In [1]:
import pandas as pd
import sqlite3


# 9.1 Reading data from SQL databases¶

So far we've only talked about reading data from CSV files. That's a pretty common way to store data, but there are many others! Pandas can read from HTML, JSON, SQL, Excel (!!!), HDF5, Stata, and a few other things. In this chapter we'll talk about reading data from SQL databases.

You can read data from a SQL database using the pd.read_sql function. read_sql will automatically convert SQL column names to DataFrame column names.

read_sql takes 2 arguments: a SELECT statement, and a database connection object. This is great because it means you can read from any kind of SQL database -- it doesn't matter if it's MySQL, SQLite, PostgreSQL, or something else.

This example reads from a SQLite database, but any other database would work the same way.

In [2]:
con = sqlite3.connect("../data/weather_2012.sqlite")
df = pd.read_sql("SELECT * from weather_2012 LIMIT 3", con)
df

Out[2]:
id date_time temp
0 1 2012-01-01 00:00:00 -1.8
1 2 2012-01-01 01:00:00 -1.8
2 3 2012-01-01 02:00:00 -1.8

read_sql doesn't automatically set the primary key (id) to be the index of the dataframe. You can make it do that by adding an index_col argument to read_sql.

If you've used read_csv a lot, you may have seen that it has an index_col argument as well. This one behaves the same way.

In [3]:
df = pd.read_sql("SELECT * from weather_2012 LIMIT 3", con, index_col='id')
df

Out[3]:
date_time temp
id
1 2012-01-01 00:00:00 -1.8
2 2012-01-01 01:00:00 -1.8
3 2012-01-01 02:00:00 -1.8

If you want your dataframe to be indexed by more than one column, you can give a list of columns to index_col:

In [4]:
df = pd.read_sql("SELECT * from weather_2012 LIMIT 3", con,
index_col=['id', 'date_time'])
df

Out[4]:
temp
id date_time
1 2012-01-01 00:00:00 -1.8
2 2012-01-01 01:00:00 -1.8
3 2012-01-01 02:00:00 -1.8

# 9.2 Writing to a SQLite database¶

Pandas has a write_frame function which creates a database table from a dataframe. Right now this only works for SQLite databases. Let's use it to move our 2012 weather data into SQL.

You'll notice that this function is in pd.io.sql. There are a ton of useful functions for reading and writing various kind of data in pd.io, and it's worth spending some time exploring them. (see the documentation!)

In [5]:
weather_df = pd.read_csv('../data/weather_2012.csv')
con = sqlite3.connect("../data/test_db.sqlite")
con.execute("DROP TABLE IF EXISTS weather_2012")
weather_df.to_sql("weather_2012", con)

/usr/lib64/python2.7/site-packages/pandas/io/sql.py:1309: UserWarning: The spaces in these column names will not be changed. In pandas versions < 0.14, spaces were converted to underscores.
warnings.warn(_SAFE_NAMES_WARNING)


We can now read from the weather_2012 table in test_db.sqlite, and we see that we get the same data back:

In [6]:
con = sqlite3.connect("../data/test_db.sqlite")
df = pd.read_sql("SELECT * from weather_2012 LIMIT 3", con)
df

Out[6]:
index Date/Time Temp (C) Dew Point Temp (C) Rel Hum (%) Wind Spd (km/h) Visibility (km) Stn Press (kPa) Weather
0 0 2012-01-01 00:00:00 -1.8 -3.9 86 4 8 101.24 Fog
1 1 2012-01-01 01:00:00 -1.8 -3.7 87 4 8 101.24 Fog
2 2 2012-01-01 02:00:00 -1.8 -3.4 89 7 4 101.26 Freezing Drizzle,Fog

The nice thing about having your data in a database is that you can do arbitrary SQL queries. This is cool especially if you're more familiar with SQL. Here's an example of sorting by the Weather column:

In [7]:
con = sqlite3.connect("../data/test_db.sqlite")
df = pd.read_sql("SELECT * from weather_2012 ORDER BY Weather LIMIT 3", con)
df

Out[7]:
index Date/Time Temp (C) Dew Point Temp (C) Rel Hum (%) Wind Spd (km/h) Visibility (km) Stn Press (kPa) Weather
0 67 2012-01-03 19:00:00 -16.9 -24.8 50 24 25 101.74 Clear
1 114 2012-01-05 18:00:00 -7.1 -14.4 56 11 25 100.71 Clear
2 115 2012-01-05 19:00:00 -9.2 -15.4 61 7 25 100.80 Clear

If you have a PostgreSQL database or MySQL database, reading from it works exactly the same way as reading from a SQLite database. You create a connection using psycopg2.connect() or MySQLdb.connect(), and then use

pd.read_sql("SELECT whatever from your_table", con)

# 9.3 Connecting to other kinds of database¶

To connect to a MySQL database:

Note: For these to work, you will need a working MySQL / PostgreSQL database, with the correct localhost, database name, etc.

import MySQLdb con = MySQLdb.connect(host="localhost", db="test")

To connect to a PostgreSQL database:

import psycopg2 con = psycopg2.connect(host="localhost")