PYTHON

df concat

pd.concat([s1, s2], ignore_index=True)

pandas concatenate

df1 = pd.DataFrame({"A": ["A0", "A1", "A2", "A3"]
                    , "B": ["B0", "B1", "B2", "B3"]
                    , "C": ["C0", "C1", "C2", "C3"]
                    , "D": ["D0", "D1", "D2", "D3"]
                    , "E": ['E0', 'E1', 'E2', 'E3']})
# since E0 isn't column in subsequent dfs their values will be NaN                  
df2 = pd.DataFrame({"A": ["A4", "A5", "A6", "A7"]
                    , "B": ["B4", "B5", "B6", "B7"]
                    , "C": ["C4", "C5", "C6", "C7"]
                    , "D": ["D4", "D5", "D6", "D7"]})
df3 = pd.DataFrame( {"A": ["A8", "A9", "A10", "A11"]
                     , "B": ["B8", "B9", "B10", "B11"]
                     , "C": ["C8", "C9", "C10", "C11"]
                    , "D": ["D8", "D9", "D10", "D11"]})
frames = [df1, df2, df3]
result = pd.concat(frames, ignore_index =True)#ignore index resets indecies
result #->

A	B	C	D	E
0	A0	B0	C0	D0	E0
1	A1	B1	C1	D1	E1
2	A2	B2	C2	D2	E2
3	A3	B3	C3	D3	E3
4	A4	B4	C4	D4	NaN
5	A5	B5	C5	D5	NaN
6	A6	B6	C6	D6	NaN
7	A7	B7	C7	D7	NaN
8	A8	B8	C8	D8	NaN
9	A9	B9	C9	D9	NaN
10	A10	B10	C10	D10	NaN
11	A11	B11	C11	D11	NaN

concatenate dataframes

# Stack the DataFrames on top of each other
#survey_sub and survey_sub_last10 are both dataframes
vertical_stack = pd.concat([survey_sub, survey_sub_last10], axis=0)

# Place the DataFrames side by side
horizontal_stack = pd.concat([survey_sub, survey_sub_last10], axis=1)

concat dataframes

result = pd.concat([df1, df2])

concat columns pandas dataframe

In [1]: df1 = pd.DataFrame(
   ...:     {
   ...:         "A": ["A0", "A1", "A2", "A3"],
   ...:         "B": ["B0", "B1", "B2", "B3"],
   ...:         "C": ["C0", "C1", "C2", "C3"],
   ...:         "D": ["D0", "D1", "D2", "D3"],
   ...:     },
   ...:     index=[0, 1, 2, 3],
   ...: )
   
In [8]: df4 = pd.DataFrame(
   ...:     {
   ...:         "B": ["B2", "B3", "B6", "B7"],
   ...:         "D": ["D2", "D3", "D6", "D7"],
   ...:         "F": ["F2", "F3", "F6", "F7"],
   ...:     },
   ...:     index=[2, 3, 6, 7],
   ...: )
   ...: 

In [9]: result = pd.concat([df1, df4], axis=1)
# This will merge columns of both the dataframes

How to Concatenate Dataframe in python

# Stack the DataFrames on top of each other
vertical_stack = pd.concat([survey_sub, survey_sub_last10], axis=0)

# Place the DataFrames side by side
horizontal_stack = pd.concat([survey_sub, survey_sub_last10], axis=1)

pandas concat

pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
       keys=None, levels=None, names=None, verify_integrity=False)

result = pd.concat([df1, df4], axis=1)
result = pd.concat(frames, keys=['x', 'y', 'z'])
result = pd.concat([df1, df4], axis=1, join='inner')
result = pd.concat([df1, df4], axis=1, join_axes=[df1.index])

concat dataframe pandas

# provide list of dataframes 
res = pd.concat([df1, df2])

pd.concat in python

s1 = pd.Series(['a', 'b'])
s2 = pd.Series(['c', 'd'])
pd.concat([s1, s2])

#GIVEN THAT BOTH HAS SAME INDEX

dataframe concatenate

# Pandas for Python

df['col1 & col2'] = df['col1']+df['col2']

#Output
#col1	col2	col1 & col2
#A1		A2		A1A2
#B1		B2		B1B2

pd df concat

pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],
...           ignore_index=True)

concate the dataframe in pandas..

df_asset_dummy=pd.concat([df_num_feature,df_nominal_dummy,df_ordinal_label],axis=1)
df_asset_dummy.head()