pandas小记：pandas高级功能

http://blog.csdn.net/pipisorry/article/details/53486777

pandas高级功能：性能优化、面板数据、字符串方法、分类、可视化。

性能优化

对Pandas逐行运算的加速

使用dask对dataframe加速

python -m pip install "dask[complete]" # Install everything

from dask import dataframe as ddf

# df[new_word_cols] = df[word_cols].applymap(lang.word2idfunc) #方式1
# chunksize = args.file_batch_size // 8 #方式2
npartitions = 4 * multiprocessing.cpu_count() #方式3
df[new_word_cols] = ddf.from_pandas(df[word_cols], npartitions=npartitions) \
.map_partitions(lambda dds: dds.applymap(lang.word2idfunc)).compute(scheduler='processes')
应用apply函数时，方式3大概比方式1节省至少一半时间。

遍历时间对比：

[Install Dask]

[对Pandas百万级文本进行中文分词加速，看这一篇就足够了]

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
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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

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分类Categoricals

Since version 0.15, pandas can include categorical data in a DataFrame. For full docs, see thecategorical introduction and theAPI documentation.

In [122]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "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

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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()

直接绘制

Plotting docs.

In [130]: ts = pd.Series(np.random.randn(1000), 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>

On DataFrame, plot() is a convenience to plot all of the columns with labels:

In [133]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,.....:                   columns=['A', 'B', 'C', 'D']).....: In [134]: df = df.cumsum()In [135]: plt.figure(); df.plot(); plt.legend(loc='best')
Out[135]: <matplotlib.legend.Legend at 0xaf499d4c>

绘制盒图

Python中有许多可视化模块，最流行的当属matpalotlib库[matplotlib绘图基础]。稍加提及，我们也可选择bokeh和seaborn模块[python高级绘图库seaborn]。

使用matplotlib

# Import the module for plotting

import matplotlib.pyplot asplt

plt.show(df.plot(kind='box'))

使用pandas模块中集成R的ggplot主题来美化图表

要使用ggplot，我们只需要在上述代码中多加一行：

importmatplotlib.pyplotasplt

pd.options.display.mpl_style='default'# Sets the plotting display theme to ggplot2

df.plot(kind='box')

比matplotlib.pyplot主题简洁太多。

更好的是引入seaborn模块

该模块是一个统计数据可视化库：

# Import the seaborn library

import seaborn assns

# Do the boxplot

plt.show(sns.boxplot(df,widths=0.5,color="pastel"))

plt.show(sns.violinplot(df,widths=0.5,color="pastel"))

绘制散点图scatter

df:

age fat_percent
0    23          9.5
1    23         26.5
2    27          7.8
3    27         17.8
4    39         31.4
5    41         25.9

plt.show(df.plot(kind='scatter', x='age', y='fat_percent'))

Note: 不指定x,y会出错： ValueError: scatter requires and x and y column

绘制直方曲线图

plt.show(sns.distplot(df.ix[:,2],rug=True,bins=15))

绘制其它图

with sns.axes_style("white"):

plt.show(sns.jointplot(df.ix[:,1],df.ix[:,2],kind="kde"))

plt.show(sns.lmplot("Benguet", "Ifugao", df))

from: http://blog.csdn.net/pipisorry/article/details/53486777

ref: 《利用Python进行数据分析》*

利用Python进行数据分析——pandas入门(五)

API Reference

pandas-docs/stable

Notebook Python: Getting Started with Data Analysis

Python数据分析入门

Python and R: Is Python really faster than R?