pandas中concatenate和combine_first的用法详解

concatenate主要作用是拼接series和dataframe的数据。

combine_first可以做来填充数据。

其中numpy和panads中都有concatenate()方法，如：np.concatenate([arr1, arr2])、pd.concat([s1, s2])

Series类型可以使用 s2 中的数值来填充 s1，如：s1.co编程客栈mbine_first(s2)

Dataframe类型同样可以使用 df2 中的数组来填充 df1， 如：df1.combine_first(df2)

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

# 设置一个随机种子，方便调试
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([android4, 5], index=['E', 'F'])
# 可以看出和numpy的效果一样
print(pd.concat([s1, s2]))
'''
A  1
B  2
C  3
E  4
F  5
dtype: int64
'''
# 用法和 np 一样 axis = 1, 等于增加了一列
print(pd.concat([s1, s2], axis=1))
# 但是，返回的是一个 <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 编程    Z
0 0.824188 0.479966 1.173468
1 0.909048 -0.571721 -0.109497
2 0.019028 -0.943761编程客栈 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 0.64057开发者_Python开发3
3    NaN -0.786443 0.608870 -0.931012
0 -0.298733 0.978222 -0.736918    NaN
1 -0.575771 -0.460587 -1http://www.devze.com.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 中的数值来填充 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]
})

# 功能同样是填充
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
'''

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