From bde8645cc7bb7f0a88b5d106cd0bd0b7e40886d0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Piotr=20Migda=C5=82?= <pmigdal@gmail.com>
Date: Sun, 3 Jan 2016 19:45:54 +0100
Subject: pep8 fixes (spaces and multiline statements)
in Python readability and code style matters
---
pythonstatcomp.html.markdown | 103 ++++++++++++++++++++++++-------------------
1 file changed, 57 insertions(+), 46 deletions(-)
(limited to 'pythonstatcomp.html.markdown')
diff --git a/pythonstatcomp.html.markdown b/pythonstatcomp.html.markdown
index 78b62e33..f8d83b98 100644
--- a/pythonstatcomp.html.markdown
+++ b/pythonstatcomp.html.markdown
@@ -9,6 +9,8 @@ This is a tutorial on how to do some typical statistical programming tasks using
```python
+
+
# 0. Getting set up ====
""" Get set up with IPython and pip install the following: numpy, scipy, pandas,
@@ -25,17 +27,17 @@ This is a tutorial on how to do some typical statistical programming tasks using
already using Python, there's a benefit to sticking with one language.
"""
-import requests # for HTTP requests (web scraping, APIs)
+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
+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")
+os.getcwd() # check what's the working directory
+f = open("learnxinyminutes.html", "wb")
f.write(r.text.encode("UTF-8"))
f.close()
@@ -44,7 +46,7 @@ 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 = open(fn, "wb")
f.write(r.text.encode("UTF-8"))
f.close()
@@ -58,7 +60,9 @@ f.close()
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
+import pandas as pd
+import numpy as np
+import scipy as sp
pets = pd.read_csv(fn)
pets
# name age weight species
@@ -74,20 +78,20 @@ pets
pets.age
pets["age"]
-pets.head(2) # prints first 2 rows
-pets.tail(1) # prints last row
+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
+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
+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
@@ -96,7 +100,8 @@ max(pets.weight) - min(pets.weight) # 20
# 3. Charts ====
-import matplotlib as mpl, matplotlib.pyplot as plt
+import matplotlib as mpl
+import matplotlib.pyplot as plt
%matplotlib inline
# To do data vizualization in Python, use matplotlib
@@ -105,13 +110,17 @@ plt.hist(pets.age);
plt.boxplot(pets.weight);
-plt.scatter(pets.age, pets.weight); plt.xlabel("age"); plt.ylabel("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");
+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
@@ -141,7 +150,7 @@ ggplot(aes(x="age",y="weight"), data=pets) + geom_point() + labs(title="pets")
url = "https://raw.githubusercontent.com/e99n09/R-notes/master/data/hre.csv"
r = requests.get(url)
fp = "hre.csv"
-f = open(fp,"wb")
+f = open(fp, "wb")
f.write(r.text.encode("UTF-8"))
f.close()
@@ -149,33 +158,33 @@ 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
+ 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
+import re # module for regular expressions
-rx = re.compile(r'\d+$') # match trailing digits
+rx = re.compile(r'\d+$') # 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
@@ -185,8 +194,9 @@ rx = re.compile(r'\d+$') # match trailing digits
- 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))
+ 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)
@@ -199,17 +209,17 @@ 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)
+(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
+slope # 0.0057672618839073328
# check the R^2 value:
-rval**2 # 0.020363950027333586
+rval**2 # 0.020363950027333586
# check the p-value
-pval # 0.34971812581498452
+pval # 0.34971812581498452
# use seaborn to make a scatterplot and plot the linear regression trend line
sns.lmplot("BirthY", "EstAge", data=hre);
@@ -223,6 +233,7 @@ sns.lmplot("BirthY", "EstAge", data=hre);
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.
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