pandas - Python: How to drop a row whose particular column is empty/NaN?

Question

I have a csv file. I read it:

import pandas as pd
data = pd.read_csv('my_data.csv', sep=',')
data.head()

It has output like:

id    city    department    sms    category
01    khi      revenue      NaN       0
02    lhr      revenue      good      1
03    lhr      revenue      NaN       0

I want to remove all the rows where sms column is empty/NaN. What is efficient way to do it?

Answer 1

Use dropna with parameter subset for specify column for check NaNs:

data = data.dropna(subset=['sms'])
print (data)
   id city department   sms  category
1   2  lhr    revenue  good         1

Another solution with boolean indexing and notnull:

data = data[data['sms'].notnull()]
print (data)
   id city department   sms  category
1   2  lhr    revenue  good         1

Alternative with query:

print (data.query("sms == sms"))
   id city department   sms  category
1   2  lhr    revenue  good         1

Timings

#[300000 rows x 5 columns]
data = pd.concat([data]*100000).reset_index(drop=True)

In [123]: %timeit (data.dropna(subset=['sms']))
100 loops, best of 3: 19.5 ms per loop

In [124]: %timeit (data[data['sms'].notnull()])
100 loops, best of 3: 13.8 ms per loop

In [125]: %timeit (data.query("sms == sms"))
10 loops, best of 3: 23.6 ms per loop