This article provides details of various ways to 'Import' different forms of data ( excel , csv , tab delimited , Rdata etc ) into your Python Environment.
Pre-requisite : To use the various functions/methods described below , you need to have the 'pandas' package installed and imported into your Python Environment.
Importing pandas :
import pandas as pd
1) Importing a CSV ( Comma Separated Value ) file from your local system-
Import_DF = pd.read_csv("C:\Users\file1.csv")
or if the csv file is available in the working directory , then you can use the following code
Import_DF = pd.read_csv("file1.csv")
1.a) By default , read_csv() assumes that your data has header record , in case your data does not contain header data , then use the following code -
Import_DF = pd.read_csv("file1.csv" , header = None)
1.b) Changing / Adding the Column Names while importing the data using read_csv()
Import_DF = pd.read_csv("file1.csv" , header = None , names =['A' , 'B'])
2) Importing a CSV file from a Website / URL -
Web_data = pd.read_csv("http://winterolympicsmedals.com/medals.csv")
3) Importing a TXT file -
Txt_data = pd.read_table("Text_Data.txt")
4) Importing a Tab Delimited / Separated file -
Tab_Sep_data = pd.read_csv("Tab_Separated_Data.txt", sep =" ")
5) Importing an Excel file -
Excel_DF = pd.read_excel('Excel_Data.xls',skiprows=1,sheet_name='Concrete')
Skiprows : defines the number of rows in the excel file at the start which has to be skipped.
sheet_name : mention the name of the sheet in excel which has to be read
Happy Learning !!