- `a=list()`
- `a=[]`
- ` list.append(item) #Adds item to the end of the list`
- ` list.insert(index, item) #Adds item to the specified index in the list`
`>>> a`
`['cat', '2', '3']` |Access by index:
`>>> a[0]`
`1`|See webpage at http://www.linuxtopia.org/online_books/programming_books/python_programming/python_ch14s07.html for some helpful methods when dealing with lists.|\n", "|**Dictionary**|Mutable| `student={}` | `>>> student={'name': 'John Doe', 'age': 22, 'college': 'MSU'}` | `>>> student['major']='Computer Science'`
`>>> student`
`{'name': 'John Doe', 'age': 22, 'college': 'MSU', 'major': 'Computer Science'}` | `del dictName[keyName] #This method removes all entries associated with the given key` | `>>> student['age'] = 23`
`>>> student`
`{'name': 'John Doe', 'age': 23, 'college': 'MSU', 'major': 'Computer Science'}`| Access by key word. Note that this key **must** be a string.
`>>>student['college']`
`MSU`| The 'in' keyword can be very helpful with dictionaries. Ex:
- `'k' in dict #Returns true if key 'k' is in dictionary dict`
- `'k' not in dict #Returns true if key 'k' is not in dicitonary dict`
- `for key in dict #This will iterate over all keys in dictionary dict`
See webpage at http://www.python-course.eu/python3_dictionaries.php for additional helpful methods and operations|\n", "|**Set**|Mutable. However the objects contained within a set **must** be immutable. | `s=set()`|`s=set(['a','b','c'])` | `s.add(item)`|
- `set.discard(item) #If item is in the set, the item is removed, otherwise nothing happens`
- `set.remove(item) #If item is in the set, the item is removed, otherwise raise a KeyError`
- `set.pop() #Remove and return an arbitrary element from the set. If the set is empty, raise a KeyError` | Sets are unordered, therefore indexing does not mean anything. To modify a set, you must directly add or remove elements. |`>>> set.pop() #This will remove and return an arbitrary element from the set`|Some helpful methods include:
- `difference()`
- `intersection()`
- `isdisjoint()`
- `union()`
- `t=()`
- `t=tuple()`
- 1-tuple:
`t=('Hello',)` - 2-tuple:
`t=('Hello', 'Goodbye')` - Access By Index:
`>>> t[0]`
`'Hello'` - Access By Slice
`>>>t[0:1:2]`
`('Hello','Goodbye')` - Packing and Unpacking
- Tuple to List: `list(tupleName)`
- `np.insert(arrayName,index,values,axis) #Inserts a value for an array at the given index.`
- `np.append(arrayName,value,axis) #Appends values to the end of an array.`
- Access By Index:
`>>> a[0] `
` 1 ` - Access By Slice:
`>>> a[0:5:2] `
`array([1, 3, 5])`
See webpage at http://www.programiz.com/python-programming/set for additional helpful methods and operations|\n", "| **Tuple** |Immutable|
- |
- |N/A|N/A|N/A|`t=('Hello','Goodbye','Goodnight')`
`array([ 1, 2, 3, 4, 12])`
For additional information on manipulating NumPy Arrays see the webpage at http://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html |
See webpage at http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html for further information about indexing of NumPy Arrays|See webpages at http://www.scipy-lectures.org/intro/numpy/array_object.html and https://docs.scipy.org/doc/numpy-dev/user/quickstart.html for additional information on NumPy Arrays|\n", " \n", "\\*Use of the NumPy Array requires the NumPy Python Module. In these examples, it is assumed that the import statement is \"import numpy as np\"\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 4:** What is the difference between mutable and immutable objects? Give an example of a scientific model where you would choose one over the other (ex. maybe a tuple vs. a list). Explain the choice. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "✎ Do This - Erase the contents of this cell and replace it with your answer to the above question! (double-click on this text to edit this cell, and hit shift+enter to save the text). Please use appropriate Markup formating to make your answer's easy to read." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 5:** Imagine a function that takes filename as a string input and counts the number of times each word appears in the file. What type of container would make the most sense for storing the words and the word counts? Why?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "✎ Do This - Erase the contents of this cell and replace it with your answer to the above question! (double-click on this text to edit this cell, and hit shift+enter to save the text). Please use appropriate Markup formating to make your answer's easy to read." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 6:** A digital image is often represented as a three dimensional array. Where the first dimension is row, the second dimension is column and the third dimension is a color. Create a three dimensional containers of numbers with 10 rows, 10 columns and 3 colors (red, green and blue) and initialize all of the numbers to zero. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#Do This - put your code here to generate a 3D container.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "###ANSWER \n", "import numpy as np\n", "n = np.zeros((10,10,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "## 3. The basics of Pandas\n", "\n", "Pandas is probably the fundamental tool we are going to use to do Data Science work. While we can do this work in straight Python, Pandas makes much of that work easier. We cannot do justice to all of Pandas, it is a big package, but we need to remind ourselves of some of the basics. We'll pick more and more of Pandas as we go along. \n", "\n", "What follows should mostly be review. Feel free to reach out and find tutorial pages that might help with reminding you of how Pandas works. In face, we'll ask some questions at the end as to your favorite tutorial pages." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Pandas Series\n", "\n", "The basic Pandas data structure is called a *Series*. It is a sequence, not unlike a numpy array, but with an associated set of labels for each value called the *index*. If you don't provide the index labels, Pandas will use the regular 0-based index as the label. Let me repeat that, it will use the numeric index as the index label. That will be important later.\n", "\n", "You can make a Series using either a python *dict*, with keys as the indicies, or by separately providing the values and indicies. You can also updated the index labels or reset the labels to the default. Note however that the `reset_index` method does *not* change the Series but returns, not a Series but a DataFrame, where the original index is preserved as a new column." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Series with indicies\n", "Type:
![](\"df.png\")
\n",
"\n",
"It is always important to know the **types** in each column as that can affect the kinds of operations you can perform on a column. Listing the `.dtypes` provides such a list. A type of `object` is likely (though not necessarily) a string.\n",
"\n",
"An index for the rows is provided by default using 0-based array indexing. The use of `[]` label indexing returns a `Series` which is a column with that heading name. The index of the entire DataFrame is used for the returned Series."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"