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pep8 fixes (spaces and multiline statements)

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in Python readability and code style matters
This commit is contained in:
Piotr Migdał 2016-01-03 19:45:54 +01:00
parent b2113480a4
commit bde8645cc7
1 changed files with 55 additions and 44 deletions
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pythonstatcomp.html.markdown
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@ -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,
@ -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
@ -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
@ -185,6 +194,7 @@ 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))
@ -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.