In [1]:
my_tuple = ('I', 'like', 'cake')
my_tuple
Out[1]:
Tuples are simple containers for data. They are ordered, meaining the order the elements are in when the tuple is created are preserved. We can get values from our tuple by using array indexing, similar to what we were doing with pandas.
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my_tuple[0]
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Recall that Python indexes start at 0. So the first element in a tuple is 0 and the last is array length - 1. You can also address from the end to the front by using negative (-) indexes, e.g.
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my_tuple[-1]
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You can also access a range of elements, e.g. the first two, the first three, by using the : to expand a range. This is called slicing.
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my_tuple[0:2]
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my_tuple[0:3]
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What do you notice about how the upper bound is referenced?
Without either end, the : expands to the entire list.
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my_tuple[1:]
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my_tuple[:-1]
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my_tuple[:]
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Tuples have a key feature that distinguishes them from other types of object containers in Python. They are immutable. This means that once the values are set, they cannot change.
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my_tuple[2]
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So what happens if I decide that I really prefer pie over cake?
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my_tuple[2] = 'pie'
Facts about tuples:
So then, what are the use cases of tuples?
Write-protects data that other pieces of code should not alterYou can alter the value of a tuple variable, e.g. change the tuple it holds, but you can't modify it.
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my_tuple
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my_tuple = ('I', 'love', 'pie')
my_tuple
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There is a really handy operator in that can be used with tuples that will return True if an element is present in a tuple and False otherwise.
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'love' in my_tuple
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Finally, tuples can contain different types of data, not just strings.
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import math
my_second_tuple = (42, 'Elephants', 'ate', math.pi)
my_second_tuple
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Numerical operators work... Sort of. What happens when you add?
my_second_tuple + 'plus'
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my_second_tuple + 'plus'
Not what you expects? What about adding two tuples?
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my_second_tuple + my_tuple
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Other operators: -, /, *
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my_list = ['I', 'like', 'cake']
my_list
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At first glance, tuples and lists look pretty similar. Notice the lists use '[' and ']' instead of '(' and ')'. But indexing and refering to the first entry as 0 and the last as -1 still works the same.
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my_list[0]
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my_list[-1]
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my_list[0:3]
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Lists, however, unlike tuples, are mutable.
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my_list[2] = 'pie'
my_list
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Multiple elements in the list can even be changed at once!
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my_list[1:] = ['love', 'puppies']
my_list
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You can still use the in operator.
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'puppies' in my_list
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'kittens' in my_list
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So when to use a tuple and when to use a list?
Ways to modify a list? You have already seen by index. Let's start with an empty list.
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my_new_list = []
my_new_list
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We can add to the list using the append method on it.
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my_new_list.append('Now')
my_new_list
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We can use the + operator to create a longer list by adding the contents of two lists together.
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my_new_list + my_list
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One of the useful things to know about a list how many elements are in it. This can be found with the len function.
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len(my_list)
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Some other handy functions with lists:
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Sometimes you have a tuple and you need to make it a list. You can cast the tuple to a list with list(my_tuple)
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list(my_tuple)
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What in the above told us it was a list?
You can also use the type function to figure out the type.
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type(tuple)
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type(list(my_tuple))
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There are other useful methods on lists, including:
| methods | description |
|---|---|
| list.append(obj) | Appends object obj to list |
| list.count(obj) | Returns count of how many times obj occurs in list |
| list.extend(seq) | Appends the contents of seq to list |
| list.index(obj) | Returns the lowest index in list that obj appears |
| list.insert(index, obj) | Inserts object obj into list at offset index |
| list.pop(obj=list[-1]) | Removes and returns last object or obj from list |
| list.remove(obj) | Removes object obj from list |
| list.reverse() | Reverses objects of list in place |
| list.sort([func]) | Sort objects of list, use compare func, if given |
Try some of them now.
my_list.count('I')
my_list
my_list.append('I')
my_list
my_list.count('I')
my_list
#my_list.index(42)
my_list.index('puppies')
my_list
my_list.insert(my_list.index('puppies'), 'furry')
my_list
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my_list.index('I')
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my_list.reverse()
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my_list
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Dictionaries are similar to tuples and lists in that they hold a collection of objects. Dictionaries, however, allow an additional indexing mode: keys. Think of a real dictionary where the elements in it are the definitions of the words and the keys to retrieve the entries are the words themselves.
| word | definition |
|---|---|
| tuple | An immutable collection of ordered objects |
| list | A mutable collection of ordered objects |
| dictionary | A mutable collection of named objects |
Let's create this data structure now. Dictionaries, like tuples and elements use a unique referencing method, '{' and its evil twin '}'.
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my_dict = { 'tuple' : 'An immutable collection of ordered objects',
'list' : 'A mutable collection of ordered objects',
'dictionary' : 'A mutable collection of objects' }
my_dict
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We access items in the dictionary by name, e.g.
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my_dict['dictionary']
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Since the dictionary is mutable, you can change the entries.
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my_dict['dictionary'] = 'A mutable collection of named objects'
my_dict
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Notice that ordering is not preserved!
And we can add new items to the list.
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my_dict['cabbage'] = 'Green leafy plant in the Brassica family'
my_dict
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To delete an entry, we can't just set it to None
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my_dict['cabbage'] = None
my_dict
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To delete it propery, we need to pop that specific entry.
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my_dict.pop('cabbage', None)
You can use other objects as names, but that is a topic for another time. You can mix and match key types, e.g.
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my_new_dict = {}
my_new_dict[1] = 'One'
my_new_dict['42'] = 42
my_new_dict
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You can get a list of keys in the dictionary by using the keys method.
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my_dict.keys()
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Similarly the contents of the dictionary with the items method.
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my_dict.items()
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We can use the keys list for fun stuff, e.g. with the in operator.
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'dictionary' in my_dict.keys()
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This is a synonym for in my_dict
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'dictionary' in my_dict
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Notice, it doesn't work for elements.
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'A mutable collection of ordered objects' in my_dict
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Other dictionary methods:
| methods | description |
|---|---|
| dict.clear() | Removes all elements from dict |
| dict.get(key, default=None) | For key key, returns value or default if key doesn't exist in dict |
| dict.items() | Returns a list of dicts (key, value) tuple pairs |
| dict.keys() | Returns a list of dictionary keys |
| dict.setdefault(key, default=None) | Similar to get, but set the value of key if it doesn't exist in dict |
| dict.update(dict2) | Add the key / value pairs in dict2 to dict |
| dict.values | Returns a list of dictionary values |
Feel free to experiment...
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my_dict.setdefault('dictionar', "None")
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my_dict
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dict2 = {'r':1,'a':2}
my_dict.update(dict2)
my_dict
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instructors = ['Dave', 'Jim', 'Dorkus the Clown']
instructors
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if 'Dorkus the Clown' in instructors:
print('#fakeinstructor')
Usually we want conditional logic on both sides of a binary condition, e.g. some action when True and some when False
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if 'Dorkus the Clown' in instructors:
print('There are fake names for class instructors in your list!')
else:
print("Nothing to see here")
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
Note: what have you noticed in this session about quotes? What is the difference between ' and "?
Another simple example:
In [135]:
if True is False:
print("I'm so confused")
else:
print("Everything is right with the world")
It is always good practice to handle all cases explicity. Conditional fall through is a common source of bugs.
Sometimes we wish to test multiple conditions. Use if, elif, and else.
In [136]:
my_favorite = 'pie'
if my_favorite is 'cake':
print("He likes cake! I'll start making a double chocolate velvet cake right now!")
elif my_favorite is 'pie':
print("He likes pie! I'll start making a cherry pie right now!")
else:
print("He likes " + my_favorite + ". I don't know how to make that.")
Conditionals can take and and or and not. E.g.
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my_favorite = 'pie'
if my_favorite is 'cake' or my_favorite is 'pie':
print(my_favorite + " : I have a recipe for that!")
else:
print("Ew! Who eats that?")
In [138]:
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!")
Dictionaries can use the keys method for iterating.
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my_dict
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for key in my_dict.keys():
if len(key) < 5:
print(my_dict[key])
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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range(3)
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Notice that Python (in the newest versions, e.g. 3+) has an object type that is a range. This saves memory and speeds up calculations vs. an explicit representation of a range as a list - but it can be automagically converted to a list on the fly by Python. To show the contents as a list we can use the type case like with the tuple above.
Sometimes, in older Python docs, you will see xrange. This used the range object back in Python 2 and range returned an actual list. Beware of this!
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list(range(3))
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Remember earlier with slicing, the syntax :3 meant [0, 1, 2]? Well, the same upper bound philosophy applies here.
In [165]:
for index in range(3):
instructor = instructors[index]
if instructor.endswith('Clown'):
print(instructor + " doesn't sound like a real instructor name!")
else:
print(instructor + " is so smart... all those gooey brains!")
This would probably be better written as
In [166]:
for index in range(len(instructors)):
instructor = instructors[index]
if instructor.endswith('Clown'):
print(instructor + " doesn't sound like a real instructor name!")
else:
print(instructor + " is so smart... all those gooey brains!")
But in all, it isn't very Pythonesque to use indexes like that (unless you have another reason in the loop) and you would opt instead for the instructor in instructors form.
More often, you are doing something with the numbers that requires them to be integers, e.g. math.
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sum = 0
for i in range(10):
sum += i
print(sum)
Note: for more on formatting strings, see: https://pyformat.info
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for i in range(1, 4):
for j in range(1, 4):
print('%d * %d = %d' % (i, j, i*j)) # Note string formatting here, %d means an integer
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for i in range(10):
if i == 4:
break
i
Out[3]:
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sum = 0
for i in range(10):
if (i == 5):
continue
else:
sum += i
print(sum)
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sum = 0
for i in range(10):
sum += i
else:
print('final i = %d, and sum = %d' % (i, sum))
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my_string = "DIRECT"
for c in my_string:
print(c)
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tr = {'a':'b'}
tr
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Objective: Replace the bash magic bits for downloading the HCEPDB data and uncompressing it with Python code. Since the download is big, check if the zip file exists first before downloading it again. Then load it into a pandas dataframe.
Notes:
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))
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| URL | filename | csv_filename |
|---|---|---|
| http://faculty.washington.edu/dacb/HCEPDB_moldata_set1.zip | HCEPDB_mol_data_set1.zip | HCEPDB_mol_data_set1.csv |
| http://faculty.washington.edu/dacb/HCEPDB_moldata_set2.zip | HCEPDB_mol_data_set2.zip | HCEPDB_mol_data_set2.csv |
| http://faculty.washington.edu/dacb/HCEPDB_moldata_set3.zip | HCEPDB_mol_data_set3.zip | HCEPDB_mol_data_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):
"""
optional 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)
return
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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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)
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)
my_global_1 = 'bad idea'
my_global_2 = 'another bad one'
my_global_3 = 'better idea'
def my_function():
print(my_global_1)
#print(my_global_1 + my_global_2)
my_global_2 = 'broke your global, man!'
global my_global_3
print(my_global_1 + 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 sum(x, y):
my_sum = x + y
return my_sum
sum(1,1)
Out[25]:
| 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 |
In [70]:
def print_name(first, last='the Clown', *k):
print('Your name is %s %s' % (first, last), len(k))
return
Play around with the above function.
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print_name(3, 5, 7, 9, 10, 12)
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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))
if age > 35:
print('You are really old.')
return
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print_name_age(age=40, last='Beck', first='Dave')
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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!
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Homework:
Save your functions to hcepdb_utils.py. Import the functions and use them to rewrite HW1. This will be laid out in the homework repo for HW2. Check the website.
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