How to delete rows from a df (negative values)-CodePudding

I would like to delete all rows with negative values (from an especific column). I´m trying this using the code below:

df = df.drop(columns = ['column_name'] < int(0)

And it´s showing the error 'SyntaxError: unexpected EOF while parsing'

What am I doing wrong?

CodePudding user response：

Try this:

df = df[df['column_name'] >= 0]

CodePudding user response：

Try this out

df = df.drop(df.index[df['col1'] < 0])

Also, instead of int(0) you can use 0.

CodePudding user response：

Let's start by creating a random dataframe

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(50, 4), columns=['A', 'B', 'C', 'D'])

[Out]:
          A         B         C         D
0 -0.776383  0.531066 -0.437706  1.297011
1  0.408125  0.924827  0.773968  0.482579
2  0.288039  1.367767  0.118284 -1.445995
3 -0.190943  0.753063 -1.224873  0.044104
4  1.057911  1.414593 -0.148705  0.495321

Now, considering that one wants each row to be greater than or equal to 0, one has various ways to do that:

• Assuming one has multiple columns (with this method one can specify which columns we want with non-negative values), start by creating a list with the column names

columns = ['A', 'B', 'C', 'D']

And then

df_new = df[df[columns].min(axis=1) >= 0]
[Out]:
           A         B         C         D
1   0.408125  0.924827  0.773968  0.482579
14  0.926402  1.357251  1.072618  1.115446
49  0.967609  1.017095  0.535051  0.017753

• Assuming one wants to apply to every column, simply run

df_new = df[df.min(axis=1) >= 0]

[Out]:
           A         B         C         D
1   0.408125  0.924827  0.773968  0.482579
14  0.926402  1.357251  1.072618  1.115446
49  0.967609  1.017095  0.535051  0.017753