pandas数据拼接的实现示例

砸漏

发布于 2020-11-04 09:40:44

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发布于 2020-11-04 09:40:44

文章被收录于专栏：恩蓝脚本

一前言

pandas数据拼接有可能会用到，比如出现重复数据，需要合并两份数据的交集，并集就是个不错的选择，知识追寻者本着技多不压身的态度蛮学习了一下下；

二数据拼接

在进行学习数据转换之前，先学习一些数拼接相关的知识

2.1 join()联结

有关merge操作知识追寻者这边不提及，有空可能后面会专门出一篇相关文章，因为其学习方式根SQL的表联结类似，不是几行能说清楚的知识点；

join操作能将 2 个DataFrame 合并为一块，前提是DataFrame 之间的列没有重复；

# -*- coding: utf-8 -*-

import pandas as pd
import numpy as np

data1 = {
  'user' : ['zszxz','craler','rose'],
  'price' : [100, 200, 300],
  'hobby' : ['reading','running','hiking']
}
index1 = ['user1','user2','user3']
frame1 = pd.DataFrame(data1,index1)

data2 = {
  'person' : ['zszxz','craler','rose'],
  'number' : [100, 2000, 3000],
  'activity' : ['swing','riding','climbing']
}
index2 = ['user1','user2','user3']
frame2 = pd.DataFrame(data2,index2)

join = frame1.join(frame2)
print(join)

输出

user price hobby person number activity user1 zszxz 100 reading zszxz 100 swing user2 craler 200 running craler 2000 riding user3 rose 300 hiking rose 3000 climbing

2.2 concat()拼接

使用 concat() 函数能将2个 Series 拼接为一个，默认按行拼接；

ser1 = pd.Series(['111','222',np.NaN])
ser2 = pd.Series(['333','444',np.NaN])
# 默认按行拼接
print(pd.concat([ser1, ser2]))

如果按列拼接则 axis = 1

ser1 = pd.Series(['111','222',np.NaN])
ser2 = pd.Series(['333','444',np.NaN])
# 按列拼接
print(pd.concat([ser1, ser2],axis=1))

输出

0 1 0 111 333 1 222 444 2 NaN NaN

更近一步，指定key 参数输出的数据格式就和 DataFrame 一样

ser1 = pd.Series(['111','222',np.NaN])
ser2 = pd.Series(['333','444',np.NaN])
# 按列拼接
data = pd.concat([ser1, ser2],axis=1, keys=['zszxz', 'rzxx'])
print(data)

输出

zszxz rzxx 0 111 333 1 222 444 2 NaN NaN

注： DataFrame 的 concat 操作和 Series 类似；

2.3 combine_first()组合

索引重复时就可以使用combine_first进行拼接

ser1 = pd.Series(['111','222',np.NaN],index=[1,2,3])
ser2 = pd.Series(['333','444',np.NaN,'555'],index=[1,2,3,4])
data = ser1.combine_first(ser2)
print(data)

输出

1 111 2 222 3 NaN 4 555 dtype: object

将Series 位置互换一下，可以看见基准将以 ser2为准；

ser1 = pd.Series(['111','222',np.NaN],index=[1,2,3])
ser2 = pd.Series(['333','444',np.NaN,'555'],index=[1,2,3,4])
data = ser2.combine_first(ser1)
print(data)

输出

1 333 2 444 3 NaN 4 555 dtype: object

2.4 轴转换

准备的数据

# -*- coding: utf-8 -*-

import pandas as pd
import numpy as np

data = {
  'user' : ['zszxz','craler','rose'],
  'price' : [100, 200, 300],
  'hobby' : ['reading','running','hiking']
}
index = ['user1','user2','user3']
frame = pd.DataFrame(data,index)
print(frame)

输出

user price hobby user1 zszxz 100 reading user2 craler 200 running user3 rose 300 hiking

stack() 将列转为行；

# -*- coding: utf-8 -*-

import pandas as pd
import numpy as np

data = {
  'user' : ['zszxz','craler','rose'],
  'price' : [100, 200, 300],
  'hobby' : ['reading','running','hiking']
}
index = ['user1','user2','user3']
frame = pd.DataFrame(data,index)
print(frame.stack())

输出

user1 user zszxz price 100 hobby reading user2 user craler price 200 hobby running user3 user rose price 300 hobby hiking dtype: object

使用 unstack()将数据结构重新返回

# -*- coding: utf-8 -*-

import pandas as pd
import numpy as np

data = {
  'user' : ['zszxz','craler','rose'],
  'price' : [100, 200, 300],
  'hobby' : ['reading','running','hiking']
}
index = ['user1','user2','user3']
frame = pd.DataFrame(data,index)
sta = frame.stack()
print(sta.unstack())

输出