## Colsum new dataframe

With this command it is possible to have a dataframe with the sum of every column

df <- data.frame(id = c(1,2,3), stock = c(3,1,4), bill = c(1,0,1), bear = c(3,2,5)) dfsum <- data.frame(colSums(df[-1]))

However this dataframe has only one column.

How is it possible to produce a dataframe with 2 column one with col names and second with the frequencies?

You can do:

stack(colSums(df[-1])) values ind 1 8 stock 2 2 bill 3 10 bear

Or using `tibble`

:

enframe(colSums(df[-1])) name value <chr> <dbl> 1 stock 8 2 bill 2 3 bear 10

**colSums function,** colSums (x, na.rm = FALSE, dims = 1) rowSums (x, na.rm = FALSE, dims = 1) colMeans(x, na.rm numeric, complex, integer or logical values, or a numeric data frame. For .colSums() etc, a numeric, integer or logical matrix (or vector of length m * n ). New example Use markdown to format your example R code blocks are� Your Dataframe before we add a new column: Your Dataframe after adding a new column: Method II.4 Using the .insert() function. You can use the.insert()function to inserting a column in a specific location. To add a new column with different values to a dataframe use: df.insert(loc=1, column="New Column", value=['value1', 'value2','value3

We can use `tidyverse`

approaches with `summarise_at`

and `pivot_longer`

library(dplyr) library(tidyr) df %>% summarise_at(vars(-id), sum) %>% pivot_longer(everything()) # name value #1 stock 8 #2 bill 2 #3 bear 10

**pandas.DataFrame.sum — pandas 1.1.1 documentation,** New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1. Given a Dataframe containing data about an event, we would like to create a new column called ‘Discounted_Price’, which is calculated after applying a discount of 10% on the Ticket price. Solution #1: We can use DataFrame.apply() function to achieve this task.

You can simply try `apply`

.

apply(df[-1], 2, sum)

Result

stock bill bear 8 2 10

For `data.frame`

(df2 <- data.frame( freq = apply(df[-1], 2, sum))) df2$var <- rownames(df2)

Result

var freq stock 8 bill 2 bear 10

**Form Row and Column Sums and Means,** numeric, complex, integer or logical values, or a numeric data frame. For . colSums() etc, a numeric, integer or logical matrix (or vector of length m * n ). na. rm. data.frame(t(colSums(demo))) a b c colSums.demo. 5.782475 10.46739 18.46751 To change the name of the row in the output, we can use rownames , for instance like this:

**How to do colsum and average on the same dataframe,** I am trying to add a new row to this dataframe below name total. Now for the columns counts,cost,views are colsums or totals but for average I� We often get into a situation where we want to add a new row or column to a dataframe after creating it. A quick and dirty solution which all of us have tried atleast once while working with pandas is re-creating the entire dataframe once again by adding that new row or column in the source i.e. csv, txt, DB etc. Pandas is a feature rich Data Analytics library and gives lot of features to

**R colSums, rowSums, colMeans & rowMeans (NA, Error & by Group),** colSums computes the sum of each column of a numeric data frame, matrix or array at the bottom right, since otherwise these cells of the data would be empty. Add new column to DataFrame in Pandas using assign() Let’s add a column ‘Marks’ i.e. mod_fd = df_obj.assign( Marks=[10, 20, 45, 33, 22, 11]) mod_fd. It will return a new dataframe with a new column ‘Marks’ in that Dataframe. Values provided in list will used as column values. Contents of new dataframe mod_fd are,

an array of two or more dimensions, containing numeric, complex, integer or logical values, or a numeric data frame. For.colSums () etc, a numeric, integer or logical matrix (or vector of length m * n).

##### Comments

`dfsum <- data.frame(n=names(df)[-1], s=colSums(df[-1]))`