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I have a large dataframe that I want to sample based on values on the target column value, which is binary : 0/1
I want to extract equal number of rows that have 0's and 1's in the "target" column. I was thinking of using the pandas sampling function but not sure how to declare the equal number of samples I want from both classes for the dataframe based on the target column.
I was thinking of using something like this:
df.sample(n=10000, weights='target', random_state=1)
Not sure how to edit it to get 10k records with 5k 1's and 5k 0's in the target column. Any help is appreciated!
808
You can use the loc and iloc functions to access columns in a Pandas DataFrame. Let's see how. If we wanted to access a certain column in our DataFrame, for example the Grades column, we could simply use the loc function and specify the name of the column in order to retrieve it.
The sample() function Here, df is the dataframe from which you want to sample the columns. By default, the sample() function returns one item, in the above case, a random column. But you can specify the number of columns to sample using the n parameter.
You can use DataFrameGroupBy.sample method as follwing:
sample_df = df.groupby("target").sample(n=5000, random_state=1)
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