data retrieval pandas provides numerous ways to retrieve and read in data we can convert data from csv filesdatabasesflat filesand so on into dataframes we can also convert list of dictionaries (python dictinto dataframe the sources of data which pandas allows us to handle cover almost all the major data sources for our introductionwe will cover three of the most important data sourceslist of dictionaries csv files databases  ist of dictionaries to dataframe this is one of the simplest methods to create dataframe it is useful in scenarios where we arrive at the data we want to analyzeafter performing some computations and manipulations on the raw data this allows us to integrate pandas based analysis into data being generated by other python processing pipelines in[ ]import pandas as pd in[ ] [{'city':'delhi',"data": }{'city':'bangalore',"data": }{'city':'mumbai',"data": }in[ ]pd dataframe(dout[ ]city data delhi bangalore mumbai in[ ]df pd dataframe(din[ ]df out[ ]city data delhi bangalore mumbai here we provided list of python dictionaries to the dataframe class of the pandas library and the dictionary was converted into dataframe two important things to note herefirst the keys of dictionary are picked up as the column names in the dataframe (we can also supply some other name as arguments for different column names)secondly we didn' supply an index and hence it picked up the default index of normal arrays csv files to dataframe csv (comma separated filesfiles are perhaps one of the most widely used ways of creating dataframe we can easily read in csvor any delimited file (like tsv)using pandas and convert into dataframe for our example we will read in the following file and convert into dataframe by using python the data in figure - is sample slice of csv file containing the data of cities of the world from