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0426pandas多重索引.py
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0426pandas多重索引.py
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# Creating a MultiIndex (hierarchical index) object
import pandas as pd
import numpy as np
# 对于多个索引，若来自数组，则使用MultiIndex.from_arrays
# 对于多个索引，若来自元组，则使用MultiIndex.from_tuples
# a crossed set of iterables (using MultiIndex.from_product)
index = pd.MultiIndex.from_product([[0,1,2],['2012-01-01','2012-01-02','2012-01-03']],names=['first', 'second'])
df = pd.DataFrame(np.random.randn(9, 4), index=index,columns=list('abcd'))
print(df)
df_1 = df.loc[(slice(None),'2012-01-01'),:] # panel数据切片
print(df_1)
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
# zip函数接受任意多个（包括0个和1个）序列作为参数，返回一个tuple列表。
# 在函数调用中使用*list/tuple的方式表示将list/tuple分开，作为位置参数传递给对应函数
tuples=list(zip(*arrays)) # 将数组转换成元组
index=pd.MultiIndex.from_tuples(tuples,names=['first','second'])
# 也可以使用
index=pd.MultiIndex.from_arrays(arrays,names=['first','second'])
s=pd.Series(np.random.randn(8),index=index)
# 还可以使用如下方法
iterables=[['bar','baz','foo','qux'],['one','two']]
# 相当于R语言中的expand.grid()
pd.MultiIndex.from_product(iterables,names=['first','second'])
# 最方便的是直接给定数组arrays
arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']),
np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])]
s=pd.Series(np.random.randn(8),index=arrays)
df=pd.DataFrame(np.random.randn(8,4),index=arrays)
df=pd.DataFrame(np.random.randn(3,8),index=['A','B','C'],columns=index)
pd.DataFrame(np.random.randn(6,6),index=index[:6],columns=index[:6])
pd.Series(np.random.randn(8), index=tuples) # 使用元组作为index
# 设置数据的显示方式
pd.set_option('display.multi_sparse',False) # 每一个数据索引都显示
pd.set_option('display.multi_sparse',True) # 只显示第一个索引
# Reconstructing the level labels
index.get_level_values(0) # 返回每一个index的索引名称
index.get_level_values('second')
index.get_level_values(1)
# Basic indexing on axis with MultiIndex
df['bar']
df['bar','one']
df['bar']['one']
df.columns
df[['foo','qux']].columns
df[['foo','qux']].columns.values # 返回真正的索引名称
df[['foo','qux']].columns.get_level_values(0)
pd.MultiIndex.from_tuples(df[['foo','qux']].columns.values)
# Data alignment and using reindex
# 数据的运算
s+s[:-2] # 与nan运算，返回nan值
s+s[::2]
s.reindex(index[:3]) # 返回前3行数据
# 按照索引元组进行返回数据
s.reindex([('foo', 'two'), ('bar', 'one'), ('qux', 'one'), ('baz', 'one')])
# Advanced indexing with hierarchical index
df.T # 进行转置
df.loc['bar']
df.loc['bar','two'] # 等同于 df['bar','one']
df.loc[('baz','two'):('qux','one')]
df.loc[('baz','two'):'foo']
df.ix[[('bar', 'two'), ('qux', 'one')]]
def mklbl(prefix,n):
return(['%s%s' % (prefix,i) for i in range(n)])
miindex=pd.MultiIndex.from_product([mklbl('A',4),mklbl('B',2),mklbl('C',4),mklbl('D',2)])
micolumns = pd.MultiIndex.from_tuples([('a','foo'),('a','bar'),('b','foo'),('b','bah')],
names=['lvl0', 'lvl1'])
dfmi = pd.DataFrame(np.arange(len(miindex)*len(micolumns)).reshape((len(miindex),len(micolumns))),index=miindex,
columns=micolumns).sort_index().sort_index(axis=1)
dfmi.loc[(slice('A1','A3'),slice(None),['C1','C3']),:]
idx=pd.IndexSlice
dfmi.loc[idx[:,:,['C1','C3']],idx[:,'foo']]
mask=dfmi[('a','foo')]>200
dfmi.loc[idx[mask,:,['C1','C3']],idx[:,'foo']]
dfmi.loc(axis=0)[:,:,['C1','C3']]
df2.loc(axis=0)[:,:,['C1','C3']] = -10
df2.loc[idx[:,:,['C1','C3']],:]=df2*1000
# Cross-section
df.xs('one',level='second') # 选取second的one一列数据
df.xs('bar',level='first') # df.xs('bar')
df.loc[(slice(None),'one'),:]
df.loc[('bar','one'),:]
df.loc[(slice('bar'),'one'),:]
df=df.T
df.xs('one', level='second', axis=1)
df.loc[:,(slice(None),'one')]
df.xs(('one', 'bar'), level=('second', 'first'), axis=1)
df.loc[:,('bar','one')]
df.xs('one', level='second', axis=1, drop_level=False) # 保留索引的名称
df.xs('one',level='second',axis=1,drop_level=True) #删除索引的名称
# Advanced reindexing and alignment
midx=pd.MultiIndex(levels=[['zero','one'],['x','y']],
labels=[[1,1,0,0],[1,0,1,0]])
df=pd.DataFrame(np.random.randn(4,2),index=midx)
df2 = df.mean(level=0)
df2.reindex(df.index,level=0)
# 将df赋值给df_aligned 将df2赋值给df2_aligned
df_aligned, df2_aligned = df.align(df2, level=0)
# Swapping levels with swaplevel()
# 转换索引名称
df[:5].swaplevel(0,1,axis=0) # 索引名称互换
# Reordering levels with reorder_levels()
df[:5].reorder_levels([1,0], axis=0)
# Sorting a MultiIndex 对于索引进行排序
import random
random.shuffle(tuples)
s=pd.Series(np.random.randn(8),index=pd.MultiIndex.from_tuples(tuples))
s.sort_index() # 默认level=0
s.sort_index(level=1)
# 通过索引name进行排序
s.index.set_names(['L1', 'L2'], inplace=True)
s.sort_index(level='L1')
df.T.sort_index(level=1, axis=1)
dfm = pd.DataFrame({'jim': [0, 0, 1, 1],
'joe':['x','x','z','y'],
'jolie':np.random.rand(4)})
dfm=dfm.set_index(['jim','joe']) # 将jim 和joe两列设置成行索引名称
dfm.loc[(1,'z')]
dfm.index.is_lexsorted() # 判断是否排序
dfm.index.is_lexsort_depth # 返回排序的深度
dfm.loc[(0,'y'):(1, 'z')]
# Take Methods
index=pd.Index(np.random.randint(0,1000,10))
positions=[0,9,3]
index[positions]
index.take(positions)
ser=pd.Series(np.random.randn(10))
ser.iloc[positions]
ser.take(positions)
frm = pd.DataFrame(np.random.randn(5, 3))
frm.take([1, 4, 3])
frm.take([0,2],axis=1)
arr=np.random.randn(10)
arr.take([False,True,False,True])
# Index Types
df.dtypes # 查看数据类型
df.B.cat.categories
indexf=pd.Index([1.5,2,3,4.5,5])
sf=pd.Series(range(5),index=indexf)
sf.loc[3.0] # 浮点型数据作为索引