Pandas is an open source library which is built on top of NumPy library. It allows user for fast analysis, data cleaning & preparation of data efficiently. Pandas is fast and it has high-performance & productivity for users.
Most of the datasets you work with are called DataFrames. DataFrames is a 2-Dimensional labeled Data Structure with index for rows and columns, where each cell is used to store a value of any type. Basically, DataFrames are Dictionary based out of NumPy Arrays.
Let’s see how to save a Pandas DataFrame as a CSV file using to_csv()
method.
Example #1: Save csv to working directory.
# importing 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 ] # dictionary of lists dict = { 'name' : nme, 'degree' : deg, 'score' : scr} df = pd.DataFrame( dict ) # saving the dataframe df.to_csv( 'file1.csv' ) |
Output:
Example #2: Saving CSV without headers and index.
# importing 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 ] # dictionary of lists dict = { 'name' : nme, 'degree' : deg, 'score' : scr} df = pd.DataFrame( dict ) # saving the dataframe df.to_csv( 'file2.csv' , header = False , index = False ) |
Output:
Example #3: Save csv file to a specified location.
# importing 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 ] # dictionary of lists dict = { 'name' : nme, 'degree' : deg, 'score' : scr} df = pd.DataFrame( dict ) # saving the dataframe df.to_csv(r 'C:\Users\Admin\Desktop\file3.csv' , index = False ) |
Output:
Example #4: Write a DataFrame to CSV file using tab separator.
import pandas as pd import numpy as np users = { 'Name' : [ 'Amit' , 'Cody' , 'Drew' ], 'Age' : [ 20 , 21 , 25 ]} df = pd.DataFrame(users, columns = [ 'Name' , 'Age' ]) #create DataFrame print ( "Original DataFrame:" ) print (df) print ( 'Data from Users.csv:' ) df.to_csv( 'Users.csv' , sep = '\t' , index = False ,header = True ) new_df = pd.read_csv( 'Users.csv' ) print (new_df) |
Output:
Original DataFrame: Name Age 0 Amit 20 1 Cody 21 2 Drew 25 Data from Users.csv: Name\tAge 0 Amit\t20 1 Cody\t21 2 Drew\t25
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