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Saving a Pandas Dataframe as a CSV

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  • Difficulty Level : Basic
  • Last Updated : 21 Aug, 2020

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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