Ignoring NaNs with str.contains

Question

I want to find rows that contain a string, like so:

DF[DF.col.str.contains(\"foo\")]

However, this fails because some elements are NaN:

Answer 1

I'm not 100% on why (actually came here to search for the answer), but this also works, and doesn't require replacing all nan values.

import pandas as pd
import numpy as np

df = pd.DataFrame([["foo1"], ["foo2"], ["bar"], [np.nan]], columns=['a'])

newdf = df.loc[df['a'].str.contains('foo') == True]

Works with or without .loc.

I have no idea why this works, as I understand it when you're indexing with brackets pandas evaluates whatever's inside the bracket as either True or False. I can't tell why making the phrase inside the brackets 'extra boolean' has any effect at all.

Answer 2

import folium
import pandas

data= pandas.read_csv("maps.txt")

lat = list(data["latitude"])
lon = list(data["longitude"])

map= folium.Map(location=[31.5204, 74.3587], zoom_start=6, tiles="Mapbox Bright")

fg = folium.FeatureGroup(name="My Map")

for lt, ln in zip(lat, lon):
c1 = fg.add_child(folium.Marker(location=[lt, ln], popup="Hi i am a Country",icon=folium.Icon(color='green')))

child = fg.add_child(folium.Marker(location=[31.5204, 74.5387], popup="Welcome to Lahore", icon= folium.Icon(color='green')))

map.add_child(fg)

map.save("Lahore.html")


Traceback (most recent call last):
  File "C:\Users\Ryan\AppData\Local\Programs\Python\Python36-32\check2.py", line 14, in <module>
    c1 = fg.add_child(folium.Marker(location=[lt, ln], popup="Hi i am a Country",icon=folium.Icon(color='green')))
  File "C:\Users\Ryan\AppData\Local\Programs\Python\Python36-32\lib\site-packages\folium\map.py", line 647, in __init__
    self.location = _validate_coordinates(location)
  File "C:\Users\Ryan\AppData\Local\Programs\Python\Python36-32\lib\site-packages\folium\utilities.py", line 48, in _validate_coordinates
    'got:\n{!r}'.format(coordinates))
ValueError: Location values cannot contain NaNs, got:
[nan, nan]

Answer 3

In addition to the above answers, I would say for columns having no single word name, you may use:-

df[df['Product ID'].str.contains("foo") == True]

Hope this helps.

Answer 4

You can also patern :

DF[DF.col.str.contains(pat = '(foo)', regex = True) ]

Answer 5

There's a flag for that:

In [11]: df = pd.DataFrame([["foo1"], ["foo2"], ["bar"], [np.nan]], columns=['a'])

In [12]: df.a.str.contains("foo")
Out[12]:
0     True
1     True
2    False
3      NaN
Name: a, dtype: object

In [13]: df.a.str.contains("foo", na=False)
Out[13]:
0     True
1     True
2    False
3    False
Name: a, dtype: bool

See the str.replace docs:

na : default NaN, fill value for missing values.

---

So you can do the following:

In [21]: df.loc[df.a.str.contains("foo", na=False)]
Out[21]:
      a
0  foo1
1  foo2

Answer 6

DF[DF.col.str.contains("foo").fillna(False)]