in [ ]city_data[columns_numericcount(out[ ]lat lng pop dtypeint in [ ]city_data[columns_numericmedian(out[ ]lat lng pop dtypefloat in [ ]city_data[columns_numericquantile( out[ ]lat lng pop dtypefloat all these operations were applied to each of the columnsthe default behavior we can also get all these statistics for each row by using different axis this will give us the calculated statistics for each row in the dataframe in [ ]city_data[columns_numericsum(axis out[ ] + + + + + pandas also provides us with another very handy function called describe this function will calculate the most important statistics for numerical data in one go so that we don' have to use individual functions in [ ]city_data[columns_numericdescribe(out[ ]lat lng pop count + mean + std + min - - - + - - + + + max +