pandas小记：pandas高级功能
http://blog.csdn.net/pipisorry/article/details/53486777
pandas高级功能：面板数据、字符串方法、分类、可视化。
面板数据
{pandas数据结构有一维Series，二维DataFrame，这是三维Panel}
pandas有一个Panel数据结构，可以将其看做一个三维版的，可以用一个由DataFrame对象组成的字典或一个三维ndarray来创建Panel对象：
import pandas.io.data as web
pdata = pd.Panel(dict((stk, web.get_data_yahoo(stk, '1/1/2009', '6/1/2012')) for stk in ['AAPL', 'GOOG', 'MSFT','DELL']))
Note: stk代表指标，6个指标；三维：stk,company,time.
Panel中的每一项（类似于DataFrame的列）都是一个DataFrame
>>> pdata
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 868 (major_axis) x 6 (minor_axis)
Items axis: AAPL to MSFT
Major_axis axis: 2009-01-02 00:00:00 to 2012-06-01 00:00:00
Minor_axis axis: Open to Adj Close
>>> pdata = pdata.swapaxes('items', 'minor')
>>>pdata['Adj Close']
三维度ix标签索引
基于ix的标签索引被推广到了三个维度，因此可以选取指定日期或日期范围的所有数据，如下所示：
>>> pdata.ix[:,'6/1/2012',:]
>>>pdata.ix['Adj Close', '5/22/2012':,:]
另一个用于呈现面板数据（尤其是对拟合统计模型）的办法是“堆积式的” DataFrame 形式：
>>> stacked=pdata.ix[:,'5/30/2012':,:].to_frame()
>>>stacked
DataFrame有一个相应的to_panel方法，它是to_frame的逆运算:
>>> stacked.to_panel()
<class 'pandas.core.panel.Panel'>
Dimensions: 6 (items) x 3 (major_axis) x 4 (minor_axis)
Items axis: Open to Adj Close
Major_axis axis: 2012-05-30 00:00:00 to 2012-06-01 00:00:00
Minor_axis axis: AAPL to MSFT
皮皮Blog
字符串方法String Methods
Series is equipped with a set of string processing methods in the strattribute that make it easy to operate on each element of the array, as in thecode snippet below. Note that pattern-matching instr generally usesregularexpressions by default (and insome cases always uses them). See more atVectorized String Methods.
In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat']) In [72]: s.str.lower() Out[72]: 0 a 1 b 2 c 3 aaba 4 baca 5 NaN 6 caba 7 dog 8 cat dtype: object
,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
Convert the raw grades to a categorical data type.
In [123]: df["grade"] = df["raw_grade"].astype("category")
In [124]: df["grade"]
Out[124]:
0 a
1 b
2 b
3 a
4 a
5 e
Name: grade, dtype: category
Categories (3, object): [a, b, e]
Rename the categories to more meaningful names (assigning to Series.cat.categories is inplace!)
In [125]: df["grade"].cat.categories = ["very good", "good", "very bad"]
Reorder the categories and simultaneously add the missing categories (methods underSeries.cat return a newSeries per default).
In [126]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"]) In [127]: df["grade"] Out[127]: 0 very good 1 good 2 good 3 very good 4 very good 5 very bad Name: grade, dtype: category Categories (5, object): [very bad, bad, medium, good, very good]
Sorting is per order in the categories, not lexical order.
In [128]: df.sort_values(by="grade") Out[128]: id raw_grade grade 5 6 e very bad 1 2 b good 2 3 b good 0 1 a very good 3 4 a very good 4 5 a very good
Grouping by a categorical column shows also empty categories.
In [129]: df.groupby("grade").size()
Out[129]:
grade
very bad 1
bad 0
medium 0
good 2
very good 3
dtype: int64
可视化Plot
DataFrame内置基于matplotlib的绘图功能
In [76]: df['GDP percap'].plot(kind='bar') In [77]: import matplotlib.pyplot as plt In [78]: plt.show()
), index=pd.date_range('1/1/2000', periods=1000))
In [131]: ts = ts.cumsum()
In [132]: ts.plot()
Out[132]: <matplotlib.axes._subplots.AxesSubplot at 0xaf49988c>
