I have the following DataFrame:

```             daysago  line_race rating        rw    wrating
line_date
2007-03-31       62         11     56  1.000000  56.000000
2007-03-10       83         11     67  1.000000  67.000000
2007-02-10      111          9     66  1.000000  66.000000
2007-01-13      139         10     83  0.880678  73.096278
2006-12-23      160         10     88  0.793033  69.786942
2006-11-09      204          9     52  0.636655  33.106077
2006-10-22      222          8     66  0.581946  38.408408
2006-09-29      245          9     70  0.518825  36.317752
2006-09-16      258         11     68  0.486226  33.063381
2006-08-30      275          8     72  0.446667  32.160051
2006-02-11      475          5     65  0.164591  10.698423
2006-01-13      504          0     70  0.142409   9.968634
2006-01-02      515          0     64  0.134800   8.627219
2005-12-06      542          0     70  0.117803   8.246238
2005-11-29      549          0     70  0.113758   7.963072
2005-11-22      556          0     -1  0.109852  -0.109852
2005-11-01      577          0     -1  0.098919  -0.098919
2005-10-20      589          0     -1  0.093168  -0.093168
2005-09-27      612          0     -1  0.083063  -0.083063
2005-09-07      632          0     -1  0.075171  -0.075171
2005-06-12      719          0     69  0.048690   3.359623
2005-05-29      733          0     -1  0.045404  -0.045404
2005-05-02      760          0     -1  0.039679  -0.039679
2005-04-02      790          0     -1  0.034160  -0.034160
2005-03-13      810          0     -1  0.030915  -0.030915
2004-11-09      934          0     -1  0.016647  -0.016647
```

I need to remove the rows where line_race is equal to 0. What's the most efficient way to do this?

Jan 4, 2021 in Python 2,183 views

## 2 answers to this question.

If I'm understanding correctly, it should be as simple as:

`df = df[df.line_race != 0]`
• 65,910 points

Pandas provide data analysts a way to delete and filter data frames using a data frames.drop() method. We can use this method to drop such rows that do not satisfy the given conditions.

Example 1: Delete rows based on condition on a column.

```# import pandas library

import pandas as pd

# dictionary with list object in values

details = {

'Name' : ['Ankit', 'Aishwarya', 'Shaurya',

'Shivangi', 'Priya', 'Swapnil'],

'Age' : [23, 21, 22, 21, 24, 25],

'University' : ['BHU', 'JNU', 'DU', 'BHU',

'Geu', 'Geu'],

}

# creating a Dataframe object

df = pd.DataFrame(details, columns = ['Name', 'Age',

'University'],

index = ['a', 'b', 'c', 'd', 'e', 'f'])

# get names of indexes for which

# column Age has value 21

index_names = df[ df['Age'] == 21 ].index

# drop these row indexes

# from dataFrame

df.drop(index_names, inplace = True)

df```

Output :

answered Jan 4, 2021 by Nikita

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