Most of the datasets you work with are named DataFrames. DataFrames — it is a two-dimensional labeled data structure with an index for rows and columns, where each cell is used to store a value of any type. Essentially, DataFrames — it is a dictionary based on NumPy arrays.

Let`s see how to save a Pandas DataFrame as a CSV file using to_csv () .

Example # 1: Save CSV to working directory.

# import pandas as pd import pandas as pd    # list of name, degree, score nme = [ "aparna" , "pankaj" , "sudhir" , "Geeku" ] deg =   [ "MBA" , " BCA " , " M.Tech " , "MBA" ] scr = [ 90 , 40 , 80 , 98 ]   # list dictionary dict = { `name` : nme, `degree` : deg, ` score` : scr}     df = pd.DataFrame ( dict )   # save data frame df.to_csv ( `file1.csv` )

Output:

Example # 2: Saving CSV without headers and index .

# import pandas as pd import pandas as pd    # list of name, degree, score nme = [ "aparna" , "pankaj" , "sudhir" , "Geeku" ] deg = [ "MBA" , "BCA" , "M.Tech" , " MBA " ] scr = [ 90 , 40 , 80 , 98 ]   # list dictionary dict = { `name` : nme, `degree` : deg, ` score` : scr}    df = pd.DataFrame ( dict )   # save data frame df.to_csv ( `file2.csv` , header = Fals e , index = False )

Output:

Example # 3: Save the CSV file to the specified place.

# import pandas as pd import pandas as pd    # list of name, degree, points nme = [ "aparna" , "pankaj" , "sudhir" , < / code> "Geeku" ] deg = [ "MBA" , "BCA" , "M.Tech" , "MBA" ] scr = [ 90 , 40 , 80 , 98 ]   # list dictionary dict = { `name` : nme, ` degree` : deg, `score ` : scr}     df = pd.DataFrame ( dict )   # save frame data df.to_csv (r `C: UsersAdminDesktopfile3.csv` , index = False )

Output: