pandas Indexing and selecting data Filter out rows with missing data (NaN, None, NaT)
Example
If you have a dataframe with missing data (NaN, pd.NaT, None) you can filter out incomplete rows
df = pd.DataFrame([[0,1,2,3],
[None,5,None,pd.NaT],
[8,None,10,None],
[11,12,13,pd.NaT]],columns=list('ABCD'))
df
# Output:
# A B C D
# 0 0 1 2 3
# 1 NaN 5 NaN NaT
# 2 8 NaN 10 None
# 3 11 12 13 NaT
DataFrame.dropna drops all rows containing at least one field with missing data
df.dropna()
# Output:
# A B C D
# 0 0 1 2 3
To just drop the rows that are missing data at specified columns use subset
df.dropna(subset=['C'])
# Output:
# A B C D
# 0 0 1 2 3
# 2 8 NaN 10 None
# 3 11 12 13 NaT
Use the option inplace = True for in-place replacement with the filtered frame.
