Pythonデータ分析実戦【第三章】2.17-Pandas接続と補修concat、combine_first【python】

【レッスン2.17】接続とパッチconcat、combine_first
接続→軸に沿った接続
1.接続:concat

s1 = pd.Series([1,2,3])
s2 = pd.Series([2,3,4])
s3 = pd.Series([1,2,3],index = ['a','c','h'])
s4 = pd.Series([2,3,4],index = ['b','e','d'])
print(pd.concat([s1,s2]))
print(pd.concat([s3,s4]).sort_index())
print('-----')
#   axis=0， + 

print(pd.concat([s3,s4], axis=1))
print('-----')
# axis=1, + ，    Dataframe
----------------------------------------------------------------------
0    1
1    2
2    3
0    2
1    3
2    4
dtype: int64
a    1
b    2
c    2
d    4
e    3
h    3
dtype: int64
-----
     0    1
a  1.0  NaN
b  NaN  2.0
c  2.0  NaN
d  NaN  4.0
e  NaN  3.0
h  3.0  NaN
-----

2.接続方式:join,join_axes

s5 = pd.Series([1,2,3],index = ['a','b','c'])
s6 = pd.Series([2,3,4],index = ['b','c','d'])
print(pd.concat([s5,s6], axis= 1))
print(pd.concat([s5,s6], axis= 1, join='inner'))
print(pd.concat([s5,s6], axis= 1, join_axes=[['a','b','d']]))
# join：{'inner'，'outer'}，   “outer”。           。outer    inner   。
# join_axes：     index
----------------------------------------------------------------------
     0    1
a  1.0  NaN
b  2.0  2.0
c  3.0  3.0
d  NaN  4.0
   0  1
b  2  2
c  3  3
     0    1
a  1.0  NaN
b  2.0  2.0
d  NaN  4.0

3.列名の上書き

sre = pd.concat([s5,s6], keys = ['one','two'])
print(sre,type(sre))
print(sre.index)
print('-----')
# keys：  ，    。                 

sre = pd.concat([s5,s6], axis=1, keys = ['one','two'])
print(sre,type(sre))
# axis = 1,     
----------------------------------------------------------------------
one  a    1
     b    2
     c    3
two  b    2
     c    3
     d    4
dtype: int64 <class 'pandas.core.series.Series'>
MultiIndex(levels=[['one', 'two'], ['a', 'b', 'c', 'd']],
           labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 1, 2, 3]])
-----
   one  two
a  1.0  NaN
b  2.0  2.0
c  3.0  3.0
d  NaN  4.0 <class 'pandas.core.frame.DataFrame'>

4.修正pd.combine_first()

df1 = pd.DataFrame([[np.nan, 3., 5.], [-4.6, np.nan, np.nan],[np.nan, 7., np.nan]])
df2 = pd.DataFrame([[-42.6, np.nan, -8.2], [-5., 1.6, 4]],index=[1, 2])
print(df1)
print(df2)
print(df1.combine_first(df2))
print('-----')
#   index，df1    df2  
#   df2 index  df1，    df1 ，  index=['a',1]

df1.update(df2)
print(df1)
# update，  df2  df1，  index  
----------------------------------------------------------------------