mirror of
https://github.com/adambard/learnxinyminutes-docs.git
synced 2025-05-05 06:18:32 +00:00
Adam Bard
commit
c6c3f33f3f
234
pythonstatcomp.markdown.html
Normal file
234
pythonstatcomp.markdown.html
Normal file
@ -0,0 +1,234 @@
|
||||
---
|
||||
language: Statistical computing with Python
|
||||
contributors:
|
||||
- ["e99n09", "https://github.com/e99n09"]
|
||||
filename: pythonstatcomp.py
|
||||
---
|
||||
|
||||
This is a tutorial on how to do some typical statistical programming tasks using Python. It's intended for people basically familiar with Python and experienced at statistical programming in a language like R, Stata, SAS, SPSS, or MATLAB.
|
||||
|
||||
```python
|
||||
|
||||
# 0. Getting set up ====
|
||||
|
||||
""" Get set up with IPython and pip install the following: numpy, scipy, pandas,
|
||||
matplotlib, seaborn, requests.
|
||||
Make sure to do this tutorial in the IPython notebook so that you get
|
||||
the inline plots and easy documentation lookup.
|
||||
"""
|
||||
|
||||
# 1. Data acquisition ====
|
||||
|
||||
""" One reason people choose Python over R is that they intend to interact a lot
|
||||
with the web, either by scraping pages directly or requesting data through
|
||||
an API. You can do those things in R, but in the context of a project
|
||||
already using Python, there's a benefit to sticking with one language.
|
||||
"""
|
||||
|
||||
import requests # for HTTP requests (web scraping, APIs)
|
||||
import os
|
||||
|
||||
# web scraping
|
||||
r = requests.get("https://github.com/adambard/learnxinyminutes-docs")
|
||||
r.status_code # if 200, request was successful
|
||||
r.text # raw page source
|
||||
print(r.text) # prettily formatted
|
||||
# save the page source in a file:
|
||||
os.getcwd() # check what's the working directory
|
||||
f = open("learnxinyminutes.html","wb")
|
||||
f.write(r.text.encode("UTF-8"))
|
||||
f.close()
|
||||
|
||||
# downloading a csv
|
||||
fp = "https://raw.githubusercontent.com/adambard/learnxinyminutes-docs/master/"
|
||||
fn = "pets.csv"
|
||||
r = requests.get(fp + fn)
|
||||
print(r.text)
|
||||
f = open(fn,"wb")
|
||||
f.write(r.text.encode("UTF-8"))
|
||||
f.close()
|
||||
|
||||
""" for more on the requests module, including APIs, see
|
||||
http://docs.python-requests.org/en/latest/user/quickstart/
|
||||
"""
|
||||
|
||||
# 2. Reading a CSV file ====
|
||||
|
||||
""" Wes McKinney's pandas package gives you 'DataFrame' objects in Python. If
|
||||
you've used R, you will be familiar with the idea of the "data.frame" already.
|
||||
"""
|
||||
|
||||
import pandas as pd, numpy as np, scipy as sp
|
||||
pets = pd.read_csv(fn)
|
||||
pets
|
||||
# name age weight species
|
||||
# 0 fluffy 3 14 cat
|
||||
# 1 vesuvius 6 23 fish
|
||||
# 2 rex 5 34 dog
|
||||
|
||||
""" R users: note that Python, like most normal programming languages, starts
|
||||
indexing from 0. R is the unusual one for starting from 1.
|
||||
"""
|
||||
|
||||
# two different ways to print out a column
|
||||
pets.age
|
||||
pets["age"]
|
||||
|
||||
pets.head(2) # prints first 2 rows
|
||||
pets.tail(1) # prints last row
|
||||
|
||||
pets.name[1] # 'vesuvius'
|
||||
pets.species[0] # 'cat'
|
||||
pets["weight"][2] # 34
|
||||
|
||||
# in R, you would expect to get 3 rows doing this, but here you get 2:
|
||||
pets.age[0:2]
|
||||
# 0 3
|
||||
# 1 6
|
||||
|
||||
sum(pets.age)*2 # 28
|
||||
max(pets.weight) - min(pets.weight) # 20
|
||||
|
||||
""" If you are doing some serious linear algebra and number-crunching, you may
|
||||
just want arrays, not DataFrames. DataFrames are ideal for combining columns
|
||||
of different types.
|
||||
"""
|
||||
|
||||
# 3. Charts ====
|
||||
|
||||
import matplotlib as mpl, matplotlib.pyplot as plt
|
||||
%matplotlib inline
|
||||
|
||||
# To do data vizualization in Python, use matplotlib
|
||||
|
||||
plt.hist(pets.age);
|
||||
|
||||
plt.boxplot(pets.weight);
|
||||
|
||||
plt.scatter(pets.age, pets.weight); plt.xlabel("age"); plt.ylabel("weight");
|
||||
|
||||
# seaborn sits atop matplotlib and makes plots prettier
|
||||
|
||||
import seaborn as sns
|
||||
|
||||
plt.scatter(pets.age, pets.weight); plt.xlabel("age"); plt.ylabel("weight");
|
||||
|
||||
# there are also some seaborn-specific plotting functions
|
||||
# notice how seaborn automatically labels the x-axis on this barplot
|
||||
sns.barplot(pets["age"])
|
||||
|
||||
# R veterans can still use ggplot
|
||||
from ggplot import *
|
||||
ggplot(aes(x="age",y="weight"), data=pets) + geom_point() + labs(title="pets")
|
||||
# source: https://pypi.python.org/pypi/ggplot
|
||||
|
||||
# there's even a d3.js port: https://github.com/mikedewar/d3py
|
||||
|
||||
# 4. Simple data cleaning and exploratory analysis ====
|
||||
|
||||
""" Here's a more complicated example that demonstrates a basic data
|
||||
cleaning workflow leading to the creation of some exploratory plots
|
||||
and the running of a linear regression.
|
||||
The data set was transcribed from Wikipedia by hand. It contains
|
||||
all the Holy Roman Emperors and the important milestones in their lives
|
||||
(birth, death, coronation, etc.).
|
||||
The goal of the analysis will be to explore whether a relationship
|
||||
exists between emperor birth year and emperor lifespan.
|
||||
data source: https://en.wikipedia.org/wiki/Holy_Roman_Emperor
|
||||
"""
|
||||
|
||||
# load some data on Holy Roman Emperors
|
||||
url = "https://raw.githubusercontent.com/e99n09/R-notes/master/data/hre.csv"
|
||||
r = requests.get(url)
|
||||
fp = "hre.csv"
|
||||
f = open(fp,"wb")
|
||||
f.write(r.text.encode("UTF-8"))
|
||||
f.close()
|
||||
|
||||
hre = pd.read_csv(fp)
|
||||
|
||||
hre.head()
|
||||
"""
|
||||
Ix Dynasty Name Birth Death Election 1
|
||||
0 NaN Carolingian Charles I 2 April 742 28 January 814 NaN
|
||||
1 NaN Carolingian Louis I 778 20 June 840 NaN
|
||||
2 NaN Carolingian Lothair I 795 29 September 855 NaN
|
||||
3 NaN Carolingian Louis II 825 12 August 875 NaN
|
||||
4 NaN Carolingian Charles II 13 June 823 6 October 877 NaN
|
||||
|
||||
Election 2 Coronation 1 Coronation 2 Ceased to be Emperor
|
||||
0 NaN 25 December 800 NaN 28 January 814
|
||||
1 NaN 11 September 813 5 October 816 20 June 840
|
||||
2 NaN 5 April 823 NaN 29 September 855
|
||||
3 NaN Easter 850 18 May 872 12 August 875
|
||||
4 NaN 29 December 875 NaN 6 October 877
|
||||
|
||||
Descent from whom 1 Descent how 1 Descent from whom 2 Descent how 2
|
||||
0 NaN NaN NaN NaN
|
||||
1 Charles I son NaN NaN
|
||||
2 Louis I son NaN NaN
|
||||
3 Lothair I son NaN NaN
|
||||
4 Louis I son NaN NaN
|
||||
"""
|
||||
|
||||
# clean the Birth and Death columns
|
||||
|
||||
import re # module for regular expressions
|
||||
|
||||
rx = re.compile(r'\d+$') # match trailing digits
|
||||
|
||||
""" This function applies the regular expression to an input column (here Birth,
|
||||
Death), flattens the resulting list, converts it to a Series object, and
|
||||
finally converts the type of the Series object from string to integer. For
|
||||
more information into what different parts of the code do, see:
|
||||
- https://docs.python.org/2/howto/regex.html
|
||||
- http://stackoverflow.com/questions/11860476/how-to-unlist-a-python-list
|
||||
- http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html
|
||||
"""
|
||||
def extractYear(v):
|
||||
return(pd.Series(reduce(lambda x,y: x+y,map(rx.findall,v),[])).astype(int))
|
||||
|
||||
hre["BirthY"] = extractYear(hre.Birth)
|
||||
hre["DeathY"] = extractYear(hre.Death)
|
||||
|
||||
# make a column telling estimated age
|
||||
hre["EstAge"] = hre.DeathY.astype(int) - hre.BirthY.astype(int)
|
||||
|
||||
# simple scatterplot, no trend line, color represents dynasty
|
||||
sns.lmplot("BirthY", "EstAge", data=hre, hue="Dynasty", fit_reg=False);
|
||||
|
||||
# use scipy to run a linear regression
|
||||
from scipy import stats
|
||||
(slope,intercept,rval,pval,stderr)=stats.linregress(hre.BirthY,hre.EstAge)
|
||||
# code source: http://wiki.scipy.org/Cookbook/LinearRegression
|
||||
|
||||
# check the slope
|
||||
slope # 0.0057672618839073328
|
||||
|
||||
# check the R^2 value:
|
||||
rval**2 # 0.020363950027333586
|
||||
|
||||
# check the p-value
|
||||
pval # 0.34971812581498452
|
||||
|
||||
# use seaborn to make a scatterplot and plot the linear regression trend line
|
||||
sns.lmplot("BirthY", "EstAge", data=hre);
|
||||
|
||||
""" For more information on seaborn, see
|
||||
- http://web.stanford.edu/~mwaskom/software/seaborn/
|
||||
- https://github.com/mwaskom/seaborn
|
||||
For more information on SciPy, see
|
||||
- http://wiki.scipy.org/SciPy
|
||||
- http://wiki.scipy.org/Cookbook/
|
||||
To see a version of the Holy Roman Emperors analysis using R, see
|
||||
- http://github.com/e99n09/R-notes/blob/master/holy_roman_emperors_dates.R
|
||||
"""
|
||||
```
|
||||
|
||||
If you want to learn more, get _Python for Data Analysis_ by Wes McKinney. It's a superb resource and I used it as a reference when writing this tutorial.
|
||||
|
||||
You can also find plenty of interactive IPython tutorials on subjects specific to your interests, like Cam Davidson-Pilon's <a href="http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/" Title="Probabilistic Programming and Bayesian Methods for Hackers">Probabilistic Programming and Bayesian Methods for Hackers</a>.
|
||||
|
||||
Some more modules to research:
|
||||
- text analysis and natural language processing: nltk, http://www.nltk.org
|
||||
- social network analysis: igraph, http://igraph.org/python/
|
||||
Loading…
Reference in New Issue
Block a user