它被称为index
,通过以下方式检查:
print (df.index)
Int64Index([102, 301, 302], dtype='int64', name='vo_11')
另请检查docs http://pandas.pydata.org/pandas-docs/stable/indexing.html:
pandas 对象中的轴标签信息有多种用途:
-使用已知指标识别数据(即提供元数据),这对于分析、可视化和交互式控制台显示很重要
-启用自动和显式数据对齐
-允许直观地获取和设置数据集的子集
If need merge http://pandas.pydata.org/pandas-docs/stable/generated/pandas.merge.html通过两者的索引DataFrames
:
df = pd.merge(df1, df2, left_index=True, right_index=True)
Or use concat http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html:
df = pd.concat([df1, df2], axis=1)
Notice:
为了匹配需要相同类型的索引 - 两者int
or object
(明显地string
)
Sample:
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
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