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Replace values in dataframe based on condition r

Delete row (s) containing specific column value (s) If you want to delete rows based on the values of a specific column, you can do so by slicing the original DataFrame. For instance, in order to drop all the rows where the colA is equal to 1.0, you can do so as shown below: df = df.drop (df.index [df ['colA'] == 1.0]) print (df) colA colB colC.

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In the above example, we used two steps, 1) create boolean variable satisfying the filtering condition 2) use boolean variable to filter rows. However, we don’t really have to create a new boolean variable and save it to do the filtering. Instead, we can directly give the boolean expression to subset the dataframe by column value as follows. 1 2 3.

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Frequently, when we're doing data manipulation in R, we need to modify data based on various possible conditions. Replace all values with NA where a certain condition is met. dat %>%.

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The column= keyword can be used if you have values in a column which need to be mapped to a color (with a certain color map). But if you already have actual color names that you want to use directly, you can use the color keyword. You can pass a list/array of colors (with the same number of values as the number of rows) to this color keyword. I'd like to learn how to conditionally replace values in R data frame using if/then statements. Suppose I have a data frame like this one: df <- data.frame ( customer_id = c (568468,568468,568468,485342,847295,847295), customer = c ('paramount','paramount','paramount','miramax','pixar','pixar'));.

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To replace a values in a column based on a condition, using numpy.where, use the following syntax. DataFrame['column_name'].where(~(condition), other=new_value, inplace=True) column_name is the column in which values has to be replaced. condition is a boolean expression that is applied for each value in the column.

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