I have a dataset in a form of a pandas dataframe, the data has this form:
file_path, label
- -
the label is either one or zero, if I run this function for checking the frequency of every label
data["labels"].value_counts()
0 197664
1 78444
I want to remove lets say 20K rows of the label 0 only How can this be done?
CodePudding user response：
You can drop the last 20K rows on some condition using pandas drop.
df.drop(df[df.labels == 0].index[-20000:], inplace=True)
CodePudding user response：
mydict = {
"file_path" : ["a", "b", "c", "d", "e" , "f", "g"],
"label" : [0, 1, 0, 1, 1, 1, 0]
}
df = pd.DataFrame(mydict)
| file_path | label | |
|---|---|---|
| 0 | a | 0 |
| 1 | b | 1 |
| 2 | c | 0 |
| 3 | d | 1 |
| 4 | e | 1 |
| 5 | f | 1 |
| 6 | g | 0 |
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
| file_path | label | |
|---|---|---|
| 1 | b | 1 |
| 3 | d | 1 |
| 4 | e | 1 |
| 5 | f | 1 |
CodePudding user response：
Usually I do split then concat
df1 = df.iloc[:20000]
df2 = df.drop(df1.index)
new = pd.concat([df1[df1['labels'] != 0], df2])