pandas.concat

pandas.concat(objs, axis=0, join=’outer’, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=False, copy=True)
沿着特定的轴用可选的设置逻辑合并DataFrame对象。

Parameters

objs Series或DataFrame对象的序列或映射
axis default _0，0或_index；1或columns
join ‘inner‘或’outer‘
ignore_index True：返回的DataFrame索引将被重置为0,1,2…n-1
keys
levels
names
verify_integrity False：检查索引是否有重复
sort 当join为’outer‘时，对非连接轴排序；当join为’inner‘时没有影响
copy False：不复制数据

Example

  1. import pandas as pd
  3. df1 = pd.DataFrame({'age': [18, 19], 'sex': ['man', 'woman']})
  4. df2 = pd.DataFrame({'age': [28, 29], 'sex': ['man', 'man']})
  6. pd.concat([df1, df2])
  7. --------------------------------------------------
  8.     age    sex
  9. 0    18    man
  10. 1    19    woman
  11. 0    28    man
  12. 1    29    man

Example

  1. import pandas as pd
  3. df1 = pd.DataFrame({'age': [18, 19], 'sex': ['man', 'woman']})
  4. df2 = pd.DataFrame({'age': [28, 29], 'sex': ['man', 'man']})
  6. pd.concat([df1, df2], ignore_index=True)
  7. ----------------------------------------------------
  8.     age    sex
  9. 0    18    man
  10. 1    19    woman
  11. 2    28    man
  12. 3    29    man

Example

  1. import pandas as pd
  3. df1 = pd.DataFrame({'age': [18, 19], 'sex': ['man', 'woman']})
  4. df2 = pd.DataFrame({'age': [28, 29], 'sex': ['man', 'man']})
  6. pd.concat([df1, df2], keys=['df1','df2'])
  7. --------------------------------------
  8.         age    sex
  9. df1    0    18    man
  10.     1    19    woman
  11. df2    0    28    man
  12.     1    29    man

Example

  1. import pandas as pd
  3. df1 = pd.DataFrame({'age': [18, 19], 'sex': ['man', 'woman']})
  4. df2 = pd.DataFrame({'age': [28, 29], 'sex': ['man', 'man']})
  6. pd.concat([df1, df2], keys=['df1','df2'], names=['series_name', 'row_id'])
  7. ---------------------------------------
  8.                     age    sex
  9. series_name    row_id        
  10. df1            0        18    man
  11.             1        19    woman
  12. df2            0        28    man
  13.             1        29    man