Reading and writing SQL files in Pandas (2023)

When I started learning Data Analytics a few years ago, the first thing I learned was SQL and Pandas. As a data analyst, it is essential to have a strong foundation in working with SQL and Pandas. Both are powerful tools that help data analysts efficiently analyze and manipulate data stored in databases.

Overview of SQL and Pandas

SQL (Structured Query Language) is a programming language used to manage and manipulate relational databases. On the other hand, Pandas is a Python library used for data manipulation and analysis.

Data analysis involves working with large amounts of data, and databases are often used to store this data. SQL and Pandas provide powerful tools for working with databases, allowing data analysts to efficiently extract, manipulate and analyze data. By leveraging these tools, data analysts can gain valuable insights from data that would otherwise be difficult to obtain.

In this article, we will explore how to use SQL and Pandas to read and write to a database.

Connects to DB

Installation of the libraries

We first need to install the necessary libraries before we can connect to the SQL database with Pandas. The two main libraries required are Pandas and SQLAlchemy. Pandas is a popular data manipulation library that allows for storing large data structures, as mentioned in the introduction. In contrast, SQLAlchemy provides an API to connect to and interact with the SQL database.

We can install both libraries using the Python package manager, pip, by running the following commands at the command prompt.

$pip install pandas$pip installer sqlalchemy

Establishing the connection

With the libraries installed, we can now use Pandas to connect to the SQL database.

To begin with, we will create a SQLAlchemy engine object withcreate_engine(). Thecreate_engine()function connects the Python code to the database. It takes as an argument a connection string specifying the database type and connection details. In this example, we use the SQLite database type and the database file path.

Create an engine object for an SQLite database using the example below:

importpandasaspdfromsqlalchemyimportcreate_engine# Create an engine objectmotor = create_engine('sqlite:///C/SQLite/student.db')

If the SQLite database file, student.db in our case, is in the same directory as the Python script, we can use the filename directly, as shown below.

motor = create_engine('sqlite:///student.db')

Reading SQL files with pandas

Now that we have established a connection, let's read the data. In this section we will look atread_sql,read_sql_table, andread_sql_queryfunctions and how to use them to work with a database.

(Video) Python Pandas Tutorial 14: Read Write Data From Database (read_sql, to_sql)

Executing SQL queries using Panda'sread_sql()Work

Theread_sql()is a Pandas library function that allows us to execute an SQL query and retrieve the results into a Pandas dataframe. Thatread_sql()function connects SQL and Python so we can take advantage of the power of both languages. The function is wrappedread_sql_table()andread_sql_query(). Theread_sql()function is routed internally based on the supplied input, meaning that if the input is to execute an SQL query, it will be routed toread_sql_query(), and if it's a database table, it will be redirected toread_sql_table().

Theread_sql()syntax is as follows:

pandas.read_sql(sql, con, index_col=No, coerce_float=Right, params=No, parse_dates=No, columns=No, chunksize=No)

SQL and con parameters are required; the rest is optional. However, we can manipulate the result using these optional parameters. Let's take a closer look at each parameter.

  • sql: Name of SQL query or database table
  • when: Connection object or connection URL
  • index_col: This parameter allows us to use one or more columns from the SQL query result as a data frame index. It can take either a single column or a list of columns.
  • coerce_float: This parameter specifies whether non-numeric values ​​should be converted to floats or left as strings. It is set to true by default. If possible, it converts non-numeric values ​​to floating types.
  • params: The parameters provide a safe method of passing dynamic values ​​to the SQL query. We can use the params parameter to pass a dictionary, tuple or list. Depending on the database, the syntax of parameters varies.
  • parse_dates: This allows us to specify which column in the resulting data frame should be interpreted as a date. It accepts a single column, a list of columns, or a dictionary with the key as the column name and the value as the column format.
  • columns: This allows us to retrieve only selected columns from the list.
  • pieces: When working with a large data set, chunk size is important. It retrieves the query result in smaller chunks, which improves performance.

Here is an example of how to useread_sql():


importpandasaspdfromsqlalchemyimportcreate_engine# Create an engine objectmotor = create_engine('sqlite:///C/SQLite/student.db')# Get all records from the Student table and manipulate the resultdf = pd.read_sql("SELECT * FROM student", motor, index_col='Roll number', parse_dates='date of birth')Print(df)Print("The data type for dateOfBirth: ", df.dateOfBirth.dtype)# Close the database connectionengine.dispose()


first name last name email dateOfBirthrollNumber1 Mark Simson[email protected]2000-02-232 Peter Griffen[email protected]2001-04-153 Meg Aniston[email protected]2001-09-20 Date type for date of birth: datetime64[ns]

After connecting to the database, we execute a query that returns all records fromStudenttable and stores them in the DataFramedf. The "Roll Number" column is converted to an index usingindex_colparameter, and the "dateOfBirth" data type is "datetime64[ns]" due toparse_dates. We can useread_sql()not only to retrieve data but also to perform other operations such as insert, delete and update.read_sql()is a generic function.

Loading specific tables or views from the database

Loading a specific table or view with Pandasread_sql_table()is another technique for reading data from the database into a Pandas dataframe.

What isread_sql_table?

Panda's library offersread_sql_tablefunction which is specifically designed to read an entire SQL table without executing any queries and return the result as a Pandas dataframe.

The syntax ofread_sql_table()is as below:

pandas.read_sql_table(tablename, con, schema=No, index_col=No, coerce_float=Right, parse_dates=No, columns=No, chunksize=No)

With the exception oftabelnavnand scheme, the parameters are explained in the same way asread_sql().

(Video) Python pandas: writing and reading dataframe to and from files

  • tabelnavn: parameterstabelnavnis the name of the SQL table in the database.
  • scheme: This optional parameter is the name of the schema that contains the table name.

After establishing a connection to the database, we will useread_sql_tablefunction to loadStudenttable into a Pandas DataFrame.

importpandasaspdfromsqlalchemyimportcreate_engine# Create an engine objectmotor = create_engine('sqlite:///C/SQLite/student.db')# Load student table from the databasedf = pd.read_sql_table('Student', motor)Print(df.head())# Close the database connectionengine.dispose()


rollNumber first name last name e-mail date of birth0 1 Mark Simson[email protected]2000-02-231 2 Peter Griffen[email protected]2001-04-152 3 Meg Aniston[email protected]2001-09-20

We assume that it is a large table that may be memory intensive. Let's explore how we can usepiecesparameter to solve this problem.

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importpandasaspdfromsqlalchemyimportcreate_engine# Create an engine objectmotor = create_engine('sqlite:///C/SQLite/student.db')# Load student table from the databasedf_iterator = pd.read_sql_table('Student', motor, chunksize =1)# Repeat the data frametodfidf_iterator:Print(df.head())# Close the database connectionengine.dispose()


rollNumber first name last name e-mail date of birth0 1 Mark Simson[email protected]2000-02-230 2 Peter Griffen[email protected]2001-04-150 3 Meg Aniston[email protected]2001-09-20

Please remember thatpiecesI use here is 1 because I only have 3 records in my table.

(Video) Reading the Data from DataBase Using Pandas

Query the database directly with Pandas' SQL syntax

Extracting insights from the database is an important part for data analysts and scientists. To do so, we will take advantage ofread_sql_query()function.

What is read_sql_query()?

Using pandasread_sql_query()function, we can run SQL queries and get the results directly into a DataFrame. Thatread_sql_query()function is created especially forCHOOSEstatement. It cannot be used for other operations, such asDELETEorUPDATE.


pandas.read_sql_query(sql, con, index_col=No, coerce_float=Right, params=No, parse_dates=No, chunksize=No, dtype=No, dtype_backend=_NoDefault.no_default)

All parameter descriptions are the same asread_sql()function. Here is an example ofread_sql_query():


importpandasaspdfromsqlalchemyimportcreate_engine# Create an engine objectmotor = create_engine('sqlite:///C/SQLite/student.db')# Query the student tabledf = pd.read_sql_query('Select firstname, lastname from student Where rollNumber = 1', motor)Print(df)# Close the database connectionengine.dispose()


first name last name0 Mark Simson

Writing SQL files with pandas

While analyzing data, suppose we discovered that a few records need to be changed or that a new table or view with the data is required. To update or insert a new record is a method to useread_sql()and write a query. However, that method can be long. Pandas provides a great method calledto_sql()for situations like this.

In this section we will first build a new table in the database and then edit an existing one.

Creating a new table in the SQL database

Before we create a new table, let's discuss firstto_sql()in details.

What isto_sql()?

Theto_sql()function of the Pandas library allows us to write or update the database. Thatto_sql()function can save DataFrame data to a SQL database.

Syntax forto_sql():

(Video) How To Read Data From SQL Server Using Python

DataFrame.to_sql(name, con, schema=No, if_exists='betray', index=Right, index_label=No, chunksize=No, dtype=No, method=No)

Whennameandwhenparameters are mandatory to runto_sql(); however, other parameters provide additional flexibility and customization options. Let's discuss each parameter in detail:

  • name: The name of the SQL table to be created or modified.
  • when: The connection object for the database.
  • scheme: The schema of the table (optional).
  • if_exists: The default value for this parameter is "fail". This parameter allows us to decide what action to perform if the table already exists. Options include "fail", "replace" and "add".
  • index: The index parameter accepts a boolean value. By default, it is set to True, which means that the index of the DataFrame will be written to the SQL table.
  • index_etiquette: This optional parameter allows us to specify a column label for the index columns. By default, the index is written to the table, but a specific name can be given using this parameter.
  • pieces: The number of rows to be written at a time to the SQL database.
  • dtype: This parameter accepts a dictionary with keys as column names and values ​​as their data types.
  • method: The method parameter allows specifying the method used to insert data into SQL. By default it is set to None, which means pandas will find the most efficient way based on the database. There are two main options for method parameters:
    • multi: It allows to insert multiple rows in a single SQL query. However, not all databases support multi-row insert.
    • Callable function: Here we can write a custom function for insertion and call it using method parameters.

Here is an example of usingto_sql():

importpandasaspdfromsqlalchemyimportcreate_engine# Create an engine objectmotor = create_engine('sqlite:///C/SQLite/student.db')# Create a new dataframe that will be our new tabledata = {'Name': ['Paul','Tom','Jerry'],'Alder': [9,8,7]}df = pd.DataFrame(data)# Create a new table called Customerdf.to_sql('Could', con=engine, if_exists='betray')# Close the database connectionengine.dispose()

A new table called Customer is created in the database with two fields called "Name" and "Age".

Database snapshot:

Updating existing tables with Pandas Dataframes

Updating data in a database is a complex task, especially when dealing with large data. But by usingto_sql()feature in Pandas can make this task much easier. To update the existing table in the database,to_sql()function can be used withif_existsparameter set to "replace". This will overwrite the existing table with the new data.

Here is an example ofto_sql()that updates the previously created oneCouldtable. Suppose, iCouldtable we want to update the age of a customer named Paul from 9 to 10. To do that we can first change the corresponding row in the DataFrame and then useto_sql()function to update the database.


importpandasaspdfromsqlalchemyimportcreate_engine# Create a connection to the SQLite databasemotor = create_engine('sqlite:///C/SQLite/student.db')# Load customer table into a data framedf = pd.read_sql_table('Could', motor)# Edit the age of the customer named Pauldf.loc[df['Name'] =='Paul','Alder'] =10# Update the customer table with the changed DataFramedf.to_sql('Could', con=engine, if_exists='substitute')# Close the database connectionengine.dispose()

In the database, Paul's age is updated:


In conclusion, Pandas and SQL are both powerful tools for data analysis tasks such as reading and writing data to the SQL database. Pandas provides an easy way to connect to the SQL database, read data from the database into a Pandas dataframe, and write dataframe data back to the database.

The Pandas library makes it easy to manipulate data in a data frame, whereas SQL provides a powerful language for querying data in a database. Using both Pandas and SQL to read and write the data can save time and effort in data analysis tasks, especially when the data is very large. Overall, leveraging SQL and Pandas together can help data analysts and scientists streamline their workflow.


How to use pandas to read SQL file? ›

I will use the following steps to explain pandas read_sql() usage.
  1. Syntax of read_sql()
  2. Create database table on sqlite3.
  3. Insert data into the table.
  4. Execute SQL query by using pands red_sql().
  5. Use read_sql_query()
  6. Use read_sql_table()
  7. Filter rows from SQL table.
Jan 20, 2023

How to read data from database in Python pandas? ›

To read a complete database table into a pandas dataframe, we can use the read_sql_table() method which accepts a Table Name and an SQLAlchemy connectable (Database URI as string), it returns a dataframe with data from SQL database table.

How to read SQL first row in pandas? ›

Get the First Row of Pandas using iloc[]

Using the Pandas iloc[] attribute we can get the single row or column by using an index, by specifying the index position 0 we can get the first row of DataFrame. iloc[0] will return the first row of DataFrame in the form of Pandas Series.

How do I write SQL queries in Python? ›

Here are simple steps to getting started.
  1. Step 1 — Importing SQLite and Pandas. To start, we will need to import SQLite into our Jupyter notebook. ...
  2. Step 2 — Connecting your database. ...
  3. Step 3 — Cursor Object. ...
  4. Step 4 — Writing a Query. ...
  5. Step 5 — Running Query. ...
  6. Step 6 — Closing your connection.

How to read data from SQL query in Python? ›

You can fetch data from MYSQL using the fetch() method provided by the mysql-connector-python. The cursor. MySQLCursor class provides three methods namely fetchall(), fetchmany() and, fetchone() where, The fetchall() method retrieves all the rows in the result set of a query and returns them as list of tuples.

How to write SQL query easily? ›

How to Create a SQL Statement
  1. Start your query with the select statement. select [all | distinct] ...
  2. Add field names you want to display. field1 [,field2, 3, 4, etc.] ...
  3. Add your statement clause(s) or selection criteria. Required: ...
  4. Review your select statement. Here's a sample statement:
Oct 13, 2022

How to write SQL query to get data from table? ›

The syntax is: SELECT column1, column2 FROM table1, table2 WHERE column2='value'; In the above SQL statement: The SELECT clause specifies one or more columns to be retrieved; to specify multiple columns, use a comma and a space between column names.

How do I write SQL queries in Jupyter notebook? ›

Meet ipython-sql

No need to write multiple lines of code to connect to the database or wrap the query in a string. ipython-sql makes querying a database from Jupyter Notebook “cleaner”. Note, each cell needs to be annotated with %%sql. This tells the Jupyter Notebook that the code in the cell is SQL.

How to read SQL query? ›

How to Understand Long and Complex SQL Queries
  1. STEP 1) Big Picture First! ...
  2. STEP 2) Focus on the Final Columns First! ...
  3. STEP 3) Understand the Final GROUP BY and WHERE Clauses. ...
  4. STEP 4) Look at the Final JOINs. ...
  5. STEP 5) Look at CTEs and Subqueries in Reverse Order. ...
  6. STEP 6) Time to Understand CTEs and Subqueries.
Feb 1, 2022

Can I use pandas as a database? ›

The Pandas dataframe system is similar to databases, but also different. Unlike database relations, within a dataframe, Pandas allows for mixed types in a column and maintains a notion of order. Dataframes also support row labels, in addition to column labels, making it easy to reference your data.

How to read tables in pandas? ›

Basic Usage
  1. import pandas as pd import numpy as np import matplotlib.pyplot as plt from unicodedata import normalize table_MN = pd. ...
  2. print(f'Total tables: {len(table_MN)}') ...
  3. table_MN = pd. ...
  4. df = table_MN[0] df.
Sep 14, 2020

How to read SQL row count in pandas? ›

Get Number of Rows in DataFrame

You can use len(df. index) to find the number of rows in pandas DataFrame, df. index returns RangeIndex(start=0, stop=8, step=1) and use it on len() to get the count.

What is the proper way to load a CSV file using pandas? ›

Pandas Read CSV
  1. ExampleGet your own Python Server. Load the CSV into a DataFrame: ...
  2. Print the DataFrame without the to_string() method: import pandas as pd. ...
  3. Check the number of maximum returned rows: import pandas as pd. ...
  4. Increase the maximum number of rows to display the entire DataFrame: import pandas as pd.

How to read first 5 rows in pandas? ›

You can use df. head() to get the first N rows in Pandas DataFrame. Alternatively, you can specify a negative number within the brackets to get all the rows, excluding the last N rows.

What is the difference between pandas and SQL? ›

Pandas is an open-source Python library that is extensively used for data analysis and manipulation. In contrast, SQL is a programming language that is used to perform operations in the relational database management system (RDBMS).

How do I write a SQL query in string format? ›

Execute SQL Query In String Format
  1. sp_executesql [ @stmt = ] statement.
  2. [
  3. { , [ @params = ] N'@parameter_name data_type [ OUT | OUTPUT ][ ,...n ]' }
  4. { , [ @param1 = ] 'value1' [ ,...n ] }
  5. ]
Aug 22, 2019

How do I write a SQL query in a string? ›

String Functions in SQL
  1. Click the Queries tab in the left menu.
  2. Click the 'Design' icon.
  3. Add the table(s) you want to query to the query design view and close the Add table dialog box.
  4. Click the small arrow next to the 'View' icon in the toolbar, select 'SQL View; from the drop down menu.

Can Python pull data from a SQL database? ›

In order to read data from SQL server to python, you need the library pyodbc. This library can be installed using below command on jupyter notebook.

How to read CSV file using SQL query? ›

Using SQL Server Management Studio Import CSV Tools
  1. From the Object Explorer, Expand the Databases Folder. ...
  2. Select the Target Database. ...
  3. Select a Flat File Source. ...
  4. Specify the CSV File. ...
  5. Configure the Columns. ...
  6. Choose the Destination (SQL Server) ...
  7. Specify the Database Table and Check Column Mappings.
Oct 28, 2021

How do you write data in a database using Python? ›

Python MySQL – Insert Data Into a Table
  1. Connect to the MySQL database server by creating a new MySQLConnection object.
  2. Initiate a MySQLCursor object from the MySQLConnection object.
  3. Execute the INSERT statement to insert data into the table.
  4. Close the database connection.

What is the first step of writing a SQL query? ›

Following are some Business Steps of Writing SQL Queries : 1. Analyse the Business Logic : The first step is to analyse the Business Logic.

How to write 1 to 10 in SQL query? ›

Most major systems provide a way to do it:
  1. In Oracle : SELECT level FROM dual CONNECT BY level <= 10.
  2. In SQL Server : WITH q AS ( SELECT 1 AS num UNION ALL SELECT num + 1 FROM q WHERE num < 10 ) SELECT * FROM q.
  3. In PostgreSQL : SELECT num FROM generate_series(1, 10) num.

What are the 5 basic SQL commands? ›

Some of The Most Important SQL Commands
  • SELECT - extracts data from a database.
  • UPDATE - updates data in a database.
  • DELETE - deletes data from a database.
  • INSERT INTO - inserts new data into a database.
  • CREATE DATABASE - creates a new database.
  • ALTER DATABASE - modifies a database.
  • CREATE TABLE - creates a new table.

How do I pull data from SQL? ›

In SQL, to retrieve data stored in our tables, we use the SELECT statement. The result of this statement is always in the form of a table that we can view with our database client software or use with programming languages to build dynamic web pages or desktop applications.

How do I pull data from a SQL database? ›

Start the SQL Server Import and Export Wizard from SQL Server Management Studio (SSMS)
  1. In SQL Server Management Studio, connect to an instance of the SQL Server Database Engine.
  2. Expand Databases.
  3. Right-click a database.
  4. Point to Tasks.
  5. Click one of the following options. Import Data. Export Data.
Mar 3, 2023

What are the 3 types of select query? ›

SELECT statement options
  • FROM is used to specify a table name where a necessary column with data is located. ...
  • AS is used to create a temporary name for the column headings. ...
  • GROUP BY is used to group results with similar data. ...
  • HAVING is used to define a search condition.
Feb 18, 2022

Is there a notebook for SQL? ›

Create a notebook

In Azure Data Studio, connect to your SQL Server. Select under the Connections in the Servers window. Then select New Notebook.

How do I read a SQL file in Jupyter? ›

Method 1: Using Pandas Read SQL Query
  1. Step 1: Install a Python package to connect to your database. We suggest installing the following packages: ...
  2. Step 2: Create a database connection in Jupyter. Connect a database to a Jupyter notebook. ...
  3. Step 3: Run SQL queries using pandas.
Feb 13, 2022

How to run SQL query in Python notebook using magic function? ›

To be able to use Python magic with sql and execute sql queries from a Python IDE we need to install ipython-sql library first. ipython-sql is the library that allows sql magic. You can simply installing it running this code from Anaconda Command Prompt or another command console you are using.

How to write SQL scripts? ›

5.5. 1 Creating a SQL Script in the Script Editor
  1. On the Workspace home page, click SQL Workshop and then SQL Scripts. The SQL Scripts page appears.
  2. Click the Create button. ...
  3. In Script Name, enter a name for the script. ...
  4. Enter the SQL statements, PL/SQL blocks you want to include in your script. ...
  5. Click Create.

What is SQL code writing? ›

SQL is, fundamentally, a programming language designed for accessing, modifying and extracting information from relational databases. As a programming language, SQL has commands and a syntax for issuing those commands.

How to write query in MySQL? ›

MySQL - Select Query
  1. You can use one or more tables separated by comma to include various conditions using a WHERE clause, but the WHERE clause is an optional part of the SELECT command.
  2. You can fetch one or more fields in a single SELECT command.
  3. You can specify star (*) in place of fields.

Is pandas a good ETL tool? ›

Pandas is a Python library that provides you with Data Structures and Analysis Tools. It simplifies ETL processes like Data Cleansing by adding R-style Data Frames. However, it is time-consuming as you would have to write your own code.

Can pandas replace SQL? ›

Both Pandas and SQL are essential tools for data scientists and analysts. There are, of course, alternatives for both but they are the predominant ones in the field. Since both Pandas and SQL operate on tabular data, similar operations or queries can be done using both.

Why use pandas instead of SQL? ›

SQL is more efficient in querying data but it has less functions whereas in pandas, there might be lag for large volumes of data but it has more functions which enable us to manipulate data in an effective way.

What is the difference between pandas read table and read CSV? ›

The difference between read_csv() and read_table() is almost nothing. In fact, the same function is called by the source: read_csv() delimiter is a comma character. read_table() is a delimiter of tab \t .

What is the difference between Read_table and read CSV? ›

read_csv vs read_table duplication

They both do the same exact thing, read in data from a text file. The only difference is that read_csv defaults the delimiter to a comma, while read_table uses tab as its default.

How do I count rows in SQL in Python? ›

Once you have the rows, you can use the rowcount property of the cursor object to determine the number of rows returned by the query. Alternatively, you can use the COUNT() function in SQL to directly count the number of rows in the table, and retrieve the result using Python.

How to check table row count in SQL Server query? ›

Use the COUNT aggregate function to count the number of rows in a table. This function takes the name of the column as its argument (e.g., id ) and returns the number of rows for this particular column in the table (e.g., 5).

How do I get unique values from a column in Pandas? ›

You can get unique values in column (multiple columns) from pandas DataFrame using unique() or Series.unique() functions. unique() from Series is used to get unique values from a single column and the other one is used to get from multiple columns.

How do I write data in a CSV file using pandas python? ›

  1. Exporting a pandas DataFrame into a CSV file. ...
  2. Change the default separator. ...
  3. Excluding the index. ...
  4. Remove the headers. ...
  5. Writing only a subset of the columns. ...
  6. Specifying the format of datetime objects. ...
  7. Change the way NaN values are written to the csv file.
Jan 13, 2023

How do I write to a CSV file in python? ›

Open the CSV file in writing (w mode) with the help of open() function. Create a CSV writer object by calling the writer() function of the csv module. Write data to CSV file by calling either the writerow() or writerows() method of the CSV writer object. Finally, close the CSV file.

How to load data into pandas DataFrame? ›

Using the read_csv() function from the pandas package, you can import tabular data from CSV files into pandas dataframe by specifying a parameter value for the file name (e.g. pd. read_csv("filename. csv") ). Remember that you gave pandas an alias ( pd ), so you will use pd to call pandas functions.

How do I select the last 10 rows in Pandas? ›

Get the Last Row of Pandas using iloc[]

Using the Pandas iloc[-1] attribute you can select the last row of the DataFrame. iloc[] is used to select the single row or column by using an index. iloc[-1] property return the last row of DataFrame in the form of Pandas Series.

How do I select top 10 rows in Pandas DataFrame? ›

Pandas Get the First N Rows of DataFrame using head()

When you wanted to extract only the top N rows after all your filtering and transformations from the Pandas DataFrame use the head() method. This function is used to get the top N rows from DataFrame or the top N elements from a Series.

How do I drop the first 20 rows in Pandas? ›

Remove First N Rows of Pandas DataFrame Using tail()

tail(df. shape[0] -n) to remove the top/first n rows of pandas DataFrame. Generally, DataFrame. tail() function is used to show the last n rows of a pandas DataFrame but you can pass a negative value to skip the rows from the beginning.

How to read data from SQL file? ›

To quickly view or edit SQL file, you can open it in a text editor like Notepad or TextEdit. You can also open SQL files in MySQL Workbench to use MySQL's database editing tools, or import an Azure SQL database into an Excel workbook.

How to read text file in Python Panda? ›

One can read a text file (txt) by using the pandas read_fwf() function, fwf stands for fixed-width lines, you can use this to read fixed length or variable length text files. Alternatively, you can also read txt file with pandas read_csv() function.

How to read data from SQL query? ›

The SQL SELECT Statement
  1. SELECT column1, column2, ... FROM table_name;
  2. SELECT * FROM table_name;
  3. ExampleGet your own SQL Server. SELECT CustomerName, City FROM Customers;
  4. Example. SELECT * FROM Customers;

How to read JSON file in Python Panda? ›

To read a JSON file via Pandas, we'll utilize the read_json() method and pass it the path to the file we'd like to read. The method returns a Pandas DataFrame that stores data in the form of columns and rows.

How do I convert a SQL file to CSV? ›

How To Export SQL Server Data From Table To a CSV File
  1. Contents.
  2. Right-click the database and navigate to Tasks > Export Data:
  3. In the SQL Server Import and Export Wizard window, click Next:
  4. Customize the data in the Choose a Data Source window:
  5. Then click Next.
  6. Customize the data in the Choose a Destination window:
Feb 14, 2022

How to output SQL query to text file? ›

Via Import/Export Wizard
  1. To begin with, right-click the database in SQL Server Management Studio or SSMS.
  2. Then, select the Import or Export data option and head to Export Data under Tasks.
  3. Next, open the SQL Server Import and Export wizard.
  4. After that, choose Microsoft OLE DB Provider as the Data Source.
May 21, 2022

How to read CSV file using SQL? ›

Using SQL Server Management Studio Import CSV Tools
  1. From the Object Explorer, Expand the Databases Folder. ...
  2. Select the Target Database. ...
  3. Select a Flat File Source. ...
  4. Specify the CSV File. ...
  5. Configure the Columns. ...
  6. Choose the Destination (SQL Server) ...
  7. Specify the Database Table and Check Column Mappings.
Oct 28, 2021

How to read columns from text file in pandas? ›

To read specific columns from a file in Pandas, we can use the read_csv(~) method and specify the usecols parameter.

How do I open a text file to read and write in Python? ›

In Python, there are six methods or access modes, which are:
  1. Read Only ('r'): This mode opens the text files for reading only. ...
  2. Read and Write ('r+'): This method opens the file for both reading and writing. ...
  3. Write Only ('w'): This mode opens the file for writing only.
Aug 26, 2022

How to read and write HTML file in pandas? ›

To read an HTML file, pandas dataframe looks for a tag . That tag is called a <td></td> tag. This tag is used for defining a table in HTML. pandas uses read_html() to read the HTML document.

How to write SQL query? ›

How to Create a SQL Statement
  1. Start your query with the select statement. select [all | distinct] ...
  2. Add field names you want to display. field1 [,field2, 3, 4, etc.] ...
  3. Add your statement clause(s) or selection criteria. Required: ...
  4. Review your select statement. Here's a sample statement:
Oct 13, 2022

How to show data from SQL database? ›

Right-click the Products table in SQL Server Object Explorer, and select View Data. The Data Editor launches.

How to read JSON file as text file in Python? ›

Read JSON file in Python
  1. Import json module.
  2. Open the file using the name of the json file witn open() function.
  3. Open the file using the name of the json file witn open() function.
  4. Read the json file using load() and put the json data into a variable.
Jun 11, 2021

How to read CSV file in Python? ›

Read A CSV File Using Python
  1. Using the CSV Library. import csv with open("./bwq.csv", 'r') as file: csvreader = csv.reader(file) for row in csvreader: print(row) ...
  2. Using the Pandas Library. import pandas as pd data = pd.read_csv("bwq.csv") data.
Apr 17, 2023

What is the difference between JSON load and read_json? ›

When you have a single JSON structure inside a json file, use read_json because it loads the JSON directly into a DataFrame. With json. loads , you've to load it into a python dictionary/list, and then into a DataFrame - an unnecessary two step process.


1. Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc
(Corey Schafer)
2. Python Read Text File with Query Statement Like SQL
(Programming Scenario Budy)
3. Reading and Writing Files With Pandas - CSV and Excel Files
(Real Python)
4. Import data From SQL Server into a DataFrame | pandas Tutorial
(Jie Jenn)
5. Pandas | Reading & Writing | Reading Dataset | Writing Data Frame into file
(Saksham Jain)
6. Read SQL Data Using Pandas And PyODBC
(Analytics School)


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