# Plotting and Tabular Data

## Overview

### Questions

• How do we make scatter plots in Matplotlib?
• How do we store data in a Pandas DataFrame?

### Objectives

• Select rows and columns from an Astropy Table.
• Use Matplotlib to make a scatter plot.
• Use Gala to transform coordinates.
• Make a Pandas DataFrame and use a Boolean Series to select rows.
• Save a DataFrame in an HDF5 file.

In the previous episode, we wrote a query to select stars from the region of the sky where we expect GD-1 to be, and saved the results in a FITS file.

Now we will read that data back in and implement the next step in the analysis, identifying stars with the proper motion we expect for GD-1.

### Outline

1. We will read back the results from the previous lesson, which we saved in a FITS file.

2. Then we will transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.

3. We will put those results into a Pandas DataFrame.

### Starting from this episode

If you are starting a new notebook for this episode, expand this section for information you will need to get started.

In the previous episode, we ran a query on the Gaia server, downloaded data for roughly 140,000 stars, and saved the data in a FITS file. We will use that data for this episode. Whether you are working from a new notebook or coming back from a checkpoint, reloading the data will save you from having to run the query again.

If you are starting this episode here or starting this episode in a new notebook, you will need to run the following lines of code.

This imports previously imported functions:

### PYTHON

import astropy.units as u
from astropy.coordinates import SkyCoord
from gala.coordinates import GD1Koposov10
from astropy.table import Table

from episode_functions import *

The following code loads in the data (instructions for downloading data can be found in the setup instructions). You may need to add a the path to the filename variable below (e.g. filename = 'student_download/backup-data/gd1_results.fits')

### PYTHON

filename = 'gd1_results.fits'

gd1_frame = GD1Koposov10()

## Selecting rows and columns

In the previous episode, we selected spatial and proper motion information from the Gaia catalog for stars around a small part of GD-1. The output was returned as an Astropy Table. We can use info to check the contents.

### PYTHON

polygon_results.info()

### OUTPUT

<Table length=140339>
name    dtype    unit                              description
--------- ------- -------- ------------------------------------------------------------------
source_id   int64          Unique source identifier (unique within a particular Data Release)
ra float64      deg                                                    Right ascension
dec float64      deg                                                        Declination
pmra float64 mas / yr                         Proper motion in right ascension direction
pmdec float64 mas / yr                             Proper motion in declination direction
parallax float64      mas                                                           Parallax

In this episode, we will see operations for selecting columns and rows from an Astropy Table. You can find more information about these operations in the Astropy documentation.

We can get the names of the columns like this:

### PYTHON

polygon_results.colnames

### OUTPUT

['source_id', 'ra', 'dec', 'pmra', 'pmdec', 'parallax']

And select an individual column like this:

### PYTHON

polygon_results['ra']

### OUTPUT

<Column name='ra' dtype='float64' unit='deg' description='Right ascension' length=140339>
142.48301935991023
142.25452941346344
142.64528557468074
142.57739430926034
142.58913564478618
141.81762228999614
143.18339801317677
142.9347319464589
142.26769745823267
142.89551292869012
[Output truncated]

The result is a Column object that contains the data, and also the data type, units, and name of the column.

### PYTHON

type(polygon_results['ra'])

### OUTPUT

astropy.table.column.Column

The rows in the Table are numbered from 0 to n-1, where n is the number of rows. We can select the first row like this:

### PYTHON

polygon_results[0]

### OUTPUT

<Row index=0>
source_id              ra                dec                pmra              pmdec             parallax
deg                deg              mas / yr           mas / yr             mas
int64             float64            float64            float64            float64            float64
------------------ ------------------ ----------------- ------------------- ----------------- -------------------
637987125186749568 142.48301935991023 21.75771616932985 -2.5168384683875766 2.941813096629439 -0.2573448962333354

The result is a Row object.

### PYTHON

type(polygon_results[0])

### OUTPUT

astropy.table.row.Row

Notice that the bracket operator can be used to select both columns and rows. You might wonder how it knows which to select. If the expression in brackets is a string, it selects a column; if the expression is an integer, it selects a row.

If you apply the bracket operator twice, you can select a column and then an element from the column.

### PYTHON

polygon_results['ra'][0]

### OUTPUT

142.48301935991023

Or you can select a row and then an element from the row.

### PYTHON

polygon_results[0]['ra']

### OUTPUT

142.48301935991023

You get the same result either way.

## Scatter plot

To see what the results look like, we will use a scatter plot. The library we will use is Matplotlib, which is the most widely-used plotting library for Python. The Matplotlib interface is based on MATLAB (hence the name), so if you know MATLAB, some of it will be familiar.

We will import like this:

### PYTHON

import matplotlib.pyplot as plt

Pyplot is part of the Matplotlib library. It is conventional to import it using the shortened name plt.

### Keeping plots in the notebook

In recent versions of Jupyter, plots appear “inline”; that is, they are part of the notebook. In some older versions, plots appear in a new window. If your plots appear in a new window, you might want to run the following Jupyter magic command in a notebook cell:

### PYTHON

%matplotlib inline

Pyplot provides two functions that can make scatter plots, plt.scatter and plt.plot.

• scatter is more versatile; for example, you can make every point in a scatter plot a different color.

• plot is more limited, but for simple cases, it can be substantially faster.

Jake Vanderplas explains these differences in The Python Data Science Handbook.

Since we are plotting more than 100,000 points and they are all the same size and color, we will use plot.

Here is a scatter plot of the stars we selected in the GD-1 region with right ascension on the x-axis and declination on the y-axis, both ICRS coordinates in degrees.

### PYTHON

x = polygon_results['ra']
y = polygon_results['dec']
plt.plot(x, y, 'ko')

plt.xlabel('ra (degree ICRS)')
plt.ylabel('dec (degree ICRS)')

### OUTPUT

<Figure size 432x288 with 1 Axes>

The arguments to plt.plot are x, y, and a string that specifies the style. In this case, the letters ko indicate that we want a black, round marker (k is for black because b is for blue). The functions xlabel and ylabel put labels on the axes.

Looking at this plot, we can see that the region we selected, which is a rectangle in GD-1 coordinates, is a non-rectanglar region in ICRS coordinates.

However, this scatter plot has a problem. It is “overplotted”, which means that there are so many overlapping points, we cannot distinguish between high and low density areas.

To fix this, we can provide optional arguments to control the size and transparency of the points.

### Exercise (5 minutes)

In the call to plt.plot, use the keyword argument markersize to make the markers smaller.

Then add the keyword argument alpha to make the markers partly transparent.

Adjust these arguments until you think the figure shows the data most clearly.

Note: Once you have made these changes, you might notice that the figure shows stripes with lower density of stars. These stripes are caused by the way Gaia scans the sky, which you can read about here. The dataset we are using, Gaia Data Release 2, covers 22 months of observations; during this time, some parts of the sky were scanned more than others.

### PYTHON

x = polygon_results['ra']
y = polygon_results['dec']
plt.plot(x, y, 'ko', markersize=0.1, alpha=0.1)

plt.xlabel('ra (degree ICRS)')
plt.ylabel('dec (degree ICRS)')

## Transform back

Remember that we selected data from a rectangle of coordinates in the GD-1 frame, then transformed them to ICRS when we constructed the query. The coordinates in the query results are in ICRS.

To plot them, we will transform them back to the GD-1 frame; that way, the axes of the figure are aligned with the orbit of GD-1, which is useful for two reasons:

• By transforming the coordinates, we can identify stars that are likely to be in GD-1 by selecting stars near the centerline of the stream, where φ2 is close to 0.

• By transforming the proper motions, we can identify stars with non-zero proper motion along the φ1 axis, which are likely to be part of GD-1.

To do the transformation, we will put the results into a SkyCoord object. In a previous episode, we created a SkyCoord object like this:

### PYTHON

skycoord = SkyCoord(ra=polygon_results['ra'], dec=polygon_results['dec'])

Notice that we did not specify the reference frame. That is because when using ra and dec in SkyCoord, the ICRS frame is assumed by default.

The SkyCoord object can keep track not just of location, but also proper motions. This means that we can initialize a SkyCoord object with location and proper motions, then use all of these quantities together to transform into the GD-1 frame.

Now we are going to do something similar, but now we will take advantage of the SkyCoord object’s capacity to include and track space motion information in addition to ra and dec. We will now also include:

• pmra and pmdec, which are proper motion in the ICRS frame, and

• distance and radial_velocity, which are important for the reflex correction and will be discussed in that section.

### PYTHON

distance = 8 * u.kpc

skycoord = SkyCoord(ra=polygon_results['ra'],
dec=polygon_results['dec'],
pm_ra_cosdec=polygon_results['pmra'],
pm_dec=polygon_results['pmdec'],
distance=distance,
radial_velocity=radial_velocity)

For the first four arguments, we use columns from polygon_results.

For distance and radial_velocity we use constants, which we explain in the section on reflex correction.

The result is an Astropy SkyCoord object, which we can transform to the GD-1 frame.

### PYTHON

transformed = skycoord.transform_to(gd1_frame)

The result is another SkyCoord object, now in the GD-1 frame.

## Reflex Correction

The next step is to correct the proper motion measurements for the effect of the motion of our solar system around the Galactic center.

When we created skycoord, we provided constant values for distance and radial_velocity rather than measurements from Gaia.

That might seem like a strange thing to do, but here is the motivation:

• Because the stars in GD-1 are so far away, parallaxes measured by Gaia are negligible, making the distance estimates unreliable.
So we replace them with our current best estimate of the mean distance to GD-1, about 8 kpc. See Koposov, Rix, and Hogg, 2010.

• For the other stars in the table, this distance estimate will be inaccurate, so reflex correction will not be correct. But that should have only a small effect on our ability to identify stars with the proper motion we expect for GD-1.

• The measurement of radial velocity has no effect on the correction for proper motion, but we have to provide a value to avoid errors in the reflex correction calculation. So we provide 0 as an arbitrary place-keeper.

With this preparation, we can use reflex_correct from Gala (documentation here) to correct for the motion of the solar system.

### PYTHON

from gala.coordinates import reflex_correct

skycoord_gd1 = reflex_correct(transformed)

The result is a SkyCoord object that contains

• phi1 and phi2, which represent the transformed coordinates in the GD-1 frame.

• pm_phi1_cosphi2 and pm_phi2, which represent the transformed proper motions that have been corrected for the motion of the solar system around the Galactic center.

We can select the coordinates and plot them like this:

### PYTHON

x = skycoord_gd1.phi1
y = skycoord_gd1.phi2
plt.plot(x, y, 'ko', markersize=0.1, alpha=0.1)

plt.xlabel('phi1 (degree GD1)')
plt.ylabel('phi2 (degree GD1)')

### OUTPUT

<Figure size 432x288 with 1 Axes>

We started with a rectangle in the GD-1 frame. When transformed to the ICRS frame, it is a non-rectangular region. Now, transformed back to the GD-1 frame, it is a rectangle again.

## Pandas DataFrame

At this point we have two objects containing different sets of the data relating to identifying stars in GD-1. polygon_results is the Astropy Table we downloaded from Gaia.

### PYTHON

type(polygon_results)

### OUTPUT

astropy.table.table.Table

And skycoord_gd1 is a SkyCoord object that contains the transformed coordinates and proper motions.

### PYTHON

type(skycoord_gd1)

### OUTPUT

astropy.coordinates.sky_coordinate.SkyCoord

On one hand, this division of labor makes sense because each object provides different capabilities. But working with multiple object types can be awkward. It will be more convenient to choose one object and get all of the data into it.

Now we can extract the columns we want from skycoord_gd1 and add them as columns in the Astropy Table polygon_results. phi1 and phi2 contain the transformed coordinates.

### PYTHON

polygon_results['phi1'] = skycoord_gd1.phi1
polygon_results['phi2'] = skycoord_gd1.phi2
polygon_results.info()

### OUTPUT

<Table length=140339>
name    dtype    unit                              description                                class
--------- ------- -------- ------------------------------------------------------------------ ------------
source_id   int64          Unique source identifier (unique within a particular Data Release) MaskedColumn
ra float64      deg                                                    Right ascension MaskedColumn
pmra float64 mas / yr                         Proper motion in right ascension direction MaskedColumn
pmdec float64 mas / yr                             Proper motion in declination direction MaskedColumn
phi1 float64      deg                                                                          Column
phi2 float64      deg                                                                          Column

pm_phi1_cosphi2 and pm_phi2 contain the components of proper motion in the transformed frame.

### PYTHON

polygon_results['pm_phi1'] = skycoord_gd1.pm_phi1_cosphi2
polygon_results['pm_phi2'] = skycoord_gd1.pm_phi2
polygon_results.info()

### OUTPUT

<Table length=140339>
name    dtype    unit                              description                                class
--------- ------- -------- ------------------------------------------------------------------ ------------
source_id   int64          Unique source identifier (unique within a particular Data Release) MaskedColumn
ra float64      deg                                                    Right ascension MaskedColumn
pmra float64 mas / yr                         Proper motion in right ascension direction MaskedColumn
pmdec float64 mas / yr                             Proper motion in declination direction MaskedColumn
phi1 float64      deg                                                                          Column
phi2 float64      deg                                                                          Column
pm_phi1 float64 mas / yr                                                                          Column
pm_phi2 float64 mas / yr                                                                          Column

### Callout

Detail If you notice that SkyCoord has an attribute called proper_motion, you might wonder why we are not using it.

We could have: proper_motion contains the same data as pm_phi1_cosphi2 and pm_phi2, but in a different format.

### Pandas DataFrames versus Astropy Tables

Two common choices are the Pandas DataFrame and Astropy Table. Pandas DataFrames and Astropy Tables share many of the same characteristics and most of the manipulations that we do can be done with either. As you become more familiar with each, you will develop a sense of which one you prefer for different tasks. For instance you may choose to use Astropy Tables to read in data, especially astronomy specific data formats, but Pandas DataFrames to inspect the data. Fortunately, Astropy makes it easy to convert between the two data types. We will choose to use Pandas DataFrame, for two reasons:

1. It provides capabilities that are (almost) a superset of the other data structures, so it is the all-in-one solution.

2. Pandas is a general-purpose tool that is useful in many domains, especially data science. If you are going to develop expertise in one tool, Pandas is a good choice.

However, compared to an Astropy Table, Pandas has one big drawback: it does not keep the metadata associated with the table, including the units for the columns. Nevertheless, we think its a useful data type to be familiar with.

It is straightforward to convert an Astropy Table to a Pandas DataFrame.

### PYTHON

import pandas as pd

results_df = polygon_results.to_pandas()

DataFrame provides shape, which shows the number of rows and columns.

### PYTHON

results_df.shape

### OUTPUT

(140339, 10)

It also provides head, which displays the first few rows. head is useful for spot-checking large results as you go along.

### PYTHON

results_df.head()

### OUTPUT

            source_id          ra        dec       pmra       pmdec   parallax        phi1       phi2    pm_phi1     pm_phi2
0  637987125186749568  142.483019  21.757716  -2.516838    2.941813  -0.257345  -54.975623  -3.659349   6.429945    6.518157
1  638285195917112960  142.254529  22.476168   2.662702  -12.165984   0.422728  -54.498247  -3.081524  -3.168637   -6.206795
2  638073505568978688  142.645286  22.166932  18.306747   -7.950660   0.103640  -54.551634  -3.554229   9.129447  -16.819570
3  638086386175786752  142.577394  22.227920   0.987786   -2.584105  -0.857327  -54.536457  -3.467966   3.837120    0.526461
4  638049655615392384  142.589136  22.110783   0.244439   -4.941079   0.099625  -54.627448  -3.542738   1.466103   -0.185292


### Attributes vs functions

shape is an attribute, so we display its value without calling it as a function.

head is a function, so we need the parentheses.

Before we go any further, we will take all of the steps that we have done and consolidate them into a single function that we can use to take the coordinates and proper motion that we get as an Astropy Table from our Gaia query, add columns representing the reflex corrected GD-1 coordinates and proper motions, and transform it into a Pandas DataFrame. This is a general function that we will use multiple times as we build different queries so we want to write it once and then call the function rather than having to copy and paste the code over and over again.

### PYTHON

def make_dataframe(table):
"""Transform coordinates from ICRS to GD-1 frame.

table: Astropy Table

returns: Pandas DataFrame
"""
#Create a SkyCoord object with the coordinates and proper motions
# in the input table
skycoord = SkyCoord(
ra=table['ra'],
dec=table['dec'],
pm_ra_cosdec=table['pmra'],
pm_dec=table['pmdec'],
distance=8*u.kpc,

# Define the GD-1 reference frame
gd1_frame = GD1Koposov10()

# Transform input coordinates to the GD-1 reference frame
transformed = skycoord.transform_to(gd1_frame)

# Correct GD-1 coordinates for solar system motion around galactic center
skycoord_gd1 = reflex_correct(transformed)

#Add GD-1 reference frame columns for coordinates and proper motions
table['phi1'] = skycoord_gd1.phi1
table['phi2'] = skycoord_gd1.phi2
table['pm_phi1'] = skycoord_gd1.pm_phi1_cosphi2
table['pm_phi2'] = skycoord_gd1.pm_phi2

# Create DataFrame
df = table.to_pandas()

return df

Here is how we use the function:

### PYTHON

results_df = make_dataframe(polygon_results)

## Saving the DataFrame

At this point we have run a successful query and combined the results into a single DataFrame. This is a good time to save the data.

To save a Pandas DataFrame, one option is to convert it to an Astropy Table, like this:

### PYTHON

from astropy.table import Table

results_table = Table.from_pandas(results_df)
type(results_table)

### OUTPUT

astropy.table.table.Table

Then we could write the Table to a FITS file, as we did in the previous lesson.

But, like Astropy, Pandas provides functions to write DataFrames in other formats; to see what they are find the functions here that begin with to_.

One of the best options is HDF5, which is Version 5 of Hierarchical Data Format.

HDF5 is a binary format, so files are small and fast to read and write (like FITS, but unlike XML).

An HDF5 file is similar to an SQL database in the sense that it can contain more than one table, although in HDF5 vocabulary, a table is called a Dataset. (Multi-extension FITS files can also contain more than one table.)

And HDF5 stores the metadata associated with the table, including column names, row labels, and data types (like FITS).

Finally, HDF5 is a cross-language standard, so if you write an HDF5 file with Pandas, you can read it back with many other software tools (more than FITS).

We can write a Pandas DataFrame to an HDF5 file like this:

### PYTHON

filename = 'gd1_data.hdf'

results_df.to_hdf(filename, 'results_df', mode='w')

Because an HDF5 file can contain more than one Dataset, we have to provide a name, or “key”, that identifies the Dataset in the file.

We could use any string as the key, but it is generally a good practice to use a descriptive name (just like your DataFrame variable name) so we will give the Dataset in the file the same name (key) as the DataFrame.

By default, writing a DataFrame appends a new dataset to an existing HDF5 file. We will use the argument mode='w' to overwrite the file if it already exists rather than append another dataset to it.

## Summary

In this episode, we re-loaded the Gaia data we saved from a previous query.

We transformed the coordinates and proper motion from ICRS to a frame aligned with the orbit of GD-1, stored the results in a Pandas DataFrame, and visualized them.

We combined all of these steps into a single function that we can reuse in the future to go straight from the output of a query with object coordinates in the ICRS reference frame directly to a Pandas DataFrame that includes object coordinates in the GD-1 reference frame.

We saved our results to an HDF5 file which we can use to restart the analysis from this stage or verify our results at some future time.

### Key Points

• When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.
• For simple scatter plots in Matplotlib, plot is faster than scatter.
• An Astropy Table and a Pandas DataFrame are similar in many ways and they provide many of the same functions. They have pros and cons, but for many projects, either one would be a reasonable choice.
• To store data from a Pandas DataFrame, a good option is an HDF5 file, which can contain multiple Datasets (we’ll dig in more in the Join lesson).