pandas - pandas 数据框中的第一列不是列？

Question

我有一个数据框:

>>df
               Column1    Column2
   vo1                          
    102        0.023002           0
    301     3571.662104           0
    302     1346.910261           0
    ...

所以有三列？但是:

>>df.dtypes
Column1      float64
Column2      float64
dtype: object

那么它有两列？第一个 (vo) 叫什么？我想使用它进行合并，但是当我这样做时，我收到错误消息，说没有名为 vo 的列。

Answer 1

它叫做index，检查它:

print (df.index)
Int64Index([102, 301, 302], dtype='int64', name='vo_11')

另请检查docs :

The axis labeling information in pandas objects serves many purposes:

-Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display
-Enables automatic and explicit data alignment
-Allows intuitive getting and setting of subsets of the data set

如果需要merge通过两个 DataFrames 的索引:

df = pd.merge(df1, df2, left_index=True, right_index=True)

或者使用concat :

df = pd.concat([df1, df2], axis=1) 

注意:

为了匹配相同类型的需要索引 - int 或 object(显然 string)

示例:

df1 = pd.DataFrame({
'Column1': {302: 10, 301: 21, 102: 2}, 
'Column2': {302: 0, 301: 0, 102: 0}})
print (df1)
    Column1  Column2
102        2        0
301       21        0
302       10        0

df2 = pd.DataFrame({
'Column1': {302: 4, 301: 5, 304: 6}, 
'Column2': {302: 0, 301: 0, 304: 0}})
print (df2)
     Column1  Column2
301        5        0
302        4        0
304        6        0

<小时/>

df = pd.merge(df1, df2, left_index=True, right_index=True)
print (df)
     Column1_x  Column2_x  Column1_y  Column2_y
301         21          0          5          0
302         10          0          4          0

df = pd.merge(df1, df2, left_index=True, right_index=True, how='outer')
print (df)
     Column1_x  Column2_x  Column1_y  Column2_y
102        2.0        0.0        NaN        NaN
301       21.0        0.0        5.0        0.0
302       10.0        0.0        4.0        0.0
304        NaN        NaN        6.0        0.0

df = pd.concat([df1, df2], axis=1) 
print (df)
     Column1  Column2  Column1  Column2
102      2.0      0.0      NaN      NaN
301     21.0      0.0      5.0      0.0
302     10.0      0.0      4.0      0.0
304      NaN      NaN      6.0      0.0

df = pd.concat([df1, df2], axis=1, join='inner') 
print (df)
     Column1  Column2  Column1  Column2
301       21        0        5        0
302       10        0        4        0