You can use pd.Series.isin. For “IN” use: something.isin(somewhere) Or for “NOT IN”: ~something.isin(somewhere) As a worked example: import pandas as pd >>> df country 0 US 1 UK 2 Germany 3 China >>> countries_to_keep [‘UK’, ‘China’] >>> df.country.isin(countries_to_keep) 0 False 1 True 2 False 3 True Name: country, dtype: bool >>> df[df.country.isin(countries_to_keep)] country 1 UK … Read more
The other answers show you how to make a list of data.frames when you already have a bunch of data.frames, e.g., d1, d2, …. Having sequentially named data frames is a problem, and putting them in a list is a good fix, but best practice is to avoid having a bunch of data.frames not in … Read more
To select rows whose column value equals a scalar, some_value, use ==: df.loc[df[‘column_name’] == some_value] To select rows whose column value is in an iterable, some_values, use isin: df.loc[df[‘column_name’].isin(some_values)] Combine multiple conditions with &: df.loc[(df[‘column_name’] >= A) & (df[‘column_name’] <= B)] Note the parentheses. Due to Python’s operator precedence rules, & binds more tightly than … Read more
You can also use the dplyr package for that purpose: library(dplyr) x %>% group_by(Category) %>% summarise(Frequency = sum(Frequency)) #Source: local data frame [3 x 2] # # Category Frequency #1 First 30 #2 Second 5 #3 Third 34 Or, for multiple summary columns (works with one column too): x %>% group_by(Category) %>% summarise(across(everything(), sum)) Here … Read more
The SettingWithCopyWarning was created to flag potentially confusing “chained” assignments, such as the following, which does not always work as expected, particularly when the first selection returns a copy. [see GH5390 and GH5597 for background discussion.] df[df[‘A’] > 2][‘B’] = new_val # new_val not set in df The warning offers a suggestion to rewrite as … Read more
Three alternative solutions: 1) With data.table: You can use the same melt function as in the reshape2 package (which is an extended & improved implementation). melt from data.table has also more parameters that the melt-function from reshape2. You can for example also specify the name of the variable-column: library(data.table) long <- melt(setDT(wide), id.vars = c(“Code”,”Country”), … Read more
Lets suppose your data frame is called DF and you want item_i = 9, you coul try: DF[DF$item_i==9,] If you want item_i = 1 or 9 then DF[DF$item_i %in% c(1,9),]
If I understand OP correctly, you want smth like this: dt[, {bigN = .N; .SD[, .N / bigN, by = subg]}, by = group] or maybe (and very similarly) this: dt[, {counts.sum = sum(counts); .SD[, counts / counts.sum, by = subg]}, by = group]