No, scratch that...
conda and pipconda for package environmentsDocs can be found online.
This works on campus machines
Find the correct link at http://continuum.io/downloads then type something like the following in your command shell:
wget <copy-paste that link>
bash Anaconda-<your version>.sh
Answer yes at the prompts. At the end, the installer asks if you want to add Anaconda to your PATH, say yes if this will be your primary Python install.
To update all of anaconda's packages at once:
conda update anacondaBut not all packages are in the conda repositories
conda install geopy
In that case, use pip
pip install geopyCreate a new environment named "nx1.7", and link all the Anaconda packages
conda create -n nx1.7 anacondaList all currently-defined environments:
conda info -eActivate our new environment:
source activate nx1.7Replace NetworkX 1.8 with 1.7:
conda install networkx=1.7Deactivate our new environment, returning to the base Anaconda env:
source deactivate nx1.7Environments are also a great way to have Python 2.7 and 3.3 side-by-side:
conda create -n py3.3 python=3.3 anaconda
Then, like before, I can just switch to py3k with a
source activate py3.3
and switch back with
source deactivate py3.3
In [1]:
import random as rd
import numpy as np
In [2]:
%timeit a = [rd.random() for x in range(1000)]
In [3]:
%timeit a = [np.random.random() for x in range(1000)]
In [4]:
%timeit a = np.random.random(1000)
There are also cell magics for use in IPython notebook
In [15]:
%%time
# This is a terrible way to do this
fib = [0, 1]
for i in range(10**5):
fib.append(fib[-1] + fib[-2])
In [95]:
from scipy.special import jn
x = linspace(0, 4*pi)
for i in range(6):
plot(x, jn(i, x))
If you're reading this, you're seeing my slides online. It's hard for me to demo this for you, but luckily there are writeups and screencasts out there of how awesome IPython notebook is.
Run this notebook in Wakari to see Python + JavaScript in action
Actually...
In [53]:
import random as rd
N = 4
lol = [[rd.random() for c in range(N)] for r in range(N)]
# OR
lol = []
for r in range(N):
row = []
for c in range(N):
row.append(rd.random())
lol.append(row)
lol
Out[53]:
List slicing: Can we get the first column of this "matrix?"?
In [55]:
lol[:,0]
Try again.
How about this?
In [56]:
lol[:][0]
Out[56]:
This is actually the first row. Seriously, go back and check.
Surely a list comprehension can save us:
In [57]:
[x[0] for x in lol]
Out[57]:
That works, but... ugh.
Do you want to write a column-wise sum?
This isn't FORTRAN. I shouldn't have to think about which way my matrix is laid out, row- or column-major. Try again with Numpy:
In [62]:
import numpy as np
npm = np.random.random((N, N))
npm
Out[62]:
In [63]:
npm[:,0]
Out[63]:
Nice. And that column-wise sum?
In [67]:
npm.sum(0)
Out[67]:
Like a boss.
In [98]:
import numpy
import scipy.ndimage
import Image
class Life(object):
def __init__(self, n, p=0.5, mode='wrap'):
self.n = n
self.mode = mode
self.array = numpy.uint8(numpy.random.random((n, n)) < p)
self.weights = numpy.array([[1,1,1],
[1,10,1],
[1,1,1]], dtype=numpy.uint8)
def step(self):
con = scipy.ndimage.filters.convolve(self.array,
self.weights,
mode=self.mode)
boolean = (con==3) | (con==12) | (con==13)
self.array = numpy.int8(boolean)
def run(self, N):
for _ in range(N):
self.step()
def draw(self, scale):
im = Image.fromarray(numpy.uint8(self.array)*255)
z = int(scale*self.n)
return im.resize((z,z))
In [99]:
l = Life(50)
imshow(l.draw(15))
Out[99]:
In [100]:
l.run(10)
imshow(l.draw(15))
Out[100]:
In [2]:
from scipy import stats
import numpy as np
x = np.random.random(10)
y = np.random.random(10)
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
print "r-squared:", r_value**2
We'll come back to this in a minute.
In [ ]:
from scipy import stats
import numpy as np
x = np.random.random(10)
y = np.random.random(10)
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
print "r-squared:", r_value**2
In [48]:
import matplotlib.pyplot as plt
sizes = 1000* np.random.random(10)
colors = np.random.random(10)
fit_x = np.linspace(0,1,100)
fit_y = slope * xx + intercept
plt.scatter(x,y, sizes, colors, alpha=0.5)
plt.plot(fit_x, fit_y, '--r')
plt.title("Fit line to random junk", fontsize=16)
plt.show()
TASK: Calculate how many responses Kinsey Reporter received, with weekly resolution, for a given tag.
First, we need to do a SQL query to get all the reports associated with a specific tag (don't worry if you're not familiar with SQL)
SELECT `option`,
DATE(timestamp) as datestamp,
count(1) as num_answers
FROM survey_event, survey_answer, survey_option
WHERE event_id = survey_event.id
AND option_id = survey_option.id
GROUP BY datestampThe results come to Python looking like this:
[
...
('smile flirt', datetime.date(2013,5,23), 12),
('smile flirt', datetime.date(2013,5,24), 9),
...
]To calculate the weekly timeline with native Python would be... uncomfortable. One must:
With Pandas, it's as simple as:
import pandas as pd
df = pd.DataFrame.from_records(kr_rows, index=datestamp)
timeline = df['num_answers'].asfreq('D').resample('W', how=sum).fillna(0)In addition to making time series easier, Pandas makes plotting a snap too.
This is a notebook where I slice and dice some automobile gas mileage data: https://www.wakari.io/sharing/bundle/clayadavis/mpg
Key concepts used in the MPG analysis:
DataFrame.group_by()Series.describe()Series.plot()DataFrame.boxplot()DataFrame.hist()Series.rolling_mean()
In [93]:
import sympy
sympy.init_printing(use_latex=True) ##or use_unicode=True in a console
x, y = sympy.symbols('x y')
We differentiate and get an answer...
In [74]:
sympy.diff(sympy.exp(x**2), x)
Out[74]:
...or we can create an unevaluated expression for further manipulation...
In [75]:
my_deriv = sympy.Derivative(sympy.exp(x**2), x, x)
my_deriv
Out[75]:
...which we can evaluate later.
In [76]:
my_deriv.doit()
Out[76]:
In [82]:
sympy.Integral(sympy.exp(-x**2 - y**2), x, y)
Out[82]:
In [84]:
from sympy import oo
sympy.integrate(sympy.exp(-x**2 - y**2), (x, -oo, oo), (y, -oo, oo))
Out[84]:
In [83]:
sympy.solve([x*y - 7, x + y - 6], [x, y])
Out[83]:
Thanks for your time. Hit me up on Twitter or email if you have any questions.
Some truths about GUIs
Why it rocks: