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|
| Table of Contents | Index |
"TR1 roughly doubles the size of the C++ standard library, and it introduces many new facilities and even new kinds of library components. TR1 has some classes, for example, where some nested types may or may not exist depending on the template arguments. To programmers whose experience stops with the standard library, this is strange and unfamiliar. This book is complete (it covers all TR1 facilities), it is easier to understand than TR1 itself, and it is technically accurate." --Matthew Austern,software engineer, Google "TR1 will help make the C++ programmer more productive than ever. In this book, Pete Becker has written the ultimate reference guide to these components, what they are, how they work, and what they're used for. This book should be on the bookshelf of anyone who wants to use these standardized components to improve both their productivity as well as their coding quality." --John Maddock, consultant and programmer The current C++ standard library extends the core C++ language with common classes and functions. In recent years, to address limitations in that library, a number of components have been developed to extend the language even further. Compiled in a comprehensive technical report (TR1), the bulk of these extensions have been approved for the next revision of the C++ standard. In this book, Pete Becker describes in detail each component in the TR1 library, explaining new facilities for utilities, containers, call wrappers, type traits, numerics, regular expressions, and C compatibility. He draws on his own experience implementing these components to illustrate their value, clarifying the specifications when necessary and providing complete, tested code examples. Most chapters include exercises of various degrees of difficulty to help programmers get hands-on practice with the new components. Answers to the exercises, along with all code examples, are available on the Web. Appendixes comprise a summary of headers included in or extended by the TR1 library, as well as guidelines on how to use the components safely in multithreaded applications. The C++ Standard Library Extensions is for any programmer who wants to get a jump on the revised standard. It also makes the perfect companion to The C++ Standard Library, by Nicolai Josuttis, both books being tutorials and references essential for using C++ more effectively. |


Relational Data
These notes provide a very brief introduction to the relational data and databases. Chances are, as a data scientist, if you’re going to be processing data stored in any more structured form than simple flat files, it’s going to be in some kind of database. And although schemaless/NoSQL/non-relational databases are popular for some applications, in a large number of cases you’re going to be dealing with data in a standard relational database (and this format honestly makes sense for the vast majority of use cases that require a database).
While obviously databases are a topic that can’t be done any kind of justice in one lecture, these notes will focus on some of the basic ideas of relational databases, and ideally will give you some hints about how to efficiently get data out of a relational database. Creating and managing such a database, let alone actually coding one, are not topics we’ll consider here.
Overview of relational data
The basic unit in any relational data is the notion of a “relation”, but since no one uses that term anymore, just note that this actually is interchangeable with the common definition we have of “table” (though a relation has the additional constraints the rows in the table cannot be duplicates). To make this concerete, let’s consider a “Person” table for people involved with this class.
Person
| ID | Last Name | First Name | Role |
|---|---|---|---|
| 1 | Kolter | Zico | Instructor |
| 2 | Xi | Edgar | TA |
| 3 | Lee | Mark | TA |
| 4 | Mani | Shouvik | TA |
| 5 | Gates | Bill | Student |
| 6 | Musk | Elon | Student |
This kind of data representation is so familiar to us that it hardly necessitates any explanation, but a few points of terminology in relational database speak are useful. In our context, rows are called tuples or records, and they represent a single instance of this relation; as mentioned above, the entirety of the row must also be unique (i.e., there cannot be two rows that have identical entries in all columns). This uniqueness requirement can be trivially satisfied by having a unique ID identifier in each tuple, and we’ll discuss this more below. Columns in the relation are called attributes, and specify some feature contained in each of the tuples.
Multiple relations
Of course, a single table isn’t particular interesting as a rich data source; we could have done (and in fact do) store the exact same information in a CSV file. Where relational data becomes interesting is when we have multiple tables and explicit relationships between them. For example, considering the case above, the “Role” attribute in our Person table is not particularly well defined. What is the data type of this attribute? A string? If so, we could enter anything we want here, which would not be ideal for maintaining a large collection of data, say if each student has to remember to type their role exactly as “Student” (with capitalization intact, of course). A better alternative is to create a separate “Role” table, that lists the allowable roles for the course:
Role
| ID | Name |
|---|---|
| 1 | Instructor |
| 2 | TA |
| 3 | Student |
Using this table, we can replace the “Role” attribute in our Person table with a “Role ID” attribute that points to the ID of the respective role for each person:
Person
| ID | Last Name | First Name | Role ID |
|---|---|---|---|
| 1 | Kolter | Zico | 1 |
| 2 | Xi | Edgar | 2 |
| 3 | Lee | Mark | 2 |
| 4 | Mani | Shouvik | 2 |
| 5 | Gates | Bill | 3 |
| 6 | Musk | Elon | 3 |
Primary and foreign keys
Of course, we haven’t really gained that much benfit here, since it’s still possible for the “Role ID” attribute to contain some number, e.g. 4, that doesn’t have a corresponding entry in the Role table. This brings us to the concepts of keys and constraints.
In the above examples, “ID” attribute serves as what is called a primary key. A primary key is a unique identifier for each row in the table. It is common to have a single column (like we do here as the “ID” column) serve as the primary key, but that is not required; the primary key can consist of multiple columns so long as they are unique in every row. Every relation (table) in the database must have exactly one primary key.
A foreign key is an attribute that “points” to the primary key of another table. Thus, in the above example, the “Role ID” attribute in the Person table is a foreign key, pointing to the primary key of the Role table. And the foreign key constraint enforces the fact that the foreign key must point to a valid primary key in the relevant table. Note, of course, that we could have simply had a single-row Role table with the name itself being the primary key, and had a similar foreign key constraint, but using the ID as a primary key is much more typical.
The foreign key constraint help enforce consistency of the database, and also forces us to be careful when we delete elements. For example, if we delete a row from Role, we must also delete all the rows from Person that point to that primary key, or the foreign key constraint would be violated.
Indexes (not indices)
Finally, the last “basic” element we’ll consider in a relational database is the notion of an index. Indexes are created to “quickly” look up rows by some of their attributes. For example, suppose we wanted to find all the people in our Person table with the last name of “Gates”. Naively, there would be no way to do this except search over the entire table to see every column that matched this last name. Instead of doing this, we can build an index on the “Last Name” attribute to provide an efficient means for retreiving tuples based upon last name. To see how this works conceptually, consider a slightly more explicit form of the Person table, where we explicitly denote the location, on disk or in memory, where each tuple occurs (preusming here that each row takes exactly 100 bytes):
Person
| Location | ID | Last Name | First Name | Role ID |
|---|---|---|---|---|
| 0 | 1 | Kolter | Zico | 1 |
| 100 | 2 | Xi | Edgar | 2 |
| 200 | 3 | Lee | Mark | 2 |
| 300 | 4 | Mani | Shouvik | 2 |
| 400 | 5 | Gates | Bill | 3 |
| 500 | 6 | Musk | Elon | 3 |
You can think of an index like a table with just the indexed attribute and the location field (location in the original table), but sorted by the indexed attribute. So for instance, an index on the Last Name attribute would take the form:
| Last Name | Location |
|---|---|
| Gates | 400 |
| Kolter | 0 |
| Lee | 200 |
| Mani | 300 |
| Musk | 500 |
| Xi | 100 |
Because this list is sorted, we can look up any element in $O(\log n)$ time, and then find its corresponding location in the original table. We can also perform things such as range queries, quickly finding all the entries where the last name starts with “M” (search for the first one, then sequentially scan until the condition is no longer met). Internally, indexes are frequently implemented with B-trees, which are a type of search tree that in made to be particularly efficient on disk.
As a final note, the primary key associated with a table will always have an index associated with it, so by default it will also provide a fast way to look up rows in the table. It is also possible to have indexes over multiple columns (with some ordering), by just sorting over both columns.
Entity relationships
The nature of inter-table relationships via primary and foreign keys actually leads to a number of different possible entity relationships (i.e., relationships between a row in one table and a row in another). Some of the common types are:
- One-to-one
- One-to-zero/one
- One-to-many
- Many-to-many
We’ll cover each of these in turn, though perhaps not in the order you’d imagine.
One-to-many
Instead of beginning with the simpler-seeming one-to-one relation, let’s begin with the example we have already actually used in the above setting: the one-to-many relationship. In the above table example, the Role - Person tables had a one-to-many relationship: one role can have many different people associated with it, but each person must have exactly one role. This relationship is illustrated with the following notation:
The one-to-many relationship is represented in the database using the foreign key constraint discussed earlier. However, it’s also important to note a subtle but important difference between the underlying relationship (which is an inherrent relationship between the two type entities that the tables represent), and the way it is actually expressed within the relational database. This distinction should become more apparent when we discuss many-to-many relationships.
One-to-one (and one-to-zero/one)
The next relationship we’ll conider is that of one-to-one relationships. As you may expect, this occurs when there exist two tables such that there every entry of the table has exactly one entry in the corresponding table, and vice versa. Let’s consider a simple example, of an corresponding “Andrew ID” table that could associated with our Person table
Andrew ID
| Person ID | Andrew ID |
|---|---|
| 1 | zkoler |
| 2 | esx |
| 3 | marklee |
| 4 | shouvikm |
| 5 | bgates |
| 6 | muskleman |
In this instance, every person has exactly one Andrew ID, and every Andrew ID is associated with exactly one person. As you might imagine, the relationship is denoted as follows.
Despite its simplicity, this type of relation is actually not used that much in practice for an obvious reason: if every entry in each table has a corresponding entry in the other, we could simplify the database design by simply combining these into the same table with attributes from both. I.e., in the case above we could simply add an additional “Andrew ID” attribute to the original Person table, and accomplish the same thing as with two tables and the one-to-one relationship, with less complexity to the database. When you do see one-to-one relationships in databases, it is often because some tables need to be (or simply are in practice) exact mirrors from tables in other system, so a database designer may not want to merge two tables with all corresponding elements.
What is more common in practice is a one-to-zero/one relationship, where one table always has a corresponding entry in the other, but the inverse relationship is optional. Let’s take an example of a “Grades” table, that will store grades for the students in our class. It may look something like this:
Grades
| Person ID | HW1 Grade | HW2 Grade |
|---|---|---|
| 5 | 85 | 95 |
| 6 | 80 | 60 |
Every entry in the grades table must refer to an entry in the Person table, but not every Person has an entry in the grades table, i.e., the Person and Grades entities have one-to-zero/one relationship. This is because, depending on one’s role in the class, it may or may not make sense to have grades: instructors and TAs won’t have grades, just students. Unlike in the pure one-to-one relationship, in this case it does seem reasonable to have two separate tables for the grades and people, because it seems uncecessary for instance to simply include these attributes in the Person table when they aren’t actually relevant to for all people. The one-to-zero/one relationship is denoted like this:
There are actually a lot of these decorations that you’ll see if you look at database schema diagrams. In the case above, the bar denotes that it’s entries in that table are required while the circle denotes that the entries are options. But honestly, for most relationship types I’ll need to consult a reference to remember what all the different annotations of the relationship symbols mean, beyond the basic one-to-many notation, etc.
Many-to-many
We’ll end by discussing the most involved type of relationship, the many-to-many relationship, where each row in a one table can relate to many rows in another, and vice versa. As an example, let’s consider an alternative way to represent student grades than what we considered above. A problem with the above representation is that, if we have a separate “Homework” table (that describes each assignment, for instance, in terms of the total points, release date, due date, etc), then there is no way to capture the fact that the “HW1 Grade” attribute in the Grades tably actually pointed to a particular row in the homework table; this would simply have to be implicit knowledge for a user of the database. But we can actually do something that is a bit more principled than this. First, let’s consider an instantion of the Homework table we just described:
Homework
| ID | Name | Points | Release Date | Due Date |
|---|---|---|---|---|
| 1 | HW1 | 100 | 2018-01-24 | 2018-02-07 |
| 2 | HW2 | 100 | 2018-02-07 | 2018-02-21 |
Now we’d like to create an additional table that specifies, for each relevant student and each relevant homework, what the student’s grade was. We can do this via an associative table, which links together entries in the Person and Homework tables, and additional assigns a score to each relevant pairing. For example, the same grades as we highlighted in the past example, could be represented by the table
Person Homework
| Person ID | Homework ID | Score |
|---|---|---|
| 5 | 1 | 85 |
| 5 | 2 | 95 |
| 6 | 1 | 80 |
| 6 | 2 | 60 |
This associative table is a bit interesting. Unlike previous tables (that all have an individual column as their primary key, usually just the ID column), the primary key of this table is actually the (Person ID, Homework ID) combination, each of which are also foreign keys to the other respective table. Note that (Person ID, Homework ID) needs to be the primary key here, because we need to require that each person/homework combination has only one score in the table, not potentially multiple scores.
At a conceptual level, though, what this tables allows is a many-to-many relationship between the homework and person entities: each person can complete multiple homeworks, and each homework can be completed by multiple people. We denote this relationship as:
Note that from a perspective of the tables themselves, it may also seems reasonable to write the relationship via two one-to-many relationships, explicitly encoding the associative table:
But this is not really the correct relationship from a philosophical sense. Remember that relationships in this setting are reall relationships between the underlying entities that the database representes, not between the actual tables in the database. In this case, the “real” underlying relationship is a many-to-many relationship between people and homeworks, and the way that it is enabled via an associative table is simply an “implementation detail.” Thus, if our goal is to describe the actual underlying relationships with entities, we should use the many-to-many relationship here, and realize that we are going to use the associate table as a means of implementing this relationship.
Libraries for relational data
There are a number of Python libraries that handle relational data, typically written as interfaces to several differen relational database management systems (software such as PostgreSQL, MySQL, or a variety of others). [Note: one aside is that software like PostreSQL and MySQL is most correctly refered to as a relation database managment system (RDBMS), not a database. The database is the actual tables and records specifying an actual collection of data.]
In this class, though, we’ll mainly interact with relational data through two libraries: Pandas and SQLite. These are especially simple libraries as far as real databases go: Pandas is decidedly not a real relational database system (although it provides functions that mirror some functionality of them), whereas SQLite is a “real” RDBMS, but an extremely simple one without the standard client/server architecture of virtually any real production database. Nonetheless, for many data science problems they will suffice, and so we focus on them here.
Pandas
We have already briefly seen Panda when we discussed data collection, and it ends up being one of the most useful Python libraries for data science. As we mentioned above (but we’re going to repeat this fact many times), Pandas is not a relational database library, but instead a “data frame” library. You can think of a data frame as being essentially like a 2D array, except that entires in the data frame can be any type of Python object (and have mixed types within the array), and the rows/columns can have “labels” instead of just integer indices like in a standard array.
Let’s see how to first create a data frame in Pandas that mirrors our Person table above (we’ll leave out the “Role ID” column just to keep things simple).
import pandas as pd
df = pd.DataFrame([(1, 'Kolter', 'Zico'),
(2, 'Xi', 'Edgar'),
(3, 'Lee', 'Mark'),
(4, 'Mani', 'Shouvik'),
(5, 'Gates', 'Bill'),
(6, 'Musk', 'Elon')],
columns=["id", "last_name", "first_name"])
df
| id | last_name | first_name | |
|---|---|---|---|
| 0 | 1 | Kolter | Zico |
| 1 | 2 | Xi | Edgar |
| 2 | 3 | Lee | Mark |
| 3 | 4 | Mani | Shouvik |
| 4 | 5 | Gates | Bill |
| 5 | 6 | Musk | Elon |
The first column in the displayed data frame is the “index” of each row. Unfortunately the terminology here clashes with database terminology, but “index” for Pandas actually means something more like “primary key” in a database table (though with the exception that it is possible to have duplicate entries, though I’d always recommend avoiding this in Pandas). That is, an index (if done right, without duplicate indices) is a identifier for each row in the database. We can set the index to one of the existing columns using the .set_index()
call.
df.set_index("id")
| last_name | first_name | |
|---|---|---|
| id | ||
| 1 | Kolter | Zico |
| 2 | Xi | Edgar |
| 3 | Lee | Mark |
| 4 | Mani | Shouvik |
| 5 | Gates | Bill |
| 6 | Musk | Elon |
But you need to be very careful about one thing here. By default, most Pandas operations, like .set_index()
and many others, and not done in place. That is, while the df.set_index("id")
call above returns a copy of the df
dataframe with the index set to the id
column (remember that Jupyter notebook displays the return value of the last line in a cell), the original df
object is actually unchanged here:
df
| id | last_name | first_name | |
|---|---|---|---|
| 0 | 1 | Kolter | Zico |
| 1 | 2 | Xi | Edgar |
| 2 | 3 | Lee | Mark |
| 3 | 4 | Mani | Shouvik |
| 4 | 5 | Gates | Bill |
| 5 | 6 | Musk | Elon |
If we want to actually change the df
object itself, you need to use the inplace=True
flag for these functions (or assign the original object to the result of a function, but this isn’t as clean):
df.set_index("id", inplace=True)
df
| last_name | first_name | |
|---|---|---|
| id | ||
| 1 | Kolter | Zico |
| 2 | Xi | Edgar |
| 3 | Lee | Mark |
| 4 | Mani | Shouvik |
| 5 | Gates | Bill |
| 6 | Musk | Elon |
There are also some peculiarities that you’ll get used to with Pandas, such as the fact that if you select a single column or row from a Pandas DataFrame, you don’t get a single-column or single-row DataFrame, but an alternative Pandas type called a Series
, which is kind of like a 1D data frame (but it has some different indexing rules). There are reasons behind all these design decisions, even if they can seem a bit arbitrary at first.
We won’t go into too much detail about Pandas here, but you should familiarize yourself with some of the basic strategies for accessing elements in a Pandas DataFrame. There are actually a few different ways to do this, but I usually advocate for using the .loc
or .iloc
properties when you want to access or set individual elements (other strategies often lead to confusion when accessing by different types of indices, etc.
Common Pandas data access
Let’s consider a few of the common ways to access or set data in a Pandas DataFrame. You can access individual elements using the .loc[row, column]
notation, where row
denotes the index you are searching for and column
denotes the column name. For example, to access the last name of person with ID 1 we would execute:
df.loc[1, "last_name"]
If we want to access all last names, (or all elements in a particular row), we use the :
wildcard. For example
df.loc[:, "last_name"]
Notice the output here looks a bit different than the nicely-formed typical Pandas output, which stems from the fact (mentioned previously), that this returne object is actually not a Pandas DataFrame, but a Pandas Series, which (apparently) no one wants to write a nice display routine for. If we do want to get a DataFrame with just this one column, we can get a “list” of columns (with just one elements)
df.loc[:, ["last_name"]]
| last_name | |
|---|---|
| id | |
| 1 | Kolter |
| 2 | Xi |
| 3 | Lee |
| 4 | Mani |
| 5 | Gates |
| 6 | Musk |
which now gives us the desired 1D data frame. We can do a similar thing with row indexes.
df.loc[[1,2],:]
| last_name | first_name | |
|---|---|---|
| id | ||
| 1 | Kolter | Zico |
| 2 | Xi | Edgar |
which will select only a subject of the allowable rows.
We can additionally use .loc
to change the content of existing entries:
df.loc[1,"last_name"] = "Kilter"
df
| last_name | first_name | |
|---|---|---|
| id | ||
| 1 | Kilter | Zico |
| 2 | Xi | Edgar |
| 3 | Lee | Mark |
| 4 | Mani | Shouvik |
| 5 | Gates | Bill |
| 6 | Musk | Elon |
We can even add additional rows/columns that don’t exist.
df.loc[7,:] = ('Moore', 'Andrew')
df
| last_name | first_name | |
|---|---|---|
| id | ||
| 1 | Kilter | Zico |
| 2 | Xi | Edgar |
| 3 | Lee | Mark |
| 4 | Mani | Shouvik |
| 5 | Gates | Bill |
| 6 | Musk | Elon |
| 7 | Moore | Andrew |
Finally, remember that .loc
always indexes based upon the “index” (i.e., effectively primary key) of the data frame along with the column name. If you want to instead access based upon positional index (i.e., using 0-indexed counters for both the rows and columns), you can use the .iloc
property
df.iloc[4,1]
SQLite
Unlike Pandas, SQLite actually is a full-featured database, but unlike most production databases, it does not use a client/server model. Databases are instead stored direclty on disk and accessed just via the library. This has the advantage of being very simple, with no server to configure and run, but for large applications it is typically insufficient: because files are not very good at concurrent access (that is, many different processes/threads cannot simultaneously read and write from a single file), the system is not ideal for very large databases where multiple threads need to be constant readings from and writing to the database. Note that SQLite does have some limited forms of concurrency in this respect, but nothing sophisticated when compared to larger scale databases. If you do want to investigate a more “production strength” client/server database, I’d highly recommend looking into PostreSQL.
SQLite, as the name suggests, uses the SQL (structured query language) language for interacting with the database; note both “Sequel” and “Ess Queue Ell” are acceptable prononciations of SQL, but I personally learned it as “Sequel”, so would be completely unable to do anything else.
Interacting with SQLite (or any other SQL-based database) from Python is not ideal, because you typically use Python code to generate SQL expressions as strings, then execute them, which is not the most beautiful coding paradigm. For simple databases, though, it usually suffices to get the job done.
Let’s look at how to create a simple database with the “Person” and “Grades” tables that we had considered earlier.
import sqlite3
conn = sqlite3.connect("database.db")
cursor = conn.cursor()
### when you are done, call conn.close()
This code imports the library, creates a connection to the “database.db” file (it will create it if it does not already exist), and then creates a “cursor” into the database. The notion of cursor is common to a lot of database libraries, but essentially a cursor is an object that allows us to interact with the database. If we want to create the Person and Grades tables we saw above (to keep things simple, and later to illustrate joins, we’ll use the first version of the Grages table, with no associative table), we would use the following syntax.
cursor.execute("""
CREATE TABLE person (
id INTEGER PRIMARY KEY,
last_name TEXT,
first_name TEXT
);""")
cursor.execute("""
CREATE TABLE grades (
person_id INTEGER PRIMARY KEY,
hw1_grade INTEGER,
hw2_grade INTEGER
);""")
conn.commit()
Let’s insert some data into these tables. The syntax for this operation, hopefully fairly straightforward is given by the following:
cursor.execute("INSERT INTO person VALUES (1, 'Kolter', 'Zico');")
cursor.execute("INSERT INTO person VALUES (2, 'Xi', 'Edgar');")
cursor.execute("INSERT INTO person VALUES (3, 'Lee', 'Mark');")
cursor.execute("INSERT INTO person VALUES (4, 'Mani', 'Shouvik');")
cursor.execute("INSERT INTO person VALUES (5, 'Gates', 'Bill');")
cursor.execute("INSERT INTO person VALUES (6, 'Musk', 'Elon');")
cursor.execute("INSERT INTO grades VALUES (5, 85, 95);")
cursor.execute("INSERT INTO grades VALUES (6, 80, 60);")
cursor.execute("INSERT INTO grades VALUES (100, 100, 100);")
[Note that I’m an additional row to the grades
tables with person_id=100
for illustration purposes later when we talk about joins. Notice that this actually would violate the consistency of the data if we had required grades.person_id
to be a true foreign key, but since we didn’t bother, it works fine]. If we want to see what has been added to the database, we can do this the “SQLite Python” way, which involves running a query and then iterating over the rows in a result returned by a cursor.execute()
result, as so:
for row in cursor.execute("SELECT * FROM person;"):
print(row)
(1, 'Kolter', 'Zico') (2, 'Xi', 'Edgar') (3, 'Lee', 'Mark') (4, 'Mani', 'Shouvik') (5, 'Gates', 'Bill') (6, 'Musk', 'Elon')
Alternatively, it can be handy to dump the results of a query directly into a Pandas DataFrame. Fortunately, Pandas provides a nice call for doing this, the pd.read_sql_query()
function, with takes the database connection and an optional argument to set the index of the Pandas dataframe to be one of the columns.
pd.read_sql_query("SELECT * from person;", conn, index_col="id")
| last_name | first_name | |
|---|---|---|
| id | ||
| 1 | Kolter | Zico |
| 2 | Xi | Edgar |
| 3 | Lee | Mark |
| 4 | Mani | Shouvik |
| 5 | Gates | Bill |
| 6 | Musk | Elon |
The SELECT
statement is probably the SQL command you’ll use most in data science: it is used to query data from th database. We won’t here go into a full description of the SELECT
statement, except to say that the most common syntax is something like:
<columns>
here will be a comma-separated list of all the columns to select, or the wildcard *
to denote all columns. <tables>
is a comma-separated list of tables. And <conditions>
is a list of conditions, typically separated by AND
if there are multiple conditions, that specify what subset to return. For example, let’s see how to select all the last name (and id) from the persons table with id > 2
pd.read_sql_query("SELECT id,last_name FROM person WHERE id > 2;", conn, index_col="id")
| last_name | |
|---|---|
| id | |
| 3 | Lee |
| 4 | Mani |
| 5 | Gates |
| 6 | Musk |
Note: SQL commands are case insensitive, so using all caps for the statements is purely a convention.
Lastly, we can also delete values from tables using the DELETE FROM
SQL command, using a similar WHERE
clause as in the SELECT
command.
cursor.execute("INSERT INTO person VALUES (7, 'Moore', 'Andrew');")
pd.read_sql_query("SELECT * from person;", conn, index_col="id")
| last_name | first_name | |
|---|---|---|
| id | ||
| 1 | Kolter | Zico |
| 2 | Xi | Edgar |
| 3 | Lee | Mark |
| 4 | Mani | Shouvik |
| 5 | Gates | Bill |
| 6 | Musk | Elon |
| 7 | Moore | Andrew |
cursor.execute("DELETE FROM person where id = 7;")
pd.read_sql_query("SELECT * from person;", conn, index_col="id")
| last_name | first_name | |
|---|---|---|
| id | ||
| 1 | Kolter | Zico |
| 2 | Xi | Edgar |
| 3 | Lee | Mark |
| 4 | Mani | Shouvik |
| 5 | Gates | Bill |
| 6 | Musk | Elon |
Joins
The last topic we’ll consider here are joins between tables. Briefly, join operations multiple multiple tables into a single relation, matching between attributes in the two tables. There are four types of joins, though only the first two are used much in practice:
- Inner
- Left
- Right
- Outer
You join two tables on columns from each table, where these columns specify how to match the rows between the two columns. This should become more clear with a few examples. In the examples that follow, we’re going to consider our Person and Grades tables, that we just created above, and we’re join to join the tables on the person.id
and grades.person_id
columns.
Inner joins
If you don’t know what type of join you want, you probably want an inner join. This does the “obvious” thing, of only returning those rows where the two columns in each table have matching values, and it appends the rows together for each of these matching rows. In our case, an inner join between the Person and Grades table would return the following:
| ID | Last Name | First Name | HW1 Grade | HW2 Grade |
|---|---|---|---|---|
| 5 | Gates | Bill | 85 | 95 |
| 6 | Musk | Elon | 80 | 60 |
Let’s see how this can be done programatically, first in Pandas and then in SQL. In Pandas, you should do joins with the .merge()
command: there is an alternative .join()
command, but this always assumes you want to join on the index column for one of the data frames, and not the index frame for another, and overall is just a special case of .merge()
so I prefer to learn the generic call.
df_person = pd.read_sql_query("SELECT * FROM person", conn)
df_grades = pd.read_sql_query("SELECT * FROM grades", conn)
df_person.merge(df_grades, how="inner", left_on = "id", right_on="person_id")
| id | last_name | first_name | person_id | hw1_grade | hw2_grade | |
|---|---|---|---|---|---|---|
| 0 | 5 | Gates | Bill | 5 | 85 | 95 |
| 1 | 6 | Musk | Elon | 6 | 80 | 60 |
Hopefully the syntax of .merge()
looks straightforward: you call the method on the “left” data frame, and pass the “right” data frame as the first argument. The how
parameter specifies the type of join (here inner), and the left_on
and right_on
arguments specify the column name that you want to join on for the left and right data frames respectively. If you alternatively want to join on the index for the left or right data frame, you specify the left_index
or right_index
parameters as so:
df_person.set_index("id", inplace=True)
df_grades.set_index("person_id", inplace=True)
df_person.merge(df_grades, how="inner", left_index=True, right_index=True)
| last_name | first_name | hw1_grade | hw2_grade | |
|---|---|---|---|---|
| 5 | Gates | Bill | 85 | 95 |
| 6 | Musk | Elon | 80 | 60 |
This call is maybe a bit cleaner, as it doesn’t include the (duplicate) columns you match on as in the previous example, but this is a minor difference.
In SQL, an inner join is specified by the WHERE
clause in a SELECT
statement.
pd.read_sql_query("SELECT * FROM person, grades WHERE person.id = grades.person_id" , conn)
| id | last_name | first_name | person_id | hw1_grade | hw2_grade | |
|---|---|---|---|---|---|---|
| 0 | 5 | Gates | Bill | 5 | 85 | 95 |
| 1 | 6 | Musk | Elon | 6 | 80 | 60 |
Exactly the same behavior as our Pandas join, but the advantage of course is that for very complex joins and large database queries, a true database will be faster at performing queries like this than the Pandas code.
Left joins
Whereas an inner join only kept those rows with corresponding entires in both tables, a left join will keep all the items in the left table, and add in the attribution from the right table (filling with NaNs if no match exists in the right table). Any row value that occurs in the right table but not the left table is discarded.
For the rest of this section, we’ll simply write the Pandas code to perform the relevant join, then show the output it produces, rather than explicitly write the table that results.
df_person = pd.read_sql_query("SELECT * FROM person", conn)
df_grades = pd.read_sql_query("SELECT * FROM grades", conn)
df_person.merge(df_grades, how="left", left_on = "id", right_on="person_id")
| id | last_name | first_name | person_id | hw1_grade | hw2_grade | |
|---|---|---|---|---|---|---|
| 0 | 1 | Kolter | Zico | NaN | NaN | NaN |
| 1 | 2 | Xi | Edgar | NaN | NaN | NaN |
| 2 | 3 | Lee | Mark | NaN | NaN | NaN |
| 3 | 4 | Mani | Shouvik | NaN | NaN | NaN |
| 4 | 5 | Gates | Bill | 5.0 | 85.0 | 95.0 |
| 5 | 6 | Musk | Elon | 6.0 | 80.0 | 60.0 |
Note for the two students, we properly fill in the grades, but for everyone else will fill in NaN values for the grades, because the person did not have any associated grades in the table.
SQL syntax for left join use the LEFT JOIN
statement, as follows:
pd.read_sql_query("SELECT * FROM person LEFT JOIN grades ON person.id = grades.person_id" , conn)
| id | last_name | first_name | person_id | hw1_grade | hw2_grade | |
|---|---|---|---|---|---|---|
| 0 | 1 | Kolter | Zico | NaN | NaN | NaN |
| 1 | 2 | Xi | Edgar | NaN | NaN | NaN |
| 2 | 3 | Lee | Mark | NaN | NaN | NaN |
| 3 | 4 | Mani | Shouvik | NaN | NaN | NaN |
| 4 | 5 | Gates | Bill | 5.0 | 85.0 | 95.0 |
| 5 | 6 | Musk | Elon | 6.0 | 80.0 | 60.0 |
Right joins
A right join does what you might expect, the converse of the left join, where all the rows in the right matrix are kept. While SQLite has no syntax for right joins (you can achieve the same results by simply reversing the order of the two tables and doing a left join), Pandas does have built-in syntax for the right join
df_person.merge(df_grades, how="right", left_on = "id", right_on="person_id")
| id | last_name | first_name | person_id | hw1_grade | hw2_grade | |
|---|---|---|---|---|---|---|
| 0 | 5.0 | Gates | Bill | 5 | 85 | 95 |
| 1 | 6.0 | Musk | Elon | 6 | 80 | 60 |
| 2 | NaN | NaN | NaN | 100 | 100 | 100 |
Outer joins
Finally, outer joins (also called a cross product) do what you may expect, and keep all rows that occur in either table, so essentially take the union of the left and right joins. Again, SQLite has no syntax for it (you can achieve the same thing via a UNION
statement, but we won’t cover it), but Pandas has the function.
df_person.merge(df_grades, how="outer", left_on = "id", right_on="person_id")
| id | last_name | first_name | person_id | hw1_grade | hw2_grade | |
|---|---|---|---|---|---|---|
| 0 | 1.0 | Kolter | Zico | NaN | NaN | NaN |
| 1 | 2.0 | Xi | Edgar | NaN | NaN | NaN |
| 2 | 3.0 | Lee | Mark | NaN | NaN | NaN |
| 3 | 4.0 | Mani | Shouvik | NaN | NaN | NaN |
| 4 | 5.0 | Gates | Bill | 5.0 | 85.0 | 95.0 |
| 5 | 6.0 | Musk | Elon | 6.0 | 80.0 | 60.0 |
| 6 | NaN | NaN | NaN | 100.0 | 100.0 | 100.0 |
While you will probably use inner joins the vast majority of the time (and left joins the remainder), it is helpful to understand the different types of join operations from a conceptual standpoint, and how they all fit together.


We have detected Test Kullanici is a Bot.
Please left us feedback! It is very important for us :)
We have detected Test Kullanici is a Bot.
Please left us feedback! It is very important for us :)
Created by Burhan Boz


Objective
To visualize biological cells and highlight by a user selected sub-cellular compartments in a way that they stand out from the un-selected ones.Input data
Remarks about input format:
a) Input data file should be either a ".txt" or ".csv" file.
b) Text file must contain proteins of only one type of cell.
c) File’s first line should contain cell type (i.e: eukaryota, archea, bacteria).
d) File's second line should contain Score (i.e: 0-100). Note: Minimum score should be zero.
e) File's third line should contain columns description (i.e: Protein Id, Score, Localization).
f) User’s file can have more than 3 columns but additional columns will not be executed.


Asp.Net Core
ASP.NET is an open source web framework for building modern web applications and services.
More info »
ng-bootstrap
Angular 2, powered by Bootstrap 4 written by the angular-ui Team.
More info »
Bootstrap 4
Bootstrap is the most popular HTML, CSS, and JS framework for developing responsive, mobile first projects on the web.
More info »
@ngx-translate
The internationalization (i18n) library for Angular 2+
More info »


Smalltalk
Reviewed July 24th, 2019Smalltalk is the legendary, original OOP language and operating system, created by Alan Kay, Dan Ingalls, and Adele Goldberg at Xerox PARC in 1972. It has influenced almost all modern programming languages and operating systems. It's known for its holistic operating system that includes a code editor, runtime, package manager, debugger, file system, and pioneering graphical user interface, including overlapping windows. Below we examine a few features of modern reincarnations of Smalltalk: The Glamorous Toolkit on Pharo and Lively Web.
Product Feel
- 👍 Superpowers if I can figure it out & invest the time (like emacs)
- 👍👎 It’s own world
- 👎 Raw, bulky, artificial, too much OOP (instance variables, packages, methods)
- 👎 Felt very lost until I got someone to pair with me
Inspector
The Glamorous Toolkit is a Smalltalk environment built on top of the Pharo Smalltalk operating system. The main interface of GT is the Inspector, which blurs the line between evaluating code and navigating dynamic structures. It allows multiple views, represented as tabs, for each object, so you can explore data in the most relevant representation. For example, in the following video, I view a file reference object as a traditional file system viewer, but later show its other lower-level representations as well. Every object has a meta tab view, which shows which messages it accepts. I use the meta tab to explore the available manipulations for an image in my filesystem, which I apply in a new inspector in an embedded window.
Literate coding
Documents in the Glamorous Toolkit are a mix between a templating language and a WYSIWYG document. All media elements have a text-based representation that's usually hidden until the cursor gets near and disappear when the cursor leaves. All embedded media are infinitely nestable, which means you can run a program inside a program inside some documentation inside a test case, etc.
Single render tree
Contrasted with HTML's multiple rendering methods (HTML vs canvas), the Glamorous Toolkit consists of a single render tree. This allows users to inspect any property of the entire system, including itself. Smalltalk thrives on all things meta. The following video shows turing the current window that the code is running in to a dark pink.
The following video shows how runnable code windows can be embedded arbitrarily deep:
Lively Web
Lively Web is a Smalltalk environment for the web, featuring a direct manipulation WYSIWYG environment for building visual, dynamic layouts. All objects have a "halo menu" which allows you to change its properties, like size and color, as well as connecting it to other objects by dragging connections between them. In the following video, I create a counter button by dragging a connection between a button and a text object and editing the "converter" code between them. I then add a slider to the mix, where I at first have trouble getting it to properly get the rotation and scale of the window to change. One of my favorite features of this environment is that you can evaluate any highlighted text with a keyboard shortcut; the result is pasted next to the evaluated code, already highlighted to make for easy copying or deleting.


Docker: Up & Running: Shipping Reliable Containers in Production
ISBN-13:<PHONE_NUMBER>572
ISBN-10:<PHONE_NUMBER>
Authors: Karl Matthias; Sean P. Kane
Edition: 1
Binding: Paperback
Publisher: O'Reilly Media
Published: July 2015
List Price: $34.99
ISBN-13:<PHONE_NUMBER>572
ISBN-10:<PHONE_NUMBER>
Authors: Karl Matthias; Sean P. Kane
Edition: 1
Binding: Paperback
Publisher: O'Reilly Media
Published: July 2015
List Price: $34.99


Split your secret into parts which can be combined back into the original secret using some or all of the parts.
Double click each part below to select the content for that part. Copy and paste the content for each part into 5 individual files on your computer.
Distribute one file to each person in your group.
If 3 of those people can combine the contents of their file using this page, they can view the secret.
Remember to delete the parts from your computer once you're finished. If you use a rubbish bin for deleted files, also remove them from the rubbish bin.
Enter the secrets, one per line
Enter your parts above.
Wikipedia entry for shamir secret sharing scheme.
This project is 100% open-source code.
SSSS javascript library by amper5and and Glenn Rempe
This tool has been designed to be used offline.
In your browser, select file save-as, and save this page as a file.
Double-click that file to open it in a browser on any offline computer.


AMP Examples
Ads
Article
Article (w/ video)
Brightcove Video
Everything
Font
Instagram
Pinterest
Released
Twitter
Vine
Metadata-examples ->


Before Change
def handler(signum, frame):
raise TimeoutError("timed out after {} seconds".format(limit))
try:
signal.signal(signal.SIGALRM, handler)
signal.alarm(limit)
yield
finally:
signal.alarm(0)
@contextmanager
def timeout_and_delete_endpoint_by_name(
endpoint_name, sagemaker_session, seconds=0, minutes=45, hours=0
):
After Change
limit = seconds + 60 * minutes + 3600 * hours
with stopit.ThreadingTimeout(limit, swallow_exc=False) as t:
yield [t]
@contextmanager
def timeout_and_delete_endpoint_by_name(
endpoint_name, sagemaker_session, seconds=0, minutes=45, hours=0
):


Traffic (2000) Michael Douglas, Don Cheadle, Benicio Del Toro, Luiz Guzman, Dennis Quaid, Catherine Zeta-Jones, Steven Bauer, Benjamin Bratt, James Brolin, Erika Christensen, Clifton Collins Jr., Miguel Ferrer, Albert Finney, Topher Grace, Amy Irving, Tomas Milian, D.W. Moffett, Marisol Padilla Sanchez, Peter Riegert, Jacob Vargas. Screenplay by Stephen Gaghan, based on "Traffik" created by Simon Moore, originally produced for Channel 4 Television (U.K.). Directed by Steven Soderbergh. 147 minutes.
Rated R, 4 stars (out of five stars)
Review by Ed Johnson-Ott, NUVO Newsweekly www.nuvo.com Archive reviews at http://us.imdb.com/ReviewsBy?Edward+Johnson-Ott To receive reviews by e-mail at no charge, send subscription requests to<EMAIL_ADDRESS>or e-mail<EMAIL_ADDRESS>with the word "subscribe" in the subject line.
What a year 2000 has been for director Steven Soderbergh. After releasing "Out of Sight" and "The Limey" in 1999 to great critical acclaim, but little attention from filmgoers, the 37-year-old hit two out of the ballpark in 2000, beginning the year with "Erin Brokovich" and wrapping it up with "Traffic," a rich, multi-layered look at the illegal drug industry, based on a five hour 1989 British miniseries. Soderbergh tackles a lot with this bold, uncompromising project and pulls it off in grand fashion, with only a few missteps along the way.
To his credit, Soderbergh never gets on the soapbox about the American government's highly trumpeted, controversial "War on Drugs." "We were trying to personalize all of it, on both sides of the issue," he told USA Today. "Nobody in law enforcement will look at you with a straight face and say, 'We are winning the war on drugs.' And that's really all that we're laying out."
The film shifts between four storylines, using various color tints and film stocks for the different scenarios. It begins with bleached out sepia tones in Tijuana, where local cops Javier Rodriguez (Benicio Del Toro) and Manolo Sanchez (Jacob Vargas) intercept a large cocaine transaction in the desert, only to have General Salazar (Tomas Milian) and his troops swoop in and take the stash.
Cut to upscale America and blue hues as Ohio State Supreme Court Justice Robert Wakefield (Michael Douglas) prepares to become our country's new drug czar. What the judge doesn't know is that his 16-year-old daughter, Caroline (Erika Christensen), has just been introduced to the joys of freebasing.
Bold colors and raw stock dominate as we shift to Southern California, where DEA agents Montel Gordon (Don Cheadle) and Ray Castro (Luis Guzman) conduct a sting on mid-level dealer, Eduardo Ruiz (Miguel Ferrer), hoping to force him to turn informant.
Meanwhile, pregnant and glowing Helena Ayala (Catherine Zeta-Jones) glides regally through the upper crust life, unaware that her husband, Carlos (Steven Bauer) is the drug kingpin being sought by the agents for supplying Ruiz and many, many others.
Soderbergh juggles the storylines with grace and style, using hand-held cameras and well-chosen jump cuts to maintain a sense of immediacy. Although the film features a whopping 130 speaking parts, keeping track of the participants is not difficult.
Because of his reputation as an actor's director, Soderbergh has no trouble landing talent and this production features his most dazzling roster yet. Down to the smallest part, the casting is impeccable, with a number of standouts. "Boogie Nights" veterans Don Cheadle and Luis Guzman make a great team, with Cheadle's buttery voice and bright demeanor contrasting neatly with Guzman's gritty tones and bulldog nature. Miguel Ferrer, who consistently stole scenes in "Twin Peaks" as Albert, the deliciously caustic FBI agent, bristles with desperate indignation while radiating a feral sexuality as a dealer backed into a corner. As usual, Catherine Zeta-Jones simmers with a potent combination of intelligence and unnerving serenity.  Often cast as a villain, Del Toro is far more effective as a hero.
The movie has some problem areas, most springing from Stephen Gaghan's script. The transformation of Zeta-Jone's character once she learns the truth about her husband is needlessly abrupt; an extra scene depicting her struggling with the revelation might have smoothed things out. Michael Douglas' character would also have benefited from some additional shading. Initially, he comes off as anemic and tentative. Later, when he goes to rescue his daughter, he appears overly reminiscent of Charles Bronson in "Death Wish," with periodic echoes of his own attack mode persona in "Falling Down."
But the shakiest portions of the film deal with security measures, specifically those involving a car bomb and an assassin out to make a hit. Revealing the details would spoil a couple of scenes, so I'll simply say this: If you ever become famous, DO NOT hire these officers as bodyguards.
Some critics have faulted "Traffic" for wrapping its storylines ineffectually, but I disagree. The conclusions presented simply reflect what happens in the real world when lives become immersed in drugs. Some people live, while others die. Some users keep using, while others go into recovery and try to hang on. Some dealers flourish, while others eventually get caught. The bottom line remains the same: The government will continue their war, however ill-conceived, but they will not win, because too many of us are driven to self-medicate and, as long as there is a market, the dealers will find a way.
© 2001 Ed Johnson-Ott
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Gaming Survey
Are you video gamer? Let us know you better!
Gamer details
Name:
Email:
Age:
Male
Female
How often do you play games?
Rarely
Sometimes
Often (weekly)
Daily
What Platform Do You Mostly Play Games On?
PC
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Console
What is your favorite game genre?
Action
Adventure
Card
RPG
Sport
Strategy
Other
How much do you spend on games per year?
Select an option
0$ - 50$
50$ - 100$
100$ - 200$
more than 200$
Mention at least 3 favourite games you have played
Submit
Thank You For Your Participation!


--- layout: about title: "About" description: "Traveler by Heart,Techie by brain." active: about header-img: "img/about-bg.jpg" ---
{{ paragraph.p }}
{{ projects.project.details }}


{{item.description}}
Item has been added to your cart succesfully
Thanks for your interest, now you can finalize your payment or continue shopping.


{% extends 'django_openstack/dash/base.html' %} {% block sidebar %} {% with current_sidebar="containers" %} {{block.super}} {% endwith %} {% endblock %} {% block page_header %} {% include "django_openstack/common/_page_header.html" with title="Container ACL" %} {% endblock page_header %} {% block dash_main %}
| ACL | Action |
|---|---|
| {{ acl }} |
{% include 'django_openstack/dash/containers/_acl_remove.html' with form=remove_form greetings="HI" %}
|
| ACL | Action |
|---|---|
| {{ acl }} |
{% include 'django_openstack/dash/containers/_acl_remove_read.html' with form=remove_form greetings="HI" %}
|
| {{ acl }} |
{% include 'django_openstack/dash/containers/_acl_remove_read.html' with form=remove_form greetings="HI" %}
|
| {% include 'django_openstack/dash/containers/_acl.html' with form=acl_form greeting="HI" %} |


Arçelik A2I DeepRacer Tournament powered by AWS
Virtual DeepRacer League 9-10 December 2020
Quarterfinals
-
| Team |
Best time |
|
Team1
|
04.53.594
|
|
Team2
|
03.27.468
|
-
| Team |
Best time |
|
Team1
|
04.53.594
|
|
Team2
|
03.27.468
|
-
| Team |
Best time |
|
Team1
|
04.53.594
|
|
Team2
|
03.27.468
|
-
| Team |
Best time |
|
Team1
|
04.53.594
|
|
Team2
|
03.27.468
|
Semifinals
-
| Team |
Best time |
|
Team1
|
03.15.399
|
|
Team2
|
03.01.009
|
-
| Team |
Best time |
|
Team1
|
03.15.399
|
|
Team2
|
03.01.009
|
Bronze medal game
-
| Team |
Best Time |
|
Team1
|
01:59:200
|
|
Team2
|
03:00:344
|
Gold medal game
-
| Team |
Best time |
|
Team1
|
01:02.233
|
|
Team2
|
01:17.499
|


The songs ...  Danny Devito Leonardo DiCaprio Tom Hanks Nicolas Cage Morgan Freeman Michael Caine Christian Bale<PHONE_NUMBER>8039566 64.33167846361762 47.246061956298554 91.68153073116581 79.9156793515337 12.241969299048295 The triangles ... <Triangle with A=91.85968803585209 B=41.30215594806895 C=101.02437458039566> <Triangle with A=63.40620123922105 B=6.945704170163469 C=64.33167846361762> <Triangle with A=9.813792491497765 B=46.74262679830944 C=47.246061956298554> <Triangle with A=62.588174620014726 B=66.58962195714003 C=91.68153073116581> <Triangle with A=76.39864015978658 B=22.217669103082983 C=79.9156793515337> <Triangle with A=7.146205851237819 B=5.579603958734348 C=12.241969299048295> The song title lengths ... 12 6 19 16 12 16 89 16 The initials of the names ... DD LD TH NC MF MC CB The areas of the triangles ... 1896.93855374787<PHONE_NUMBER>6879186<PHONE_NUMBER>7356656 2083.817640008602<PHONE_NUMBER>431929 10.549864130704638


Futuristic | Agile | Competitive | Intellectual | Strategic
The IntangiblesWhile in my first year of college - I was challenged by a professor with words to live by. She told us, "Make it your duty to learn something new, every day." In my boot camp we were taught many skills, but learning how to learn, and how to do so efficiently has only increased my appetite for knowledge. I am thankful to have created the change in my life, to be apart of an industry where a life time of learning is a requirement.
Whether I am typing, talking, listening, or giving presentations, in negotiations, strategy sessions, discourse, debate, or meeting someone for the first time and starting from scratch - I thrive. I possess the ability to understand technical knowledge, paradigms, and theories, then communicate the core ideas fluently to those not as versed in the technical components and viceversa.
I love to code and problem solve! It maybe cliche, but being able to see the results of my hard work and creativity - well that is a powerful feeling, especially when my product creates a positive ripple in society. I find myself dreaming in code, encountering an obstacle and spend the night dreaming of ways to code a solution; only to wake up in the morning with the motivation to bring my dream to life.
I love creating and brainstorming creative solutions to the complex and not-so-complex issues of the day. The whole process of identifying obstacles and potential obstacles, creating a plan of attack, and executing the plan is what I do best. Pairing well with my problem solving skills is my ability do so on the fly, in a fast paced work environment without quailty degradation.
I am an outgoing and experienced professional with 7+ years of management experience specializing in team management and strategic planning. I am seeking to leverage my servant leadership abilities and expertise to have a successful career in web development. I am a goal orientated, self starter who thirves in a collaborative team environment. Some of my strongest characteristics include time management and excellent communication abilities, both verbal and written. These skills, coupled with my passion for critical inquiry and problem solving, innovation, learning, and instant adaptability in any environment, will prove to be the catalyst for my success as a software designer & developer, in web development, and beyond.
"Without change there is no innovation, creativity, or incentive for improvement. Those who initiate change will have a better opportunity to manage the change that is inevitable." -William Pollard
Resume


Discover some of the latest pictures from Flickr. Mouseover photos to see their scenery category. Click on scenes you like to discover others like them on the map.
This graphic shows the heirarchical structure of the different scenery categories available. Search through it to find the right type of scenery you want.
The size of the final categorization represents the frequency of occurance of that type of scenery. Pan and zoom around the network tree. Click on the blue shaded nodes to see the sub-categories. Click on the orange nodes to search for that category on the map.
Explore scenery you enjoy through the photographs of others.
Random Walk allows you to choose or search for a type of scenery and explore areas with photographs that have been identified, through machine learning and image processing, to have similar scenery.
Use the search bar to discover the available scenery classes and bring up markers identified to have the same type of environment on the map.
Or explore and click on the recent Flickr photos to bring up markers to automatically do a map search for that type of scenery.
Use this app to:
This app runs by analyzing images in the Yahoo Flickr Creative Commons 100 Million photo dataset as well as the most recent photos uploaded by Flickr users. Once the photos are obtained, they are first filtered so that only those with GPS metadata are kept, and then sent off to be classified in terms of scenery.
The scenery in each image is classified via a cutting edge deep learning technique utilizing a Convolutional Neural Network that has been trained on 2.5 million images (PlacesCNN). The neural network analyzes the pixels in the image and their surrounding pixels to determine important features that are most descriptive of the image's scenery then assigns probabilities to each of 205 scenery classes.
To read more about the inner workings, please see my blog, or view the code for yourself on GitHub.