How to remove some rows from a Pandas dataframe to balance it

Question

I have a csv file and after reading it with pandas it has the following structure:

file_path, label
   -        -

The labels are only zeros and ones, and the frequency count is as follows:

data["labels"].value_counts()

0    197664
1     78444

I would like to remove an amount of rows which has the value 0, lets say 20k for example so that the frequency counts will have these values.

data["labels"].value_counts()

0    195664
1     78444

Asked By: Omar

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Answer 1

You can drop the last 20K rows on some condition using pandas drop.

df.drop(df[df.labels == 0].index[-20000:], inplace=True)

Answered By: Himanshuman

Answer 2

Usually I do split then concat

df1 = df.iloc[:20000]
df2 = df.drop(df1.index)
new = pd.concat([df1[df1['labels'] != 0], df2])

Answered By: BENY

Answer 3

mydict = {
  "file_path" : ["a", "b", "c", "d", "e" , "f", "g"],
  "label" : [0, 1, 0, 1, 1, 1, 0]
}
df = pd.DataFrame(mydict)

if your labels are 1 or 0 and you want get only "1" label, you can group your dataset by "label" column and then use get_group() :

get_1 = df.groupby("label").get_group(1)
get_1

Answered By: Galaxy

Question: