# Translating SQL to Pandas, Part 1

I wrote a three part pandas tutorial for SQL users that you can find here.

UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.

For some reason, I've always found SQL to a much more intuitive tool for exploring a tabular dataset than I have other languages (namely R and Python).

If you know SQL well, you can do a whole lot with it, and since data is often in a relational database anyway, it usually makes sense to stick with it. I find that my workflow often includes writing a lot of queries in SQL (using Sequel Pro) to get the data the way I want it, reading it into R (with RStudio), and then maybe a bit more exploration, modeling, and visualization (with ggplot2).

Not too long ago though, I came across Wes McKinney's pandas package and my interest was immediately piqued. Pandas adds a bunch of functionality to Python, but most importantly, it allows for a DataFrame data structure - much like a database table or R's data frame.

Given the great things I've been reading about pandas lately, I wanted to make a conscious effort to play around with it. Instead of my typical workflow being a couple disjointed steps with SQL + R + (sometimes) Python, my thought is that it might make sense to have pandas work its way in and take over the R work. While I probably won't be able to completely give up R (too much ggplot2 love over here), I get bored if I'm not learning something new, so pandas it is.

I intend to document the process a bit - hopefully a couple posts illustrating the differences between SQL and pandas (and maybe some R too).

Throughout the rest of this post, we're going to be working with data from the City of Chicago's open data - specifically the Towed Vechicles data.

##### Using SQLite

To be able to use SQL with this dataset, we'd first have to create the table. Using SQLite syntax, we'd run the following:

CREATE TABLE towed (
tow_date text,
make text,
style text,
model text,
color text,
plate text,
state text,
phone text,
inventory text
);


Because SQLite uses a very generic type system, we don't get the strict data types that we would in most other databases (such as MySQL and PostgreSQL); therefore, all of our data is going to be stored as text. In other databases, we'd store tow_date as a date or datetime field.

Before we read the data into SQLite, we need to tell the database to that the fields are separated by a comma. Then we can use the import command to read the file into our table.

.separator ','
.import ./Towed_Vehicles.csv towed


Note that the downloaded CSV contains two header rows, so we'll need to delete those from our table since we don't need them.

DELETE FROM towed WHERE tow_date = 'Tow Date';


We should have 5,068 records in our table now (note: the City of Chicago regularly updates this dataset, so you might get a different number).

SELECT COUNT(*) FROM towed; -- 5068

##### Using Python + pandas

Let do the same with pandas now.

import pandas as pd

col_names = ["tow_date", "make", "style", "model", "color", "plate", "state",
skiprows=2, parse_dates=["tow_date"])


The read_csv function in pandas actually allowed us to skip the two header columns and translate the tow_date field to a datetime field.

Let's check our count just to make sure.

len(towed) # 5068


#### Selecting data

##### SQL

Selection data with SQL is fairly intuitive - just SELECT the columns you want FROM the particular table you're interested in. You can also take advantage of the LIMIT clause to only see a subset of your data.

-- Return every column for every record in the towed table
SELECT * FROM towed;

-- Return the tow_date, make, style, model, and color for every record in the towed table
SELECT tow_date, make, style, model, color FROM towed;

-- Return every column for the first five records of the towed table
SELECT * FROM towed LIMIT 5;

-- Return every column in the towed table - start at the fifth record and show the next ten
SELECT * FROM towed LIMIT 5, 10; -- records 5-14


Additionally, you can throw a WHERE or ORDER BY (or both) into your queries for proper filtering and ordering of the data returned:

SELECT * FROM towed WHERE state = 'TX'; -- Only towed vehicles from Texas

SELECT * FROM towed WHERE make = 'KIA' AND state = 'TX'; -- KIAs with Texas plates

SELECT * FROM towed WHERE make = 'KIA' ORDER BY color; -- All KIAs ordered by color (A to Z)

##### Python + pandas

Let's do some of the same, but this time let's use pandas:

# show only the make column for all records
towed["make"]

# tow_date, make, style, model, and color for the first ten records
towed[["tow_date", "make", "style", "model", "color"]][:10]

towed[:5] # first five rows (alternatively, you could use towed.head())


Because pandas is built on top of NumPy, we're able to use boolean indexing. Since we're going to replicate similar statements to the ones we did in SQL, we know we're going to need towed cars from TX made by KIA.

towed[towed["state"] == "TX"] # all columns and records where the car was from TX

towed[(towed["state"] == "TX") & (towed["make"] == "KIA")] # made by KIA AND from TX

towed[(towed["state"] == "MA") | (towed["make"] == "JAGU")] # made by Jaguar OR from MA

towed[towed["make"] == "KIA"].sort("color") # made by KIA, ordered by color (A to Z)

##### Conclusion, Part 1

This was obviously a very basic start, but there are a lot of good things about pandas - it's certainly concise and readable. Plus, since it works well with the various science + math packages (SciPy, NumPy, Matplotlib, statsmodels, etc.), there's the potential to work almost entirely in one language for analysis tasks.

I plan on covering aggregate functions, pivots, and maybe some matplotlib in my next post.