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Importing Data in Python
When running python programs, we need to use datasets for data analysis. Python has various modules which help us in importing the external data in various file formats to a python program. In this example we will see how to import data of various formats to a python program.
Import csv file
The csv module enables us to read each of the row in the file using a comma as a delimiter. We first open the file in read only mode and then assign the delimiter. Finally use a for loop to read each row from the csv file.
Example
import csv
with open("E:\customers.csv",'r') as custfile:
rows=csv.reader(custfile,delimiter=',')
for r in rows:
print(r)Output
Running the above code gives us the following result −
['customerID', 'gender', 'Contract', 'PaperlessBilling', 'Churn'] ['7590-VHVEG', 'Female', 'Month-to-month', 'Yes', 'No'] ['5575-GNVDE', 'Male', 'One year', 'No', 'No'] ['3668-QPYBK', 'Male', 'Month-to-month', 'Yes', 'Yes'] ['7795-CFOCW', 'Male', 'One year', 'No', 'No'] …… …….
With pandas
The pandas library can actually handle most of the file types inclusing csv file. In this program let see how pandas library handles the excel file using the read_excel module. In the below example we read the excel version of the above file and get the same result when we read the file.
Example
import pandas as pd
df = pd.ExcelFile("E:\customers.xlsx")
data=df.parse("customers")
print(data.head(10))Output
Running the above code gives us the following result −
customerID gender Contract PaperlessBilling Churn 0 7590-VHVEG Female Month-to-month Yes No 1 5575-GNVDE Male One year No No 2 3668-QPYBK Male Month-to-month Yes Yes 3 7795-CFOCW Male One year No No 4 9237-HQITU Female Month-to-month Yes Yes 5 9305-CDSKC Female Month-to-month Yes Yes 6 1452-KIOVK Male Month-to-month Yes No 7 6713-OKOMC Female Month-to-month No No 8 7892-POOKP Female Month-to-month Yes Yes 9 6388-TABGU Male One year No No
With pyodbc
We can also connect to database servers using a module called pyodbc. This will help us import data from relational sources using a sql query. Ofcourse we also have to define the connection details to the db before passing on the query.
Example
import pyodbc
sql_conn = pyodbc.connect("Driver={SQL Server};Server=serverName;UID=UserName;PWD=Password;Database=sqldb;")
data_sql = pd.read_sql_query(SQL QUERY’, sql_conn)
data_sql.head()
Output
Depending the SQL query the result will be displayed.
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