CSV files are the “comma-separated values”, these values are separated by commas, this file can be viewed like an excel file. In Python, Pandas is the most important library coming to data science. We need to deal with huge datasets while analyzing the data, which usually can get in CSV file format. Creating a pandas data frame using CSV files can be achieved in multiple ways.
Note: Get the csv file used in the below examples from here.
Method #1: Using read_csv() method: read_csv() is an important pandas function to read csv files and do operations on it.
Example:
Python3
# Python program to illustrate# creating a data frame using CSV files# import pandas moduleimport pandas as pd# creating a data framedf = pd.read_csv("CardioGoodFitness.csv")print(df.head()) |
Output:
Method #2: Using read_table() method: read_table() is another important pandas function to read csv files and create data frame from it.
Example:
Python3
# Python program to illustrate# creating a data frame using CSV files# import pandas moduleimport pandas as pd# creating a data framedf = pd.read_table("CardioGoodFitness.csv", delimiter =", ")print(df.head()) |
Output:
Method #3: Using the csv module: One can directly import the csv files using the csv module and then create a data frame using that csv file.
Example:
Python3
# Python program to illustrate# creating a data frame using CSV files# import pandas moduleimport pandas as pd# import csv moduleimport csvwith open("CardioGoodFitness.csv") as csv_file: # read the csv file csv_reader = csv.reader(csv_file) # now we can use this csv files into the pandas df = pd.DataFrame([csv_reader], index = None)# iterating values of first columnfor val in list(df[1]): print(val) |
Output:
['TM195', '18', 'Male', '14', 'Single', '3', '4', '29562', '112']
