I have a data frame and tried to select only the observations I'm interested in by this:
data[data["Var1"]>10]
Unfortunately, this command destroys the data.frame
structure and returns a long vector
.
What I want to get is the data.frame
shortened by the observations that don't match my criteria.
Filter Rows by Condition You can use df[df["Courses"] == 'Spark'] to filter rows by a condition in pandas DataFrame. Not that this expression returns a new DataFrame with selected rows.
How to apply a filter on dataframe in R ? A filter () function is used to filter out specified elements from a dataframe that return TRUE value for the given condition (s). filter () helps to reduce a huge dataset into small chunks of datasets.
You are missing a comma in your statement.
Try this:
data[data[, "Var1"]>10, ]
Or:
data[data$Var1>10, ]
Or:
subset(data, Var1>10)
As an example, try it on the built-in dataset, mtcars
data(mtcars) mtcars[mtcars[, "mpg"]>25, ] mpg cyl disp hp drat wt qsec vs am gear carb Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 mtcars[mtcars$mpg>25, ] mpg cyl disp hp drat wt qsec vs am gear carb Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 subset(mtcars, mpg>25) mpg cyl disp hp drat wt qsec vs am gear carb Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
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