Read and write data using Pandas Dataframe from different sources

Pandas

read_csv()Data input to Pandas DataFrameto_csv()
read_clipboard() to_clipboard()
read_excel() to_excel()
read_html() to_html()
read_json()to_json()
read_pickle()to_pickle()
read_sql()to_sql()
read_table()
To read and store data there are different methods available in Pandas. By using these tools we can read from sources like Excel , CSV file , Tabular data from HTML pages or from Database. Similarly after processing we can save data in Excel, CSV file, Generate HTML table or save directly in a Database table.

We can even read or write to our Clipboard. So Next time if you like a table in any webpage then just select and copy the table and run few lines of standard Python code to save the copied table to a Excel or CSV file.

Input to Pandas ( Reading to DataFrame )
read_csvReading data from csv file
read_clipboardReading data from Clipboard and storing in csv file
read_sqlUsing MySql records to create Pandas DataFrame
read_excelReading data from Excel file
read_jsonReading data from Json file
read_tableReading Tab separated data from .tsv file
read_htmlReading Tabular data from Web page
read_pickleReading DataFrame object from pickled file
Output from Pandas
to_clipboardData From DataFrame copied to clipboard
to_sqlUsing DataFrame managing MySql database
to_csvSaving data to CSV file
to_jsonSaving / output data in Json format
to_excelSaving data to Excel file
to_htmlSaving DataFrame data as HTML table in file
to_pickleSaving DataFrame data by pickling in a file


Pandas read_clipboard() read_html() read_csv() read_excel() to_excel()


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