pandas Missing Data Dropping missing values
Example
When creating a DataFrame None (python's missing value) is converted to NaN (pandas' missing value):
In [11]: df = pd.DataFrame([[1, 2, None, 3], [4, None, 5, 6],
[7, 8, 9, 10], [None, None, None, None]])
Out[11]:
0 1 2 3
0 1.0 2.0 NaN 3.0
1 4.0 NaN 5.0 6.0
2 7.0 8.0 9.0 10.0
3 NaN NaN NaN NaN
Drop rows if at least one column has a missing value
In [12]: df.dropna()
Out[12]:
0 1 2 3
2 7.0 8.0 9.0 10.0
This returns a new DataFrame. If you want to change the original DataFrame, either use the inplace parameter (df.dropna(inplace=True)) or assign it back to original DataFrame (df = df.dropna()).
Drop rows if all values in that row are missing
In [13]: df.dropna(how='all')
Out[13]:
0 1 2 3
0 1.0 2.0 NaN 3.0
1 4.0 NaN 5.0 6.0
2 7.0 8.0 9.0 10.0
Drop columns that don't have at least 3 non-missing values
In [14]: df.dropna(axis=1, thresh=3)
Out[14]:
0 3
0 1.0 3.0
1 4.0 6.0
2 7.0 10.0
3 NaN NaN
