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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