pandas小记：pandas高级功能

「爱情、让人受尽委屈。」 2022-07-14 10:10 191阅读 0赞

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

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BpcGlzb3JyeQ_size_16_color_FFFFFF_t_70][]  
\[[Install Dask][]\]

\[[对Pandas百万级文本进行中文分词加速，看这一篇就足够了][Pandas]\]

## 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][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 uses[regularexpressions][] by default (and insome cases always uses them). See more at[*Vectorized String Methods*][Vectorized 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

[皮皮Blog][Blog]

## 分类Categoricals ##

Since version 0.15, pandas can include categorical data in a DataFrame. For full docs, see the[*categorical introduction*][categorical introduction] and the[*API documentation*][API 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

[皮皮blog][Blog]

## 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>

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BpcGlzb3JyeQ_size_16_color_FFFFFF_t_70 1][]

On DataFrame, [plot()][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>

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BpcGlzb3JyeQ_size_16_color_FFFFFF_t_70 2][]

**绘制盒图**

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

**使用matplotlib**

\# Import the module for plotting

import matplotlib.pyplot asplt

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

![2531a3848ddd367297b33ae74fa070c0.png][]

**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')

![34a1ae1df26d2a3c92c01a6d6096e99e.png][]

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"))

![16e25d056425be76453ba03f5542eb4d.png][]

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

![57c29fb30501620e3ae554d59d184dd9.png][]

### 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'))

![Center][]

**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))

![701e1a69c2b1c92d29b1e7df52d868b1.png][]

**绘制其它图**

with sns.axes\_style("white"):

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

![569430a41c8fd301a705578b1700f921.png][]

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

![3bce54dc8d3c0cd465e16558db02734e.png][]

from: [http://blog.csdn.net/pipisorry/article/details/53486777][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)][Python_pandas]

[API Reference][]

[pandas-docs/stable][pandas-docs_stable]

[Notebook Python: Getting Started with Data Analysis][Notebook Python_ Getting Started with Data Analysis]

[Python数据分析入门][Python]

[Python and R: Is Python really faster than R?][Python and R_ Is Python really faster than R]

[blog.csdn.net_pipisorry_article_details_53486777]: http://blog.csdn.net/pipisorry/article/details/53486777
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BpcGlzb3JyeQ_size_16_color_FFFFFF_t_70]: /images/20220714/fd4b3dcaf4c34fe095f8bec62bad25f2.png
[Install Dask]: https://docs.dask.org/en/latest/install.html#pip
[Pandas]: https://www.pythonf.cn/read/50549
[Blog]: http://blog.csdn.net/pipisorry
[regularexpressions]: https://docs.python.org/2/library/re.html
[Vectorized String Methods]: http://pandas.pydata.org/pandas-docs/stable/text.html#text-string-methods
[categorical introduction]: http://pandas.pydata.org/pandas-docs/stable/categorical.html#categorical
[API documentation]: http://pandas.pydata.org/pandas-docs/stable/api.html#api-categorical
[Plotting]: http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BpcGlzb3JyeQ_size_16_color_FFFFFF_t_70 1]: /images/20220714/747e3fe8273541f4b9a6db17e0bde661.png
[plot]: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html#pandas.DataFrame.plot
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3BpcGlzb3JyeQ_size_16_color_FFFFFF_t_70 2]: /images/20220714/8a0df9b33e264152a828b0dc126a70f0.png
[matplotlib]: http://blog.csdn.net/pipisorry/article/details/37742423
[python_seaborn]: http://blog.csdn.net/pipisorry/article/details/49515745
[2531a3848ddd367297b33ae74fa070c0.png]: /images/20220714/c44aac7ec5e1409baf6d04ab9248c6d4.png
[ggplot]: http://docs.ggplot2.org/current/index.html
[34a1ae1df26d2a3c92c01a6d6096e99e.png]: /images/20220714/8e580d1b2cbc4cb3873003bc8c678746.png
[16e25d056425be76453ba03f5542eb4d.png]: /images/20220714/1bd9758abcc64401998b885f9a12a1f9.png
[57c29fb30501620e3ae554d59d184dd9.png]: /images/20220714/bbe178e0823d49cdbe9ad8b9b3bb30a2.png
[Center]: /images/20220714/bcfc2c549ea54c16b10e2ee61751ff0e.png
[701e1a69c2b1c92d29b1e7df52d868b1.png]: /images/20220714/f339b9410e0a492caea87bdc15407fb3.png
[569430a41c8fd301a705578b1700f921.png]: /images/20220714/3fc51c6d7825498aa16b8152f086fdab.png
[3bce54dc8d3c0cd465e16558db02734e.png]: /images/20220714/282ee356796041219ba335cb184dd6bf.png
[Python_pandas]: http://www.xuebuyuan.com/2180573.html
[API Reference]: http://pandas.pydata.org/pandas-docs/stable/api.html
[pandas-docs_stable]: http://pandas.pydata.org/pandas-docs/stable/#
[Notebook Python_ Getting Started with Data Analysis]: http://nuttenscl.be/Python_Getting_Started_with_Data_Analysis.html
[Python]: http://python.jobbole.com/81133/
[Python and R_ Is Python really faster than R]: http://alstatr.blogspot.com/2014/01/python-and-r-is-python-really-faster.html#disqus_thread