Python Pandas: How can I drop rows using df.drop and df.loc?

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Suppose I have the following dataframe:

import numpy as np
import pandas as pd

df = pd.DataFrame(
    {
        'user': ['Adam', 'Barry', 'Cindy', 'Dirk', 'Ella'],
        'income': [50000, 0, 100000, 30000, 0],
        'net worth': [250000, 1000000, 2000000, 50000, 0]
    }
)

enter image description here

So far, I've been removing rows based on conditions using the following:

df2 = df[df.income != 0]

enter image description here

And using multiple conditions like so:

df3 = df[(df['income'] != 0) & (df['net worth'] > 100000)]

enter image description here

Question: Is this the preferred way to drop rows? If not, what is? Is it possible to do this via df.drop and df.loc? What would the syntax be?

pandas python
1 Answers

.loc creates a subset of the rows you want to keep rather than .drop filter rows you want to remove. drop need the row label (index name).

The equivalent of your last filter with drop is:

>>> df.drop(df[~((df['income'] != 0) & (df['net worth'] > 100000))].index)

    user  income  net worth
0   Adam   50000     250000
2  Cindy  100000    2000000

# OR a bit smart:
>>> df.drop(df[(df['income'] == 0) | (df['net worth'] <= 100000)].index)

    user  income  net worth
0   Adam   50000     250000
2  Cindy  100000    2000000

Which syntax do you prefer?

user_152399510
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