text stringlengths 1 81 | start float64 0 10.1k | duration float64 0 24.9 |
|---|---|---|
value from another in other scenarios | 1,737.24 | 4.26 |
you might very well want to keep that | 1,739.58 | 3.54 |
line ending because it's a very long | 1,741.5 | 3.72 |
series of text or a parallel graph or | 1,743.12 | 3.36 |
something like that where you want to | 1,745.22 | 2.939 |
keep it distinct from the others but | 1,746.48 | 3.9 |
it's just a convention we have to use | 1,748.159 | 4.861 |
something presumably to separate one | 1,750.38 | 4.919 |
chunk of text from another there are | 1,753.02 | 4.32 |
other functions in Python that will in | 1,755.299 | 4.021 |
fact handle the removal of that white | 1,757.34 | 4.439 |
space for you read lines though does | 1,759.32 | 4.02 |
literally that though it reads all of | 1,761.779 | 4.561 |
the lines as is well allow me to turn | 1,763.34 | 5.16 |
our attention back to where we left off | 1,766.34 | 4.559 |
here which is just names to propose that | 1,768.5 | 5.159 |
with names.text we have an ability it | 1,770.899 | 4.38 |
seems to store each of these names | 1,773.659 | 3.721 |
pretty straightforwardly but what if we | 1,775.279 | 3.241 |
wanted to keep track of other | 1,777.38 | 3.299 |
information as well suppose that we | 1,778.52 | 5.039 |
wanted to store information including a | 1,780.679 | 6.301 |
student's uh name and their house at | 1,783.559 | 5.521 |
Hogwarts be it Gryffindor or Slytherin | 1,786.98 | 4.439 |
or something else well where do we go | 1,789.08 | 4.26 |
about putting that you know Hermione | 1,791.419 | 3.36 |
lives in Gryffindor so we could do | 1,793.34 | 3 |
something like this in our text file | 1,794.779 | 3.9 |
Harry lives in Gryffindor so we could do | 1,796.34 | 4.559 |
that Ron lives in Gryffindor so we could | 1,798.679 | 4.441 |
do that and Draco lives in Slytherin so | 1,800.899 | 5.701 |
we could do that but I worry here | 1,803.12 | 5.52 |
but I worry now that we're mixing apples | 1,806.6 | 4.079 |
and oranges so to speak like some lines | 1,808.64 | 4.32 |
or names some lines are houses so this | 1,810.679 | 4.201 |
probably isn't the best design if only | 1,812.96 | 3.78 |
because it's confusing or it's ambiguous | 1,814.88 | 4.08 |
so maybe what we could do is Adopt A | 1,816.74 | 3.84 |
convention and indeed this is in fact | 1,818.96 | 4.079 |
what a lot of programmers do they change | 1,820.58 | 5.16 |
this file not to be names.text but | 1,823.039 | 4.64 |
instead let me create a new file called | 1,825.74 | 5.46 |
names.csv CSV stands for comma separated | 1,827.679 | 5.38 |
values and it's a very common convention | 1,831.2 | 4.02 |
to store multiple pieces of information | 1,833.059 | 5.401 |
that are related in the same file and so | 1,835.22 | 5.1 |
to do this I'm going to separate each of | 1,838.46 | 4.02 |
these types of data not with another new | 1,840.32 | 4.5 |
line but simply with a comma I'm going | 1,842.48 | 4.199 |
to keep each student on their own line | 1,844.82 | 3.3 |
but I'm going to separate the | 1,846.679 | 3.541 |
information about each student using a | 1,848.12 | 4.679 |
comma instead and so now we sort of have | 1,850.22 | 4.92 |
a two-dimensional file if you will row | 1,852.799 | 4.561 |
by row we have our students but if you | 1,855.14 | 4.44 |
think of these commas as representing a | 1,857.36 | 3.72 |
column even though it's not perfectly | 1,859.58 | 2.939 |
straight because of the lengths of these | 1,861.08 | 4.199 |
names it's a little it's a little Jagged | 1,862.519 | 4.02 |
you can think of these commas as | 1,865.279 | 3.601 |
representing a column and it turns out | 1,866.539 | 4.98 |
these CSV files are very commonly used | 1,868.88 | 4.44 |
when you use something like Microsoft | 1,871.519 | 3.54 |
Excel Apple numbers or Google | 1,873.32 | 3.54 |
spreadsheets and you want to export the | 1,875.059 | 4.5 |
data to share with someone else as a CSV | 1,876.86 | 5.699 |
file or conversely if you want to import | 1,879.559 | 5.401 |
a CSV file into your preferred | 1,882.559 | 4.441 |
spreadsheet software like Excel or | 1,884.96 | 3.9 |
numbers or Google spreadsheets you can | 1,887 | 4.2 |
do that as well so CSV is a very common | 1,888.86 | 4.74 |
very simple text format that just | 1,891.2 | 5.04 |
separates values with commas and | 1,893.6 | 4.079 |
different types of values ultimately | 1,896.24 | 3.72 |
with new lines as well let me go ahead | 1,897.679 | 5.22 |
and run code of students.csv to create a | 1,899.96 | 4.559 |
brand new file that's initially empty | 1,902.899 | 3.841 |
and we'll add to it those same names but | 1,904.519 | 4.561 |
also some other information as well so | 1,906.74 | 5.279 |
if I now have this new file students.csv | 1,909.08 | 5.459 |
inside of which is one column of name so | 1,912.019 | 4.921 |
to speak and one column of houses how do | 1,914.539 | 4.5 |
I go about changing my code to read not | 1,916.94 | 4.02 |
just those names but also those names | 1,919.039 | 3.661 |
and houses so that they're not all on | 1,920.96 | 3.9 |
one line we somehow have access to both | 1,922.7 | 4.74 |
type of value separately play well let | 1,924.86 | 3.72 |
me go ahead and create a new program | 1,927.44 | 2.78 |
here called | 1,928.58 | 4.56 |
students.pi and in this program let's go | 1,930.22 | 5.199 |
about reading not a text file per se but | 1,933.14 | 4.74 |
a specific type of text file a CSV a | 1,935.419 | 4.921 |
comma separated values file and to do | 1,937.88 | 3.84 |
this I'm going to use similar code as | 1,940.34 | 4.38 |
before I'm going to say with open quote | 1,941.72 | 4.04 |
unquote | 1,944.72 | 3.179 |
students.csv I'm not going to bother | 1,945.76 | 3.88 |
specifying quote unquote R because again | 1,947.899 | 3.241 |
that's the default but I'm going to give | 1,949.64 | 4.44 |
myself a variable name of file and then | 1,951.14 | 4.44 |
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