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Concatenating two Pandas DataFrames while maintaining index order


Concatenating two Pandas DataFrames while maintaining index order

By : user3099962
Date : January 11 2021, 05:14 PM
To fix the issue you can do Use concat with parameter sort for avoid warning and then DataFrame.sort_index:
code :
df = pd.concat([df, df2], sort=False).sort_index()

print(df)
   Cities  Houses
0       1      15
1       3      10
2       4      20
3       8      25
4       7      30
5      11      35
6       6      40
7      14      45
8       1      50
9       4      55


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Concatenating pandas dataframes along time series index without duplicating columns

Concatenating pandas dataframes along time series index without duplicating columns


By : Abhishek Jain
Date : March 29 2020, 07:55 AM
this one helps. Assuming, you want to 'exclude all duplicate values on both indices', this should work
code :
unique_indices = np.setdiff1d(np.unioin1d(old_close.index.to_list(), new_close.index.to_list()), 
                              np.intersect1d(old_close.index.to_list(), new_close.index.to_list()))
merged_close = pd.concat([old_close, new_close]).ix[unique_indices]
Pandas - concatenating two multi-index dataframes

Pandas - concatenating two multi-index dataframes


By : Motasem
Date : March 29 2020, 07:55 AM
it fixes the issue First, you're way better off setting your index to be ['Month', 'Roll no.', 'Student Name']. That will simplify your concat syntaxes a lot and ensure you match on the name of the students too.
code :
df.set_index('Student Name', append=True, inplace=True)
# Creating a dictionnary with the first df from your question
df_dict = {'IS': df}

# Iterate....
   # Append the new df to the df_dict
   df_dict['CC'] = df
df_dict

In [10]: df_dict

Out[10]:
{'CC':                                             Q1   Q2   Q3
 Month      Roll No Student Name                         
 2016-08-01 0       Save Mithil Vinay       0.0  0.0  0.0
            1       Abraham Ancy Chandy     6.0  5.0  5.0
            2       Barabde Pranjal Sanjiv  7.0  5.0  5.0
            3       Bari Siddhesh Kisho     8.0  5.0  3.0
            4       Barretto Cleon Domnic   1.0  5.0  4.0,
 'IS':                                             Q1   Q2   Q3
 Month      Roll No Student Name                         
 2016-08-01 0       Save Mithil Vinay       0.0  0.0  0.0
            1       Abraham Ancy Chandy     8.0  5.0  5.0
            2       Barabde Pranjal Sanjiv  7.0  5.0  4.0
            3       Bari Siddhesh Kisho     8.0  4.0  3.0
            4       Barretto Cleon Domnic   2.0  3.0  4.0}
In [11]: big_df = pd.concat(df_dict, axis=1)
         big_df

Out[11]: 
df.columns = pd.MultiIndex.from_tuples([('IS', x) for x in df.columns])

# Then you can concat, give the same result as the picture above.
pd.concat([big_df, df], axis=1)
Pandas concat DataFrames - keep original order of index

Pandas concat DataFrames - keep original order of index


By : Fusesa Line
Date : March 29 2020, 07:55 AM
To fix this issue You can try reindex if values of df1.index contains values of df2.index:
code :
df3  = pd.concat([df2, df1], axis=1).reindex(df1.index)
print (df3)
        2017-02-01  'Total/Max/Min'
CU Pmt                             
h  b          15.0               20
   d          12.0               23
   a          13.0               22
a  b           NaN               16
   d           NaN               13
   a           NaN               14
Pandas keep index order when vectorised calc between dataframes with duplicate indexes

Pandas keep index order when vectorised calc between dataframes with duplicate indexes


By : enzo
Date : March 29 2020, 07:55 AM
seems to work fine You can create helper MultiIndex by assign neste lists or add parameter append=True to DataFrame.set_index, then subtract by second level and last remove first helper level:
code :
df1.index = [np.arange(len(df1)), df1.index]
s = df1['column'].subtract(df2["column"], level=1).reset_index(level=0, drop=True)
print (s)
identifier
a   -1.0
b    NaN
a    1.0
b    NaN
c    2.0
Name: column, dtype: float64
df1 = pd.DataFrame({'identifier': list("ababc"), 
                    'column': [1, 2, 3, 4, 5]}).set_index('identifier', append=True)

s = df1['column'].subtract(df2["column"], level=1).reset_index(level=0, drop=True)
Index error while concatenating two dataframes in pandas

Index error while concatenating two dataframes in pandas


By : user2838701
Date : March 29 2020, 07:55 AM
hope this fix your issue The problem is that df is not uniquely indexed. So you need to either reset the index
code :
pd.concat([df.reset_index(),tdf], axis=1)
pd.concat([df.reset_index(drop=True),tdf], axis=1)
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