text stringlengths 1 81 | start float64 0 10.1k | duration float64 0 24.9 |
|---|---|---|
What table would I like to update? | 1,593.153 | 1.417 |
I'd like to update the flights table. | 1,594.57 | 2.34 |
What would I like to do? | 1,596.91 | 1.47 |
Well I'd like to set the
duration equal to 430. | 1,598.38 | 4.29 |
So whatever the value of
duration happens to be right now, | 1,602.67 | 2.7 |
change it to 430. | 1,605.37 | 2.04 |
But of course I don't want to change
it to 430 for every single flight. | 1,607.41 | 3.75 |
Just as in a SELECT clause, I could say
WHERE something = something or WHERE | 1,611.16 | 3.73 |
and then some other
clause to specify where | 1,614.89 | 2.57 |
I want the rows to be selected from. | 1,617.46 | 2.14 |
Likewise, to an Update I can
say set duration equal to 430 | 1,619.6 | 4.22 |
WHERE a particular condition is true. | 1,623.82 | 2.29 |
So here I'm going to look
through the flights table, | 1,626.11 | 2.45 |
find myself all of the flights
where the origin is New York | 1,628.56 | 3.6 |
and the destination is London. | 1,632.16 | 2.34 |
For those rows I'm going to update
the value of the duration column | 1,634.5 | 3.51 |
setting the duration
column equal to 430. | 1,638.01 | 2.91 |
So using that syntax I can
take data and update it, | 1,640.92 | 2.61 |
changing one value to another value
by pinpointing which row or rows I | 1,643.53 | 4.32 |
would like to change. | 1,647.85 | 0.99 |
If I only want to change one row, I
might do Update flights SET duration= | 1,648.84 | 4.77 |
something WHERE id= a particular id. | 1,653.61 | 3.33 |
To be able to pinpoint one
id and then take that row | 1,656.94 | 3.36 |
and make some modification to it. | 1,660.3 | 2.46 |
In addition to inserting data,
selecting data, and updating data, | 1,662.76 | 3.31 |
the last main command we'll
concern ourselves with | 1,666.07 | 2.24 |
is the ability to delete data. | 1,668.31 | 1.538 |
The ability to take a row and
say, I'd like to get rid of it. | 1,669.848 | 2.542 |
Or take multiple rows
and get rid of them. | 1,672.39 | 2.31 |
And so a command like delete from
flights, where destination= "Tokyo" | 1,674.7 | 4.41 |
as you might imagine deletes from the
flights table all of the rows that | 1,679.11 | 3.54 |
satisfy this condition where the
destination is equal to Tokyo. | 1,682.65 | 4.482 |
So a number of different operations
now that we have the ability to do. | 1,687.132 | 2.958 |
The ability to delete from a particular
table where a condition is true, | 1,690.09 | 3.6 |
the ability to update a table based
on particular conditions, the ability | 1,693.69 | 3.45 |
to select data from a table, and
insert data into a table as well. | 1,697.14 | 4.62 |
There are a couple other clauses that
can be used to add SQL queries as well. | 1,701.76 | 3.81 |
Just to add additional functionality. | 1,705.57 | 2.28 |
If I don't want all of the rows to
come back from a particular SQL query, | 1,707.85 | 3.6 |
I can limit the results that come back. | 1,711.45 | 2.04 |
So normally SELECT *
from flights would get me | 1,713.49 | 2.67 |
all of the flights inside of the table. | 1,716.16 | 2.16 |
But I could say SELECT * from
flights, LIMIT 5 to just say, | 1,718.32 | 3.9 |
I only want five results to
come back from this table. | 1,722.22 | 3.9 |
ORDER BY allows me to decide how the
results are ordered inside the results | 1,726.12 | 4.02 |
to come back. | 1,730.14 | 0.84 |
So I can say SELECT * from flights,
ORDER BY destination, or ORDER BY | 1,730.98 | 3.99 |
duration to get all of the flights
in order by how long they are. | 1,734.97 | 3.9 |
GROUP BY allows me to group a
whole bunch of rows together. | 1,738.87 | 3.97 |
So if I wanted to group all
of the flights by their origin | 1,742.84 | 2.913 |
so I can get all of the flights leaving
New York and all the flights leaving | 1,745.753 | 3.167 |
London and so forth, I could
do something like SELECT * | 1,748.92 | 2.73 |
from flights GROUP BY origin to group
flights by their origin as well. | 1,751.65 | 4.83 |
HAVING is a constraint
I can place on GROUP BY | 1,756.48 | 2.73 |
to say that I would like to select
all of the flights grouping them | 1,759.21 | 4.62 |
by their origin, but they need to
have a count of at least three. | 1,763.83 | 3.27 |
Meaning there needs to be
at least three flights that | 1,767.1 | 2.55 |
are leaving from that particular city. | 1,769.65 | 1.74 |
For all of these
particular clauses, these | 1,772.23 | 1.83 |
are helpful to know if you're
going to be directly writing SQL. | 1,774.06 | 2.64 |
We won't worry about them
too much here, in particular | 1,776.7 | 2.31 |
because fairly shortly we're not
going to be writing SQL ourselves. | 1,779.01 | 3.09 |
We're going to just
be writing Python code | 1,782.1 | 2.13 |
and Django is going to, under the
hood, be manipulating the database | 1,784.23 | 3.27 |
and creating the SQL
commands that it is going | 1,787.5 | 2.43 |
to run on the underlying database. | 1,789.93 | 2.09 |
So we will see how we don't
actually need to worry | 1,792.02 | 2.29 |
about writing the specific syntax. | 1,794.31 | 1.8 |
But Django is going to
handle much of that for us. | 1,796.11 | 3.97 |
So here now we have a
flights table, a table | 1,800.08 | 2.42 |
that keeps track of all of
the flights that we have, | 1,802.5 | 2.46 |
organizing them by their id and
their origin and their destination | 1,804.96 | 3.54 |
and their duration. | 1,808.5 | 1.2 |
But oftentimes when we're dealing with
data, especially in a larger database, | 1,809.7 | 3.61 |
we don't just have one table of data. | 1,813.31 | 2.15 |
We have multiple tables of data. | 1,815.46 | 1.87 |
And those multiple tables might
relate to each other in some way. | 1,817.33 | 4.198 |
Let's take a look at an example
of how that might come about. | 1,821.528 | 2.542 |
We're going to introduce a concept
that will call foreign keys, | 1,824.07 | 2.7 |
and we'll see what that
means in just a moment. | 1,826.77 | 2.77 |
So here again is our flights table. | 1,829.54 | 2.15 |
The flights table has four columns-- an
id, origin, destination, and duration. | 1,831.69 | 5.58 |
But of course in New York,
there are multiple airports. | 1,837.27 | 3.945 |
And so it might not make
sense for me to just | 1,841.215 | 1.875 |
label each origin or each destination
just by the name of the city. | 1,843.09 | 3.75 |
Maybe I also want to give
the three letter airport | 1,846.84 | 2.7 |
code that corresponds to the airport
to which I'm referring in this case. | 1,849.54 | 4.53 |
So how would I encode into this
table, not only the origin, but also | 1,854.07 | 3.66 |
that city's airport code? | 1,857.73 | 1.44 |
And not only for the destination
the name of the city, | 1,859.17 | 2.26 |
but also the airport code
for that airport as well? | 1,861.43 | 3.02 |
Well I could just add more columns. | 1,864.45 | 2 |
I could say something
like, all right, now | 1,866.45 | 1.75 |
we have this table that has an id, an
origin, an origin code, a destination, | 1,868.2 | 6.33 |
a destination code, and a duration. | 1,874.53 | 2.88 |
But here now, the table is
starting to get fairly wide. | 1,877.41 | 3.64 |
There are a lot of columns
here and in particular, there | 1,881.05 | 2.68 |
is some duplicate data. | 1,883.73 | 3.37 |
Paris is associated with
this particular three letter | 1,887.1 | 2.91 |
code, and the same thing for New
York and other airports as well. | 1,890.01 | 3.67 |
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