Python pandas Filtering out nan from a data selection of a column of strings
PythonPandasDataframePython Problem Overview
Without using groupby how would I filter out data without NaN?
Let say I have a matrix where customers will fill in 'N/A','n/a' or any of its variations and others leave it blank:
import pandas as pd
import numpy as np
df = pd.DataFrame({'movie': ['thg', 'thg', 'mol', 'mol', 'lob', 'lob'],
'rating': [3., 4., 5., np.nan, np.nan, np.nan],
'name': ['John', np.nan, 'N/A', 'Graham', np.nan, np.nan]})
nbs = df['name'].str.extract('^(N/A|NA|na|n/a)')
nms=df[(df['name'] != nbs) ]
output:
>>> nms
movie name rating
0 thg John 3
1 thg NaN 4
3 mol Graham NaN
4 lob NaN NaN
5 lob NaN NaN
How would I filter out NaN values so I can get results to work with like this:
movie name rating
0 thg John 3
3 mol Graham NaN
I am guessing I need something like ~np.isnan but the tilda does not work with strings.
Python Solutions
Solution 1 - Python
Just drop them:
nms.dropna(thresh=2)
this will drop all rows where there are at least two non-NaN.
Then you could then drop where name is NaN:
In [87]:
nms
Out[87]:
movie name rating
0 thg John 3
1 thg NaN 4
3 mol Graham NaN
4 lob NaN NaN
5 lob NaN NaN
[5 rows x 3 columns]
In [89]:
nms = nms.dropna(thresh=2)
In [90]:
nms[nms.name.notnull()]
Out[90]:
movie name rating
0 thg John 3
3 mol Graham NaN
[2 rows x 3 columns]
EDIT
Actually looking at what you originally want you can do just this without the dropna call:
nms[nms.name.notnull()]
UPDATE
Looking at this question 3 years later, there is a mistake, firstly thresh arg looks for at least n non-NaN values so in fact the output should be:
In [4]:
nms.dropna(thresh=2)
Out[4]:
movie name rating
0 thg John 3.0
1 thg NaN 4.0
3 mol Graham NaN
It's possible that I was either mistaken 3 years ago or that the version of pandas I was running had a bug, both scenarios are entirely possible.
Solution 2 - Python
Simplest of all solutions:
filtered_df = df[df['name'].notnull()]
Thus, it filters out only rows that doesn't have NaN values in 'name' column.
For multiple columns:
filtered_df = df[df[['name', 'country', 'region']].notnull().all(1)]
Solution 3 - Python
df.dropna(subset=['columnName1', 'columnName2'])
Solution 4 - Python
df = pd.DataFrame({'movie': ['thg', 'thg', 'mol', 'mol', 'lob', 'lob'],'rating': [3., 4., 5., np.nan, np.nan, np.nan],'name': ['John','James', np.nan, np.nan, np.nan,np.nan]})
for col in df.columns:
df = df[~pd.isnull(df[col])]
Solution 5 - Python
You can also use query:
out = df.query("name.notna() & name !='N/A'", engine='python')
Output:
movie rating name
0 thg 3.0 John
3 mol NaN Graham
Solution 6 - Python
Inside query() pass column_name == column_name to keep the rows where column_name is not NA.
For your case:
nms.query('name == name')