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I have a dataframe that looks like shown below
mean
comp_name date
Appdynamics 2012-05-01 00:18:15.910000
2012-05-01 NaT
2012-05-01 NaT
2012-05-02 00:20:12.145200
2012-05-02 NaT
2012-05-02 NaT
Here the comp_name and date form multiindex. I want to get rid of the NaT values and obtain only those rows where the mean(timedelta64) is not NaT.
mean
comp_name date
Appdynamics 2012-05-01 00:18:15.910000
2012-05-02 00:20:12.145200
Any ideas on this?
200
By using dropna() method you can drop rows with NaN (Not a Number) and None values from pandas DataFrame. Note that by default it returns the copy of the DataFrame after removing rows. If you wanted to remove from the existing DataFrame, you should use inplace=True .
Use drop() method to delete rows based on column value in pandas DataFrame, as part of the data cleansing, you would be required to drop rows from the DataFrame when a column value matches with a static value or on another column value.
Use df. isna() to find NaN , NaT , and None values. They all evaluate to True with this method. A boolean DataFrame is returned if df.
dropna() to drop columns having Nan values.
pandas.notnull() takes a series and returns a Boolean series which is True where the input series is not null (None, np.NaN, np.NaT). Then you can slice a dataframe by the Boolean series:
df[pandas.notnull(df['mean'])]
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