How to open and convert sqlite database to pandas dataframe

Question

I have downloaded some datas as a sqlite database (data.db) and I want to open this database in python and then convert it into pandas dataframe.

This is so far I ha

Answer 1

Search sqlalchemy, engine and database name in google (sqlite in this case):

import pandas as pd
import sqlalchemy

db_name = "data.db"
table_name = "LITTLE_BOBBY_TABLES"

engine = sqlalchemy.create_engine("sqlite:///%s" % db_name, execution_options={"sqlite_raw_colnames": True})
df = pd.read_sql_table(table_name, engine)

Answer 2

i have stored my data in database.sqlite table name is Reviews

import sqlite3
con=sqlite3.connect("database.sqlite")

data=pd.read_sql_query("SELECT * FROM Reviews",con)
print(data)

Answer 3

The line

data = sqlite3.connect('data.db')

opens a connection to the database. There are no records queried up to this. So you have to execute a query afterward and provide this to the pandas DataFrame constructor.

It should look similar to this

import sqlite3
import pandas as pd

dat = sqlite3.connect('data.db')
query = dat.execute("SELECT * From <TABLENAME>")
cols = [column[0] for column in query.description]
results= pd.DataFrame.from_records(data = query.fetchall(), columns = cols)

I am not really firm with SQL commands, so you should check the correctness of the query. should be the name of the table in your database.

Answer 4

I wrote a piece of code up that saves tables in a database file such as .sqlite or .db and creates an excel file out of it with each table as a sheet or makes individual tables into csvs.

Note: You don't need to know the table names in advance!

import os, fnmatch
import sqlite3
import pandas as pd

#creates a directory without throwing an error
def create_dir(dir):
  if not os.path.exists(dir):
    os.makedirs(dir)
    print("Created Directory : ", dir)
  else:
    print("Directory already existed : ", dir)
  return dir

#finds files in a directory corresponding to a regex query
def find(pattern, path):
    result = []
    for root, dirs, files in os.walk(path):
        for name in files:
            if fnmatch.fnmatch(name, pattern):
                result.append(os.path.join(root, name))
    return result



#convert sqlite databases(.db,.sqlite) to pandas dataframe(excel with each table as a different sheet or individual csv sheets)
def save_db(dbpath=None,excel_path=None,csv_path=None,extension="*.sqlite",csvs=True,excels=True):
    if (excels==False and csvs==False):
      print("Atleast one of the parameters need to be true: csvs or excels")
      return -1

    #little code to find files by extension
    if dbpath==None:
      files=find(extension,os.getcwd())
      if len(files)>1:
        print("Multiple files found! Selecting the first one found!")
        print("To locate your file, set dbpath=<yourpath>")
      dbpath = find(extension,os.getcwd())[0] if dbpath==None else dbpath
      print("Reading database file from location :",dbpath)

    #path handling

    external_folder,base_name=os.path.split(os.path.abspath(dbpath))
    file_name=os.path.splitext(base_name)[0] #firstname without .
    exten=os.path.splitext(base_name)[-1]   #.file_extension

    internal_folder="Saved_Dataframes_"+file_name
    main_path=os.path.join(external_folder,internal_folder)
    create_dir(main_path)


    excel_path=os.path.join(main_path,"Excel_Multiple_Sheets.xlsx") if excel_path==None else excel_path
    csv_path=main_path if csv_path==None else csv_path

    db = sqlite3.connect(dbpath)
    cursor = db.cursor()
    cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
    tables = cursor.fetchall()
    print(len(tables),"Tables found :")

    if excels==True:
      #for writing to excel(xlsx) we will be needing this!
      try:
        import XlsxWriter
      except ModuleNotFoundError:
        !pip install XlsxWriter

    if (excels==True and csvs==True):
      writer = pd.ExcelWriter(excel_path, engine='xlsxwriter')
      i=0
      for table_name in tables:
          table_name = table_name[0]
          table = pd.read_sql_query("SELECT * from %s" % table_name, db)
          i+=1
          print("Parsing Excel Sheet ",i," : ",table_name)
          table.to_excel(writer, sheet_name=table_name, index=False)
          print("Parsing CSV File ",i," : ",table_name)
          table.to_csv(os.path.join(csv_path,table_name + '.csv'), index_label='index')

      writer.save()


    elif excels==True:
      writer = pd.ExcelWriter(excel_path, engine='xlsxwriter')
      i=0
      for table_name in tables:
          table_name = table_name[0]
          table = pd.read_sql_query("SELECT * from %s" % table_name, db)
          i+=1
          print("Parsing Excel Sheet ",i," : ",table_name)
          table.to_excel(writer, sheet_name=table_name, index=False)

      writer.save()

    elif csvs==True:
      i=0
      for table_name in tables:
          table_name = table_name[0]
          table = pd.read_sql_query("SELECT * from %s" % table_name, db)
          i+=1
          print("Parsing CSV File ",i," : ",table_name)
          table.to_csv(os.path.join(csv_path,table_name + '.csv'), index_label='index')
    cursor.close()
    db.close()
    return 0
save_db(); 

Answer 5

Despite sqlite being part of the Python Standard Library and is a nice and easy interface to SQLite databases, the Pandas tutorial states:

Note In order to use read_sql_table(), you must have the SQLAlchemy optional dependency installed. http://pandas.pydata.org/pandas-docs/stable/io.html#reading-tables

But Pandas still supports sqlite3 access if you want to avoid installing SQLAlchemy:

import sqlite3
import pandas as pd
# Create your connection.
cnx = sqlite3.connect('file.db')

df = pd.read_sql_query("SELECT * FROM table_name", cnx)

But you need to know the name of the used table in advance.

Hope it helps!

http://pandas.pydata.org/pandas-docs/stable/io.html#sqlite-fallback