How to Merge and Sum Two Data Frames

Pandas DataFrame merge summing column

In [41]: pd.merge(df1, df2, on=['id', 'name']).set_index(['id', 'name']).sum(axis=1)
Out[41]: 
id  name
2   B       25
3   C       20
dtype: int64

how to merge two dataframes and sum the values of columns

I think need set_index for both DataFrames, add and last reset_index:

df = df1.set_index('Name').add(df2.set_index('Name'), fill_value=0).reset_index()
print (df)
  Name  class  value
0  Ram    2.0    8.0
1  Sri    2.0   10.0
2  viv    7.0    8.0

If values in Name are not unique use groupby and aggregate sum:

df = df1.groupby('Name').sum().add(df2.groupby('Name').sum(), fill_value=0).reset_index()

Merge data frames and sum columns with the same name

One way would be:

library(dplyr)

bind_rows(df1, df2) %>%
  #mutate_if(is.numeric, tidyr::replace_na, 0) %>% #in case of having NAs
  group_by(country) %>%
  summarise_all(., sum, na.rm = TRUE)
  

# # A tibble: 4 x 3
# country year1 year2
# <chr>     <dbl>   <dbl>
# 1 a             2       2
# 2 b             4       4
# 3 c             3       3
# 4 d             3       3

or a base r solution

aggregate(. ~ country, rbind(df1, df2), sum, na.rm = TRUE, na.action = NULL)

which would generate the same output.

How to merge and sum two data frames

With dplyr,

library(dplyr)

# add rownames as a column in each data.frame and bind rows
bind_rows(df1 %>% add_rownames(), 
          df2 %>% add_rownames()) %>% 
    # evaluate following calls for each value in the rowname column
    group_by(rowname) %>% 
    # add all non-grouping variables
    summarise_all(sum)

## # A tibble: 7 x 4
##   rowname     x     y     z
##     <chr> <int> <int> <int>
## 1       A     1     2     3
## 2       B     2     3     4
## 3       C     4     6     8
## 4       D     6     8    10
## 5       E     8    10    12
## 6       F     4     5     6
## 7       G     5     6     7

Pandas- merging two dataframe by sum the values of columns and index

You can use :df1.add(df2, fill_value=0). It will add df2 into df1 also it will replace NAN value with 0.

>>> import numpy as np
>>> import pandas as pd
>>> df2 = pd.DataFrame([(10,9),(8,4),(7,np.nan)], columns=['a','b'])
>>> df1 = pd.DataFrame([(1,2),(3,4),(5,6)], columns=['a','b'])
>>> df1.add(df2, fill_value=0)

    a     b
0  11  11.0
1  11   8.0
2  12   6.0

Pandas Merge and Sum Data Frames

To join dataframes in pandas use pd.merge. In the given case join is applied on columns with similar names, thus it's enough to pass the list of those column names as on parameter:

merged = pd.merge(df_1, df_2, on=["ID1", "ID2"], how="left").fillna(0)

Next, calculate necessary columns using, for example, df.assign:

merged = merged.assign(
    VAL1 = lambda x: x.VAL1_x + x.VAL1_y,
    VAL2 = lambda x: x.VAL2_x + x.VAL2_y)

Result:

columns = df_1.columns 
merged[columns]

>>> ID1     ID2     VAL1    VAL2
0   CAR     RED     6.0     6.0
1   TRUCK   RED     6.0     6.0
2   CAR     BLUE    1.0     1.0

how to sum two dataframes python

Try this:

df = pd.concat([df1, df2]).groupby(['dt']).sum().reset_index()

print(df)

PS: This is ensure all datetimes to exist.

multiply and sum two columns in two dataframes in Python

You will can just use pandas abstractions for it.

result = df['col1'] * df['col3']

If then you want to get the sum of those result values you can just do:

sum(results)

Python Pandas Summing columns in different data frames which have same column names, same index values but not same same length of index

No need to merge, let's use pandas intrinsic data aligment with indexes:

df1.set_index("Buckets")\
   .add(df2.set_index("Buckets"), fill_value=0)\
   .reset_index()

Output:

  Buckets    EUR
0     20Y  200.0
1     25Y  200.0
2     30Y  200.0
3     35Y  200.0

Note: You can leave out the set_index if Buckets is already in the index.
Do, df1.add(df2, fill_value=0)

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