pandas資料拼接

pandas資料拼接有可能會用到，比如出現重複資料，需要合併兩份資料的交集，並集就是個不錯的選擇，知識追尋者本著技多不壓身的態度蠻學習了一下下；

知識追尋者(inheriting the spirit of open source, spreading technology knowledge;)

join操作能將 2 個dataframe 合併為一塊，前提是dataframe 之間的列沒有重複；

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

import pandas as pd
import numpy as np
data1 = 
index1 = ['user1','user2','user3']
frame1  = pd.dataframe(data1,index1)
data2 = 
index2 = ['user1','user2','user3']
frame2  = pd.dataframe(data2,index2)
join = frame1.join(frame2)
print(join)

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

ser1 = pd.series(['111','222',np.nan])

ser2 = pd.series(['333','444',np.nan])
# 預設按行拼接
print(pd.concat([ser1, ser2]))

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))

0    1

0  111  333
1  222  444
2  nan  nan

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)

zszxz rzxx

0   111  333
1   222  444
2   nan  nan

索引重複時就可以使用combine_first進行拼接

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)

1    111

2    222
3    nan
4    555
dtype: object

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)

1    333

2    444
3    nan
4    555
dtype: object

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

import pandas as pd
import numpy as np
data = 
index = ['user1','user2','user3']
frame  = pd.dataframe(data,index)
print(frame)

user  price    hobby

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

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

import pandas as pd
import numpy as np
data = 
index = ['user1','user2','user3']
frame  = pd.dataframe(data,index)
print(frame.stack())

user1  user       zszxz

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

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

import pandas as pd
import numpy as np
data = 
index = ['user1','user2','user3']
frame  = pd.dataframe(data,index)
sta = frame.stack()
print(sta.unstack())

user price    hobby

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

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一 前言 pandas資料拼接有可能會用到，比如出現重複資料，需要合併兩份資料的交集，並集就是個不錯的選擇，知識追尋者本著技多不壓身的態度蠻學習了一下下 二 資料拼接 在進行學習資料轉換之前，先學習一些數拼接相關的知識 2.1 join 聯結 有關merge操作知識追尋者這邊不提及，有空可能後面會專...

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