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
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:
