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I have a very big data.frame and want to sum the values in every column.
So I used the following code:
sum(production[,4],na.rm=TRUE)
or
sum(production$X1961,na.rm=TRUE)
The problem is that the data.frame is very big. And I only want to sum 40 certain columns with different names of my data.frame. And I don't want to list every single column. Is there a smarter solution?
At the end I also want to store the sum of every column in a new data.frame.
Thanks in advance!
338
Use DataFrame. groupby(). sum() to group rows based on one or multiple columns and calculate sum agg function. groupby() function returns a DataFrameGroupBy object which contains an aggregate function sum() to calculate a sum of a given column for each group.
Combine Two Columns Using + Operator By use + operator simply you can combine/merge two or multiple text/string columns in pandas DataFrame. Note that when you apply + operator on numeric columns it actually does addition instead of concatenation.
To sum all the rows of a DataFrame, use the sum() function and set the axis value as 1. The value axis 1 will add the row values.
Add multiple columns to a data frame using Dataframe. assign() method. Using DataFrame. assign() method, we can set column names as parameters and pass values as list to replace/create the columns.
Try this:
colSums(df[sapply(df, is.numeric)], na.rm = TRUE)
where sapply(df, is.numeric) is used to detect all the columns that are numeric.
If you just want to sum a few columns, then do:
colSums(df[c("X1961", "X1962", "X1999")], na.rm = TRUE)
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