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Python: how to merge two dataframe based only on different columns?


Python: how to merge two dataframe based only on different columns?

By : Charles Richards
Date : October 25 2020, 07:10 PM
To fix the issue you can do Use Index.isin with inverse mask or Index.difference:
code :
df22 = df2.loc[:, ~df2.columns.isin(df1.columns)]
df = df1.join(df22)
df22 = df2[df2.columns.difference(df1.columns)]
df = df1.join(df22)
print (df)
   A  B  AB  C
0  4  2   8  3
1  3  3   9  2
2  1  2   2  4


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pandas merge dataframe based on same value in columns

pandas merge dataframe based on same value in columns


By : Kaby
Date : March 29 2020, 07:55 AM
around this issue Use merge and pass the columns to merge on for left_param and right_param respectively:
code :
In [27]:
df.merge(df1, left_on='student_name', right_on='member_name')

Out[27]:
  student_name  student_id member_id member_name school_name
0          may           0        B0         may         NIT
1         june        1111        C0        june         LIT
Merge rows from one dataframe that do not match specific columns in another dataframe Python Pandas

Merge rows from one dataframe that do not match specific columns in another dataframe Python Pandas


By : Clinton
Date : March 29 2020, 07:55 AM
hope this fix your issue I have two dataframes and want to merge all the rows from one dataframe that do not have a set of matching columns to another dataframe. Below is an example: , You need an outer join:
code :
df_1.merge(df_2, how = "outer")
Merge and then sort columns of a dataframe based on the columns of the merging dataframe

Merge and then sort columns of a dataframe based on the columns of the merging dataframe


By : MANOHAR YV
Date : November 06 2020, 03:01 PM
fixed the issue. Will look into that further Using an OrderedDict, as you suggested.
code :
from collections import OrderedDict
from itertools import chain

c = df3.columns.tolist()
o = OrderedDict()

for x in c:
    o.setdefault(x.split('_')[0], []).append(x)

c = list(chain.from_iterable(o.values()))
df3 = df3[c]
# https://stackoverflow.com/a/46839182/4909087
p = [s[0] for s in c]
c = sorted(c, key=lambda x: (p.index(x[0]), x))
df = df[c]
Merge and fill missing values based on multiple columns from another dataframe in Python

Merge and fill missing values based on multiple columns from another dataframe in Python


By : user3347973
Date : March 29 2020, 07:55 AM
it fixes the issue As mentioned in the question you can also use update depending on your data and needs:
code :
df1 = df1.set_index(['year', 'city'])
df1.update(
    df2
    .set_index(['year', 'city'])\
    .rename(columns={'gdp':'gdp_value','rate':'growth_rate'})\
)
df1 = df1.reset_index()
df1.set_index(['year','city'])\
   .combine_first(df2.set_index(['year','city'])
                     .rename(columns={'gdp':'gdp_value','rate':'growth_rate'}))\
   .reset_index()
   year city  gdp_value  growth_rate
0  2015   bj        7.0         0.01
1  2015   sh        6.0         0.04
2  2016   bj        3.0         0.03
3  2016   sh        5.0         0.07
4  2017   bj        2.0        -0.03
5  2017   sh        3.0        -0.03
6  2018   bj        5.0         0.05
7  2018   sh        6.0         0.05
8  2019   bj        4.0         0.02
9  2019   sh        4.0         0.02
merge two dataframe based on matching two exchangable columns in each dataframe

merge two dataframe based on matching two exchangable columns in each dataframe


By : aravindhan
Date : March 29 2020, 07:55 AM
wish of those help The following works, but no doubt can be improved.
I first create a little helper function that performs a row-wise sort on A and B (and renames it to V1 and V2).
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