pandas合并、转换、映射、替换

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我是靠谱客的博主简单雨，最近开发中收集的这篇文章主要介绍pandas合并、转换、映射、替换，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

import numpy as np
import pandas as pd
df1=pd.DataFrame({'a':[1,np.nan,5,np.nan],
'b':[np.nan,2,np.nan,6],
'c':range(2,18,4)})
df2=pd.DataFrame({'a':[5,4,np.nan,3,7],
'b':[np.nan,3,4,6,8]})
In[71]: df1.combine_first(df2)
Out[71]:
a
b
c
0
1.0
NaN
2.0
1
4.0
2.0
6.0
2
5.0
4.0
10.0
3
3.0
6.0
14.0
4
7.0
8.0
NaN

data=pd.DataFrame(np.arange(6).reshape((2,3)),
index=pd.Index(['oo','cc'],name='state'),
columns=pd.Index(['one','two','three'],name='number'))
result=data.stack()
In[72]: result
Out[72]:
state
number
oo
one
0
two
1
three
2
cc
one
3
two
4
three
5
dtype: int32
In[73]: result.unstack()
Out[73]:
number
one
two
three
state
oo
0
1
2
cc
3
4
5
In[74]: result.unstack(0)
Out[74]:
state
oo
cc
number
one
0
3
two
1
4
three
2
5
In[75]: result.unstack('state')
Out[75]:
state
oo
cc
number
one
0
3
two
1
4
three
2
5

s1=pd.Series([0,1,2,3],index=['a','b','c','d'])
s2=pd.Series([4,5,6],index=['c','d','e'])
data2=pd.concat([s1,s2],keys=['one','two'])
In[77]: data2
Out[77]:
one
a
0
b
1
c
2
d
3
two
c
4
d
5
e
6
dtype: int64
In[78]: data2.unstack()
Out[78]:
a
b
c
d
e
one
0.0
1.0
2.0
3.0
NaN
two
NaN
NaN
4.0
5.0
6.0
In[79]: data2.unstack().stack()
Out[79]:
one
a
0.0
b
1.0
c
2.0
d
3.0
two
c
4.0
d
5.0
e
6.0
dtype: float64

data=pd.DataFrame({'k1':['one'] * 3 + ['two'] * 4,
'k2':[1,1,2,3,3,4,4]})
data['v1']=range(7)
In[80]: data.duplicated()
Out[80]:
0
False
1
False
2
False
3
False
4
False
5
False
6
False
dtype: bool
In[81]: data.drop_duplicates()
Out[81]:
k1
k2
v1
0
one
1
0
1
one
1
1
2
one
2
2
3
two
3
3
4
two
3
4
5
two
4
5
6
two
4
6
In[82]: data.drop_duplicates(['k1'])
Out[82]:
k1
k2
v1
0
one
1
0
3
two
3
3
In[83]: data.drop_duplicates(['k1','k2'])
Out[83]:
k1
k2
v1
0
one
1
0
2
one
2
2
3
two
3
3
5
two
4
5
In[84]: data.drop_duplicates(['k1','k2'],keep='last')
Out[84]:
k1
k2
v1
1
one
1
1
2
one
2
2
4
two
3
4
6
two
4
6

data=pd.DataFrame({'food':['aa','bb','cc','dd','ee','ff','gg'],
'price':[11,22,33,44,55,66,77]})
In[86]: data
Out[86]:
food
price
0
aa
11
1
bb
22
2
cc
33
3
dd
44
4
ee
55
5
ff
66
6
gg
77
In[87]: data['food'].map(lambda x:x.upper())
Out[87]:
0
AA
1
BB
2
CC
3
DD
4
EE
5
FF
6
GG
Name: food, dtype: object
data['food'].map(str.upper)
animal={'aa':'pig','bb':'dog','cc':'horse','dd':'dog','ee':'pig','ff':'dog','gg':'horse'}
#data['animal']=data['food'].map(animal)
data['animal']=data['food'].map(lambda x:animal[x])
In[89]: data['animal']
Out[89]:
0
pig
1
dog
2
horse
3
dog
4
pig
5
dog
6
horse
Name: animal, dtype: object

dataa=pd.Series([1,-999,2,-999,-1000,3])
dataa.replace(-999,np.nan)
dataa.replace([-999,-1000],np.nan)
dataa.replace([-999,-1000],[np.nan,0])
dataa.replace({-999:np.nan,-1000:0})
In[90]: dataa.replace([-999,-1000],[np.nan,0])
Out[90]:
0
1.0
1
NaN
2
2.0
3
NaN
4
0.0
5
3.0
dtype: float64