pandas concat function

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enay answered on June 2, 2021 Popularity 9/10 Helpfulness 3/10

answer pandas concat function

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pandas concat function

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import pandas as pd

# Assuming you have two DataFrames called 'df1' and 'df2'
concatenated_df = pd.concat([df1, df2])

print(concatenated_df)

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Tags: concat function pandas python

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Contributed on Jul 15 2023

enay

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concat dataframe pandas

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# Horizontal concatenation = concat side by side, increase no of columns
horizontal_concat_df = pd.concat([df1,df2,df3], axis=1)
# Vertical concatenation = concat on top of another, increase no of rows
vertical_concat_df = pd.concat([df1,df2,df3], axis=0,
                               verify_integrity=False, # Verifies duplicate entries
                        ignore_index=False, # Retain index of source dfs
                        keys=['df1','df2','df3']) # Provide key for each source df 
# Vertical concatenation : another approach
df = pd.DataFrame()
df = df.append(df_new)

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Contributed on Jan 15 2023

Innocent Iguana

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df concat

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

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Source: pandas.pydata.org

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Contributed on Aug 18 2022

Athul Mathew

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concatenate dataframes

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# 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)

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Contributed on May 06 2022

Yesid Cano Castro

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concat dataframes

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

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Source: pandas.pydata.org

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Contributed on Nov 18 2021

D Goglia

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concat columns pandas dataframe

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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

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Source: pandas.pydata.org

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Contributed on Jun 02 2021

Friendly Falcon

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pandas concatenate

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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

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Contributed on Sep 19 2022

rusty denton

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pandas concat

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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])

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Source: blog.csdn.net

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Contributed on Jul 26 2022

Spotless Swiftlet

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concat dataframe pandas

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

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Contributed on Aug 14 2021

Frail Fish

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pd.concat in python

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

#GIVEN THAT BOTH HAS SAME INDEX

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Contributed on May 12 2022

Curious Chicken

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pandas concat function

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