Menu
I'm using python 2.7.3 and Pandas version 0.12.0.
I want to drop the row with the NaN index so that I only have valid site_id values.
print df.head() special_name site_id NaN Banana OMG Apple df.drop(df.index[0]) TypeError: 'NoneType' object is not iterable If I try dropping a range, like this:
df.drop(df.index[0:1]) I get this error:
AttributeError: 'DataFrame' object has no attribute 'special_name' 
728
To drop all the rows with the NaN values, you may use df. dropna().
To drop rows based on certain conditions, select the index of the rows which pass the specific condition and pass that index to the drop() method. In this code, (df['Unit_Price'] >400) & (df['Unit_Price'] < 600) is the condition to drop the rows.
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 .
With pandas version >= 0.20.0 you can:
df = df[df.index.notnull()] With older versions:
df = df[pandas.notnull(df.index)] To break it down:
notnull generates a boolean mask, e.g. [False, False, True], where True denotes the value at the corresponding position is null (numpy.nan or None). We then select the rows whose index corresponds to a true value in the mask by using df[boolean_mask].
125
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
