import numpy as np
import pandas as pd
from pandas import Series, DataFrame

# 設置一個隨機種子，方便調試
np.random.seed(666)

# Series
arr1 = np.arange(9).reshape(3, 3)
arr2 = np.arange(9).reshape(3, 3)

# numpy的 concatenate 用法
print(np.concatenate([arr1, arr2]))
'''
[[0 1 2]
?[3 4 5]
?[6 7 8]
?[0 1 2]
?[3 4 5]
?[6 7 8]]
'''

print(np.concatenate([arr1, arr2], axis=1))
'''
[[0 1 2 0 1 2]
?[3 4 5 3 4 5]
?[6 7 8 6 7 8]]
'''


s1 = Series([1, 2, 3], index=['A', 'B', 'C'])
s2 = Series([4, 5], index=['E', 'F'])
# 可以看出和numpy的效果一樣
print(pd.concat([s1, s2]))
'''
A ? ?1
B ? ?2
C ? ?3
E ? ?4
F ? ?5
dtype: int64
'''
# 用法和 np 一樣 axis = 1, 等于增加了一列
print(pd.concat([s1, s2], axis=1))
# 但是，返回的是一個 <class 'pandas.core.frame.DataFrame'>
print(type(pd.concat([s1, s2], axis=1)))
'''
? ? ?0 ? ?1
A ?1.0 ?NaN
B ?2.0 ?NaN
C ?3.0 ?NaN
E ?NaN ?4.0
F ?NaN ?5.0
'''


df1 = DataFrame(np.random.randn(4, 3), columns=['X', 'Y', 'Z'])
print(df1)
'''
? ? ? ? ? X ? ? ? ? Y ? ? ? ? Z
0 ?0.824188 ?0.479966 ?1.173468
1 ?0.909048 -0.571721 -0.109497
2 ?0.019028 -0.943761 ?0.640573
3 -0.786443 ?0.608870 -0.931012
'''

df2 = DataFrame(np.random.randn(3, 3), columns=['X', 'Y', 'A'])
print(df2)
'''
? ? ? ? ? X ? ? ? ? Y ? ? ? ? A
0 ?0.978222 -0.736918 -0.298733
1 -0.460587 -1.088793 -0.575771
2 -1.682901 ?0.229185 -1.756625
'''

print(pd.concat([df1, df2]))
'''
? ? ? ? ? A ? ? ? ? X ? ? ? ? Y ? ? ? ? Z
0 ? ? ? NaN ?0.824188 ?0.479966 ?1.173468
1 ? ? ? NaN ?0.909048 -0.571721 -0.109497
2 ? ? ? NaN ?0.019028 -0.943761 ?0.640573
3 ? ? ? NaN -0.786443 ?0.608870 -0.931012
0 -0.298733 ?0.978222 -0.736918 ? ? ? NaN
1 -0.575771 -0.460587 -1.088793 ? ? ? NaN
2 -1.756625 -1.682901 ?0.229185 ? ? ? NaN
'''

# combine

s1 = Series([2, np.nan, 4, np.nan], index=['A', 'B', 'C', 'D'])
s2 = Series([1, 2, 3, 4], index=['A', 'B', 'C', 'D'])

# 用 s2 中的數(shù)值來填充 s1
print(s1.combine_first(s2))

'''
A ? ?2.0
B ? ?2.0
C ? ?4.0
D ? ?4.0
dtype: float64
'''

df1 = DataFrame({
? ? 'X':[1, np.nan, 3, np.nan],
? ? 'Y':[5, np.nan, 7, np.nan],
? ? 'Z':[9, np.nan, 11, np.nan]
})

df2 = DataFrame({
? ? 'Z':[np.nan, 10, np.nan, 12],
? ? 'A':[1, 2, 3, 4]
})

# 功能同樣是填充
print(df1.combine_first(df2))
'''
? ? ?A ? ?X ? ?Y ? ? Z
0 ?1.0 ?1.0 ?5.0 ? 9.0
1 ?2.0 ?NaN ?NaN ?10.0
2 ?3.0 ?3.0 ?7.0 ?11.0
3 ?4.0 ?NaN ?NaN ?12.0
'''