How to Merge Two Dataframes Side-By-Side

Join two data-frames side by side

Say I have the following dataframes;

df1=pd.DataFrame({'a':[1,2,3,4,5,6], 'b':[6,7,8,9,9,0],'c':[6,7,8,9,9,0],'d':[6,7,8,9,9,0],'e':[6,7,8,9,9,0]})
df2=pd.DataFrame({'a':[1,2,13,41,5,6], 'b':[61,7,83,9,9,60],'c':[61,7,83,9,9,60],'d':[61,7,83,9,9,60],'e':[61,7,83,9,9,60]})

Set a common index(You must be careful though, make sure you set an index common among the datframes.In this case, I just reset them to make it start from 0 on wards)

df1= df.iloc[0:3, 1:3]
df1.reset_index(drop=True, inplace=True)
df2=df.iloc[3:6, 1:3]
df2.reset_index(drop=True, inplace=True)

Do an inner concat

result = pd.concat([df1, df2], axis=1, join='inner')
result

Combining multiple dataframes side by side and renaming them

Use concat.

If Activity are indexes in both dataframes use:

df = pd.concat([df1, df2], axis=1, keys=('China Analysis','America Analysis'))

If not, first set_index:

df = pd.concat([df1.set_index('Activity'), 
                df2.set_index('Activity')], 
                axis=1, 
                keys=('China Analysis','America Analysis'))
print (df)
           China Analysis America Analysis
             No of people     No of people
Activity                                  
Activity 1            100              400
Activity 2            200              500
Activity 3            300              500

Create index and merge two dataframes side by side after transpose

Assuming df1 is the name of the first table and transpose is the name of the transposed table, you can use:

transpose.join(df1.set_index(transpose.index))

# Result: 

       Metal  Rock  NumberofTracks  NumberofAlbums
count      4     2             200              12

Here, we make use of .set_index() to set the index of df1 to the same index of dataframe transpose. Thus, enabling them to .join() together because of having same index.

Paste 2 data frames side by side without any key

If the number of rows are same use

cbind(df1, df2)
#   A  B  E  H  A  B
#1 x1 x2 x3 x6 y1 y2
#2 x1 x2 x4 x7 y1 y2
#3 x1 x2 x5 x8 y1 y2

Or in dplyr

library(dplyr)
library(stringr)
df2 %>% 
       rename_all(~ str_c(., ".1")) %>%
       bind_cols(df1, .)

In some versions of dplyr (0.8.5), it would rename correctly when there are duplicate column names

bind_cols(df1, df2)

NOTE: It is not recommended to have same column names in data.frame so we could change the column names with make.unique

If we have two datasets with unequal number of rows

library(rowr)
cbind.fill(df1, df2new, fill = NA)
#   A  B  E  H    A    B
#1 x1 x2 x3 x6   y1   y2
#2 x1 x2 x4 x7   y1   y2
#3 x1 x2 x5 x8 <NA> <NA>

Or with base R

mxn <- max(nrow(df1), nrow(df2new))
df2new[(nrow(df2new)+1):mxn,] <- NA
cbind(df1, df2new)
#   A  B  E  H    A    B
#1 x1 x2 x3 x6   y1   y2
#2 x1 x2 x4 x7   y1   y2
#3 x1 x2 x5 x8 <NA> <NA>

data

df1 <- structure(list(A = c("x1", "x1", "x1"), B = c("x2", "x2", "x2"
), E = c("x3", "x4", "x5"), H = c("x6", "x7", "x8")),
    class = "data.frame", row.names = c(NA, 
-3L))

df2 <- structure(list(A = c("y1", "y1", "y1"), B = c("y2", "y2", "y2"
)), class = "data.frame", row.names = c(NA, -3L))

df2new <- structure(list(A = c("y1", "y1"), B = c("y2", "y2")), class = "data.frame", row.names = c(NA, 
-2L))

how to append two columns in a dataframe side by side in pandas

You can slice df_final into two dataframes, and then you can concatenate both frames two one as we did here:

df_cost = df_final.iloc[:, 0:3]
df_total_cn = df_final.iloc[:, 3:6]
df = pd.concat([df_cost, df_total_cn], axis=1, keys=['cost', 'total cn']).swaplevel(axis=1).sort_index('columns').fillna(0)
print(df)

             2022-01           2022-02           2022-03
             cost total cn     cost total cn     cost total cn
location
AGH       2062.25        0  6561.43        0  3154.43        0
AIM       3122.74        0  3277.56        0  3631.45        0
AJL       1641.82        0  3229.49        0  4937.23        0