concatenating dataframes most data science projects will have data from more than one data source these data sources will mostly have data that' related in some way to each other and the subsequent steps in data analysis will require them to be concatenated or joined pandas provides rich set of functions that allow us to merge different data sources we cover small subset of such methods in this sectionwe explore and learn about two methods that can be used to perform all kinds of amalgamations of dataframes concatenating using the concat method the first method to concatenate different dataframes in pandas is by using the concat method the majority of the concatenation operations on dataframes will be possible by tweaking the parameters of the concat method let' look at couple of examples to understand how the concat method works the simplest scenario of concatenating is when we have more than one fragment of the same dataframe (which may happen if you are reading it from stream or in chunksin that casewe can just supply the constituent dataframes to the concat function as follows in [ ]city_data city_data sample( in [ ]city_data city_data sample( in [ ]city_data_combine pd concat([city_data ,city_data ]in [ ]city_data_combine out[ ]city city_ascii lat lng pop groningen groningen tambov tambov karibib karibib - focsani focsani pleven pleven indianapolis indianapolis - country iso iso province netherlands nl nld groningen russia ru rus tambov namibia nan nam erongo romania ro rou vrancea bulgaria bg bgr pleven united states of america us usa indiana another common scenario of concatenating is when we have information about the columns of same dataframe split across different dataframes then we can use the concat method again to combine all the dataframes consider the following example in [ ]df pd dataframe({'col '['col ''col ''col ''col ']'col '['col ''col ''col ''col ']'col '['col ''col ''col ''col ']'col '['col ''col ''col ''col ']}index=[ ]