最近在看Python数据分析这本书,随手记录一下读书笔记。
本书中推荐了edm和ipython作为数据分析的环境,我还是刚开始使用这种集成的环境,觉得交互方面,比传统的命令行方式提高了不少。
使用方法
#edm shell
(edm)bash-3.2$ ipython
Python 2.7.13 |Enthought, Inc. (x86_64)| (default, Mar 2 2017, 08:20:50)
Type "copyright", "credits" or "license" for more information.
IPython 5.3.0 -- An enhanced Interactive Python.
从 github 的网站上下载了美国的人口数据,按照书上的敲代码,到 pivot_table 这里过不去,用 help 查了一下,我这个版本的已经更新了,修改一下后就可以跑了。
In [7]: import pandas as pd
In [8]: names1880 = pd.read_csv('yob1880.txt',names=['name','sex','births'])
In [9]: names1880
Out[9]:
name sex births
0 Mary F 7065
1 Anna F 2604
2 Emma F 2003
3 Elizabeth F 1939
4 Minnie F 1746
5 Margaret F 1578
...
1998 York M 5
1999 Zachariah M 5
[2000 rows x 3 columns]
In [10]: names1880.groupby('sex').births.sum()
Out[10]:
sex
F 90993
M 110493
Name: births, dtype: int64
In [12]: years = range(1880,2011)
In [13]: pieces=[]
In [14]: columns=['name','sex','births']
In [15]: for year in years:
...: path='yob%d.txt' % year
...: frame=pd.read_csv(path,names=columns)
...: frame['year']=year
...: pieces.append(frame)
...:
In [16]: names=pd.concat(pieces,ignore_index=True)
In [17]: names
Out[17]:
name sex births year
0 Mary F 7065 1880
1 Anna F 2604 1880
2 Emma F 2003 1880
3 Elizabeth F 1939 1880
4 Minnie F 1746 1880
1690781 Zyquarius M 5 2010
1690782 Zyran M 5 2010
1690783 Zzyzx M 5 2010
[1690784 rows x 4 columns]
In [25]: total_birth=names.pivot_table('births',index
...: ='year',columns='sex',aggfunc=sum)
In [26]: total_birth.tail()
Out[26]:
sex F M
year
2006 1896468 2050234
2007 1916888 2069242
2008 1883645 2032310
2009 1827643 1973359
2010 1759010 1898382
In [27]: total_birth.plot(title="Total births by sex and year")
Out[27]: <matplotlib.axes._subplots.AxesSubplot at 0x11864af50>
In [31]: import matplotlib.pyplot as plt
In [32]: plt.show()
本文为作者原创。
参考资料: 1、edm 2、pydata 3、matplotlib