Today’s note on GitHub:

https://github.com/jumpingchu/Notes/blob/master/Seaborn%20Practice.ipynb

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Series.str.endswith(pat, na=nan)

Test if the end of each string element matches a pattern.

>>> s = pd.Series(['bat', 'bear', 'caT', np.nan])
>>> s.str.endswith('t') 
0     True 
1    False 
2    False 
3      NaN

>>> s.str.endswith('t', na=False)
0     True
1    False
2    False
3    False

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Series.corr(other, method=’pearson’, min_periods=None)

Compute correlation with other Series, excluding missing values.

>>> histogram_intersection = lambda a, b: np.minimum(a, b).sum().round(decimals=1)
>>> s1 = pd.Series([.2, .0, .6, .2])
>>> s2 = pd.Series([.3, .6, .0, .1])
>>> s1.corr(s2, method=histogram_intersection)
0.3

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DataFrame.nunique(axis=0, dropna=True)

Count distinct observations over requested axis.

>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})
>>> df.nunique()
A    3
B    1

各index(A,B)中，有多少個不同的值

>>> df.nunique(axis=1)
0    1
1    2
2    2

[1, 2, 3]
[1, 1, 1]
各column中
column[0]有1個不同的值
column[1]、[2]有2個不同的值

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