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instructors = ['Dave', 'Jim', 'Dorkus the Clown']
if 'Dorkus the Clown' in instructors:
print('#fakeinstructor')
There is a special do nothing word: pass that skips over some arm of a conditional, e.g.
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if 'Jim' in instructors:
print("Congratulations! Jim is teaching, your class won't stink!")
else:
pass
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for instructor in instructors:
print(instructor)
You can combine loops and conditionals:
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for instructor in instructors:
if instructor.endswith('Clown'):
print(instructor + " doesn't sound like a real instructor name!")
else:
print(instructor + " is so smart... all those gooey brains!")
Since for operates over lists, it is common to want to do something like:
NOTE: C-like
for (i = 0; i < 3; ++i) {
print(i);
}The Python equivalent is:
for i in [0, 1, 2]:
do something with iWhat happens when the range you want to sample is big, e.g.
NOTE: C-like
for (i = 0; i < 1000000000; ++i) {
print(i);
}That would be a real pain in the rear to have to write out the entire list from 1 to 1000000000.
Enter, the range() function. E.g.
range(3) is [0, 1, 2]
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sum = 0
for i in range(10):
sum += i
print(sum)
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data.head()
| URL | filename | csv_filename |
|---|---|---|
| http://faculty.washington.edu/dacb/HCEPDB_moldata_set1.zip | HCEPDB_moldata_set1.zip | HCEPDB_moldata_set1.csv |
| http://faculty.washington.edu/dacb/HCEPDB_moldata_set2.zip | HCEPDB_moldata_set2.zip | HCEPDB_moldata_set2.csv |
| http://faculty.washington.edu/dacb/HCEPDB_moldata_set3.zip | HCEPDB_moldata_set3.zip | HCEPDB_moldata_set3.csv |
What pieces of the data structures and flow control that we talked about earlier can you use?
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How did you solve this problem?
For loops let you repeat some code for every item in a list. Functions are similar in that they run the same lines of code for new values of some variable. They are different in that functions are not limited to looping over items.
Functions are a critical part of writing easy to read, reusable code.
Create a function like:
def function_name (parameters):
"""
docstring
"""
function expressions
return [variable]Note: Sometimes I use the word argument in place of parameter.
Here is a simple example. It prints a string that was passed in and returns nothing.
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def print_string(str):
"""This prints out a string passed as the parameter."""
print(str)
for c in str:
print(c)
if c == 'r':
break
print("done")
return
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print_string("string")
To call the function, use:
print_string("Dave is awesome!")Note: The function has to be defined before you can call it!
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print_string("Dave is awesome!")
If you don't provide an argument or too many, you get an error.
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print_string()
Parameters (or arguments) in Python are all passed by reference. This means that if you modify the parameters in the function, they are modified outside of the function.
See the following example:
def change_list(my_list):
"""This changes a passed list into this function"""
my_list.append('four');
print('list inside the function: ', my_list)
return
my_list = [1, 2, 3];
print('list before the function: ', my_list)
change_list(my_list);
print('list after the function: ', my_list)
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def change_list(my_list):
"""This changes a passed list into this function"""
my_list.append('four');
print('list inside the function: ', my_list)
return
my_list = [1, 2, 3];
print('list before the function: ', my_list)
change_list(my_list);
print('list after the function: ', my_list)
Variables have scope: global and local
In a function, new variables that you create are not saved when the function returns - these are local variables. Variables defined outside of the function can be accessed but not changed - these are global variables, Note there is a way to do this with the global keyword. Generally, the use of global variables is not encouraged, instead use parameters.
my_global_1 = 'bad idea'
my_global_2 = 'another bad one'
my_global_3 = 'better idea'
def my_function():
print(my_global_1)
my_global_2 = 'broke your global, man!'
global my_global_3
my_global_3 = 'still a better idea'
return
my_function()
print(my_global_2)
print(my_global_3)
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my_global_1 = 'bad idea'
my_global_2 = 'another bad one'
my_global_3 = 'better idea'
def my_function():
print(my_global_1)
my_global_2 = 'broke your global, man!'
print(my_global_2)
global my_global_3
my_global_3 = 'still a better idea'
return
my_function()
print(my_global_2)
print(my_global_3)
In general, you want to use parameters to provide data to a function and return a result with the return. E.g.
def sum(x, y):
my_sum = x + y
return my_sumIf you are going to return multiple objects, what data structure that we talked about can be used? Give and example below.
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def a_function(parameter):
return None
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foo = a_function('bar')
print(foo)
| type | behavior |
|---|---|
| required | positional, must be present or error, e.g. my_func(first_name, last_name) |
| keyword | position independent, e.g. my_func(first_name, last_name) can be called my_func(first_name='Dave', last_name='Beck') or my_func(last_name='Beck', first_name='Dave') |
| default | keyword params that default to a value if not provided |
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def print_name(first, last='the Clown'):
print('Your name is %s %s' % (first, last))
return
Take a minute and play around with the above function. Which are required? Keyword? Default?
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def massive_correlation_analysis(data, method='pearson'):
pass
return
Functions can contain any code that you put anywhere else including:
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def print_name_age(first, last, age):
print_name(first, last)
print('Your age is %d' % (age))
print('Your age is ' + str(age))
if age > 35:
print('You are really old.')
return
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print_name_age(age=40, last='Beck', first='Dave')
Once you have some code that is functionalized and not going to change, you can move it to a file that ends in .py, check it into version control, import it into your notebook and use it!
Let's do this now for the above two functions.
...
See you after the break!Import the function...
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Call them!
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Notes from last class:
os package has tools for checking if a file exists: os.path.exists
import os
filename = 'HCEPDB_moldata.zip'
if os.path.exists(filename):
print("wahoo!")requests package to get the file given a url (got this from the requests docs)
import requests
url = 'http://faculty.washington.edu/dacb/HCEPDB_moldata.zip'
req = requests.get(url)
assert req.status_code == 200 # if the download failed, this line will generate an error
with open(filename, 'wb') as f:
f.write(req.content)zipfile package to decompress the file while reading it into pandas
import pandas as pd
import zipfile
csv_filename = 'HCEPDB_moldata.csv'
zf = zipfile.ZipFile(filename)
data = pd.read_csv(zf.open(csv_filename))Here was my solution
import os
import requests
import pandas as pd
import zipfile
filename = 'HCEPDB_moldata.zip'
url = 'http://faculty.washington.edu/dacb/HCEPDB_moldata.zip'
csv_filename = 'HCEPDB_moldata.csv'
if os.path.exists(filename):
pass
else:
req = requests.get(url)
assert req.status_code == 200 # if the download failed, this line will generate an error
with open(filename, 'wb') as f:
f.write(req.content)
zf = zipfile.ZipFile(filename)
data = pd.read_csv(zf.open(csv_filename))HCEPDB_utils.py and import it!
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def download_if_not_exists(filename):
if os.path.exists(filename):
pass
else:
req = requests.get(url)
assert req.status_code == 200 # if the download failed, this line will generate an error
with open(filename, 'wb') as f:
f.write(req.content)
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How many functions did you use?
Why did you choose to use functions for these pieces?
Let's say we have three molecules (A, B, C) with three measurements each (v1, v2, v3). So for each molecule we have a vector of measurements:
$$X=\begin{bmatrix} X_{v_{1}} \\ X_{v_{2}} \\ X_{v_{3}} \\ \end{bmatrix} $$Where X is a molecule and the components are the values for each of the measurements. These make up the rows in our matrix.
Often, we want to compare molecules to determine how similar or different they are. One measure is the Pearson correlation.
Pearson correlation:
Expressed graphically, when you plot the paired measurements for two samples (in this case molecules) against each other you can see positively correlated, no correlation, and negatively correlated. Eg.
Simple input dataframe (note when you are writing code it is always a good idea to have a simple test case where you can readily compute by hand or know the output):
| index | v1 | v2 | v3 |
|---|---|---|---|
| A | -1 | 0 | 1 |
| B | 1 | 0 | -1 |
| C | .5 | 0 | .5 |
For our test case, what will the output be?
| A | B | C | |
|---|---|---|---|
| A | 1 | -1 | 0 |
| B | -1 | 1 | 0 |
| C | 0 | 0 | 1 |
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for and possibly if..py file in the directory with the Jupyter notebook, import and run!
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import pandas as pd
import math
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df = pd.read_csv('HCEPDB_moldata.csv')
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df
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def typ(x,y):
sol = x.mean() + y.mean()
return sol
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typ(df['mass'],df['pce'])
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