- - - - - - - nan - - - in [ ]df fillna ( out[ ] - - - - - - - - - - here we have substituted the missing value with default value we can use variety of methods to arrive at the substituting value (meanmedianand so onwe will see more methods of missing value treatment (like imputationin subsequent descriptive statistics functions general practice of dealing with datasets is to know as much about them as possible descriptive statistics of dataframe give data scientists comprehensive look into important information about any attributes and features in the dataset pandas packs bunch of functionswhich facilitate easy access to these statistics consider the cities dataframe (city_datathat we consulted in the earlier section we will use pandas functions to gather some descriptive statistical information about the attributes of that dataframe as we only have three numeric columns in that particular dataframewe will deal with subset of the dataframe which contains only those three values in [ ]columns_numeric ['lat','lng','pop'in [ ]city_data[columns_numericmean(out[ ]lat lng pop dtypefloat in [ ]city_data[columns_numericsum(out[ ]lat + lng + pop + dtypefloat