Pandas Primer

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Introduction to Pandas¶

Pandas works hand-in-hand with other python libraries (e.g. matplotlib and numpy) to make manipulating data (what the Pandas team calls "Data Munging") easy. With pandas it is easy to

1. Easily access data using variable names, but have full linear algebra capabilities of numpy
2. Group data by values (for example, calculate the mean income by state)
3. Plot and summarize values
4. Join (combine) different sources of data
5. Powerful time series and panel data capabilities (beyond scope of course)

Note: If you want to reinforce some of these concepts on your own, I recommend this superb youtube video (https://www.youtube.com/watch?v=5JnMutdy6Fw) and accompanying coursework (https://github.com/brandon-rhodes/pycon-pandas-tutorial) by Brandon Rhodes.

There are numerous ways to get data into Pandas:

• Import excel and comma delimited data
• Import stata, sas, matlab, and other datasets
• Import data from an SQL server
• Import data scraped from the web
• Manually building (by typing in values)

In this tutorial we will focus on the first two methods for reading data, but just know that there is probably a way to get your data into pandas irrespective of what format it is in.

Loading and Cleaning Data¶

We will be loading 2010 "Trip" data from the NOAA Fisheries Service Recreational Fisheries Statistics Survey (called MRIP). More detail here: https://www.fisheries.noaa.gov/topic/recreational-fishing-data

We will also load the Census Bureau's county and state fips file, obtained here: https://www.census.gov/2010census/xls/fips_codes_website.xls

The MRIPs data needs to be downloaded from my website (you only need to do this once).

Trips is a very big dataset with lots of columns we'll never use. Let's trim it down:

This is summary statistics for numeric data columns:

Using Pandas DataFrames¶

Referencing columns in data:¶

Referencing rows in data:¶

We can use numpy-like slicing:

We can select rows of data based on column values. Let's select all the rows where st is 9 (Connecticut)

Find all rows where days spent fishing during past 12 months exceeds 10:

Math on columns:¶

Note: You can combine pandas with numpy. This is the standard deviation of the column ffdays12:

We can perform matrix/vector operations on pandas data.

Here, we can transpose a slice of the data:

We can do matrix multiplication:

Note: the remainder of this notebook is not particularly relevant for Bayesian Econometrics¶

While we won't use it in our course, you may find the following very useful for data munging.

Summary Statistics¶

Find mean of ffdays12 and ffdays2

Find mean of ffdays12 and ffdays2 by state:

What happened? We trimmed down the columns of t with the statement t[['ffdays12','ffdays2']] and pandas couldn't find the columns st. Let's try this again:

Combining DataFrames¶

The state fips codes are hardly intuitive and even worse for the county fips codes. The goal of this section is to

1. Add text-based labels
2. Be able to use these labels for fast data selection

To do this, we will attempt to match text based state and county labels to our trips dataframe. Pandas calls this a merge.

The fips codes (numeric) uniquely identify counties in our trips file. So let's look at the fips file (that we grabbed from Census) above and combine it with our trips data. These column names are messy: let's clean it up.

For me, column names should always:

1. Be lower case
2. Have no spaces or funky characters

The merge statement takes two pandas dataframes and combines them. Notice in the merge command below we have trips,fips. The dataframe trips is known as the "left" dataframe and fips, the "right". I want to combine these columns based on the numeric fips values (state and county). These fields have different names in each of the dataframes, so I need to specify the names (in the same order) for the left (trips) and right (fips) dataframes. The option how='left' tells pandas to always keep rows from trips whether a match is made with fips or not. Note that if a row in the fips table doesn't match our trips data, it is discarded.

The result we get is disconcerting. What we wanted to achieve with the merge was to add text names to the trips table, and our merged dataset has 1.5 million rows rather than 104,709 (what we want). This probably occured because there were duplicate (or redundant information on fips codes in the fips table). To check that use the duplicate method to find rows with duplicate state and county fips codes.

Unfortunately, this file is (apparently) for census workers in the field who may encounter lots of different place names, and this file helps their data-entry program map into the correct state and county fips codes. This isn't ideal for our purposes. Since our trips table already has the correct numeric fips codes, we need a fips table that has the correct and unique county/city names.

Grab another fips table from here: https://www.census.gov/geo/reference/codes/cou.html and choose United States.

For convenience, I'll save to my website.

Having worked with the data before, I know that counties/entities like Virginia Beach City are labeled "Virginia Beach city". Let's fix that:

Check for duplicates:

Let's rename our state and intercept column to make it more intuitive.

Working with Indices¶

Let's create an index on these new state and county fields:

Why use an index for these state and county codes? The biggest reason is that we can easily access values for looking at our data. Here we have 2 month avidity for anglers sampled in James City County:

Ok, indexes are pretty cool, but what if we need the county data for doing summary statistics? Example, suppose we want to get a frequency count of trips by county in VA targeting Striped Bass? Notice, we can't just access index values like you would a column of data:

So the strategy will be to copy the index values back into our table so we can use it for other purposes:

(Note: you can also keep these columns in your data by including the option drop=False when you set_index())

This allows us to use index lookups and also the index values for calculating interesting summary data. The following calculates the top 10 targeted species in VA:

This calculates the top targeted species in Gloucester County, VA.

What about top 10 counties in VA with respect to numbers of intercepted trips?

This tells us when summer flounder is being targeted in VA:

Note we used the data/time field intercept_date to extract the month using the dt.month method. Pandas has immensely rich datetime capabilities that we won't demonstrate here.

Groupby¶

We can compute interesting data by grouping over column values and using our index for fast selection. Suppose we want to know total number of trips in VA by county of intercept for each wave (wave 1 = Jan,Feb; 2 = Mar,Apr; etc). Groupby lets us do this:

Stack and Unstack¶

We can manipulate the result above further to make it more amenable for comparisons and graphing (e.g. we want all of the wave 2 records on the same row):

Or we could put waves on the same row:

We can combine these with plot to quickly make cool charts. Let's focus on the peninsula:

We can also stack data we have unstacked:

Pivot Table¶

Borrowing from excel, pandas can create pivot tables of summary statistics. Suppose we want counties as rows and waves as columns, with the average and standard deviation of 2 and 12 month avidity.

Exporting Data from Pandas¶

You may want to export dataframes and other information to other packages (e.g. latex, excel, or stata). Pandas has a lot of to_ methods on pandas dataframes and series. For example we can export some interesting information to latex table code:

Econometrics in Python¶