pandas基础命令

pandas

• pandas = pannel data + data analysis
• anthor:Wes McKinney

Series

#给数据的索引命名，提高可读性
from pandas import Series
obj=Series([4,7,-5,2],['b','d','c','a'])

DataFrame

#表格形数据结构
#create dataframe by passing a dict object
data = {'state':['Ohio','Ohio','Ohio','Nevada','Nevada'],
... 'year':[2000,2001,2002,2001,2002],
... 'pop':[1.5,1.7,3.6,2.4,2.9]}
print DataFrame(data,columns = ['state','year','pop'])

#create dataframe by passing numpy array
date=pd.date_range('20160101',periods=10)
df=pd.DataFrame(np.random.randn(10,4),index=date,columns=list('ABCD')

Index

#索引对象
from pandas import DataFram,Series



#reindex
obj=Series('bule','purple','yellow',index=[0,2,4])
print obj.reindex(range(6),method='fill')

#drop index
obj.drop('1')

#axis 0行 1列
import numpy as np
data=DataFrame(np.arange(16).reshape(4,4),index=['Chio','Colorado','Utah','NewYork'],columns=['one','two','three','four'])
data.ix['Colorado',['two','three']]

匿名函数

import numpy as np
from pandas import Series,DataFrame
frame=DataFrame(np.random.randn(4,3),columns=list('bde'),index=['Utah','Chio','Texas','Oregon']
np.abs(frame)
#函数应用到某列或某行
f=lambda x:x.max()-x.min()
print frame.apply(f)
print frame.apply(f,axix=1)

#函数map到每个元素
_format=lambda x:'%.2f' % x
print frame.applymap(_format)
print frame['e'].map(_format)

排序

from pandas import DataFream,Series
frame=DataFrame(np.random.randn(4,3),columns=list('bde'),index=['Utah','Chio','Texas','Oregon']
frame.sort_index()

join

import pandas as pd
left = pd.DataFrame({'key':['foo','foo'],'lval':[1,2]})
right = pd.DataFrame({'key':['foo','foo'],'rval':[4,5]})
print left
   key  lval
0  foo     1
1  foo     2
print right
   key  rval
0  foo     4
1  foo     5
pd.merge(left,right,on='key')
   key  lval  rval
0  foo     1     4
1  foo     1     5
2  foo     2     4
3  foo     2     5

groupby

import pandas as pd
df=pd.DataFrame({'A':['boo','bar','boo','bar'],'B':['one','two','three','four'],'C':np.random.randn(4),'D':np.random.randn(4)})
df.groupby('A').sum()
            C         D
A                      
bar  0.558913  0.163351
boo -0.936902 -1.450482