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pep8 fixes (spaces and multiline statements)
in Python readability and code style matters
This commit is contained in:
Piotr Migdał
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b2113480a4
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bde8645cc7
@ -9,6 +9,8 @@ This is a tutorial on how to do some typical statistical programming tasks using
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```python
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# 0. Getting set up ====
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""" Get set up with IPython and pip install the following: numpy, scipy, pandas,
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@ -25,17 +27,17 @@ This is a tutorial on how to do some typical statistical programming tasks using
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already using Python, there's a benefit to sticking with one language.
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"""
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import requests # for HTTP requests (web scraping, APIs)
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import requests # for HTTP requests (web scraping, APIs)
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import os
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# web scraping
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r = requests.get("https://github.com/adambard/learnxinyminutes-docs")
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r.status_code # if 200, request was successful
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r.text # raw page source
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print(r.text) # prettily formatted
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r.status_code # if 200, request was successful
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r.text # raw page source
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print(r.text) # prettily formatted
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# save the page source in a file:
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os.getcwd() # check what's the working directory
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f = open("learnxinyminutes.html","wb")
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os.getcwd() # check what's the working directory
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f = open("learnxinyminutes.html", "wb")
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f.write(r.text.encode("UTF-8"))
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f.close()
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@ -44,7 +46,7 @@ fp = "https://raw.githubusercontent.com/adambard/learnxinyminutes-docs/master/"
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fn = "pets.csv"
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r = requests.get(fp + fn)
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print(r.text)
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f = open(fn,"wb")
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f = open(fn, "wb")
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f.write(r.text.encode("UTF-8"))
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f.close()
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@ -58,7 +60,9 @@ f.close()
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you've used R, you will be familiar with the idea of the "data.frame" already.
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"""
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import pandas as pd, numpy as np, scipy as sp
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import pandas as pd
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import numpy as np
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import scipy as sp
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pets = pd.read_csv(fn)
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pets
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# name age weight species
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@ -74,20 +78,20 @@ pets
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pets.age
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pets["age"]
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pets.head(2) # prints first 2 rows
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pets.tail(1) # prints last row
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pets.head(2) # prints first 2 rows
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pets.tail(1) # prints last row
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pets.name[1] # 'vesuvius'
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pets.species[0] # 'cat'
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pets["weight"][2] # 34
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pets.name[1] # 'vesuvius'
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pets.species[0] # 'cat'
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pets["weight"][2] # 34
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# in R, you would expect to get 3 rows doing this, but here you get 2:
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pets.age[0:2]
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# 0 3
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# 1 6
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sum(pets.age)*2 # 28
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max(pets.weight) - min(pets.weight) # 20
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sum(pets.age) * 2 # 28
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max(pets.weight) - min(pets.weight) # 20
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""" If you are doing some serious linear algebra and number-crunching, you may
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just want arrays, not DataFrames. DataFrames are ideal for combining columns
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@ -96,7 +100,8 @@ max(pets.weight) - min(pets.weight) # 20
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# 3. Charts ====
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import matplotlib as mpl, matplotlib.pyplot as plt
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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%matplotlib inline
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# To do data vizualization in Python, use matplotlib
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@ -105,13 +110,17 @@ plt.hist(pets.age);
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plt.boxplot(pets.weight);
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plt.scatter(pets.age, pets.weight); plt.xlabel("age"); plt.ylabel("weight");
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plt.scatter(pets.age, pets.weight)
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plt.xlabel("age")
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plt.ylabel("weight");
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# seaborn sits atop matplotlib and makes plots prettier
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import seaborn as sns
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plt.scatter(pets.age, pets.weight); plt.xlabel("age"); plt.ylabel("weight");
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plt.scatter(pets.age, pets.weight)
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plt.xlabel("age")
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plt.ylabel("weight");
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# there are also some seaborn-specific plotting functions
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# notice how seaborn automatically labels the x-axis on this barplot
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@ -141,7 +150,7 @@ ggplot(aes(x="age",y="weight"), data=pets) + geom_point() + labs(title="pets")
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url = "https://raw.githubusercontent.com/e99n09/R-notes/master/data/hre.csv"
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r = requests.get(url)
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fp = "hre.csv"
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f = open(fp,"wb")
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f = open(fp, "wb")
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f.write(r.text.encode("UTF-8"))
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f.close()
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@ -149,33 +158,33 @@ hre = pd.read_csv(fp)
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hre.head()
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"""
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Ix Dynasty Name Birth Death Election 1
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0 NaN Carolingian Charles I 2 April 742 28 January 814 NaN
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1 NaN Carolingian Louis I 778 20 June 840 NaN
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2 NaN Carolingian Lothair I 795 29 September 855 NaN
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3 NaN Carolingian Louis II 825 12 August 875 NaN
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4 NaN Carolingian Charles II 13 June 823 6 October 877 NaN
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Ix Dynasty Name Birth Death Election 1
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0 NaN Carolingian Charles I 2 April 742 28 January 814 NaN
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1 NaN Carolingian Louis I 778 20 June 840 NaN
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2 NaN Carolingian Lothair I 795 29 September 855 NaN
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3 NaN Carolingian Louis II 825 12 August 875 NaN
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4 NaN Carolingian Charles II 13 June 823 6 October 877 NaN
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Election 2 Coronation 1 Coronation 2 Ceased to be Emperor
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0 NaN 25 December 800 NaN 28 January 814
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1 NaN 11 September 813 5 October 816 20 June 840
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2 NaN 5 April 823 NaN 29 September 855
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3 NaN Easter 850 18 May 872 12 August 875
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4 NaN 29 December 875 NaN 6 October 877
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Election 2 Coronation 1 Coronation 2 Ceased to be Emperor
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0 NaN 25 December 800 NaN 28 January 814
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1 NaN 11 September 813 5 October 816 20 June 840
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2 NaN 5 April 823 NaN 29 September 855
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3 NaN Easter 850 18 May 872 12 August 875
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4 NaN 29 December 875 NaN 6 October 877
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Descent from whom 1 Descent how 1 Descent from whom 2 Descent how 2
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0 NaN NaN NaN NaN
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1 Charles I son NaN NaN
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2 Louis I son NaN NaN
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3 Lothair I son NaN NaN
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4 Louis I son NaN NaN
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Descent from whom 1 Descent how 1 Descent from whom 2 Descent how 2
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0 NaN NaN NaN NaN
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1 Charles I son NaN NaN
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2 Louis I son NaN NaN
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3 Lothair I son NaN NaN
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4 Louis I son NaN NaN
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"""
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# clean the Birth and Death columns
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import re # module for regular expressions
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import re # module for regular expressions
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rx = re.compile(r'\d+$') # match trailing digits
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rx = re.compile(r'\d+$') # match trailing digits
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""" This function applies the regular expression to an input column (here Birth,
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Death), flattens the resulting list, converts it to a Series object, and
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@ -185,8 +194,9 @@ rx = re.compile(r'\d+$') # match trailing digits
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- http://stackoverflow.com/questions/11860476/how-to-unlist-a-python-list
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- http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html
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"""
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def extractYear(v):
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return(pd.Series(reduce(lambda x,y: x+y,map(rx.findall,v),[])).astype(int))
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return(pd.Series(reduce(lambda x, y: x + y, map(rx.findall, v), [])).astype(int))
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hre["BirthY"] = extractYear(hre.Birth)
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hre["DeathY"] = extractYear(hre.Death)
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@ -199,17 +209,17 @@ sns.lmplot("BirthY", "EstAge", data=hre, hue="Dynasty", fit_reg=False);
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# use scipy to run a linear regression
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from scipy import stats
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(slope,intercept,rval,pval,stderr)=stats.linregress(hre.BirthY,hre.EstAge)
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(slope, intercept, rval, pval, stderr) = stats.linregress(hre.BirthY, hre.EstAge)
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# code source: http://wiki.scipy.org/Cookbook/LinearRegression
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# check the slope
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slope # 0.0057672618839073328
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slope # 0.0057672618839073328
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# check the R^2 value:
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rval**2 # 0.020363950027333586
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rval**2 # 0.020363950027333586
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# check the p-value
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pval # 0.34971812581498452
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pval # 0.34971812581498452
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# use seaborn to make a scatterplot and plot the linear regression trend line
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sns.lmplot("BirthY", "EstAge", data=hre);
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@ -223,6 +233,7 @@ sns.lmplot("BirthY", "EstAge", data=hre);
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To see a version of the Holy Roman Emperors analysis using R, see
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- http://github.com/e99n09/R-notes/blob/master/holy_roman_emperors_dates.R
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"""
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```
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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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