show()

with plt.xkcd():
    ax = plt.subplot(111)
    t = np.arange(0.0, 5.0, 0.01)
    s = np.cos(2 * np.pi * t)
    line, = plt.plot(t, s, lw=2)
    plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5), arrowprops=dict(facecolor='k', shrink=0.05))
    plt.ylim(-2, 2)
    plt.show()

with plt.xkcd():
    x = np.array(range(100))
    line1, = plt.plot(x, x, 'r', label='$Y=x$')
    line2, = plt.plot(x, x ** 1.5, 'g--', label='$Y=x^2$')
    plt.legend([line1, line2], ['For $Y=x$', 'For $Y=x^{1.5}$'], loc=2, bbox_to_anchor=(1.05, 1), borderaxespad=0.0)
    plt.show()

Credit

http://matplotlib.org/users/beginner.html

Python basic memo - Part 2

Table of content

1. Environment

1.1. Command line

1.2. pip

1.3. pyenv

1.4. virtualenv

1.5. iPython Notebook

Install iPython Notebook with pip

Start iPython Notebook

Using iPython Notebook

2. Module

Basic on module

Import patterns

Global variable __name__

Module can be executed directly

Module search path

3. File access

Reading from file

Read all data from file

Read one line

Read all lines

Write to file

Reading from CSV

Writing to CSV

Serialization with pickle

Writing data with pickle

Reading data with pickle

4. Database access with mysql-connector-python

Install mysql-connector-python package

Install manually

Install through pip

Connector/Python API Reference

Connect to DB

SELECT data

UPDATE data

INSERT data

DELETE data

Disconnect

5. Plotting with matplotlib

Introduction

Install

Basic

Controlling line properties

Working with multiple figures and axes

Working with text

Annotating text

Working with legend

Creating artists specifically for adding to the legend (aka. Proxy artists)

Legend location

Multiple legends on the same Axes

Financial charts

Candle stick chart

(From http://matplotlib.org/examples/pylab_examples/finance_demo.html)

Stock prices with indicators

(From http://matplotlib.org/examples/pylab_examples/finance_work2.html)

XKCD-style sketch plots

Credit

Global variable `name`