The Pandas package provides a powerful, high-performance table object via the DataFrame class. Pandas has a few downsides, including its lack of support for multidimensional table columns, but Pandas is the generally-used Python tables packange, and so we will use it here as well. Pandas DataFrame functionality is very complementary to astropy Tables so astropy 1.1 and later provides interfaces for converting between astropy Tables and DataFrames. If you wish to learn more about Pandas, there are many resources available on-line. A good starting point is the main tutorials site at http://pandas.pydata.org/pandas-docs/stable/tutorials.html.

1.1 Constructing Tables

There is great deal of flexibility in the way that a table can be initially constructed:

Read an existing table from a file or web URL
Add columns of data one by one
Add rows of data one by one
From an existing data structure in memory:
- List of data columns
- Dict of data columns
- List of row dicts
- Numpy homgeneous array or structured array
- List of row records

See the documentation section on Constructing a table for the gory details and plenty of examples.



In [ ]:

    
t = Table()
t['name'] = ['larry', 'curly', 'moe', 'shemp']
t['flux'] = [1.2, 2.2, 3.1, 4.3]

1.2 Displaying Tables in Notebook

In IPython notebook, showing a table will produce a nice HTML representation of the table:



In [ ]:

    
t

If you did the same in a terminal session you get a different view that isn't as pretty but does give a bit more information about the table:

>>> t
<Table rows=4 names=('name','flux')>
array([('source 1', 1.2), ('source 2', 2.2), ('source 3', 3.1),
       ('source 4', 4.3)], 
      dtype=[('name', 'S8'), ('flux', '<f8')])

To get a plain view which is the same in notebook and terminal use print():



In [ ]:

    
print(t)



In [ ]:

    
##similar, but nicer when there are lots and lots of rows/columns
t.pprint()

To get the table column names and data types using the colnames and dtype properties:



In [ ]:

    
t.colnames



In [ ]:

    
t.dtype

Astropy 1.1 and later provides a show_in_notebook() method that allows more interactive exploration of tables. It can be especially handy for large tables.



In [ ]:

    
t.show_in_notebook()

1.3 Indexing Tables

We can access the columns and rows in a way similar to accessing discionary entries (with dict[key]), but here the syntax is table[column]. Table objects can also be indexed by row or column, and the column index can be swapped with column name.



In [ ]:

    
t['flux']  # Flux column (notice meta attributes)



In [ ]:

    
t['flux'][1]  # Row 1 of flux column



In [ ]:

    
t[1]['flux']  # equivalent!



In [ ]:

    
t[1][1]  # also equivalent. Which is the column index? Play with this to find out.



In [ ]:

    
t[1]  # one index = row number



In [ ]:

    
t[1:3]  # 2nd and 3rd rows in a new table (remember that the a:b indexing is not inclusive of b)



In [ ]:

    
t[1:3]['flux']



In [ ]:

    
t[[1, 3]] # the second and fourth rows of t in a new table

1.4 Modifying Tables

Once the table exists with defined columns there are a number of ways to modify the table in place. These are fully documented in the section Modifying a Table.

To give a couple of simple examples, you can add rows with the add_row() method or add new columns using dict-style assignment:



In [ ]:

    
t.add_row(('joe', 10.1))  # Add a new source at the end
t['logflux'] = log10(t['flux'])  # Compute the log10 of the flux
t

Notice that the logflux column really has too many output digits given the precision of the input values. We can fix this by setting the format using normal Python formatting syntax:

The format operator in python acts on an object and reformatts it according to your specifications. The syntax is alwasy object.format = '%format_string', where format_string tells it how to format the output. For now let's just deal with two of the more useful types:

Float Formatting Floats are denoted with '%A.Bf', where A is the number of total characters you want, including the decimal point, and B is the number of characters that you want after the decimal. The f tells it that you would like the output as a float. If you don't specify A, python will keep as many characters as are currently to the left of the decimal point. If you specify more characters to the left of the decimal than are there, python will usually print the extra space as blank characters. If you want it to print leading zeroes instead, use the format '%0A.Bf'. This is not the case in tables though, where white space and leading zeroes will be ignored.

Scientific Notation Formatting Sometimes in tables, we will be dealing with very large numbers. Exponential formatting is similar to float formatting in that you are formatting the float that comes before the "e" (meaning 10 to some power). Numbers in scientific notation print as X.YeNN where NN is the power of the exponent. The formatting string for floating point exponentials looks like "%A.Be" or "%A.BE", where e and E print lowwercase and capital es, respectively.

Should you need it in the future, here is a more detailed reference regarding string formatting.

Also useful is printing numbers in a given format, for which you use the syntax print('%format code'% object), as demonstrated below. Play around with the cells below to make sure you understand the subtelties here before moving on.



In [ ]:

    
t['flux'].format = '%.2f'
t['logflux'].format = '%.2f'
t



In [ ]:

    
print('%11.2f'% 100000)
print('%8.2f'% 100000)



In [ ]:

    
t['flux'].format = '%5.2e'
t['logflux'].format = '%.2E'
t



In [ ]:

    
print('%5.2e'% 0.0005862341)
print('%4.2E'% 246001)

1.5 Converting Tables to Numpy

Sometimes you may not want or be able to use a Table object and prefer to work with a plain numpy array (like if you read in data and then want to manipulate it. This is easily done by passing the table to the np.array() constructor.

This makes a copy of the data. If you have a huge table and don't want to waste memory, supply copy=False to the constructor, but be warned that changing the output numpy array will change the original table.



In [ ]:

    
array(t)



In [ ]:

    
array(t['flux'])

1.6 Masking Tables

One of the most powerful concepts in table manipulation is using boolean selection masks to select only table entries that meet certain criteria.



In [ ]:

    
mask = t['flux'] > 3.0  # Define boolean (True/False) mask for all flux values > 3
mask



In [ ]:

    
t[mask]  # Create a new table with only the "True" rows



In [ ]:

    
t2 = Table([['x', 'y', 'z'], 
            [1.1, 2.2, 3.3]],
           names=['name', 'value'],
           masked=True)
t2



In [ ]:

    
t2['value'].mask = [False, True, False]



In [ ]:

    
print(t2)



In [ ]:

    
t2['value'].fill_value = -99
print(t2.filled())

1.7 High-Level Table Operations

So far we've just worked with one table at a time and viewed that table as a monolithic entity. Astropy also supports high-level Table operations that manipulate multiple tables or view one table as a collection of sub-tables (groups).

Documentation	Description
Grouped operations	Group tables and columns by keys
Stack vertically	Concatenate input tables along rows
Stack horizontally	Concatenate input tables along columns
Join	Database-style join of two tables

Here we'll just introduce the join operation but go into more detail on the others in the exercises.



In [ ]:

    
from astropy.table import join

Now recall our original table t:



In [ ]:

    
t

Now say that we now got some additional flux values from a different reference for a different, but overlapping sample of sources:



In [ ]:

    
t2 = Table()
t2['name'] = ['larry', 'moe', 'groucho']
t2['flux2'] = [1.4, 3.5, 8.6]

Now we can get a master table of flux measurements which are joined matching the values the name column. This includes every row from each of the two tables, which is known as an outer join.



In [ ]:

    
t3 = join(t, t2, keys=['name'], join_type='outer')
print(t3)



In [ ]:

    
mean(t3['flux2'])

Alternately we could choose to keep only rows where both tables had a valid measurement using an inner join:



In [ ]:

    
join(t, t2, keys=['name'], join_type='inner')

1.8 Writing and Reading Tabular Data

You can write data using the Table.write() method:



In [ ]:

    
t3.write('test.fits', overwrite=True)



In [ ]:

    
t3.write('test.vot', format='votable', overwrite=True)

You can read data using the Table.read() method:



In [ ]:

    
t4 = Table.read('test.fits')
t4

Some formats, such as FITS and HDF5, are automatically identified by file extention while most others will require format to be explicitly provided. A number of common ascii formats are supported such as IPAC, sextractor, daophot, and CSV. Refer to the documentation for a full listing.



In [ ]:

    
Table.read?



In [ ]:

    
t_2mass = Table.read("data/2mass.tbl", format="ascii.ipac")
t_2mass.show_in_notebook()

2. Pandas

Although astropy Tables has some nice functionality that Pandas doesn't and is also a simpler, easier to use package, Pandas is the more versatile and commonly used table manipluator for Python so I recommend you use it wherever possible.

Astropy 1.1 includes new to_pandas() and from_pandas() methods that facilitate conversion to/from pandas DataFrame objects. There are a few caveats in making these conversions:

Tables with multi-dimensional columns cannot be converted.
Masked values are converted to numpy.nan. Numerical columns, int or float, are thus converted to numpy.float while string columns with missing values are converted to object columns with numpy.nan values to indicate missing or masked data. Therefore, one cannot always round-trip between Table and DataFrame.



In [ ]:

    
import pandas as pd

Data frames are defined like dictionaries with a column header/label (similar to a key) and a list of entries.



In [ ]:

    
df = pd.DataFrame({'a': [10,20,30],
                   'b': [40,50,60]})



In [ ]:

    
df

think of DataFrames as numpy arrays plus some extra pointers to their columns and the indices of the row entries that make them amenable for tables



In [ ]:

    
df.columns



In [ ]:

    
df.index



In [ ]:

    
#hit shift + tab tab in the cell below to read more about dataframe objects and operations
df.

pandas has built-in functions for reading all kinds of types of data. In the cell below, hit tab once after the r to see all of the read functions. In our case here, read_table will work fine



In [ ]:

    
pd.r

we can also convert the table that we already made with Astropy Tables to pandas dataframe format



In [ ]:

    
pd_2mass = t_2mass.to_pandas()
pd_2mass

And the opposite operation (conversion from pandas dataframe to astropy table) works as well



In [ ]:

    
t_pd = Table.from_pandas(pd_2mass)
t_pd.show_in_notebook()

Unlike astropy Tables, pandas can also read excel spreadsheets



In [ ]:

    
asteroids = pd.read_excel("data/asteroids5000.xlsx")
asteroids
#excel_data = Table.from_pandas(pd.read_excel("2mass.xls"))
#excel_data.show_in_notebook()

pandas dataframe columns can be called as python series using the syntax dataframe.columnlabel, as below, which is why it usually makes sense to define a column name/label that is short and has no spaces



In [ ]:

    
asteroids.ra

this calling method allows you to do use some useful built-in functions as well



In [ ]:

    
#this one counts how many occurrences there are in the table for each unique value
asteroids.ph_qual.value_counts()

To pull up individual rows or entries, the fact that pandas dataframes always print the indices of rows off of their lefthand side helps. You index dataframes with .loc (if using column name) or .iloc (if using column index), as below



In [ ]:

    
asteroids.loc[4,"ra"]



In [ ]:

    
asteroids.iloc[4,0] #same because column 0 is "ra"

you can always check that the column you're indexing is the one you want as below



In [ ]:

    
asteroids.columns[0]

Although indices are nice for reference, sometimes you might want the row labels to be more descriptive. What is the line below doing?



In [ ]:

    
# make the row names more interesting than numbers starting from zero
asteroids.index = ['Asteroid %d'%(i+1) for i in asteroids.index]



In [ ]:

    
#and you can index multiple columns/rows in the usual way 
asteroids.iloc[:10,:2]

You can do lots more with this as well, including logical operations to parse the table



In [ ]:

    
asteroids.columns
ast_new = asteroids[asteroids.dist < 500]
ast_new

3. Exercises

In these exercises, you will be dealing with two tables of information, described below. We'll be doing lots of manipulation of pandas dataframes in Labs 9, 11 and 13, so these exercises focus mostly on special functions of Astropy tables, but you should, wherever possible, try to figure out how to do the same thing with a Pandas dataframe.

master_sources

Each distinct X-ray source identified on the sky is represented in the catalog by a single "master source" entry and one or more "source observation" entries, one for each observation in which the source has been detected. The master source entry records the best estimates of the properties of a source, based on the data extracted from the set of observations in which the source has been detected. The subset of fields in our exercise table file are:

Name	Description
msid	Master source ID
name	Source name in the Chandra catalog
ra	Source RA (deg)
dec	Source Dec (deg)

obs_sources

The individual source entries record all of the properties about a detection extracted from a single observation, as well as associated file-based data products, which are observation-specific. The subset of fields in our exercise table file are:

Name	Description
obsid	Observation ID
obi	Observation interval
targname	Target name
gti_obs	Observation date
flux_aper_b	Broad band (0.5 - 7 keV) flux (erg/cm2/sec)
src_cnts_aper_b	Broad band source counts
ra_b	Source RA (deg)
dec_b	Source Dec (deg)
livetime	Observation duration (sec)
posid	Position ID
theta	Off-axis angle (arcmin)
msid	Master source ID

Exercise 1 - Read the data

To start with, read in the two data files representing the master source list and observations source list. The fields for the two tables are respectively documented in:

Read them in as astropy tables and then convert them to pandas. In the end you should have four table objects, two astropy tables and two pandas dataframes



In [ ]:

    
## code to read in source data here



In [ ]:

    
## code to convert to pandas dataframes here

Get a list of the column names for each table.

Hint: use <TAB> completion to easily discover all the attributes and methods, e.g. type master_sources. and then hit the <TAB> key. This will reveal some built-in methods to do things like print column names, as well as some of the other things below



In [ ]:

    
## code to print list of column names

Find and print the length of each table.



In [ ]:

    
## code to print the length of each table here

Find the column datatypes for each table (also a built-in method).



In [ ]:

    
## code to print the column datatypes

Display all the rows of the master_sources table using its pprint() method (astropy tables only).



In [ ]:

    
## code to pprint the rows of master_sources

Exercise 2 - Modifying tables

Remove the obi column from the obs_sources table.



In [ ]:

    
#code to remove column here

The gti_obs column name is a bit obscure (GTI is "good time interval", but it really just means "date"). Rename the gti_obs column to obs_date.



In [ ]:

    
# code to rename column here

The source count column tells you how many photons were collected by the detector, but it would also be nice to have a count rate (number of photons per second). Add a new column src_rate_aper_b which is the source counts divided by observation duration in sec.



In [ ]:

    
# code to create and add new column here

Exercise 3 - Visualizing Data

Use the matplotlib hist() function to make a histogram of the source flux column. Since the fluxes vary by orders of magnitude, use numpy.log10 to put the fluxes in log space.



In [ ]:

    
# code to create histogram

Let's now remove any sources that we think might not be associated with the source we pointed at ("background/foreground objects" - things that appear near the location of our source in the sky, but that aren't physically associated with it and are actually either much closer or much farther away). To remove these potentially unassociated objects, make the same plot but using only observations where the source was within 4 arcminutes of the place where the telescope was pointed. HINT: use a boolean mask to select values of theta that are less than 4.0.



In [ ]:

    
# code to mask table and create new histogram

Exercise 4 - Join the master_sources and obs_sources tables

The master_sources and obs_sources tables share a common msid column. What we now want is to join the master list of sky positions (RA and Dec columns - essentially celestial longitude and latitude) and source names with the individual observations table.

Use the table.join() function to make a single table called sources that has the master RA, Dec, and name included for each observation source.

HINT: the defaults for keys and join_type='inner' are correct in this case, so the simplest possible call to join() will work!



In [ ]:

    
# code to join tables

Is the length of the new sources the same as obs_sources? What happened? Use specific examples in your explanations.



In [ ]:

    
# code to investigate lengths

insert explanation here

Exercise 5 - Grouped properties of `sources`

When using tables, we may occasionally wish to group entries based on various properties, which is done using the group_by() functionality.

This method makes a new table in which all the sources with the same entry for some property (that property is specified in the function call) are next to one another.

Make a new table g_sources which is the sources table grouped by the msid key using the group_by() method.



In [ ]:

    
## code to group sources by the msid key and write into new table g_sources

The new g_sources table is just a regular table with all the sources in a particular order. The attribute g_sources.groups has also been created and is an object that provides access to the msid sub-groups. You can access the $i^{th}$ group with g_sources.groups[i].

In addition the g_sources.groups.indices attribute is an array with the indicies of the group boundaries.

Using np.diff() find the number of repeat observations of each master sources. HINT: use the indices, Luke.



In [ ]:

    
## code to find the number of observations for each source

Print the 50th group and note which columns are the same for all group members and which are different. Does this make sense? In these few observations how many different target names were provided by observers?



In [ ]:

    
## code to print info for the 50th group

Exercise 6 - Aggregation

The real power of grouping comes in the ability to create aggregate values for each of the groups, for instance the mean flux for each unique source. This is done with the aggregate() method, which takes a function reference as its input. This function must take as input an array of values and return a single value.

Aggregate returns a new table that has a length equal to the number of groups.

Compute the mean of all columns for each unique source (i.e. each group) using aggregate and the np.mean function. Call this table g_sources_mean.



In [ ]:

    
## code to create a new table with the group means

Notice that aggregation cannot form a mean for certain columns and these are dropped from the output. Use the join() function to restore the master_sources information to g_sources_mean.



In [ ]:

    
## code to add back in the columns from master_sources



In [1]:

    
from IPython.core.display import HTML
def css_styling():
    styles = open("../custom.css", "r").read()
    return HTML(styles)
css_styling()









    Out[1]:

Names: [Insert Your Names Here]

Lab 7 - Python Tables

Lab 7 Contents

1. Astropy Tables

Sidebar - Tables vs. Pandas DataFrames

1.1 Constructing Tables

1.2 Displaying Tables in Notebook

1.3 Indexing Tables

1.4 Modifying Tables

Sidebar - Formatting Output

1.5 Converting Tables to Numpy

1.6 Masking Tables

1.7 High-Level Table Operations

1.8 Writing and Reading Tabular Data

2. Pandas

3. Exercises

Exercise 1 - Read the data

Exercise 2 - Modifying tables

Exercise 3 - Visualizing Data

Exercise 4 - Join the master_sources and obs_sources tables

Exercise 5 - Grouped properties of `sources`

Exercise 6 - Aggregation

Names: [Insert Your Names Here]

Lab 7 - Python Tables

Lab 7 Contents

1. Astropy Tables

Sidebar - Tables vs. Pandas DataFrames

1.1 Constructing Tables

1.2 Displaying Tables in Notebook

1.3 Indexing Tables

1.4 Modifying Tables

Sidebar - Formatting Output

1.5 Converting Tables to Numpy

1.6 Masking Tables

1.7 High-Level Table Operations

1.8 Writing and Reading Tabular Data

2. Pandas

3. Exercises

Exercise 1 - Read the data

Exercise 2 - Modifying tables

Exercise 3 - Visualizing Data

Exercise 4 - Join the master_sources and obs_sources tables

Exercise 5 - Grouped properties of sources

Exercise 6 - Aggregation

Exercise 5 - Grouped properties of `sources`