diff --git a/book/01-module/lesson-1a.ipynb b/book/01-module/lesson-1a.ipynb index ec84763..b99c142 100644 --- a/book/01-module/lesson-1a.ipynb +++ b/book/01-module/lesson-1a.ipynb @@ -94,7 +94,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -200,7 +200,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -457,7 +457,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/01-module/lesson-1b.ipynb b/book/01-module/lesson-1b.ipynb index 7b6198a..ae6afc9 100644 --- a/book/01-module/lesson-1b.ipynb +++ b/book/01-module/lesson-1b.ipynb @@ -46,7 +46,7 @@ "## Determining the type\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "First, we will use Python's built-in `type()` function to determine the type of some variables." @@ -1028,7 +1028,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1226,7 +1226,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/01-module/lesson-1c.ipynb b/book/01-module/lesson-1c.ipynb index 6cc2f4f..a30e327 100644 --- a/book/01-module/lesson-1c.ipynb +++ b/book/01-module/lesson-1c.ipynb @@ -613,7 +613,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -905,7 +905,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1177,7 +1177,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1616,7 +1616,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/01-module/lesson-1d.ipynb b/book/01-module/lesson-1d.ipynb index f36dfa1..f5ef80a 100644 --- a/book/01-module/lesson-1d.ipynb +++ b/book/01-module/lesson-1d.ipynb @@ -30,7 +30,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -717,7 +717,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1036,7 +1036,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1353,7 +1353,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/02-module/lesson-2a.ipynb b/book/02-module/lesson-2a.ipynb index cc783c0..d343b3e 100644 --- a/book/02-module/lesson-2a.ipynb +++ b/book/02-module/lesson-2a.ipynb @@ -37,7 +37,7 @@ "## Terminology\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "People often use the terms _\"package\"_, _\"library\"_, and _\"module\"_ synonymously. If you are familiar with the R programming language you've probably heard some of these terms before. Although there are some semantical differences between R and Python, here is how you can think of the two terms:\n", @@ -422,7 +422,7 @@ "In the code above you saw the `import` statement. The `import` statement allows us to import code from another module. There are a few ways to use the statement, which we will mention briefly here, from most recommended to least recommended.\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "### Explicit module import\n", @@ -638,7 +638,7 @@ "## Standard library\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "In the above code we've seen a few different libraries installed (`math` and `numpy`). These libraries represent two of the many readily available libraries at your disposal. However, we refer to the `math` library is part of the **standard library** and the `numpy` library is considered a **third party library**.\n", @@ -769,7 +769,7 @@ "## Third-party modules\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "One of the things that makes Python useful, especially within the world of data science, is its ecosystem of third-party modules. These can be imported just as the built-in modules, but first the modules must be installed on your system. As previously mentioned, [Python Package Index](https://pypi.org/) (PyPI for short) and [Anaconda](https://anaconda.org/anaconda/repo) are the two primary public package managers for Python. To install packages from these locations we just use the following:\n", diff --git a/book/02-module/lesson-2b.ipynb b/book/02-module/lesson-2b.ipynb index afb1955..4b4caff 100644 --- a/book/02-module/lesson-2b.ipynb +++ b/book/02-module/lesson-2b.ipynb @@ -30,7 +30,7 @@ "## Data & memory\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "Python stores its data in memory - this makes it relatively quickly accessible but can cause size limitations in certain fields. In this class we will mainly work with small to moderate data sets, which means we should not run into any space limitations. \n", @@ -59,7 +59,7 @@ "## Delimited files\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "Text files are a popular way to hold and exchange tabular data as almost any data application supports exporting data to the CSV (or other text file) format. Text file formats use delimiters to separate the different elements in a line, and each line of data is in its own line in the text file. Therefore, importing different kinds of text files can follow a fairly consistent process once you’ve identified the delimiter.\n", @@ -708,7 +708,7 @@ "### File paths\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "It's important to understand where files exist on your computer and how to reference those paths. There are two main approaches:\n", @@ -1020,7 +1020,7 @@ "### Attributes & methods\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "We've seen that we can use the dot-notation to access functions in libraries (i.e. `pd.read_csv()`). We can use this same approach to access things inside of _objects_. What's an object? Basically, a variable that contains other data or functionality inside of it that is exposed to users. Consequently, our DataFrame item is an object.\n", diff --git a/book/02-module/lesson-2c.ipynb b/book/02-module/lesson-2c.ipynb index 3fee2cc..d1cc882 100644 --- a/book/02-module/lesson-2c.ipynb +++ b/book/02-module/lesson-2c.ipynb @@ -114,7 +114,7 @@ "## What Are DataFrames Made of?\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "Accessing an individual column of a DataFrame can be done by passing the column name as a string, in brackets (`[]`)." @@ -496,7 +496,7 @@ "## What Can You Do with a Series?\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -953,7 +953,7 @@ "## DataFrame Indexes\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "It's not just Series that have indexes! DataFrames have them too. Take a look at the carrier DataFrame again and note the bold numbers on the left." diff --git a/book/03-module/lesson-3a.ipynb b/book/03-module/lesson-3a.ipynb index 590b6df..09d225e 100644 --- a/book/03-module/lesson-3a.ipynb +++ b/book/03-module/lesson-3a.ipynb @@ -179,7 +179,7 @@ "## Subsetting dimensions\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "We don't always want all of the data in a DataFrame, so we need to take subsets of the DataFrame. In general, **subsetting** is extracting a small portion of a DataFrame -- making the DataFrame smaller. Since the DataFrame is two-dimensional, there are two dimensions on which to subset.\n", @@ -903,7 +903,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2156,7 +2156,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2491,7 +2491,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/03-module/lesson-3b.ipynb b/book/03-module/lesson-3b.ipynb index 38bf044..55b017b 100644 --- a/book/03-module/lesson-3b.ipynb +++ b/book/03-module/lesson-3b.ipynb @@ -1013,7 +1013,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1720,7 +1720,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2700,7 +2700,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/03-module/lesson-3c.ipynb b/book/03-module/lesson-3c.ipynb index d9e2801..277b602 100644 --- a/book/03-module/lesson-3c.ipynb +++ b/book/03-module/lesson-3c.ipynb @@ -274,7 +274,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -859,7 +859,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2318,7 +2318,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/04-module/lesson-4a.ipynb b/book/04-module/lesson-4a.ipynb index e4ecee9..2bf07d4 100644 --- a/book/04-module/lesson-4a.ipynb +++ b/book/04-module/lesson-4a.ipynb @@ -23,7 +23,7 @@ "Once a DataFrame is tidy, it becomes much easier to compute summary statistics, join with other datasets, visualize, apply machine learning models, etc. In this lesson we will focus on ways to reshape DataFrames so that they meet the tidy guidelines.\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -458,8 +458,8 @@ "source": [ "ames_wide.melt(\n", " id_vars='neighborhood',\n", - " value_vars=['2007', '2008', '2009', '2010'], \n", - " var_name='year', \n", + " value_vars=['2007', '2008', '2009', '2010'],\n", + " var_name='year',\n", " value_name='homes_sold'\n", " )" ] @@ -498,7 +498,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1202,7 +1202,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/04-module/lesson-4b.ipynb b/book/04-module/lesson-4b.ipynb index 9f1dc98..f9c3951 100644 --- a/book/04-module/lesson-4b.ipynb +++ b/book/04-module/lesson-4b.ipynb @@ -1480,7 +1480,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1655,7 +1655,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/04-module/lesson-4c.ipynb b/book/04-module/lesson-4c.ipynb index 30e6ab3..c0248f1 100644 --- a/book/04-module/lesson-4c.ipynb +++ b/book/04-module/lesson-4c.ipynb @@ -1009,7 +1009,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -3761,7 +3761,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/06-module/lesson-6a.ipynb b/book/06-module/lesson-6a.ipynb index ad8afaf..ccda7b3 100644 --- a/book/06-module/lesson-6a.ipynb +++ b/book/06-module/lesson-6a.ipynb @@ -120,7 +120,7 @@ } ], "source": [ - "# multiple lines in the statement are fine as long as they have the \n", + "# multiple lines in the statement are fine as long as they have the\n", "# same indentation\n", "if x < 0:\n", " new_value = abs(x)\n", @@ -254,7 +254,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -339,7 +339,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -520,18 +520,18 @@ "def produce_revenue(sqft, visits, trend):\n", " total = 9.91 * sqft * visits * trend\n", " return round(total, 2)\n", - " \n", + "\n", "def frozen_revenue(sqft, visits, trend):\n", " prod = produce_revenue(sqft, visits, trend)\n", - " total = 3.28 * sqft * visits * trend - prod * .005 \n", + " total = 3.28 * sqft * visits * trend - prod * .005\n", " return round(total, 2)\n", - " \n", + "\n", "expected_annual_revenue = {\n", - " 'produce': produce_revenue, \n", - " 'frozen': frozen_revenue, \n", + " 'produce': produce_revenue,\n", + " 'frozen': frozen_revenue,\n", " 'pharmacy': lambda: 16.11 * visits * trend\n", " }\n", - " \n", + "\n", "choice = 'frozen'\n", "expected_annual_revenue.get(choice, 'Bad choice')(sqft=937, visits=465, trend=0.98)" ] @@ -564,7 +564,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1166,7 +1166,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/book/06-module/lesson-6b.ipynb b/book/06-module/lesson-6b.ipynb index 9de962c..614eb29 100644 --- a/book/06-module/lesson-6b.ipynb +++ b/book/06-module/lesson-6b.ipynb @@ -285,7 +285,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -405,7 +405,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -539,7 +539,7 @@ ], "source": [ "squared_certain_values = [\n", - " number * number for number in range(10) \n", + " number * number for number in range(10)\n", " if number % 2 != 0 and number != 5\n", " ]\n", "\n", @@ -611,7 +611,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -660,7 +660,7 @@ "while not ten_of_a_kind:\n", " # flip coin 10 times\n", " ten_coin_flips = [random.choice(coin) for flip in range(11)]\n", - " \n", + "\n", " # check if there\n", " ten_of_a_kind = len(set(ten_coin_flips)) == 1\n", "\n", @@ -710,7 +710,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -904,7 +904,7 @@ "for col in range(len(df.columns)):\n", " value = df.iloc[:, col].nunique()\n", " unique_values.append(value)\n", - " \n", + "\n", "unique_values" ] }, diff --git a/book/06-module/lesson-6c.ipynb b/book/06-module/lesson-6c.ipynb index e723904..c072fc1 100644 --- a/book/06-module/lesson-6c.ipynb +++ b/book/06-module/lesson-6c.ipynb @@ -76,7 +76,7 @@ "source": [ "# array containing 4 sets of 10 random numbers\n", "x = np.random.random_sample((4, 10))\n", - " \n", + "\n", "x[0] = (x[0] - x[0].min()) / (x[0].max() - x[0].min())\n", "x[1] = (x[1] - x[1].min()) / (x[1].max() - x[1].min())\n", "x[2] = (x[2] - x[2].min()) / (x[1].max() - x[2].min())\n", @@ -119,7 +119,7 @@ "def rescale(array):\n", " for index, vector in enumerate(array):\n", " array[index] = (vector - vector.min()) / (vector.max() - vector.min())\n", - " \n", + "\n", " return(array)\n", "\n", "rescale(x)" @@ -176,7 +176,7 @@ } ], "source": [ - "# method \n", + "# method\n", "x.sum(axis = 0)" ] }, @@ -292,8 +292,8 @@ "def yell(text):\n", " new_text = text.upper()\n", " return new_text\n", - " \n", - "yell('hello world!') " + "\n", + "yell('hello world!')" ] }, { @@ -346,7 +346,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -392,7 +392,7 @@ ], "source": [ "# implicitly computing store sales for store 46 during week 43\n", - "store_sales(df, 46, 43) " + "store_sales(df, 46, 43)" ] }, { @@ -413,7 +413,7 @@ ], "source": [ "# implicitly computing store sales for store 43 (does not exist) during week 46\n", - "store_sales(df, 43, 46) " + "store_sales(df, 43, 46)" ] }, { @@ -445,7 +445,7 @@ ], "source": [ "# explicitly computing store sales for store 46 during week 43\n", - "store_sales(data=df, week=43, store=46) " + "store_sales(data=df, week=43, store=46)" ] }, { @@ -543,7 +543,7 @@ "def yell(*args):\n", " new_text = ' '.join(args).upper()\n", " return new_text\n", - " \n", + "\n", "yell('hello world!', 'I', 'love', 'Python!!')" ] }, @@ -571,7 +571,7 @@ "# **kwargs just creates a dictionary\n", "def students(**kwargs):\n", " print(kwargs)\n", - " \n", + "\n", "students(student1='John', student2='Robert', student3='Sally')" ] }, @@ -595,8 +595,8 @@ "def print_student_names(**kwargs):\n", " for key, value in kwargs.items():\n", " print(f'{key} = {value}')\n", - " \n", - "print_student_names(student1='John', student2='Robert', student3='Sally') " + "\n", + "print_student_names(student1='John', student2='Robert', student3='Sally')" ] }, { @@ -629,7 +629,7 @@ "source": [ "def some_function(name, age):\n", " return f'{name} is {age} years old'\n", - " \n", + "\n", "some_function('Tom', 27)" ] }, @@ -661,7 +661,7 @@ "source": [ "def some_function(name: str, age: int) -> str:\n", " return f'{name} is {age} years old'\n", - " \n", + "\n", "some_function('Tom', 27)" ] }, @@ -741,7 +741,7 @@ " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", " return total_sales\n", - " \n", + "\n", "store_sales(data=df, store=309, week=48)" ] }, @@ -765,7 +765,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -793,11 +793,11 @@ "def store_sales(data: pd.DataFrame, store: int, week: int) -> float:\n", " \"\"\"\n", " Compute total store sales.\n", - " \n", + "\n", " This function computes the total sales for a given\n", " store and week based on a user supplied DataFrame that\n", " contains sales in a column named `sales_value`.\n", - " \n", + "\n", " Parameters\n", " ----------\n", " data : DataFrame\n", @@ -806,16 +806,16 @@ " Integer value representing store number\n", " week : int\n", " Integer value representing week of year\n", - " \n", + "\n", " Returns\n", " -------\n", " float\n", " A float object representing total store sales\n", - " \n", + "\n", " See Also\n", " --------\n", " store_visits : Computes total store visits\n", - " \n", + "\n", " Examples\n", " --------\n", " >>> store_sales(data=df, store=309, week=48)\n", @@ -849,7 +849,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -897,13 +897,13 @@ " if not isinstance(data, pd.DataFrame): raise Exception('`data` should be a Pandas DataFrame')\n", " if not isinstance(store, int): raise Exception('`store` should be an integer')\n", " if not isinstance(week, int): raise Exception('`week` should be an integer')\n", - " \n", + "\n", " # computation\n", " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", " return total_sales\n", - " \n", - "store_sales(data=df, store='309', week=48) " + "\n", + "store_sales(data=df, store='309', week=48)" ] }, { @@ -937,13 +937,13 @@ " if not isinstance(data, pd.DataFrame): raise TypeError('`data` should be a Pandas DataFrame')\n", " if not isinstance(store, int): raise TypeError('`store` should be an integer')\n", " if not isinstance(week, int): raise TypeError('`week` should be an integer')\n", - " \n", + "\n", " # computation\n", " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", " return total_sales\n", - " \n", - "store_sales(data=df, store='309', week=48) " + "\n", + "store_sales(data=df, store='309', week=48)" ] }, { @@ -1004,10 +1004,10 @@ " if not isinstance(data, pd.DataFrame): raise TypeError('`data` should be a Pandas DataFrame')\n", " if not isinstance(store, int): raise TypeError('`store` should be an integer')\n", " if not isinstance(week, int): raise TypeError('`week` should be an integer')\n", - " if store not in data.store_id.unique(): \n", + " if store not in data.store_id.unique():\n", " raise ValueError(f'`store` {store} does not exist in the supplied DataFrame')\n", - " \n", - " \n", + "\n", + "\n", " # computation\n", " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", @@ -1127,8 +1127,8 @@ " price = round(product['price'] * (1.0 - discount), 2)\n", " assert 0 <= price <= product['price'], 'Invalid discount applied'\n", " return price\n", - " \n", - "apply_discount(milk, 2.00) " + "\n", + "apply_discount(milk, 2.00)" ] }, { @@ -1257,7 +1257,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1300,8 +1300,8 @@ "\n", "def func(x):\n", " return x + 1\n", - " \n", - "func(x = 50) " + "\n", + "func(x = 50)" ] }, { @@ -1359,8 +1359,8 @@ "\n", "def func(x):\n", " return x + ' ' + y\n", - " \n", - "func(x = 'Brad') " + "\n", + "func(x = 'Brad')" ] }, { @@ -1404,7 +1404,7 @@ " def my_paste():\n", " return x + sep + y\n", " return my_paste()\n", - " \n", + "\n", "my_name(sep=' ')" ] }, @@ -1447,7 +1447,7 @@ " y = firstpart + '.' + secondpart\n", " return y\n", "\n", - "convert(8451) " + "convert(8451)" ] }, { diff --git a/docs/_build/.doctrees/01-module/lesson-1a.doctree b/docs/_build/.doctrees/01-module/lesson-1a.doctree index 19b0a9b..f8e667c 100644 Binary files a/docs/_build/.doctrees/01-module/lesson-1a.doctree and b/docs/_build/.doctrees/01-module/lesson-1a.doctree differ diff --git 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8cf688e..f1dd3a5 100644 Binary files a/docs/_build/.doctrees/06-module/lesson-6c.doctree and b/docs/_build/.doctrees/06-module/lesson-6c.doctree differ diff --git a/docs/_build/.doctrees/environment.pickle b/docs/_build/.doctrees/environment.pickle index b916903..632467b 100644 Binary files a/docs/_build/.doctrees/environment.pickle and b/docs/_build/.doctrees/environment.pickle differ diff --git a/docs/_build/html/01-module/lesson-1a.html b/docs/_build/html/01-module/lesson-1a.html index 2989d6c..3c544d7 100644 --- a/docs/_build/html/01-module/lesson-1a.html +++ b/docs/_build/html/01-module/lesson-1a.html @@ -664,7 +664,7 @@

Hello, world. and the print() functionHello, world. to the screen. To do this, we used Python’s built-in print() function. The print() function takes as an argument a string. It then prints that string to the screen. We will learn more about function syntax later, but we can already see the rough syntax with the print() function.

Video 🎥:

- +
@@ -729,7 +729,7 @@

.py filesRunning tab at the left of the JupyterLab window and click on SHUTDOWN next to the console.

Video 🎥:

- +

@@ -904,7 +904,7 @@

Quick keys

Video 🎥:

- +

@@ -951,7 +951,7 @@

Computing environment 
Python implementation: CPython
 Python version       : 3.9.12
-IPython version      : 8.2.0
+IPython version      : 8.4.0
 
 jupyterlab: 3.3.2
diff --git a/docs/_build/html/01-module/lesson-1b.html b/docs/_build/html/01-module/lesson-1b.html index 9b578e5..c6ce586 100644 --- a/docs/_build/html/01-module/lesson-1b.html +++ b/docs/_build/html/01-module/lesson-1b.html @@ -641,7 +641,7 @@

Learning objectives

Video 🎥:

- +

First, we will use Python’s built-in type() function to determine the type of some variables.

@@ -1259,7 +1259,7 @@

Knowledge check

Video 🎥:

- +

@@ -1374,7 +1374,7 @@

Knowledge check

Video 🎥:

- + diff --git a/docs/_build/html/01-module/lesson-1c.html b/docs/_build/html/01-module/lesson-1c.html index 9e35662..015f8b5 100644 --- a/docs/_build/html/01-module/lesson-1c.html +++ b/docs/_build/html/01-module/lesson-1c.html @@ -1008,7 +1008,7 @@

Knowledge check

Video 🎥:

- + @@ -1182,7 +1182,7 @@

Knowledge check

Video 🎥:

- + @@ -1385,7 +1385,7 @@

Knowledge check

Video 🎥:

- + @@ -1656,7 +1656,7 @@

Knowledge check

Video 🎥:

- + @@ -1684,7 +1684,7 @@

Computing environment 
Python implementation: CPython
 Python version       : 3.9.12
-IPython version      : 8.2.0
+IPython version      : 8.4.0
 
 jupyterlab: 3.3.2
diff --git a/docs/_build/html/01-module/lesson-1d.html b/docs/_build/html/01-module/lesson-1d.html index a5c0bc4..2f57b45 100644 --- a/docs/_build/html/01-module/lesson-1d.html +++ b/docs/_build/html/01-module/lesson-1d.html @@ -708,7 +708,7 @@

Learning objectives

Video 🎥:

- +

@@ -1305,7 +1305,7 @@

Knowledge check

Video 🎥:

- + @@ -1508,7 +1508,7 @@

Knowledge check

Video 🎥:

- + diff --git a/docs/_build/html/02-module/lesson-2a.html b/docs/_build/html/02-module/lesson-2a.html index e404404..f5c3607 100644 --- a/docs/_build/html/02-module/lesson-2a.html +++ b/docs/_build/html/02-module/lesson-2a.html @@ -646,7 +646,7 @@

Learning objectives

Video 🎥:

- +

People often use the terms “package”, “library”, and “module” synonymously. If you are familiar with the R programming language you’ve probably heard some of these terms before. Although there are some semantical differences between R and Python, here is how you can think of the two terms:

A package generally refers to source code that is bundled up in a way that a package manager can host. PyPI and Anaconda are the two primary public package managers for Python. When you pip install pkg or conda install pkg you are installing the pkg package from PyPI or Anaconda, respectively, to your computer or the server you’re working on.

@@ -662,394 +662,443 @@

Terminology -
Package                       Version
------------------------------ --------------------
-aiohttp                       3.8.1
-aiosignal                     1.2.0
-alabaster                     0.7.12
-anaconda-client               1.9.0
-anaconda-navigator            2.1.4
-anaconda-project              0.10.2
-anyio                         3.5.0
-appdirs                       1.4.4
-applaunchservices             0.2.1
-appnope                       0.1.2
-appscript                     1.1.2
-argon2-cffi                   21.3.0
-argon2-cffi-bindings          21.2.0
-arrow                         1.2.2
-astroid                       2.6.6
-astropy                       5.0.4
-asttokens                     2.0.5
-async-timeout                 4.0.1
-atomicwrites                  1.4.0
-attrs                         20.3.0
-Automat                       20.2.0
-autopep8                      1.6.0
-Babel                         2.9.1
-backcall                      0.2.0
-backports.functools-lru-cache 1.6.4
-backports.tempfile            1.0
-backports.weakref             1.0.post1
-bcrypt                        3.2.0
-beautifulsoup4                4.11.1
-binaryornot                   0.4.4
-bitarray                      2.4.1
-bkcharts                      0.2
-black                         21.12b0
-blackcellmagic                0.0.3
-bleach                        4.1.0
-bokeh                         2.4.2
-boto3                         1.21.32
-botocore                      1.24.32
-Bottleneck                    1.3.4
-brotlipy                      0.7.0
-cachetools                    4.2.2
-certifi                       2022.5.18.1
-cffi                          1.15.0
-cftime                        1.5.1.1
-chardet                       4.0.0
-charset-normalizer            2.0.4
-click                         8.0.4
-click-completion              0.5.2
-click-log                     0.3.2
-cloudpickle                   2.0.0
-clyent                        1.2.2
-colorama                      0.4.4
-colorcet                      2.0.6
-commonmark                    0.9.1
-completejourney-py            0.0.3
-component-logger              1.11.1
-conda                         4.12.0
-conda-build                   3.21.8
-conda-content-trust           0+unknown
-conda-pack                    0.6.0
-conda-package-handling        1.8.1
-conda-repo-cli                1.0.4
-conda-token                   0.3.0
-conda-verify                  3.4.2
-constantly                    15.1.0
-cookiecutter                  1.7.3
-cryptography                  3.4.8
-cssselect                     1.1.0
-cycler                        0.11.0
-Cython                        0.29.28
-cytoolz                       0.11.0
-daal4py                       2021.5.0
-dask                          2022.2.1
-datashader                    0.13.0
-datashape                     0.5.4
-dateparser                    1.1.1
-debugpy                       1.5.1
-decorator                     5.1.1
-defusedxml                    0.7.1
-diff-match-patch              20200713
-distributed                   2022.2.1
-docutils                      0.16
-e451-py-security              1.0.57
-effo-cookiecutter             0.4.0
-effo411                       0.2.0
-effodata                      2.5.0
-entrypoints                   0.4
-et-xmlfile                    1.1.0
-executing                     0.8.3
-fastjsonschema                2.15.1
-filelock                      3.6.0
-flake8                        3.9.2
-Flask                         1.1.2
-fonttools                     4.25.0
-frozenlist                    1.2.0
-fsspec                        2022.2.0
-future                        0.18.2
-gensim                        4.1.2
-gitdb                         4.0.9
-GitPython                     3.1.27
-glob2                         0.7
-gmpy2                         2.1.2
-google-api-core               1.25.1
-google-auth                   1.33.0
-google-cloud-core             1.7.1
-google-cloud-storage          1.31.0
-google-crc32c                 1.1.2
-google-resumable-media        1.3.1
-googleapis-common-protos      1.53.0
-greenlet                      1.1.1
-grpcio                        1.42.0
-h5py                          3.6.0
-HeapDict                      1.0.1
-holoviews                     1.14.8
-hvplot                        0.7.3
-hyperlink                     21.0.0
-hypothesis                    6.29.3
-idna                          3.3
-imagecodecs                   2021.8.26
-imageio                       2.9.0
-imagesize                     1.3.0
-importlib-metadata            4.11.3
-incremental                   21.3.0
-inflection                    0.5.1
-iniconfig                     1.1.1
-intake                        0.6.5
-intervaltree                  3.1.0
-ipykernel                     6.9.1
-ipython                       8.2.0
-ipython-genutils              0.2.0
-ipywidgets                    7.6.5
-iqplot                        0.2.4
-isort                         5.9.3
-itemadapter                   0.3.0
-itemloaders                   1.0.4
-itsdangerous                  2.0.1
-jdcal                         1.4.1
-jedi                          0.18.1
-Jinja2                        2.11.3
-jinja2-time                   0.2.0
-jmespath                      0.10.0
-joblib                        1.1.0
-json5                         0.9.6
-jsonschema                    3.2.0
-jupyter                       1.0.0
-jupyter-bokeh                 3.0.4
-jupyter-book                  0.12.3
-jupyter-cache                 0.4.3
-jupyter-client                6.1.12
-jupyter-console               6.4.0
-jupyter-core                  4.9.2
-jupyter-server                1.13.5
-jupyter-server-mathjax        0.2.5
-jupyter-sphinx                0.3.2
-jupyterlab                    3.3.2
-jupyterlab-pygments           0.1.2
-jupyterlab-server             2.10.3
-jupyterlab-spellchecker       0.7.2
-jupyterlab-widgets            1.0.0
-jupytext                      1.10.3
-kayday                        2.0.0
-keyring                       23.4.0
-kiwisolver                    1.3.2
-kpi-metrics                   1.3.4
-latexcodec                    2.0.1
-lazy-object-proxy             1.6.0
-libarchive-c                  2.9
-linkify-it-py                 1.0.3
-llvmlite                      0.38.0
-locket                        0.2.1
-lxml                          4.8.0
-Markdown                      3.3.4
-markdown-it-py                1.1.0
-MarkupSafe                    2.0.1
-matplotlib                    3.5.1
-matplotlib-inline             0.1.2
-mccabe                        0.6.1
-mdit-py-plugins               0.2.8
-mistune                       0.8.4
-mkl-fft                       1.3.1
-mkl-random                    1.2.2
-mkl-service                   2.4.0
-mock                          4.0.3
-mpmath                        1.2.1
-msgpack                       1.0.2
-multidict                     5.2.0
-multipledispatch              0.6.0
-munkres                       1.1.4
-mypy-extensions               0.4.3
-myst-nb                       0.13.2
-myst-parser                   0.15.2
-navigator-updater             0.2.1
-nbclassic                     0.3.5
-nbclient                      0.5.13
-nbconvert                     5.6.1
-nbdime                        3.1.1
-nbformat                      5.3.0
-nest-asyncio                  1.5.5
-nested-lookup                 0.2.23
-netCDF4                       1.5.7
-networkx                      2.7.1
-nltk                          3.7
-nose                          1.3.7
-notebook                      6.4.8
-numba                         0.55.1
-numexpr                       2.8.1
-numpy                         1.21.5
-numpydoc                      1.2
-olefile                       0.46
-openpyxl                      3.0.9
-packaging                     21.3
-pandas                        1.4.2
-pandocfilters                 1.5.0
-panel                         0.13.0
-param                         1.12.0
-parsel                        1.6.0
-parso                         0.8.3
-partd                         1.2.0
-pathspec                      0.9.0
-patsy                         0.5.2
-pep8                          1.7.1
-pexpect                       4.8.0
-pickleshare                   0.7.5
-Pillow                        9.0.1
-pip                           21.2.4
-pkginfo                       1.8.2
-platformdirs                  2.5.2
-plotly                        5.6.0
-pluggy                        1.0.0
-poyo                          0.5.0
-prometheus-client             0.13.1
-prompt-toolkit                3.0.20
-Protego                       0.1.16
-protobuf                      3.19.1
-psutil                        5.8.0
-ptyprocess                    0.7.0
-pure-eval                     0.2.2
-py                            1.11.0
-py4j                          0.10.9.3
-pyarrow                       8.0.0
-pyasn1                        0.4.8
-pyasn1-modules                0.2.8
-pybtex                        0.24.0
-pybtex-docutils               1.0.1
-pycodestyle                   2.7.0
-pycosat                       0.6.3
-pycparser                     2.21
-pyct                          0.4.6
-pycurl                        7.44.1
-pydata-sphinx-theme           0.7.2
-PyDispatcher                  2.0.5
-pydocstyle                    6.1.1
-pyerfa                        2.0.0
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-Pygments                      2.11.2
-PyHamcrest                    2.0.2
-PyJWT                         2.1.0
-pylint                        2.9.6
-pyls-spyder                   0.4.0
-pyluach                       1.4.2
-pyodbc                        4.0.32
-pyOpenSSL                     21.0.0
-pyparsing                     3.0.4
-pypi-simple                   0.9.0
-pyrsistent                    0.18.0
-pyserial                      3.5
-PySocks                       1.7.1
-pyspark                       3.2.1
-pytest                        7.1.1
-python-dateutil               2.8.2
-python-lsp-black              1.0.0
-python-lsp-jsonrpc            1.0.0
-python-lsp-server             1.2.4
-python-slugify                5.0.2
-python-snappy                 0.6.0
-pytz                          2021.3
-pyviz-comms                   2.0.2
-PyWavelets                    1.3.0
-PyYAML                        6.0
-pyzmq                         22.3.0
-QDarkStyle                    3.0.2
-qstylizer                     0.1.10
-QtAwesome                     1.0.3
-qtconsole                     5.3.0
-QtPy                          2.0.1
-queuelib                      1.5.0
-regex                         2022.4.24
-requests                      2.27.1
-requests-file                 1.5.1
-rich                          12.4.3
-rope                          0.22.0
-rsa                           4.7.2
-Rtree                         0.9.7
-ruamel-yaml-conda             0.15.100
-s3transfer                    0.5.0
-scikit-image                  0.19.2
-scikit-learn                  1.0.2
-scikit-learn-intelex          2021.20220215.132722
-scipy                         1.7.3
-Scrapy                        2.6.1
-seaborn                       0.11.2
-Send2Trash                    1.8.0
-service-identity              18.1.0
-setuptools                    65.2.0
-shellingham                   1.4.0
-sip                           4.19.13
-six                           1.16.0
-smart-open                    5.1.0
-smmap                         3.0.5
-sniffio                       1.2.0
-snowballstemmer               2.2.0
-sortedcollections             2.1.0
-sortedcontainers              2.4.0
-soupsieve                     2.3.1
-Sphinx                        3.5.3
-sphinx-book-theme             0.1.10
-sphinx-comments               0.0.3
-sphinx-copybutton             0.5.0
-sphinx-external-toc           0.2.4
-sphinx-jupyterbook-latex      0.4.6
-sphinx-multitoc-numbering     0.1.3
-sphinx-panels                 0.6.0
-sphinx-thebe                  0.1.2
-sphinx-togglebutton           0.3.1
-sphinxcontrib-applehelp       1.0.2
-sphinxcontrib-bibtex          2.4.2
-sphinxcontrib-devhelp         1.0.2
-sphinxcontrib-htmlhelp        2.0.0
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-sphinxcontrib-qthelp          1.0.3
-sphinxcontrib-serializinghtml 1.1.5
-spyder                        5.1.5
-spyder-kernels                2.1.3
-SQLAlchemy                    1.4.32
-sqlparse                      0.4.2
-stack-data                    0.2.0
-statsmodels                   0.13.2
-sympy                         1.10.1
-tables                        3.6.1
-tabulate                      0.8.9
-TBB                           0.2
-tblib                         1.7.0
-tenacity                      8.0.1
-terminado                     0.13.1
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+
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Note: you may need to restart the kernel to use updated packages.
@@ -1073,7 +1122,8 @@ 
Terminologyimport statement. The import statement allows us to import code from another module. There are a few ways to use the statement, which we will mention briefly here, from most recommended to least recommended.

 

 
Video 🎥:

-
+
 

 

 
Explicit module import

@@ -1230,7 +1280,7 @@ 
Implicit import of module contents

 

 
Video 🎥:

-
+
 

 
In the above code we’ve seen a few different libraries installed (math and numpy). These libraries represent two of the many readily available libraries at your disposal. However, we refer to the math library is part of the standard library and the numpy library is considered a third party library.

 
So far we’ve seen several data types that Python offers out-of-the-box.
@@ -1290,7 +1340,7 @@ 
Knowledge check

 

 
Video 🎥:

-
+
 

 
One of the things that makes Python useful, especially within the world of data science, is its ecosystem of third-party modules. These can be imported just as the built-in modules, but first the modules must be installed on your system. As previously mentioned, Python Package Index (PyPI for short) and Anaconda are the two primary public package managers for Python.  To install packages from these locations we just use the following:

 
# install from PyPI
diff --git a/docs/_build/html/02-module/lesson-2b.html b/docs/_build/html/02-module/lesson-2b.html
index b468390..02ec1c0 100644
--- a/docs/_build/html/02-module/lesson-2b.html
+++ b/docs/_build/html/02-module/lesson-2b.html
@@ -618,7 +618,7 @@ 
Learning objectives

 

 
Video 🎥:

-
+
 

 
Python stores its data in memory - this makes it relatively quickly accessible but can cause size limitations in certain fields. In this class we will mainly work with small to moderate data sets, which means we should not run into any space limitations.

 

@@ -639,7 +639,7 @@ 
Data & memory

 

 
Video 🎥:

-
+
 

 
Text files are a popular way to hold and exchange tabular data as almost any data application supports exporting data to the CSV (or other text file) format. Text file formats use delimiters to separate the different elements in a line, and each line of data is in its own line in the text file. Therefore, importing different kinds of text files can follow a fairly consistent process once you’ve identified the delimiter.

 
Although there are other approaches to importing delimited files, Pandas is often the preferred approach as it greatly simplifies the proess and imports the data directly into a DataFrame – the data structure of choice for tabular data in Python.

@@ -1164,7 +1164,7 @@ 
Delimited files

 

 
Video 🎥:

-
+
 

 
It’s important to understand where files exist on your computer and how to reference those paths. There are two main approaches:

 
    
@@ -1397,7 +1397,7 @@ 
    Metadata
    
 
    
 
    Video 🎥:
    
-
+
 
    
 
    We’ve seen that we can use the dot-notation to access functions in libraries (i.e. pd.read_csv()). We can use this same approach to access things inside of objects. What’s an object? Basically, a variable that contains other data or functionality inside of it that is exposed to users. Consequently, our DataFrame item is an object.
    
 
    In the above code, we saw that we can make different calls with our DataFrame such as ames.shape and ames.head(). An observant reader probably noticed the difference between the two – one has parentheses and the other does not.
    
@@ -2047,7 +2047,7 @@ 
    Computing environment
 
    Python implementation: CPython
 Python version       : 3.9.12
-IPython version      : 8.2.0
+IPython version      : 8.4.0
 
 jupyterlab: 3.3.2
 pandas    : 1.4.2
diff --git a/docs/_build/html/02-module/lesson-2c.html b/docs/_build/html/02-module/lesson-2c.html
index 85a5843..c38f474 100644
--- a/docs/_build/html/02-module/lesson-2c.html
+++ b/docs/_build/html/02-module/lesson-2c.html
@@ -581,7 +581,7 @@ 
    Learning objectives
    
 
    
 
    Video 🎥:
    
-
+
 
    
 
    Accessing an individual column of a DataFrame can be done by passing the column name as a string, in brackets ([]).
    
 
    
@@ -807,7 +807,7 @@ 
    What Are DataFrames Made of?
    
 
    
 
    Video 🎥:
    
-
+
 
    
 
    First, let’s create our own Series object from scratch – they don’t need to come from a DataFrame. Here, we pass a list in as an argument and it will be converted to a Series.
    
 
    
@@ -1038,7 +1038,7 @@ 
    What Can You Do with a Series?
    
 
    
 
    Video 🎥:
    
-
+
 
    
 
    It’s not just Series that have indexes! DataFrames have them too. Take a look at the carrier DataFrame again and note the bold numbers on the left.
    
 
    
diff --git a/docs/_build/html/03-module/lesson-3a.html b/docs/_build/html/03-module/lesson-3a.html
index 2f3f01b..a8c5a7a 100644
--- a/docs/_build/html/03-module/lesson-3a.html
+++ b/docs/_build/html/03-module/lesson-3a.html
@@ -743,7 +743,7 @@ 
    Prerequisites
    
 
    
 
    Video 🎥:
    
-
+
 
    
 
    We don’t always want all of the data in a DataFrame, so we need to take subsets of the DataFrame. In general, subsetting is extracting a small portion of a DataFrame – making the DataFrame smaller. Since the DataFrame is two-dimensional, there are two dimensions on which to subset.
    
 
    Dimension 1: We may only want to consider certain variables. For example, we may only care about the year and engines variables:
    
@@ -1172,7 +1172,7 @@ 
    Knowledge check
 
    Video 🎥:
    
-
+
 
    
 
    
 
    
@@ -2066,7 +2066,7 @@ 
    Knowledge check
 
    Video 🎥:
    
-
+
 
    
 
    
 

@@ -2276,7 +2276,7 @@ 
Knowledge check
 
Video 🎥:

-
+
 

 

 

@@ -2365,7 +2365,7 @@ 
Views vs copies
-
/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_97171/2190037627.py:1: SettingWithCopyWarning: 
+
/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_69390/2190037627.py:1: SettingWithCopyWarning: 
 A value is trying to be set on a copy of a slice from a DataFrame.
 Try using .loc[row_indexer,col_indexer] = value instead
 
@@ -2438,8 +2438,8 @@ 
Views vs copies
-
The id of the original dataframe is: 140606100927728
- The id of the indexed dataframe is: 140606114994256
+
The id of the original dataframe is: 140438716342624
+ The id of the indexed dataframe is: 140438730364480
 

 

 

@@ -2537,7 +2537,7 @@ 
Computing environment
 
Python implementation: CPython
 Python version       : 3.9.12
-IPython version      : 8.2.0
+IPython version      : 8.4.0
 
 jupyterlab: 3.3.2
 pandas    : 1.4.2
diff --git a/docs/_build/html/03-module/lesson-3b.html b/docs/_build/html/03-module/lesson-3b.html
index 05d792d..c6a58cf 100644
--- a/docs/_build/html/03-module/lesson-3b.html
+++ b/docs/_build/html/03-module/lesson-3b.html
@@ -1411,7 +1411,7 @@ 
Renaming columns
 
Video 🎥:

-
+
 

 

 
 

 

@@ -2650,7 +2650,7 @@ 
Knowledge check
 
Video 🎥:

-
+
 

 

 

@@ -4219,11 +4219,11 @@ 
Computing environment
 
Python implementation: CPython
 Python version       : 3.9.12
-IPython version      : 8.2.0
+IPython version      : 8.4.0
 
 jupyterlab: 3.3.2
 pandas    : 1.4.2
-numpy     : 1.21.5
+numpy     : 1.22.4
 seaborn   : 0.11.2
 

 

diff --git a/docs/_build/html/03-module/lesson-3c.html b/docs/_build/html/03-module/lesson-3c.html
index a94b1f2..1a09411 100644
--- a/docs/_build/html/03-module/lesson-3c.html
+++ b/docs/_build/html/03-module/lesson-3c.html
@@ -738,7 +738,7 @@ 
Knowledge check
 
Video 🎥:

-
+
@@ -2223,7 +2223,7 @@

Computing environment 
Python implementation: CPython
 Python version       : 3.9.12
-IPython version      : 8.2.0
+IPython version      : 8.4.0
 
 jupyterlab: 3.3.2
 pandas    : 1.4.2
diff --git a/docs/_build/html/04-module/lesson-4a.html b/docs/_build/html/04-module/lesson-4a.html
index 9898435..9aba6b8 100644
--- a/docs/_build/html/04-module/lesson-4a.html
+++ b/docs/_build/html/04-module/lesson-4a.html
@@ -567,7 +567,7 @@ 
Lession 4a: Tidy dataOnce a DataFrame is tidy, it becomes much easier to compute summary statistics, join with other datasets, visualize, apply machine learning models, etc. In this lesson we will focus on ways to reshape DataFrames so that they meet the tidy guidelines.

 

 
Video 🎥:

-
+
@@ -1651,7 +1651,7 @@

Knowledge check

Video 🎥:

- +

diff --git a/docs/_build/html/04-module/lesson-4b.html b/docs/_build/html/04-module/lesson-4b.html index 0caca48..0d2680a 100644 --- a/docs/_build/html/04-module/lesson-4b.html +++ b/docs/_build/html/04-module/lesson-4b.html @@ -1756,7 +1756,7 @@

Knowledge check

Video 🎥:

- + @@ -1871,7 +1871,7 @@

Knowledge check

Video 🎥:

- + diff --git a/docs/_build/html/04-module/lesson-4c.html b/docs/_build/html/04-module/lesson-4c.html index 54ebe8d..0bfe530 100644 --- a/docs/_build/html/04-module/lesson-4c.html +++ b/docs/_build/html/04-module/lesson-4c.html @@ -1402,7 +1402,7 @@

Knowledge check

Video 🎥:

- + @@ -2654,7 +2654,7 @@

Character classes -
/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_50389/1279328171.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.
+
/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_69791/1279328171.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.
   products['package_size']
 

 

@@ -3199,7 +3199,7 @@ 
Repetition
-
/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_50389/3233621946.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.
+
/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_69791/3233621946.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.
   prod_id_8s = products['product_id'].astype(str).str.contains('(8)\\1', na=False)
 

 

@@ -3294,7 +3294,7 @@ 
Repetition
-
/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_50389/1949681077.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.
+
/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_69791/1949681077.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.
   prod_id_8s = products['product_id'].astype(str).str.contains('(8)\\1{2}', na=False)
 

 

@@ -3410,7 +3410,7 @@ 
Putting it altogether
 

 

-
/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_50389/3801605409.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.
+
/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_69791/3801605409.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.
   size_filter = products['package_size'].str.contains('^\d{2,}(\.)?.*lb', case=False, na=False)
 

 

@@ -3479,7 +3479,7 @@ 
Knowledge check
 
Video 🎥:

-
+
 

 

 

@@ -3507,7 +3507,7 @@ 
Computing environment
 
Python implementation: CPython
 Python version       : 3.9.12
-IPython version      : 8.2.0
+IPython version      : 8.4.0
 
 jupyterlab        : 3.3.2
 pandas            : 1.4.2
diff --git a/docs/_build/html/06-module/lesson-6a.html b/docs/_build/html/06-module/lesson-6a.html
index ef3cbe5..43b517e 100644
--- a/docs/_build/html/06-module/lesson-6a.html
+++ b/docs/_build/html/06-module/lesson-6a.html
@@ -643,7 +643,7 @@ 
if statement
 

-
# multiple lines in the statement are fine as long as they have the 
+
# multiple lines in the statement are fine as long as they have the
 # same indentation
 if x < 0:
     new_value = abs(x)
@@ -740,7 +740,7 @@ 
Knowledge check
 
Video 🎥:

-
+
 

 

 

@@ -806,7 +806,7 @@ 
Knowledge check
 
Video 🎥:

-
+
 

 

 

@@ -909,18 +909,18 @@ 
Switch statements
def produce_revenue(sqft, visits, trend):
     total = 9.91 * sqft * visits * trend
     return round(total, 2)
-  
+
 def frozen_revenue(sqft, visits, trend):
     prod = produce_revenue(sqft, visits, trend)
-    total = 3.28 * sqft * visits * trend - prod * .005 
+    total = 3.28 * sqft * visits * trend - prod * .005
     return round(total, 2)
-  
+
 expected_annual_revenue = {
-    'produce':    produce_revenue, 
-    'frozen':     frozen_revenue, 
+    'produce':    produce_revenue,
+    'frozen':     frozen_revenue,
     'pharmacy':   lambda: 16.11 * visits * trend
     }
-  
+
 choice = 'frozen'
 expected_annual_revenue.get(choice, 'Bad choice')(sqft=937, visits=465, trend=0.98)
 

@@ -951,7 +951,7 @@ 
Knowledge check
 
Video 🎥:

-
+
 

 

 

@@ -1405,7 +1405,7 @@ 
Knowledge check
 
Video 🎥:

-
+
@@ -1441,7 +1441,7 @@

Computing environment 
Python implementation: CPython
 Python version       : 3.9.12
-IPython version      : 8.2.0
+IPython version      : 8.4.0
 
 pandas            : 1.4.2
 jupyterlab        : 3.3.2
diff --git a/docs/_build/html/06-module/lesson-6b.html b/docs/_build/html/06-module/lesson-6b.html
index d941ac9..f055a83 100644
--- a/docs/_build/html/06-module/lesson-6b.html
+++ b/docs/_build/html/06-module/lesson-6b.html
@@ -786,7 +786,7 @@ 
Knowledge check
 
Video 🎥:

-
+
@@ -867,7 +867,7 @@

Knowledge check

Video 🎥:

- + @@ -937,7 +937,7 @@

List comprehensions 
squared_certain_values = [
-    number * number for number in range(10) 
+    number * number for number in range(10)
     if number % 2 != 0 and number != 5
     ]
 
@@ -986,7 +986,7 @@ 
Knowledge check
 
Video 🎥:

-
+
@@ -1014,7 +1014,7 @@

whilewhile not ten_of_a_kind: # flip coin 10 times ten_coin_flips = [random.choice(coin) for flip in range(11)] - + # check if there ten_of_a_kind = len(set(ten_coin_flips)) == 1 @@ -1027,7 +1027,7 @@

while
-
After 330 flips: ['tails', 'tails', 'tails', 'tails', 'tails', 'tails', 'tails', 'tails', 'tails', 'tails', 'tails']
+
After 2335 flips: ['heads', 'heads', 'heads', 'heads', 'heads', 'heads', 'heads', 'heads', 'heads', 'heads', 'heads']
@@ -1060,7 +1060,7 @@

Knowledge check

Video 🎥:

- +

@@ -1182,7 +1182,7 @@

Iterablesfor col in range(len(df.columns)): value = df.iloc[:, col].nunique() unique_values.append(value) - + unique_values diff --git a/docs/_build/html/06-module/lesson-6c.html b/docs/_build/html/06-module/lesson-6c.html index 887ce30..2f812a2 100644 --- a/docs/_build/html/06-module/lesson-6c.html +++ b/docs/_build/html/06-module/lesson-6c.html @@ -776,7 +776,7 @@

When to write functions 
# array containing 4 sets of 10 random numbers
 x = np.random.random_sample((4, 10))
-          
+
 x[0] = (x[0] - x[0].min()) / (x[0].max() - x[0].min())
 x[1] = (x[1] - x[1].min()) / (x[1].max() - x[1].min())
 x[2] = (x[2] - x[2].min()) / (x[1].max() - x[2].min())
@@ -793,7 +793,7 @@ 
When to write functionsdef rescale(array):
     for index, vector in enumerate(array):
         array[index] = (vector - vector.min()) / (vector.max() - vector.min())
-    
+
     return(array)
 
 rescale(x)
@@ -801,14 +801,14 @@ 
When to write functions
-
array([[0.        , 0.25838401, 0.41338666, 0.22670219, 0.14207045,
-        0.6372547 , 1.        , 0.66500104, 0.97430082, 0.67975973],
-       [0.23875146, 0.25624387, 0.09282163, 0.39901557, 0.65988219,
-        0.08264166, 0.53262924, 0.        , 0.98924274, 1.        ],
-       [0.86209879, 0.83270803, 0.7993648 , 0.        , 0.21927493,
-        0.55366273, 0.00574432, 0.98300226, 1.        , 0.64223695],
-       [0.56997021, 0.76553595, 1.        , 0.        , 0.64481501,
-        0.68118805, 0.95526039, 0.91795629, 0.39921224, 0.95423523]])
+
array([[0.79882196, 0.        , 0.63291805, 1.        , 0.67628976,
+        0.85449383, 0.21740158, 0.3928153 , 0.84347625, 0.05630605],
+       [1.        , 0.38420133, 0.78614167, 0.        , 0.60469202,
+        0.47085719, 0.39803966, 0.91226131, 0.73877887, 0.9079893 ],
+       [0.09984341, 1.        , 0.13157581, 0.40402155, 0.43002542,
+        0.        , 0.00857417, 0.87397615, 0.78929133, 0.4674958 ],
+       [0.65530407, 0.10196804, 0.        , 0.83969465, 1.        ,
+        0.68729707, 0.0015783 , 0.354641  , 0.34662782, 0.52565252]])
@@ -826,22 +826,22 @@

Functions vs methods

-
array([1.67082046, 2.11287186, 2.30557308, 0.62571776, 1.66604259,
-       1.95474713, 2.49363395, 2.56595959, 3.3627558 , 3.27623192])
+
array([2.55396944, 1.48616936, 1.55063553, 2.24371621, 2.7110072 ,
+       2.01264809, 0.62559371, 2.53369375, 2.71817427, 1.95744367])
-
# method 
+
# method
 x.sum(axis = 0)
-
array([1.67082046, 2.11287186, 2.30557308, 0.62571776, 1.66604259,
-       1.95474713, 2.49363395, 2.56595959, 3.3627558 , 3.27623192])
+
array([2.55396944, 1.48616936, 1.55063553, 2.24371621, 2.7110072 ,
+       2.01264809, 0.62559371, 2.53369375, 2.71817427, 1.95744367])
@@ -855,7 +855,7 @@

Functions vs methods

-
22.034354137152878
+
20.393051216518487
@@ -868,8 +868,8 @@

Functions vs methods

-
array([1.67082046, 2.11287186, 2.30557308, 0.62571776, 1.66604259,
-       1.95474713, 2.49363395, 2.56595959, 3.3627558 , 3.27623192])
+
array([2.55396944, 1.48616936, 1.55063553, 2.24371621, 2.7110072 ,
+       2.01264809, 0.62559371, 2.53369375, 2.71817427, 1.95744367])
@@ -882,7 +882,7 @@

Functions vs methods

-
array([4.9968596 , 4.25122836, 5.89809281, 6.88817336])
+
array([5.47252277, 6.20296134, 4.20480363, 4.51276347])
@@ -903,8 +903,8 @@

Defining functions
def yell(text):
     new_text = text.upper()
     return new_text
-  
-yell('hello world!') 
+
+yell('hello world!')

@@ -943,7 +943,7 @@

Knowledge check

Video 🎥:

- +

@@ -961,7 +961,7 @@

Keyword arguments 
# implicitly computing store sales for store 46 during week 43
-store_sales(df, 46, 43) 
+store_sales(df, 46, 43)
@@ -974,7 +974,7 @@

Keyword arguments 
# implicitly computing store sales for store 43 (does not exist) during week 46
-store_sales(df, 43, 46) 
+store_sales(df, 43, 46)
@@ -992,7 +992,7 @@

Keyword arguments 
# explicitly computing store sales for store 46 during week 43
-store_sales(data=df, week=43, store=46) 
+store_sales(data=df, week=43, store=46)
@@ -1055,7 +1055,7 @@

*args and **kwargs
def yell(*args):
     new_text = ' '.join(args).upper()
     return new_text
-  
+
 yell('hello world!', 'I', 'love', 'Python!!')

@@ -1072,7 +1072,7 @@

*args and **kwargs
# **kwargs just creates a dictionary
 def students(**kwargs):
     print(kwargs)
-    
+
 students(student1='John', student2='Robert', student3='Sally')

@@ -1089,8 +1089,8 @@

*args and **kwargsdef print_student_names(**kwargs): for key, value in kwargs.items(): print(f'{key} = {value}') - -print_student_names(student1='John', student2='Robert', student3='Sally') + +print_student_names(student1='John', student2='Robert', student3='Sally')

@@ -1111,7 +1111,7 @@

Type hints 
def some_function(name, age):
     return f'{name} is {age} years old'
- 
+
 some_function('Tom', 27)
@@ -1128,7 +1128,7 @@

Type hints 
def some_function(name: str, age: int) -> str:
     return f'{name} is {age} years old'
- 
+
 some_function('Tom', 27)
@@ -1179,7 +1179,7 @@

Type hintsfilt = (data['store_id'] == store) & (data['week'] == week) total_sales = data['sales_value'][filt].sum() return total_sales - + store_sales(data=df, store=309, week=48)

@@ -1203,7 +1203,7 @@

Knowledge check

Video 🎥:

- + @@ -1220,11 +1220,11 @@

Docstrings
def store_sales(data: pd.DataFrame, store: int, week: int) -> float:
     """
     Compute total store sales.
-    
+
     This function computes the total sales for a given
     store and week based on a user supplied DataFrame that
     contains sales in a column named `sales_value`.
-    
+
     Parameters
     ----------
     data : DataFrame
@@ -1233,16 +1233,16 @@ 
Docstrings        Integer value representing store number
     week : int
         Integer value representing week of year
-    
+
     Returns
     -------
     float
         A float object representing total store sales
-    
+
     See Also
     --------
     store_visits : Computes total store visits
-    
+
     Examples
     --------
     >>> store_sales(data=df, store=309, week=48)
@@ -1269,7 +1269,7 @@ 
Knowledge check
 
Video 🎥:

-
+
@@ -1287,13 +1287,13 @@

Validating argumentsif not isinstance(data, pd.DataFrame): raise Exception('`data` should be a Pandas DataFrame') if not isinstance(store, int): raise Exception('`store` should be an integer') if not isinstance(week, int): raise Exception('`week` should be an integer') - + # computation filt = (data['store_id'] == store) & (data['week'] == week) total_sales = data['sales_value'][filt].sum() return total_sales - -store_sales(data=df, store='309', week=48) + +store_sales(data=df, store='309', week=48) @@ -1326,13 +1326,13 @@

Validating argumentsif not isinstance(data, pd.DataFrame): raise TypeError('`data` should be a Pandas DataFrame') if not isinstance(store, int): raise TypeError('`store` should be an integer') if not isinstance(week, int): raise TypeError('`week` should be an integer') - + # computation filt = (data['store_id'] == store) & (data['week'] == week) total_sales = data['sales_value'][filt].sum() return total_sales - -store_sales(data=df, store='309', week=48) + +store_sales(data=df, store='309', week=48) @@ -1378,10 +1378,10 @@

Validating argumentsif not isinstance(data, pd.DataFrame): raise TypeError('`data` should be a Pandas DataFrame') if not isinstance(store, int): raise TypeError('`store` should be an integer') if not isinstance(week, int): raise TypeError('`week` should be an integer') - if store not in data.store_id.unique(): + if store not in data.store_id.unique(): raise ValueError(f'`store` {store} does not exist in the supplied DataFrame') - - + + # computation filt = (data['store_id'] == store) & (data['week'] == week) total_sales = data['sales_value'][filt].sum() @@ -1478,8 +1478,8 @@

Assert statementsprice = round(product['price'] * (1.0 - discount), 2) assert 0 <= price <= product['price'], 'Invalid discount applied' return price - -apply_discount(milk, 2.00) + +apply_discount(milk, 2.00) @@ -1598,7 +1598,7 @@

Knowledge check

Video 🎥:

- + @@ -1620,8 +1620,8 @@

Searching for variablesdef func(x): return x + 1 - -func(x = 50) + +func(x = 50) @@ -1651,8 +1651,8 @@

Searching for variablesdef func(x): return x + ' ' + y - -func(x = 'Brad') + +func(x = 'Brad') @@ -1683,7 +1683,7 @@

Searching for variablesdef my_paste(): return x + sep + y return my_paste() - + my_name(sep=' ') @@ -1713,7 +1713,7 @@

Changing variablesy = firstpart + '.' + secondpart return y -convert(8451) +convert(8451) diff --git a/docs/_build/html/_images/lesson-3b_72_0.png b/docs/_build/html/_images/lesson-3b_72_0.png index 01f30db..74f1757 100644 Binary files a/docs/_build/html/_images/lesson-3b_72_0.png and b/docs/_build/html/_images/lesson-3b_72_0.png differ diff --git a/docs/_build/html/_sources/01-module/lesson-1a.ipynb b/docs/_build/html/_sources/01-module/lesson-1a.ipynb index ec84763..b99c142 100644 --- a/docs/_build/html/_sources/01-module/lesson-1a.ipynb +++ b/docs/_build/html/_sources/01-module/lesson-1a.ipynb @@ -94,7 +94,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -200,7 +200,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -457,7 +457,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/01-module/lesson-1b.ipynb b/docs/_build/html/_sources/01-module/lesson-1b.ipynb index 7b6198a..ae6afc9 100644 --- a/docs/_build/html/_sources/01-module/lesson-1b.ipynb +++ b/docs/_build/html/_sources/01-module/lesson-1b.ipynb @@ -46,7 +46,7 @@ "## Determining the type\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "First, we will use Python's built-in `type()` function to determine the type of some variables." @@ -1028,7 +1028,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1226,7 +1226,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/01-module/lesson-1c.ipynb b/docs/_build/html/_sources/01-module/lesson-1c.ipynb index 6cc2f4f..a30e327 100644 --- a/docs/_build/html/_sources/01-module/lesson-1c.ipynb +++ b/docs/_build/html/_sources/01-module/lesson-1c.ipynb @@ -613,7 +613,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -905,7 +905,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1177,7 +1177,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1616,7 +1616,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/01-module/lesson-1d.ipynb b/docs/_build/html/_sources/01-module/lesson-1d.ipynb index f36dfa1..f5ef80a 100644 --- a/docs/_build/html/_sources/01-module/lesson-1d.ipynb +++ b/docs/_build/html/_sources/01-module/lesson-1d.ipynb @@ -30,7 +30,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -717,7 +717,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1036,7 +1036,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1353,7 +1353,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/02-module/lesson-2a.ipynb b/docs/_build/html/_sources/02-module/lesson-2a.ipynb index cc783c0..d343b3e 100644 --- a/docs/_build/html/_sources/02-module/lesson-2a.ipynb +++ b/docs/_build/html/_sources/02-module/lesson-2a.ipynb @@ -37,7 +37,7 @@ "## Terminology\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "People often use the terms _\"package\"_, _\"library\"_, and _\"module\"_ synonymously. If you are familiar with the R programming language you've probably heard some of these terms before. Although there are some semantical differences between R and Python, here is how you can think of the two terms:\n", @@ -422,7 +422,7 @@ "In the code above you saw the `import` statement. The `import` statement allows us to import code from another module. There are a few ways to use the statement, which we will mention briefly here, from most recommended to least recommended.\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "### Explicit module import\n", @@ -638,7 +638,7 @@ "## Standard library\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "In the above code we've seen a few different libraries installed (`math` and `numpy`). These libraries represent two of the many readily available libraries at your disposal. However, we refer to the `math` library is part of the **standard library** and the `numpy` library is considered a **third party library**.\n", @@ -769,7 +769,7 @@ "## Third-party modules\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "One of the things that makes Python useful, especially within the world of data science, is its ecosystem of third-party modules. These can be imported just as the built-in modules, but first the modules must be installed on your system. As previously mentioned, [Python Package Index](https://pypi.org/) (PyPI for short) and [Anaconda](https://anaconda.org/anaconda/repo) are the two primary public package managers for Python. To install packages from these locations we just use the following:\n", diff --git a/docs/_build/html/_sources/02-module/lesson-2b.ipynb b/docs/_build/html/_sources/02-module/lesson-2b.ipynb index afb1955..4b4caff 100644 --- a/docs/_build/html/_sources/02-module/lesson-2b.ipynb +++ b/docs/_build/html/_sources/02-module/lesson-2b.ipynb @@ -30,7 +30,7 @@ "## Data & memory\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "Python stores its data in memory - this makes it relatively quickly accessible but can cause size limitations in certain fields. In this class we will mainly work with small to moderate data sets, which means we should not run into any space limitations. \n", @@ -59,7 +59,7 @@ "## Delimited files\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "Text files are a popular way to hold and exchange tabular data as almost any data application supports exporting data to the CSV (or other text file) format. Text file formats use delimiters to separate the different elements in a line, and each line of data is in its own line in the text file. Therefore, importing different kinds of text files can follow a fairly consistent process once you’ve identified the delimiter.\n", @@ -708,7 +708,7 @@ "### File paths\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "It's important to understand where files exist on your computer and how to reference those paths. There are two main approaches:\n", @@ -1020,7 +1020,7 @@ "### Attributes & methods\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "We've seen that we can use the dot-notation to access functions in libraries (i.e. `pd.read_csv()`). We can use this same approach to access things inside of _objects_. What's an object? Basically, a variable that contains other data or functionality inside of it that is exposed to users. Consequently, our DataFrame item is an object.\n", diff --git a/docs/_build/html/_sources/02-module/lesson-2c.ipynb b/docs/_build/html/_sources/02-module/lesson-2c.ipynb index 3fee2cc..d1cc882 100644 --- a/docs/_build/html/_sources/02-module/lesson-2c.ipynb +++ b/docs/_build/html/_sources/02-module/lesson-2c.ipynb @@ -114,7 +114,7 @@ "## What Are DataFrames Made of?\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "Accessing an individual column of a DataFrame can be done by passing the column name as a string, in brackets (`[]`)." @@ -496,7 +496,7 @@ "## What Can You Do with a Series?\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -953,7 +953,7 @@ "## DataFrame Indexes\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "It's not just Series that have indexes! DataFrames have them too. Take a look at the carrier DataFrame again and note the bold numbers on the left." diff --git a/docs/_build/html/_sources/03-module/lesson-3a.ipynb b/docs/_build/html/_sources/03-module/lesson-3a.ipynb index 590b6df..09d225e 100644 --- a/docs/_build/html/_sources/03-module/lesson-3a.ipynb +++ b/docs/_build/html/_sources/03-module/lesson-3a.ipynb @@ -179,7 +179,7 @@ "## Subsetting dimensions\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "We don't always want all of the data in a DataFrame, so we need to take subsets of the DataFrame. In general, **subsetting** is extracting a small portion of a DataFrame -- making the DataFrame smaller. Since the DataFrame is two-dimensional, there are two dimensions on which to subset.\n", @@ -903,7 +903,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2156,7 +2156,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2491,7 +2491,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/03-module/lesson-3b.ipynb b/docs/_build/html/_sources/03-module/lesson-3b.ipynb index 38bf044..55b017b 100644 --- a/docs/_build/html/_sources/03-module/lesson-3b.ipynb +++ b/docs/_build/html/_sources/03-module/lesson-3b.ipynb @@ -1013,7 +1013,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1720,7 +1720,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2700,7 +2700,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/03-module/lesson-3c.ipynb b/docs/_build/html/_sources/03-module/lesson-3c.ipynb index d9e2801..277b602 100644 --- a/docs/_build/html/_sources/03-module/lesson-3c.ipynb +++ b/docs/_build/html/_sources/03-module/lesson-3c.ipynb @@ -274,7 +274,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -859,7 +859,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2318,7 +2318,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/04-module/lesson-4a.ipynb b/docs/_build/html/_sources/04-module/lesson-4a.ipynb index e4ecee9..2bf07d4 100644 --- a/docs/_build/html/_sources/04-module/lesson-4a.ipynb +++ b/docs/_build/html/_sources/04-module/lesson-4a.ipynb @@ -23,7 +23,7 @@ "Once a DataFrame is tidy, it becomes much easier to compute summary statistics, join with other datasets, visualize, apply machine learning models, etc. In this lesson we will focus on ways to reshape DataFrames so that they meet the tidy guidelines.\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -458,8 +458,8 @@ "source": [ "ames_wide.melt(\n", " id_vars='neighborhood',\n", - " value_vars=['2007', '2008', '2009', '2010'], \n", - " var_name='year', \n", + " value_vars=['2007', '2008', '2009', '2010'],\n", + " var_name='year',\n", " value_name='homes_sold'\n", " )" ] @@ -498,7 +498,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1202,7 +1202,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/04-module/lesson-4b.ipynb b/docs/_build/html/_sources/04-module/lesson-4b.ipynb index 9f1dc98..f9c3951 100644 --- a/docs/_build/html/_sources/04-module/lesson-4b.ipynb +++ b/docs/_build/html/_sources/04-module/lesson-4b.ipynb @@ -1480,7 +1480,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1655,7 +1655,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/04-module/lesson-4c.ipynb b/docs/_build/html/_sources/04-module/lesson-4c.ipynb index 30e6ab3..c0248f1 100644 --- a/docs/_build/html/_sources/04-module/lesson-4c.ipynb +++ b/docs/_build/html/_sources/04-module/lesson-4c.ipynb @@ -1009,7 +1009,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -3761,7 +3761,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/06-module/lesson-6a.ipynb b/docs/_build/html/_sources/06-module/lesson-6a.ipynb index ad8afaf..ccda7b3 100644 --- a/docs/_build/html/_sources/06-module/lesson-6a.ipynb +++ b/docs/_build/html/_sources/06-module/lesson-6a.ipynb @@ -120,7 +120,7 @@ } ], "source": [ - "# multiple lines in the statement are fine as long as they have the \n", + "# multiple lines in the statement are fine as long as they have the\n", "# same indentation\n", "if x < 0:\n", " new_value = abs(x)\n", @@ -254,7 +254,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -339,7 +339,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -520,18 +520,18 @@ "def produce_revenue(sqft, visits, trend):\n", " total = 9.91 * sqft * visits * trend\n", " return round(total, 2)\n", - " \n", + "\n", "def frozen_revenue(sqft, visits, trend):\n", " prod = produce_revenue(sqft, visits, trend)\n", - " total = 3.28 * sqft * visits * trend - prod * .005 \n", + " total = 3.28 * sqft * visits * trend - prod * .005\n", " return round(total, 2)\n", - " \n", + "\n", "expected_annual_revenue = {\n", - " 'produce': produce_revenue, \n", - " 'frozen': frozen_revenue, \n", + " 'produce': produce_revenue,\n", + " 'frozen': frozen_revenue,\n", " 'pharmacy': lambda: 16.11 * visits * trend\n", " }\n", - " \n", + "\n", "choice = 'frozen'\n", "expected_annual_revenue.get(choice, 'Bad choice')(sqft=937, visits=465, trend=0.98)" ] @@ -564,7 +564,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1166,7 +1166,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/html/_sources/06-module/lesson-6b.ipynb b/docs/_build/html/_sources/06-module/lesson-6b.ipynb index 9de962c..614eb29 100644 --- a/docs/_build/html/_sources/06-module/lesson-6b.ipynb +++ b/docs/_build/html/_sources/06-module/lesson-6b.ipynb @@ -285,7 +285,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -405,7 +405,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -539,7 +539,7 @@ ], "source": [ "squared_certain_values = [\n", - " number * number for number in range(10) \n", + " number * number for number in range(10)\n", " if number % 2 != 0 and number != 5\n", " ]\n", "\n", @@ -611,7 +611,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -660,7 +660,7 @@ "while not ten_of_a_kind:\n", " # flip coin 10 times\n", " ten_coin_flips = [random.choice(coin) for flip in range(11)]\n", - " \n", + "\n", " # check if there\n", " ten_of_a_kind = len(set(ten_coin_flips)) == 1\n", "\n", @@ -710,7 +710,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -904,7 +904,7 @@ "for col in range(len(df.columns)):\n", " value = df.iloc[:, col].nunique()\n", " unique_values.append(value)\n", - " \n", + "\n", "unique_values" ] }, diff --git a/docs/_build/html/_sources/06-module/lesson-6c.ipynb b/docs/_build/html/_sources/06-module/lesson-6c.ipynb index e723904..c072fc1 100644 --- a/docs/_build/html/_sources/06-module/lesson-6c.ipynb +++ b/docs/_build/html/_sources/06-module/lesson-6c.ipynb @@ -76,7 +76,7 @@ "source": [ "# array containing 4 sets of 10 random numbers\n", "x = np.random.random_sample((4, 10))\n", - " \n", + "\n", "x[0] = (x[0] - x[0].min()) / (x[0].max() - x[0].min())\n", "x[1] = (x[1] - x[1].min()) / (x[1].max() - x[1].min())\n", "x[2] = (x[2] - x[2].min()) / (x[1].max() - x[2].min())\n", @@ -119,7 +119,7 @@ "def rescale(array):\n", " for index, vector in enumerate(array):\n", " array[index] = (vector - vector.min()) / (vector.max() - vector.min())\n", - " \n", + "\n", " return(array)\n", "\n", "rescale(x)" @@ -176,7 +176,7 @@ } ], "source": [ - "# method \n", + "# method\n", "x.sum(axis = 0)" ] }, @@ -292,8 +292,8 @@ "def yell(text):\n", " new_text = text.upper()\n", " return new_text\n", - " \n", - "yell('hello world!') " + "\n", + "yell('hello world!')" ] }, { @@ -346,7 +346,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -392,7 +392,7 @@ ], "source": [ "# implicitly computing store sales for store 46 during week 43\n", - "store_sales(df, 46, 43) " + "store_sales(df, 46, 43)" ] }, { @@ -413,7 +413,7 @@ ], "source": [ "# implicitly computing store sales for store 43 (does not exist) during week 46\n", - "store_sales(df, 43, 46) " + "store_sales(df, 43, 46)" ] }, { @@ -445,7 +445,7 @@ ], "source": [ "# explicitly computing store sales for store 46 during week 43\n", - "store_sales(data=df, week=43, store=46) " + "store_sales(data=df, week=43, store=46)" ] }, { @@ -543,7 +543,7 @@ "def yell(*args):\n", " new_text = ' '.join(args).upper()\n", " return new_text\n", - " \n", + "\n", "yell('hello world!', 'I', 'love', 'Python!!')" ] }, @@ -571,7 +571,7 @@ "# **kwargs just creates a dictionary\n", "def students(**kwargs):\n", " print(kwargs)\n", - " \n", + "\n", "students(student1='John', student2='Robert', student3='Sally')" ] }, @@ -595,8 +595,8 @@ "def print_student_names(**kwargs):\n", " for key, value in kwargs.items():\n", " print(f'{key} = {value}')\n", - " \n", - "print_student_names(student1='John', student2='Robert', student3='Sally') " + "\n", + "print_student_names(student1='John', student2='Robert', student3='Sally')" ] }, { @@ -629,7 +629,7 @@ "source": [ "def some_function(name, age):\n", " return f'{name} is {age} years old'\n", - " \n", + "\n", "some_function('Tom', 27)" ] }, @@ -661,7 +661,7 @@ "source": [ "def some_function(name: str, age: int) -> str:\n", " return f'{name} is {age} years old'\n", - " \n", + "\n", "some_function('Tom', 27)" ] }, @@ -741,7 +741,7 @@ " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", " return total_sales\n", - " \n", + "\n", "store_sales(data=df, store=309, week=48)" ] }, @@ -765,7 +765,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -793,11 +793,11 @@ "def store_sales(data: pd.DataFrame, store: int, week: int) -> float:\n", " \"\"\"\n", " Compute total store sales.\n", - " \n", + "\n", " This function computes the total sales for a given\n", " store and week based on a user supplied DataFrame that\n", " contains sales in a column named `sales_value`.\n", - " \n", + "\n", " Parameters\n", " ----------\n", " data : DataFrame\n", @@ -806,16 +806,16 @@ " Integer value representing store number\n", " week : int\n", " Integer value representing week of year\n", - " \n", + "\n", " Returns\n", " -------\n", " float\n", " A float object representing total store sales\n", - " \n", + "\n", " See Also\n", " --------\n", " store_visits : Computes total store visits\n", - " \n", + "\n", " Examples\n", " --------\n", " >>> store_sales(data=df, store=309, week=48)\n", @@ -849,7 +849,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -897,13 +897,13 @@ " if not isinstance(data, pd.DataFrame): raise Exception('`data` should be a Pandas DataFrame')\n", " if not isinstance(store, int): raise Exception('`store` should be an integer')\n", " if not isinstance(week, int): raise Exception('`week` should be an integer')\n", - " \n", + "\n", " # computation\n", " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", " return total_sales\n", - " \n", - "store_sales(data=df, store='309', week=48) " + "\n", + "store_sales(data=df, store='309', week=48)" ] }, { @@ -937,13 +937,13 @@ " if not isinstance(data, pd.DataFrame): raise TypeError('`data` should be a Pandas DataFrame')\n", " if not isinstance(store, int): raise TypeError('`store` should be an integer')\n", " if not isinstance(week, int): raise TypeError('`week` should be an integer')\n", - " \n", + "\n", " # computation\n", " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", " return total_sales\n", - " \n", - "store_sales(data=df, store='309', week=48) " + "\n", + "store_sales(data=df, store='309', week=48)" ] }, { @@ -1004,10 +1004,10 @@ " if not isinstance(data, pd.DataFrame): raise TypeError('`data` should be a Pandas DataFrame')\n", " if not isinstance(store, int): raise TypeError('`store` should be an integer')\n", " if not isinstance(week, int): raise TypeError('`week` should be an integer')\n", - " if store not in data.store_id.unique(): \n", + " if store not in data.store_id.unique():\n", " raise ValueError(f'`store` {store} does not exist in the supplied DataFrame')\n", - " \n", - " \n", + "\n", + "\n", " # computation\n", " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", @@ -1127,8 +1127,8 @@ " price = round(product['price'] * (1.0 - discount), 2)\n", " assert 0 <= price <= product['price'], 'Invalid discount applied'\n", " return price\n", - " \n", - "apply_discount(milk, 2.00) " + "\n", + "apply_discount(milk, 2.00)" ] }, { @@ -1257,7 +1257,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1300,8 +1300,8 @@ "\n", "def func(x):\n", " return x + 1\n", - " \n", - "func(x = 50) " + "\n", + "func(x = 50)" ] }, { @@ -1359,8 +1359,8 @@ "\n", "def func(x):\n", " return x + ' ' + y\n", - " \n", - "func(x = 'Brad') " + "\n", + "func(x = 'Brad')" ] }, { @@ -1404,7 +1404,7 @@ " def my_paste():\n", " return x + sep + y\n", " return my_paste()\n", - " \n", + "\n", "my_name(sep=' ')" ] }, @@ -1447,7 +1447,7 @@ " y = firstpart + '.' + secondpart\n", " return y\n", "\n", - "convert(8451) " + "convert(8451)" ] }, { diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js index d61f66a..f1bd123 100644 --- a/docs/_build/html/searchindex.js +++ b/docs/_build/html/searchindex.js @@ -1 +1 @@ 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0: Configuring your computer","Lesson 1a: Introduction to JupyterLab","Lesson 1b: Variables, operators & types","Lesson 1c: More operators & conditionals","Lesson 1d: Data structures","Overview","Lesson 2a: Packages, libraries & modules","Lesson 2b: Importing data","Lesson 2c: Deeper dive on DataFrames","Overview","Lesson 3a: Subsetting data","Lesson 3b: Manipulating data","Lesson 3c: Summarizing data","Overview","Lession 4a: Tidy data","Lesson 4b: Relational data","Lesson 4c: Handling text data","Overview","Lession 5a: Plotting with Pandas","Lesson 5b: Plotting with Matplotlib","Lesson 5c: Plotting with Bokeh","Overview","Lession 6a: Conditional statements","Lesson 6b: Iteration statement","Lesson 6C: Writing functions","Overview","Lesson 7a: Data Exploration","Lesson 7b: First model with scikit-learn","Lesson 7c: Feature Engineering","Lesson 7d: Model Evaluation & Selection","Overview","Statistical 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0: Configuring your computer","Lesson 1a: Introduction to JupyterLab","Lesson 1b: Variables, operators & types","Lesson 1c: More operators & conditionals","Lesson 1d: Data structures","Overview","Lesson 2a: Packages, libraries & modules","Lesson 2b: Importing data","Lesson 2c: Deeper dive on DataFrames","Overview","Lesson 3a: Subsetting data","Lesson 3b: Manipulating data","Lesson 3c: Summarizing data","Overview","Lession 4a: Tidy data","Lesson 4b: Relational data","Lesson 4c: Handling text data","Overview","Lession 5a: Plotting with Pandas","Lesson 5b: Plotting with Matplotlib","Lesson 5c: Plotting with Bokeh","Overview","Lession 6a: Conditional statements","Lesson 6b: Iteration statement","Lesson 6C: Writing functions","Overview","Lesson 7a: Data Exploration","Lesson 7b: First model with scikit-learn","Lesson 7c: Feature Engineering","Lesson 7d: Model Evaluation & Selection","Overview","Statistical 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\ No newline at end of file diff --git a/docs/_build/jupyter_execute/01-module/lesson-1a.ipynb b/docs/_build/jupyter_execute/01-module/lesson-1a.ipynb index 45b518b..8ef6cdd 100644 --- a/docs/_build/jupyter_execute/01-module/lesson-1a.ipynb +++ b/docs/_build/jupyter_execute/01-module/lesson-1a.ipynb @@ -94,7 +94,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -200,7 +200,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -457,7 +457,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -522,7 +522,7 @@ "text": [ "Python implementation: CPython\n", "Python version : 3.9.12\n", - "IPython version : 8.2.0\n", + "IPython version : 8.4.0\n", "\n", "jupyterlab: 3.3.2\n", "\n" diff --git a/docs/_build/jupyter_execute/01-module/lesson-1a.py b/docs/_build/jupyter_execute/01-module/lesson-1a.py index e7ebf41..e67cec6 100644 --- a/docs/_build/jupyter_execute/01-module/lesson-1a.py +++ b/docs/_build/jupyter_execute/01-module/lesson-1a.py @@ -55,7 +55,7 @@ # Hooray! We just printed `Hello, world.` to the screen. To do this, we used Python's built-in `print()` function. The `print()` function takes as an **argument** a **string**. It then prints that string to the screen. We will learn more about function syntax later, but we can already see the rough syntax with the `print()` function. # ```{admonition} Video 🎥: -# +# # ``` # ## .py files @@ -113,7 +113,7 @@ # To shut down the console, you can click on the `Running` tab at the left of the JupyterLab window and click on `SHUTDOWN` next to the console. # ```{admonition} Video 🎥: -# +# # ``` # ## Jupyter @@ -268,7 +268,7 @@ # There are many others (and they are shown in the pulldown menus within JupyterLab), but these are the ones I seem to encounter most often. # ```{admonition} Video 🎥: -# +# # ``` # ## Exercises diff --git a/docs/_build/jupyter_execute/01-module/lesson-1b.ipynb b/docs/_build/jupyter_execute/01-module/lesson-1b.ipynb index fd37422..09091e6 100644 --- a/docs/_build/jupyter_execute/01-module/lesson-1b.ipynb +++ b/docs/_build/jupyter_execute/01-module/lesson-1b.ipynb @@ -46,7 +46,7 @@ "## Determining the type\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "First, we will use Python's built-in `type()` function to determine the type of some variables." @@ -1028,7 +1028,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1226,7 +1226,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/jupyter_execute/01-module/lesson-1b.py b/docs/_build/jupyter_execute/01-module/lesson-1b.py index 250d1a5..0e6253c 100644 --- a/docs/_build/jupyter_execute/01-module/lesson-1b.py +++ b/docs/_build/jupyter_execute/01-module/lesson-1b.py @@ -28,7 +28,7 @@ # ## Determining the type # # ```{admonition} Video 🎥: -# +# # ``` # # First, we will use Python's built-in `type()` function to determine the type of some variables. @@ -379,7 +379,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Variables and assignment operators @@ -461,7 +461,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Type conversion diff --git a/docs/_build/jupyter_execute/01-module/lesson-1c.ipynb b/docs/_build/jupyter_execute/01-module/lesson-1c.ipynb index 1bec28a..0fff718 100644 --- a/docs/_build/jupyter_execute/01-module/lesson-1c.ipynb +++ b/docs/_build/jupyter_execute/01-module/lesson-1c.ipynb @@ -613,7 +613,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -905,7 +905,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1177,7 +1177,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1616,7 +1616,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1657,7 +1657,7 @@ "text": [ "Python implementation: CPython\n", "Python version : 3.9.12\n", - "IPython version : 8.2.0\n", + "IPython version : 8.4.0\n", "\n", "jupyterlab: 3.3.2\n", "\n" diff --git a/docs/_build/jupyter_execute/01-module/lesson-1c.py b/docs/_build/jupyter_execute/01-module/lesson-1c.py index 437ece0..37d1d79 100644 --- a/docs/_build/jupyter_execute/01-module/lesson-1c.py +++ b/docs/_build/jupyter_execute/01-module/lesson-1c.py @@ -227,7 +227,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Identity operators @@ -352,7 +352,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Logical operators @@ -464,7 +464,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Operators we left out @@ -662,7 +662,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Exercises diff --git a/docs/_build/jupyter_execute/01-module/lesson-1d.ipynb b/docs/_build/jupyter_execute/01-module/lesson-1d.ipynb index f133545..0eff5ed 100644 --- a/docs/_build/jupyter_execute/01-module/lesson-1d.ipynb +++ b/docs/_build/jupyter_execute/01-module/lesson-1d.ipynb @@ -30,7 +30,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -717,7 +717,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1036,7 +1036,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1353,7 +1353,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/jupyter_execute/01-module/lesson-1d.py b/docs/_build/jupyter_execute/01-module/lesson-1d.py index a7a9d1b..ca58855 100644 --- a/docs/_build/jupyter_execute/01-module/lesson-1d.py +++ b/docs/_build/jupyter_execute/01-module/lesson-1d.py @@ -17,7 +17,7 @@ # * Create and manage these data structures along with how to apply operators on them. # ```{admonition} Video 🎥: -# +# # ``` # ## Lists @@ -288,7 +288,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Tuples @@ -418,7 +418,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Dictionaries @@ -539,7 +539,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Quick Review diff --git a/docs/_build/jupyter_execute/02-module/lesson-2a.ipynb b/docs/_build/jupyter_execute/02-module/lesson-2a.ipynb index 865840f..0709af3 100644 --- a/docs/_build/jupyter_execute/02-module/lesson-2a.ipynb +++ b/docs/_build/jupyter_execute/02-module/lesson-2a.ipynb @@ -37,7 +37,7 @@ "## Terminology\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "People often use the terms _\"package\"_, _\"library\"_, and _\"module\"_ synonymously. If you are familiar with the R programming language you've probably heard some of these terms before. Although there are some semantical differences between R and Python, here is how you can think of the two terms:\n", @@ -60,394 +60,443 @@ "name": "stdout", "output_type": "stream", "text": [ - "Package Version\r\n", - "----------------------------- --------------------\r\n", - "aiohttp 3.8.1\r\n", - "aiosignal 1.2.0\r\n", - "alabaster 0.7.12\r\n", - "anaconda-client 1.9.0\r\n", - "anaconda-navigator 2.1.4\r\n", - "anaconda-project 0.10.2\r\n", - "anyio 3.5.0\r\n", - "appdirs 1.4.4\r\n", - "applaunchservices 0.2.1\r\n", - "appnope 0.1.2\r\n", - "appscript 1.1.2\r\n", - "argon2-cffi 21.3.0\r\n", - "argon2-cffi-bindings 21.2.0\r\n", - "arrow 1.2.2\r\n", - "astroid 2.6.6\r\n", - "astropy 5.0.4\r\n", - "asttokens 2.0.5\r\n", - "async-timeout 4.0.1\r\n", - "atomicwrites 1.4.0\r\n", - "attrs 20.3.0\r\n", - "Automat 20.2.0\r\n", - "autopep8 1.6.0\r\n", - "Babel 2.9.1\r\n", - "backcall 0.2.0\r\n", - "backports.functools-lru-cache 1.6.4\r\n", - "backports.tempfile 1.0\r\n", - "backports.weakref 1.0.post1\r\n", - "bcrypt 3.2.0\r\n", - "beautifulsoup4 4.11.1\r\n", - "binaryornot 0.4.4\r\n", - "bitarray 2.4.1\r\n", - "bkcharts 0.2\r\n", - "black 21.12b0\r\n", - "blackcellmagic 0.0.3\r\n", - "bleach 4.1.0\r\n", - "bokeh 2.4.2\r\n", - "boto3 1.21.32\r\n", - "botocore 1.24.32\r\n", - "Bottleneck 1.3.4\r\n", - "brotlipy 0.7.0\r\n", - "cachetools 4.2.2\r\n", - "certifi 2022.5.18.1\r\n", - "cffi 1.15.0\r\n", - "cftime 1.5.1.1\r\n", - "chardet 4.0.0\r\n", - "charset-normalizer 2.0.4\r\n", - "click 8.0.4\r\n", - "click-completion 0.5.2\r\n", - "click-log 0.3.2\r\n", - "cloudpickle 2.0.0\r\n", - "clyent 1.2.2\r\n", - "colorama 0.4.4\r\n", - "colorcet 2.0.6\r\n", - "commonmark 0.9.1\r\n", - "completejourney-py 0.0.3\r\n", - "component-logger 1.11.1\r\n", - "conda 4.12.0\r\n", - "conda-build 3.21.8\r\n", - "conda-content-trust 0+unknown\r\n", - "conda-pack 0.6.0\r\n", - "conda-package-handling 1.8.1\r\n", - "conda-repo-cli 1.0.4\r\n", - "conda-token 0.3.0\r\n", - "conda-verify 3.4.2\r\n", - "constantly 15.1.0\r\n", - "cookiecutter 1.7.3\r\n", - "cryptography 3.4.8\r\n", - "cssselect 1.1.0\r\n", - "cycler 0.11.0\r\n", - "Cython 0.29.28\r\n", - "cytoolz 0.11.0\r\n", - "daal4py 2021.5.0\r\n", - "dask 2022.2.1\r\n", - "datashader 0.13.0\r\n", - "datashape 0.5.4\r\n", - "dateparser 1.1.1\r\n", - "debugpy 1.5.1\r\n", - "decorator 5.1.1\r\n", - "defusedxml 0.7.1\r\n", - "diff-match-patch 20200713\r\n", - "distributed 2022.2.1\r\n", - "docutils 0.16\r\n", - "e451-py-security 1.0.57\r\n", - "effo-cookiecutter 0.4.0\r\n", - "effo411 0.2.0\r\n", - "effodata 2.5.0\r\n", - "entrypoints 0.4\r\n", - "et-xmlfile 1.1.0\r\n", - "executing 0.8.3\r\n", - "fastjsonschema 2.15.1\r\n", - "filelock 3.6.0\r\n", - "flake8 3.9.2\r\n", - "Flask 1.1.2\r\n", - "fonttools 4.25.0\r\n", - "frozenlist 1.2.0\r\n", - "fsspec 2022.2.0\r\n", - "future 0.18.2\r\n", - "gensim 4.1.2\r\n", - "gitdb 4.0.9\r\n", - "GitPython 3.1.27\r\n", - "glob2 0.7\r\n", - "gmpy2 2.1.2\r\n", - "google-api-core 1.25.1\r\n", - 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0.6.0\r\n", + "pytz 2021.3\r\n", + "pyviz-comms 2.0.2\r\n", + "PyWavelets 1.3.0\r\n", + "PyYAML 6.0\r\n", + "pyzmq 22.3.0\r\n", + "QDarkStyle 3.0.2\r\n", + "qstylizer 0.1.10\r\n", + "QtAwesome 1.0.3\r\n", + "qtconsole 5.3.0\r\n", + "QtPy 2.0.1\r\n", + "queuelib 1.5.0\r\n", + "regex 2022.3.2\r\n", + "requests 2.28.1\r\n", + "requests-file 1.5.1\r\n", + "requests-oauthlib 1.3.1\r\n", + "rich 12.4.3\r\n", + "rope 0.22.0\r\n", + "rsa 4.7.2\r\n", + "Rtree 0.9.7\r\n", + "ruamel-yaml-conda 0.15.100\r\n", + "s3transfer 0.5.0\r\n", + "scikit-image 0.19.2\r\n", + "scikit-learn 1.0.2\r\n", + "scikit-learn-intelex 2021.20220215.132722\r\n", + "scipy 1.9.3\r\n", + "Scrapy 2.6.1\r\n", + "seaborn 0.11.2\r\n", + "Send2Trash 1.8.0\r\n", + "service-identity 18.1.0\r\n", + "setuptools 65.2.0\r\n", + "shellingham 1.4.0\r\n", + "simpy 4.0.1\r\n", + "sip 4.19.13\r\n", + "six 1.16.0\r\n", + "smart-open 5.1.0\r\n", + "smmap 3.0.5\r\n", + "sniffio 1.2.0\r\n", + "snowballstemmer 2.2.0\r\n", + "sortedcollections 2.1.0\r\n", + "sortedcontainers 2.4.0\r\n", + "soupsieve 2.3.1\r\n", + "Sphinx 3.5.3\r\n", + "sphinx-book-theme 0.1.10\r\n", + "sphinx-comments 0.0.3\r\n", + "sphinx-copybutton 0.5.0\r\n", + "sphinx-external-toc 0.2.4\r\n", + "sphinx-jupyterbook-latex 0.4.6\r\n", + "sphinx-multitoc-numbering 0.1.3\r\n", + "sphinx-panels 0.6.0\r\n", + "sphinx-rtd-theme 1.0.0\r\n", + "sphinx-thebe 0.1.2\r\n", + "sphinx-togglebutton 0.3.1\r\n", + "sphinxcontrib-applehelp 1.0.2\r\n", + "sphinxcontrib-bibtex 2.4.2\r\n", + "sphinxcontrib-devhelp 1.0.2\r\n", + "sphinxcontrib-htmlhelp 2.0.0\r\n", + "sphinxcontrib-jsmath 1.0.1\r\n", + "sphinxcontrib-napoleon 0.7\r\n", + "sphinxcontrib-qthelp 1.0.3\r\n", + "sphinxcontrib-serializinghtml 1.1.5\r\n", + "spyder 5.1.5\r\n", + "spyder-kernels 2.1.3\r\n", + "SQLAlchemy 1.4.32\r\n", + "sqlalchemy2-stubs 0.0.2a29\r\n", + "sqlmodel 0.0.8\r\n", + "sqlparse 0.4.2\r\n", + "stack-data 0.2.0\r\n", + "statsmodels 0.13.2\r\n", + "sympy 1.10.1\r\n", + "tables 3.6.1\r\n", + "tabulate 0.8.9\r\n", + "TBB 0.2\r\n", + "tblib 1.7.0\r\n", + "tenacity 8.0.1\r\n", + "terminado 0.13.1\r\n", + "testpath 0.5.0\r\n", + "text-unidecode 1.3\r\n", + "textdistance 4.2.1\r\n", + "threadpoolctl 2.2.0\r\n", + "three-merge 0.1.1\r\n", + "tifffile 2021.7.2\r\n", + "tinycss 0.4\r\n", + "tldextract 3.2.0\r\n", + "toml 0.10.2\r\n", + "tomli 1.2.2\r\n", + "toolz 0.11.2\r\n", + "tornado 6.1\r\n", + "tqdm 4.64.0\r\n", + "traitlets 5.1.1\r\n", + "Twisted 22.2.0\r\n", + "typed-ast 1.4.3\r\n", + "typing_extensions 4.1.1\r\n", + "tzlocal 2.1\r\n", + "uc-micro-py 1.0.1\r\n", + "ujson 5.1.0\r\n", + "Unidecode 1.2.0\r\n", + "urllib3 1.26.9\r\n", + "virtualenv 20.16.5\r\n", + "w3lib 1.21.0\r\n", + "watchdog 2.1.6\r\n", + "watermark 2.3.0\r\n", + "wcwidth 0.2.5\r\n", + "webencodings 0.5.1\r\n", + "websocket-client 0.58.0\r\n", + "Werkzeug 2.0.3\r\n", + "wheel 0.37.1\r\n", + "widgetsnbextension 3.5.2\r\n", + "wrapt 1.12.1\r\n", + "wurlitzer 3.0.2\r\n", + "xarray 0.20.1\r\n", + "xlrd 2.0.1\r\n", + "XlsxWriter 3.0.3\r\n", + "xlwings 0.24.9\r\n", + "yapf 0.31.0\r\n", + "yarl 1.6.3\r\n", + "zict 2.0.0\r\n", + "zipp 3.7.0\r\n", + "zope.interface 5.4.0\r\n" ] }, { @@ -483,7 +532,8 @@ " '/opt/anaconda3/lib/python3.9/lib-dynload',\n", " '',\n", " '/opt/anaconda3/lib/python3.9/site-packages',\n", - " '/opt/anaconda3/lib/python3.9/site-packages/aeosa']" + " '/opt/anaconda3/lib/python3.9/site-packages/aeosa',\n", + " '/Users/b294776/Desktop/workspace/packages/dynamic-positioner/src']" ] }, "execution_count": 2, @@ -545,7 +595,7 @@ "In the code above you saw the `import` statement. The `import` statement allows us to import code from another module. There are a few ways to use the statement, which we will mention briefly here, from most recommended to least recommended.\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "### Explicit module import\n", @@ -761,7 +811,7 @@ "## Standard library\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "In the above code we've seen a few different libraries installed (`math` and `numpy`). These libraries represent two of the many readily available libraries at your disposal. However, we refer to the `math` library is part of the **standard library** and the `numpy` library is considered a **third party library**.\n", @@ -892,7 +942,7 @@ "## Third-party modules\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "One of the things that makes Python useful, especially within the world of data science, is its ecosystem of third-party modules. These can be imported just as the built-in modules, but first the modules must be installed on your system. As previously mentioned, [Python Package Index](https://pypi.org/) (PyPI for short) and [Anaconda](https://anaconda.org/anaconda/repo) are the two primary public package managers for Python. To install packages from these locations we just use the following:\n", diff --git a/docs/_build/jupyter_execute/02-module/lesson-2a.py b/docs/_build/jupyter_execute/02-module/lesson-2a.py index 5f48bf6..84e3a8c 100644 --- a/docs/_build/jupyter_execute/02-module/lesson-2a.py +++ b/docs/_build/jupyter_execute/02-module/lesson-2a.py @@ -21,7 +21,7 @@ # ## Terminology # # ```{admonition} Video 🎥: -# +# # ``` # # People often use the terms _"package"_, _"library"_, and _"module"_ synonymously. If you are familiar with the R programming language you've probably heard some of these terms before. Although there are some semantical differences between R and Python, here is how you can think of the two terms: @@ -74,7 +74,7 @@ # In the code above you saw the `import` statement. The `import` statement allows us to import code from another module. There are a few ways to use the statement, which we will mention briefly here, from most recommended to least recommended. # # ```{admonition} Video 🎥: -# +# # ``` # # ### Explicit module import @@ -166,7 +166,7 @@ # ## Standard library # # ```{admonition} Video 🎥: -# +# # ``` # # In the above code we've seen a few different libraries installed (`math` and `numpy`). These libraries represent two of the many readily available libraries at your disposal. However, we refer to the `math` library is part of the **standard library** and the `numpy` library is considered a **third party library**. @@ -224,7 +224,7 @@ # ## Third-party modules # # ```{admonition} Video 🎥: -# +# # ``` # # One of the things that makes Python useful, especially within the world of data science, is its ecosystem of third-party modules. These can be imported just as the built-in modules, but first the modules must be installed on your system. As previously mentioned, [Python Package Index](https://pypi.org/) (PyPI for short) and [Anaconda](https://anaconda.org/anaconda/repo) are the two primary public package managers for Python. To install packages from these locations we just use the following: diff --git a/docs/_build/jupyter_execute/02-module/lesson-2b.ipynb b/docs/_build/jupyter_execute/02-module/lesson-2b.ipynb index a9a8d78..8af162a 100644 --- a/docs/_build/jupyter_execute/02-module/lesson-2b.ipynb +++ b/docs/_build/jupyter_execute/02-module/lesson-2b.ipynb @@ -30,7 +30,7 @@ "## Data & memory\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "Python stores its data in memory - this makes it relatively quickly accessible but can cause size limitations in certain fields. In this class we will mainly work with small to moderate data sets, which means we should not run into any space limitations. \n", @@ -59,7 +59,7 @@ "## Delimited files\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "Text files are a popular way to hold and exchange tabular data as almost any data application supports exporting data to the CSV (or other text file) format. Text file formats use delimiters to separate the different elements in a line, and each line of data is in its own line in the text file. Therefore, importing different kinds of text files can follow a fairly consistent process once you’ve identified the delimiter.\n", @@ -708,7 +708,7 @@ "### File paths\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "It's important to understand where files exist on your computer and how to reference those paths. There are two main approaches:\n", @@ -1020,7 +1020,7 @@ "### Attributes & methods\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "We've seen that we can use the dot-notation to access functions in libraries (i.e. `pd.read_csv()`). We can use this same approach to access things inside of _objects_. What's an object? Basically, a variable that contains other data or functionality inside of it that is exposed to users. Consequently, our DataFrame item is an object.\n", @@ -1923,7 +1923,7 @@ "text": [ "Python implementation: CPython\n", "Python version : 3.9.12\n", - "IPython version : 8.2.0\n", + "IPython version : 8.4.0\n", "\n", "jupyterlab: 3.3.2\n", "pandas : 1.4.2\n", diff --git a/docs/_build/jupyter_execute/02-module/lesson-2b.py b/docs/_build/jupyter_execute/02-module/lesson-2b.py index 2944a3a..6dc004b 100644 --- a/docs/_build/jupyter_execute/02-module/lesson-2b.py +++ b/docs/_build/jupyter_execute/02-module/lesson-2b.py @@ -17,7 +17,7 @@ # ## Data & memory # # ```{admonition} Video 🎥: -# +# # ``` # # Python stores its data in memory - this makes it relatively quickly accessible but can cause size limitations in certain fields. In this class we will mainly work with small to moderate data sets, which means we should not run into any space limitations. @@ -41,7 +41,7 @@ # ## Delimited files # # ```{admonition} Video 🎥: -# +# # ``` # # Text files are a popular way to hold and exchange tabular data as almost any data application supports exporting data to the CSV (or other text file) format. Text file formats use delimiters to separate the different elements in a line, and each line of data is in its own line in the text file. Therefore, importing different kinds of text files can follow a fairly consistent process once you’ve identified the delimiter. @@ -85,7 +85,7 @@ # ### File paths # # ```{admonition} Video 🎥: -# +# # ``` # # It's important to understand where files exist on your computer and how to reference those paths. There are two main approaches: @@ -207,7 +207,7 @@ # ### Attributes & methods # # ```{admonition} Video 🎥: -# +# # ``` # # We've seen that we can use the dot-notation to access functions in libraries (i.e. `pd.read_csv()`). We can use this same approach to access things inside of _objects_. What's an object? Basically, a variable that contains other data or functionality inside of it that is exposed to users. Consequently, our DataFrame item is an object. diff --git a/docs/_build/jupyter_execute/02-module/lesson-2c.ipynb b/docs/_build/jupyter_execute/02-module/lesson-2c.ipynb index ce4b407..148afcb 100644 --- a/docs/_build/jupyter_execute/02-module/lesson-2c.ipynb +++ b/docs/_build/jupyter_execute/02-module/lesson-2c.ipynb @@ -114,7 +114,7 @@ "## What Are DataFrames Made of?\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "Accessing an individual column of a DataFrame can be done by passing the column name as a string, in brackets (`[]`)." @@ -496,7 +496,7 @@ "## What Can You Do with a Series?\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -953,7 +953,7 @@ "## DataFrame Indexes\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "It's not just Series that have indexes! DataFrames have them too. Take a look at the carrier DataFrame again and note the bold numbers on the left." diff --git a/docs/_build/jupyter_execute/02-module/lesson-2c.py b/docs/_build/jupyter_execute/02-module/lesson-2c.py index f06385a..d9f736f 100644 --- a/docs/_build/jupyter_execute/02-module/lesson-2c.py +++ b/docs/_build/jupyter_execute/02-module/lesson-2c.py @@ -26,7 +26,7 @@ # ## What Are DataFrames Made of? # # ```{admonition} Video 🎥: -# +# # ``` # # Accessing an individual column of a DataFrame can be done by passing the column name as a string, in brackets (`[]`). @@ -128,7 +128,7 @@ # ## What Can You Do with a Series? # # ```{admonition} Video 🎥: -# +# # ``` # First, let's create our own Series object from scratch -- they don't need to come from a DataFrame. Here, we pass a list in as an argument and it will be converted to a Series. @@ -228,7 +228,7 @@ # ## DataFrame Indexes # # ```{admonition} Video 🎥: -# +# # ``` # # It's not just Series that have indexes! DataFrames have them too. Take a look at the carrier DataFrame again and note the bold numbers on the left. diff --git a/docs/_build/jupyter_execute/03-module/lesson-3a.ipynb b/docs/_build/jupyter_execute/03-module/lesson-3a.ipynb index 0450187..33818e6 100644 --- a/docs/_build/jupyter_execute/03-module/lesson-3a.ipynb +++ b/docs/_build/jupyter_execute/03-module/lesson-3a.ipynb @@ -179,7 +179,7 @@ "## Subsetting dimensions\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```\n", "\n", "We don't always want all of the data in a DataFrame, so we need to take subsets of the DataFrame. In general, **subsetting** is extracting a small portion of a DataFrame -- making the DataFrame smaller. Since the DataFrame is two-dimensional, there are two dimensions on which to subset.\n", @@ -903,7 +903,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2156,7 +2156,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2491,7 +2491,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2628,7 +2628,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_97171/2190037627.py:1: SettingWithCopyWarning: \n", + "/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_69390/2190037627.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -2735,8 +2735,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The id of the original dataframe is: 140606100927728\n", - " The id of the indexed dataframe is: 140606114994256\n" + "The id of the original dataframe is: 140438716342624\n", + " The id of the indexed dataframe is: 140438730364480\n" ] } ], @@ -2891,7 +2891,7 @@ "text": [ "Python implementation: CPython\n", "Python version : 3.9.12\n", - "IPython version : 8.2.0\n", + "IPython version : 8.4.0\n", "\n", "jupyterlab: 3.3.2\n", "pandas : 1.4.2\n", diff --git a/docs/_build/jupyter_execute/03-module/lesson-3a.py b/docs/_build/jupyter_execute/03-module/lesson-3a.py index afc0461..658cd13 100644 --- a/docs/_build/jupyter_execute/03-module/lesson-3a.py +++ b/docs/_build/jupyter_execute/03-module/lesson-3a.py @@ -33,7 +33,7 @@ # ## Subsetting dimensions # # ```{admonition} Video 🎥: -# +# # ``` # # We don't always want all of the data in a DataFrame, so we need to take subsets of the DataFrame. In general, **subsetting** is extracting a small portion of a DataFrame -- making the DataFrame smaller. Since the DataFrame is two-dimensional, there are two dimensions on which to subset. @@ -166,7 +166,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Subsetting rows @@ -276,7 +276,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Selecting variables and filtering rows @@ -328,7 +328,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Views vs copies diff --git a/docs/_build/jupyter_execute/03-module/lesson-3b.ipynb b/docs/_build/jupyter_execute/03-module/lesson-3b.ipynb index 206f0a7..896e049 100644 --- a/docs/_build/jupyter_execute/03-module/lesson-3b.ipynb +++ b/docs/_build/jupyter_execute/03-module/lesson-3b.ipynb @@ -1013,7 +1013,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1720,7 +1720,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2700,7 +2700,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -4048,15 +4048,16 @@ "outputs": [ { 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { "filenames": { "image/png": "/Users/b294776/Desktop/workspace/training/UC/uc-bana-6043/docs/_build/jupyter_execute/03-module/lesson-3b_72_0.png" - } + }, + "needs_background": "light" }, "output_type": "display_data" } @@ -4816,11 +4817,11 @@ "text": [ "Python implementation: CPython\n", "Python version : 3.9.12\n", - "IPython version : 8.2.0\n", + "IPython version : 8.4.0\n", "\n", "jupyterlab: 3.3.2\n", "pandas : 1.4.2\n", - "numpy : 1.21.5\n", + "numpy : 1.22.4\n", "seaborn : 0.11.2\n", "\n" ] diff --git a/docs/_build/jupyter_execute/03-module/lesson-3b.py b/docs/_build/jupyter_execute/03-module/lesson-3b.py index 27bda8d..3889058 100644 --- a/docs/_build/jupyter_execute/03-module/lesson-3b.py +++ b/docs/_build/jupyter_execute/03-module/lesson-3b.py @@ -88,7 +88,7 @@ # ```{admonition} Video 🎥: -# +# # ``` # ## Calculations using columns @@ -182,7 +182,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Overwriting columns @@ -246,7 +246,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Non-numeric column operations diff --git a/docs/_build/jupyter_execute/03-module/lesson-3b_72_0.png b/docs/_build/jupyter_execute/03-module/lesson-3b_72_0.png index 01f30db..74f1757 100644 Binary files a/docs/_build/jupyter_execute/03-module/lesson-3b_72_0.png and b/docs/_build/jupyter_execute/03-module/lesson-3b_72_0.png differ diff --git a/docs/_build/jupyter_execute/03-module/lesson-3c.ipynb b/docs/_build/jupyter_execute/03-module/lesson-3c.ipynb index f6668c8..8f60a10 100644 --- a/docs/_build/jupyter_execute/03-module/lesson-3c.ipynb +++ b/docs/_build/jupyter_execute/03-module/lesson-3c.ipynb @@ -274,7 +274,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -859,7 +859,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1629,7 +1629,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 18, @@ -2318,7 +2318,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2360,7 +2360,7 @@ "text": [ "Python implementation: CPython\n", "Python version : 3.9.12\n", - "IPython version : 8.2.0\n", + "IPython version : 8.4.0\n", "\n", "jupyterlab: 3.3.2\n", "pandas : 1.4.2\n", diff --git a/docs/_build/jupyter_execute/03-module/lesson-3c.py b/docs/_build/jupyter_execute/03-module/lesson-3c.py index 0a84df9..dfe2520 100644 --- a/docs/_build/jupyter_execute/03-module/lesson-3c.py +++ b/docs/_build/jupyter_execute/03-module/lesson-3c.py @@ -109,7 +109,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ### Describe method @@ -251,7 +251,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ### Describe method @@ -389,7 +389,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Exercises diff --git a/docs/_build/jupyter_execute/04-module/lesson-4a.ipynb b/docs/_build/jupyter_execute/04-module/lesson-4a.ipynb index a295791..300e45b 100644 --- a/docs/_build/jupyter_execute/04-module/lesson-4a.ipynb +++ b/docs/_build/jupyter_execute/04-module/lesson-4a.ipynb @@ -23,7 +23,7 @@ "Once a DataFrame is tidy, it becomes much easier to compute summary statistics, join with other datasets, visualize, apply machine learning models, etc. In this lesson we will focus on ways to reshape DataFrames so that they meet the tidy guidelines.\n", "\n", "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -458,8 +458,8 @@ "source": [ "ames_wide.melt(\n", " id_vars='neighborhood',\n", - " value_vars=['2007', '2008', '2009', '2010'], \n", - " var_name='year', \n", + " value_vars=['2007', '2008', '2009', '2010'],\n", + " var_name='year',\n", " value_name='homes_sold'\n", " )" ] @@ -579,7 +579,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1365,7 +1365,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/jupyter_execute/04-module/lesson-4a.py b/docs/_build/jupyter_execute/04-module/lesson-4a.py index 8da72ed..028d4b5 100644 --- a/docs/_build/jupyter_execute/04-module/lesson-4a.py +++ b/docs/_build/jupyter_execute/04-module/lesson-4a.py @@ -20,7 +20,7 @@ # Once a DataFrame is tidy, it becomes much easier to compute summary statistics, join with other datasets, visualize, apply machine learning models, etc. In this lesson we will focus on ways to reshape DataFrames so that they meet the tidy guidelines. # # ```{admonition} Video 🎥: -# +# # ``` # ## Learning objectives @@ -87,8 +87,8 @@ ames_wide.melt( id_vars='neighborhood', - value_vars=['2007', '2008', '2009', '2010'], - var_name='year', + value_vars=['2007', '2008', '2009', '2010'], + var_name='year', value_name='homes_sold' ) @@ -115,7 +115,7 @@ # ```{admonition} Video 🎥: -# +# # ``` # ## Pivoting long data @@ -162,7 +162,7 @@ # ```{admonition} Video 🎥: -# +# # ``` # ## Pivoting with special needs diff --git a/docs/_build/jupyter_execute/04-module/lesson-4b.ipynb b/docs/_build/jupyter_execute/04-module/lesson-4b.ipynb index 9ef04f8..8b577c0 100644 --- a/docs/_build/jupyter_execute/04-module/lesson-4b.ipynb +++ b/docs/_build/jupyter_execute/04-module/lesson-4b.ipynb @@ -1480,7 +1480,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1655,7 +1655,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, diff --git a/docs/_build/jupyter_execute/04-module/lesson-4b.py b/docs/_build/jupyter_execute/04-module/lesson-4b.py index 62b09bc..e7823ae 100644 --- a/docs/_build/jupyter_execute/04-module/lesson-4b.py +++ b/docs/_build/jupyter_execute/04-module/lesson-4b.py @@ -306,7 +306,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Merge indicator @@ -352,7 +352,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Exercises diff --git a/docs/_build/jupyter_execute/04-module/lesson-4c.ipynb b/docs/_build/jupyter_execute/04-module/lesson-4c.ipynb index ea0512f..5e52d47 100644 --- a/docs/_build/jupyter_execute/04-module/lesson-4c.ipynb +++ b/docs/_build/jupyter_execute/04-module/lesson-4c.ipynb @@ -1009,7 +1009,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -2624,7 +2624,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_50389/1279328171.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", + "/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_69791/1279328171.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", " products['package_size']\n" ] }, @@ -3311,7 +3311,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_50389/3233621946.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", + "/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_69791/3233621946.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", " prod_id_8s = products['product_id'].astype(str).str.contains('(8)\\\\1', na=False)\n" ] }, @@ -3442,7 +3442,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_50389/1949681077.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", + "/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_69791/1949681077.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", " prod_id_8s = products['product_id'].astype(str).str.contains('(8)\\\\1{2}', na=False)\n" ] }, @@ -3583,7 +3583,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_50389/3801605409.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", + "/var/folders/8f/c06lv6q17tjbyjv2nkt0_s4s1sh0tg/T/ipykernel_69791/3801605409.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", " size_filter = products['package_size'].str.contains('^\\d{2,}(\\.)?.*lb', case=False, na=False)\n" ] }, @@ -3686,7 +3686,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -3728,7 +3728,7 @@ "text": [ "Python implementation: CPython\n", "Python version : 3.9.12\n", - "IPython version : 8.2.0\n", + "IPython version : 8.4.0\n", "\n", "jupyterlab : 3.3.2\n", "pandas : 1.4.2\n", diff --git a/docs/_build/jupyter_execute/04-module/lesson-4c.py b/docs/_build/jupyter_execute/04-module/lesson-4c.py index 87399ae..44e9df7 100644 --- a/docs/_build/jupyter_execute/04-module/lesson-4c.py +++ b/docs/_build/jupyter_execute/04-module/lesson-4c.py @@ -174,7 +174,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Regular expressions @@ -579,7 +579,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Exercises diff --git a/docs/_build/jupyter_execute/06-module/lesson-6a.ipynb b/docs/_build/jupyter_execute/06-module/lesson-6a.ipynb index 9e99b81..6b36e4f 100644 --- a/docs/_build/jupyter_execute/06-module/lesson-6a.ipynb +++ b/docs/_build/jupyter_execute/06-module/lesson-6a.ipynb @@ -120,7 +120,7 @@ } ], "source": [ - "# multiple lines in the statement are fine as long as they have the \n", + "# multiple lines in the statement are fine as long as they have the\n", "# same indentation\n", "if x < 0:\n", " new_value = abs(x)\n", @@ -254,7 +254,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -339,7 +339,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -520,18 +520,18 @@ "def produce_revenue(sqft, visits, trend):\n", " total = 9.91 * sqft * visits * trend\n", " return round(total, 2)\n", - " \n", + "\n", "def frozen_revenue(sqft, visits, trend):\n", " prod = produce_revenue(sqft, visits, trend)\n", - " total = 3.28 * sqft * visits * trend - prod * .005 \n", + " total = 3.28 * sqft * visits * trend - prod * .005\n", " return round(total, 2)\n", - " \n", + "\n", "expected_annual_revenue = {\n", - " 'produce': produce_revenue, \n", - " 'frozen': frozen_revenue, \n", + " 'produce': produce_revenue,\n", + " 'frozen': frozen_revenue,\n", " 'pharmacy': lambda: 16.11 * visits * trend\n", " }\n", - " \n", + "\n", "choice = 'frozen'\n", "expected_annual_revenue.get(choice, 'Bad choice')(sqft=937, visits=465, trend=0.98)" ] @@ -564,7 +564,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1166,7 +1166,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1209,7 +1209,7 @@ "text": [ "Python implementation: CPython\n", "Python version : 3.9.12\n", - "IPython version : 8.2.0\n", + "IPython version : 8.4.0\n", "\n", "pandas : 1.4.2\n", "jupyterlab : 3.3.2\n", diff --git a/docs/_build/jupyter_execute/06-module/lesson-6a.py b/docs/_build/jupyter_execute/06-module/lesson-6a.py index 9cd1cec..b5ca88d 100644 --- a/docs/_build/jupyter_execute/06-module/lesson-6a.py +++ b/docs/_build/jupyter_execute/06-module/lesson-6a.py @@ -63,7 +63,7 @@ # In[4]: -# multiple lines in the statement are fine as long as they have the +# multiple lines in the statement are fine as long as they have the # same indentation if x < 0: new_value = abs(x) @@ -134,7 +134,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Multiway branching @@ -193,7 +193,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Switch statements @@ -278,18 +278,18 @@ def produce_revenue(sqft, visits, trend): total = 9.91 * sqft * visits * trend return round(total, 2) - + def frozen_revenue(sqft, visits, trend): prod = produce_revenue(sqft, visits, trend) - total = 3.28 * sqft * visits * trend - prod * .005 + total = 3.28 * sqft * visits * trend - prod * .005 return round(total, 2) - + expected_annual_revenue = { - 'produce': produce_revenue, - 'frozen': frozen_revenue, + 'produce': produce_revenue, + 'frozen': frozen_revenue, 'pharmacy': lambda: 16.11 * visits * trend } - + choice = 'frozen' expected_annual_revenue.get(choice, 'Bad choice')(sqft=937, visits=465, trend=0.98) @@ -313,7 +313,7 @@ def frozen_revenue(sqft, visits, trend): # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Applying in Pandas @@ -405,7 +405,7 @@ def flag(df): # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Exercises diff --git a/docs/_build/jupyter_execute/06-module/lesson-6b.ipynb b/docs/_build/jupyter_execute/06-module/lesson-6b.ipynb index d56a5b8..ce20615 100644 --- a/docs/_build/jupyter_execute/06-module/lesson-6b.ipynb +++ b/docs/_build/jupyter_execute/06-module/lesson-6b.ipynb @@ -285,7 +285,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -405,7 +405,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -539,7 +539,7 @@ ], "source": [ "squared_certain_values = [\n", - " number * number for number in range(10) \n", + " number * number for number in range(10)\n", " if number % 2 != 0 and number != 5\n", " ]\n", "\n", @@ -611,7 +611,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -643,7 +643,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "After 330 flips: ['tails', 'tails', 'tails', 'tails', 'tails', 'tails', 'tails', 'tails', 'tails', 'tails', 'tails']\n" + "After 2335 flips: ['heads', 'heads', 'heads', 'heads', 'heads', 'heads', 'heads', 'heads', 'heads', 'heads', 'heads']\n" ] } ], @@ -660,7 +660,7 @@ "while not ten_of_a_kind:\n", " # flip coin 10 times\n", " ten_coin_flips = [random.choice(coin) for flip in range(11)]\n", - " \n", + "\n", " # check if there\n", " ten_of_a_kind = len(set(ten_coin_flips)) == 1\n", "\n", @@ -710,7 +710,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -904,7 +904,7 @@ "for col in range(len(df.columns)):\n", " value = df.iloc[:, col].nunique()\n", " unique_values.append(value)\n", - " \n", + "\n", "unique_values" ] }, diff --git a/docs/_build/jupyter_execute/06-module/lesson-6b.py b/docs/_build/jupyter_execute/06-module/lesson-6b.py index 3aa11af..f9d060a 100644 --- a/docs/_build/jupyter_execute/06-module/lesson-6b.py +++ b/docs/_build/jupyter_execute/06-module/lesson-6b.py @@ -118,7 +118,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Controlling sequences @@ -178,7 +178,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## List comprehensions @@ -235,7 +235,7 @@ squared_certain_values = [ - number * number for number in range(10) + number * number for number in range(10) if number % 2 != 0 and number != 5 ] @@ -274,7 +274,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## `while` loop @@ -306,7 +306,7 @@ while not ten_of_a_kind: # flip coin 10 times ten_coin_flips = [random.choice(coin) for flip in range(11)] - + # check if there ten_of_a_kind = len(set(ten_coin_flips)) == 1 @@ -347,7 +347,7 @@ # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Iterables @@ -424,7 +424,7 @@ for col in range(len(df.columns)): value = df.iloc[:, col].nunique() unique_values.append(value) - + unique_values diff --git a/docs/_build/jupyter_execute/06-module/lesson-6c.ipynb b/docs/_build/jupyter_execute/06-module/lesson-6c.ipynb index d2d8766..e8f7f22 100644 --- a/docs/_build/jupyter_execute/06-module/lesson-6c.ipynb +++ b/docs/_build/jupyter_execute/06-module/lesson-6c.ipynb @@ -76,7 +76,7 @@ "source": [ "# array containing 4 sets of 10 random numbers\n", "x = np.random.random_sample((4, 10))\n", - " \n", + "\n", "x[0] = (x[0] - x[0].min()) / (x[0].max() - x[0].min())\n", "x[1] = (x[1] - x[1].min()) / (x[1].max() - x[1].min())\n", "x[2] = (x[2] - x[2].min()) / (x[1].max() - x[2].min())\n", @@ -98,14 +98,14 @@ { "data": { "text/plain": [ - "array([[0. , 0.25838401, 0.41338666, 0.22670219, 0.14207045,\n", - " 0.6372547 , 1. , 0.66500104, 0.97430082, 0.67975973],\n", - " [0.23875146, 0.25624387, 0.09282163, 0.39901557, 0.65988219,\n", - " 0.08264166, 0.53262924, 0. , 0.98924274, 1. ],\n", - " [0.86209879, 0.83270803, 0.7993648 , 0. , 0.21927493,\n", - " 0.55366273, 0.00574432, 0.98300226, 1. , 0.64223695],\n", - " [0.56997021, 0.76553595, 1. , 0. , 0.64481501,\n", - " 0.68118805, 0.95526039, 0.91795629, 0.39921224, 0.95423523]])" + "array([[0.79882196, 0. , 0.63291805, 1. , 0.67628976,\n", + " 0.85449383, 0.21740158, 0.3928153 , 0.84347625, 0.05630605],\n", + " [1. , 0.38420133, 0.78614167, 0. , 0.60469202,\n", + " 0.47085719, 0.39803966, 0.91226131, 0.73877887, 0.9079893 ],\n", + " [0.09984341, 1. , 0.13157581, 0.40402155, 0.43002542,\n", + " 0. , 0.00857417, 0.87397615, 0.78929133, 0.4674958 ],\n", + " [0.65530407, 0.10196804, 0. , 0.83969465, 1. ,\n", + " 0.68729707, 0.0015783 , 0.354641 , 0.34662782, 0.52565252]])" ] }, "execution_count": 3, @@ -119,7 +119,7 @@ "def rescale(array):\n", " for index, vector in enumerate(array):\n", " array[index] = (vector - vector.min()) / (vector.max() - vector.min())\n", - " \n", + "\n", " return(array)\n", "\n", "rescale(x)" @@ -144,8 +144,8 @@ { "data": { "text/plain": [ - "array([1.67082046, 2.11287186, 2.30557308, 0.62571776, 1.66604259,\n", - " 1.95474713, 2.49363395, 2.56595959, 3.3627558 , 3.27623192])" + "array([2.55396944, 1.48616936, 1.55063553, 2.24371621, 2.7110072 ,\n", + " 2.01264809, 0.62559371, 2.53369375, 2.71817427, 1.95744367])" ] }, "execution_count": 4, @@ -166,8 +166,8 @@ { "data": { "text/plain": [ - "array([1.67082046, 2.11287186, 2.30557308, 0.62571776, 1.66604259,\n", - " 1.95474713, 2.49363395, 2.56595959, 3.3627558 , 3.27623192])" + "array([2.55396944, 1.48616936, 1.55063553, 2.24371621, 2.7110072 ,\n", + " 2.01264809, 0.62559371, 2.53369375, 2.71817427, 1.95744367])" ] }, "execution_count": 5, @@ -176,7 +176,7 @@ } ], "source": [ - "# method \n", + "# method\n", "x.sum(axis = 0)" ] }, @@ -195,7 +195,7 @@ { "data": { "text/plain": [ - "22.034354137152878" + "20.393051216518487" ] }, "execution_count": 6, @@ -216,8 +216,8 @@ { "data": { "text/plain": [ - "array([1.67082046, 2.11287186, 2.30557308, 0.62571776, 1.66604259,\n", - " 1.95474713, 2.49363395, 2.56595959, 3.3627558 , 3.27623192])" + "array([2.55396944, 1.48616936, 1.55063553, 2.24371621, 2.7110072 ,\n", + " 2.01264809, 0.62559371, 2.53369375, 2.71817427, 1.95744367])" ] }, "execution_count": 7, @@ -238,7 +238,7 @@ { "data": { "text/plain": [ - "array([4.9968596 , 4.25122836, 5.89809281, 6.88817336])" + "array([5.47252277, 6.20296134, 4.20480363, 4.51276347])" ] }, "execution_count": 8, @@ -292,8 +292,8 @@ "def yell(text):\n", " new_text = text.upper()\n", " return new_text\n", - " \n", - "yell('hello world!') " + "\n", + "yell('hello world!')" ] }, { @@ -346,7 +346,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -392,7 +392,7 @@ ], "source": [ "# implicitly computing store sales for store 46 during week 43\n", - "store_sales(df, 46, 43) " + "store_sales(df, 46, 43)" ] }, { @@ -413,7 +413,7 @@ ], "source": [ "# implicitly computing store sales for store 43 (does not exist) during week 46\n", - "store_sales(df, 43, 46) " + "store_sales(df, 43, 46)" ] }, { @@ -445,7 +445,7 @@ ], "source": [ "# explicitly computing store sales for store 46 during week 43\n", - "store_sales(data=df, week=43, store=46) " + "store_sales(data=df, week=43, store=46)" ] }, { @@ -543,7 +543,7 @@ "def yell(*args):\n", " new_text = ' '.join(args).upper()\n", " return new_text\n", - " \n", + "\n", "yell('hello world!', 'I', 'love', 'Python!!')" ] }, @@ -571,7 +571,7 @@ "# **kwargs just creates a dictionary\n", "def students(**kwargs):\n", " print(kwargs)\n", - " \n", + "\n", "students(student1='John', student2='Robert', student3='Sally')" ] }, @@ -595,8 +595,8 @@ "def print_student_names(**kwargs):\n", " for key, value in kwargs.items():\n", " print(f'{key} = {value}')\n", - " \n", - "print_student_names(student1='John', student2='Robert', student3='Sally') " + "\n", + "print_student_names(student1='John', student2='Robert', student3='Sally')" ] }, { @@ -629,7 +629,7 @@ "source": [ "def some_function(name, age):\n", " return f'{name} is {age} years old'\n", - " \n", + "\n", "some_function('Tom', 27)" ] }, @@ -661,7 +661,7 @@ "source": [ "def some_function(name: str, age: int) -> str:\n", " return f'{name} is {age} years old'\n", - " \n", + "\n", "some_function('Tom', 27)" ] }, @@ -741,7 +741,7 @@ " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", " return total_sales\n", - " \n", + "\n", "store_sales(data=df, store=309, week=48)" ] }, @@ -765,7 +765,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -793,11 +793,11 @@ "def store_sales(data: pd.DataFrame, store: int, week: int) -> float:\n", " \"\"\"\n", " Compute total store sales.\n", - " \n", + "\n", " This function computes the total sales for a given\n", " store and week based on a user supplied DataFrame that\n", " contains sales in a column named `sales_value`.\n", - " \n", + "\n", " Parameters\n", " ----------\n", " data : DataFrame\n", @@ -806,16 +806,16 @@ " Integer value representing store number\n", " week : int\n", " Integer value representing week of year\n", - " \n", + "\n", " Returns\n", " -------\n", " float\n", " A float object representing total store sales\n", - " \n", + "\n", " See Also\n", " --------\n", " store_visits : Computes total store visits\n", - " \n", + "\n", " Examples\n", " --------\n", " >>> store_sales(data=df, store=309, week=48)\n", @@ -849,7 +849,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -897,13 +897,13 @@ " if not isinstance(data, pd.DataFrame): raise Exception('`data` should be a Pandas DataFrame')\n", " if not isinstance(store, int): raise Exception('`store` should be an integer')\n", " if not isinstance(week, int): raise Exception('`week` should be an integer')\n", - " \n", + "\n", " # computation\n", " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", " return total_sales\n", - " \n", - "store_sales(data=df, store='309', week=48) " + "\n", + "store_sales(data=df, store='309', week=48)" ] }, { @@ -937,13 +937,13 @@ " if not isinstance(data, pd.DataFrame): raise TypeError('`data` should be a Pandas DataFrame')\n", " if not isinstance(store, int): raise TypeError('`store` should be an integer')\n", " if not isinstance(week, int): raise TypeError('`week` should be an integer')\n", - " \n", + "\n", " # computation\n", " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", " return total_sales\n", - " \n", - "store_sales(data=df, store='309', week=48) " + "\n", + "store_sales(data=df, store='309', week=48)" ] }, { @@ -1004,10 +1004,10 @@ " if not isinstance(data, pd.DataFrame): raise TypeError('`data` should be a Pandas DataFrame')\n", " if not isinstance(store, int): raise TypeError('`store` should be an integer')\n", " if not isinstance(week, int): raise TypeError('`week` should be an integer')\n", - " if store not in data.store_id.unique(): \n", + " if store not in data.store_id.unique():\n", " raise ValueError(f'`store` {store} does not exist in the supplied DataFrame')\n", - " \n", - " \n", + "\n", + "\n", " # computation\n", " filt = (data['store_id'] == store) & (data['week'] == week)\n", " total_sales = data['sales_value'][filt].sum()\n", @@ -1127,8 +1127,8 @@ " price = round(product['price'] * (1.0 - discount), 2)\n", " assert 0 <= price <= product['price'], 'Invalid discount applied'\n", " return price\n", - " \n", - "apply_discount(milk, 2.00) " + "\n", + "apply_discount(milk, 2.00)" ] }, { @@ -1257,7 +1257,7 @@ "metadata": {}, "source": [ "```{admonition} Video 🎥:\n", - "\n", + "\n", "```" ] }, @@ -1300,8 +1300,8 @@ "\n", "def func(x):\n", " return x + 1\n", - " \n", - "func(x = 50) " + "\n", + "func(x = 50)" ] }, { @@ -1359,8 +1359,8 @@ "\n", "def func(x):\n", " return x + ' ' + y\n", - " \n", - "func(x = 'Brad') " + "\n", + "func(x = 'Brad')" ] }, { @@ -1404,7 +1404,7 @@ " def my_paste():\n", " return x + sep + y\n", " return my_paste()\n", - " \n", + "\n", "my_name(sep=' ')" ] }, @@ -1447,7 +1447,7 @@ " y = firstpart + '.' + secondpart\n", " return y\n", "\n", - "convert(8451) " + "convert(8451)" ] }, { diff --git a/docs/_build/jupyter_execute/06-module/lesson-6c.py b/docs/_build/jupyter_execute/06-module/lesson-6c.py index df2f1bb..baba78f 100644 --- a/docs/_build/jupyter_execute/06-module/lesson-6c.py +++ b/docs/_build/jupyter_execute/06-module/lesson-6c.py @@ -46,7 +46,7 @@ # array containing 4 sets of 10 random numbers x = np.random.random_sample((4, 10)) - + x[0] = (x[0] - x[0].min()) / (x[0].max() - x[0].min()) x[1] = (x[1] - x[1].min()) / (x[1].max() - x[1].min()) x[2] = (x[2] - x[2].min()) / (x[1].max() - x[2].min()) @@ -63,7 +63,7 @@ def rescale(array): for index, vector in enumerate(array): array[index] = (vector - vector.min()) / (vector.max() - vector.min()) - + return(array) rescale(x) @@ -85,7 +85,7 @@ def rescale(array): # In[5]: -# method +# method x.sum(axis = 0) @@ -129,8 +129,8 @@ def rescale(array): def yell(text): new_text = text.upper() return new_text - -yell('hello world!') + +yell('hello world!') # Of course, your functions will get more complex as you go along: you can add `for` loops, flow control, and more to it to make it more finegrained. Let's build a function that finds the total sales for a store, for a given day. The parameters required for the function are the `data` frame being analyzed, the `store_id`, and the specific `week` for which we need the sales. @@ -155,7 +155,7 @@ def store_sales(data, store, week): # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Parameters vs arguments @@ -176,14 +176,14 @@ def store_sales(data, store, week): # implicitly computing store sales for store 46 during week 43 -store_sales(df, 46, 43) +store_sales(df, 46, 43) # In[12]: # implicitly computing store sales for store 43 (does not exist) during week 46 -store_sales(df, 43, 46) +store_sales(df, 43, 46) # Using keyword, rather than positional, arguments makes your function call more explicit and allows you to specify them in different orders. @@ -196,7 +196,7 @@ def store_sales(data, store, week): # explicitly computing store sales for store 46 during week 43 -store_sales(data=df, week=43, store=46) +store_sales(data=df, week=43, store=46) # ### Default arguments @@ -242,7 +242,7 @@ def store_sales(data, store, week, qty_greater_than=0): def yell(*args): new_text = ' '.join(args).upper() return new_text - + yell('hello world!', 'I', 'love', 'Python!!') @@ -254,7 +254,7 @@ def yell(*args): # **kwargs just creates a dictionary def students(**kwargs): print(kwargs) - + students(student1='John', student2='Robert', student3='Sally') @@ -265,8 +265,8 @@ def students(**kwargs): def print_student_names(**kwargs): for key, value in kwargs.items(): print(f'{key} = {value}') - -print_student_names(student1='John', student2='Robert', student3='Sally') + +print_student_names(student1='John', student2='Robert', student3='Sally') # ### Type hints @@ -280,7 +280,7 @@ def print_student_names(**kwargs): def some_function(name, age): return f'{name} is {age} years old' - + some_function('Tom', 27) @@ -293,7 +293,7 @@ def some_function(name, age): def some_function(name: str, age: int) -> str: return f'{name} is {age} years old' - + some_function('Tom', 27) @@ -336,7 +336,7 @@ def store_sales(data: pd.DataFrame, store: int, week: int) -> float: filt = (data['store_id'] == store) & (data['week'] == week) total_sales = data['sales_value'][filt].sum() return total_sales - + store_sales(data=df, store=309, week=48) @@ -351,7 +351,7 @@ def store_sales(data: pd.DataFrame, store: int, week: int) -> float: # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Docstrings @@ -370,11 +370,11 @@ def store_sales(data: pd.DataFrame, store: int, week: int) -> float: def store_sales(data: pd.DataFrame, store: int, week: int) -> float: """ Compute total store sales. - + This function computes the total sales for a given store and week based on a user supplied DataFrame that contains sales in a column named `sales_value`. - + Parameters ---------- data : DataFrame @@ -383,16 +383,16 @@ def store_sales(data: pd.DataFrame, store: int, week: int) -> float: Integer value representing store number week : int Integer value representing week of year - + Returns ------- float A float object representing total store sales - + See Also -------- store_visits : Computes total store visits - + Examples -------- >>> store_sales(data=df, store=309, week=48) @@ -417,7 +417,7 @@ def store_sales(data: pd.DataFrame, store: int, week: int) -> float: # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Errors and exceptions @@ -439,13 +439,13 @@ def store_sales(data: pd.DataFrame, store: int, week: int) -> float: if not isinstance(data, pd.DataFrame): raise Exception('`data` should be a Pandas DataFrame') if not isinstance(store, int): raise Exception('`store` should be an integer') if not isinstance(week, int): raise Exception('`week` should be an integer') - + # computation filt = (data['store_id'] == store) & (data['week'] == week) total_sales = data['sales_value'][filt].sum() return total_sales - -store_sales(data=df, store='309', week=48) + +store_sales(data=df, store='309', week=48) # Note that `Exception()` is used to create a generic exception object/output. Python has [many built-in exception types](https://docs.python.org/3/library/exceptions.html) that can be used to indicate a specific error has occured. For example, we could replace `Exception()` with `TypeError()` in the previous example to make it more specific that the error is due to an invalid argument type. @@ -458,13 +458,13 @@ def store_sales(data: pd.DataFrame, store: int, week: int) -> float: if not isinstance(data, pd.DataFrame): raise TypeError('`data` should be a Pandas DataFrame') if not isinstance(store, int): raise TypeError('`store` should be an integer') if not isinstance(week, int): raise TypeError('`week` should be an integer') - + # computation filt = (data['store_id'] == store) & (data['week'] == week) total_sales = data['sales_value'][filt].sum() return total_sales - -store_sales(data=df, store='309', week=48) + +store_sales(data=df, store='309', week=48) # We can expand this as much as necessary. For example, say we want to ensure users only use a store value that exists in our data. Currently, if the user supplies a store value that does not exist (i.e. 35), they simply get a return value of 0. @@ -485,10 +485,10 @@ def store_sales(data: pd.DataFrame, store: int, week: int) -> float: if not isinstance(data, pd.DataFrame): raise TypeError('`data` should be a Pandas DataFrame') if not isinstance(store, int): raise TypeError('`store` should be an integer') if not isinstance(week, int): raise TypeError('`week` should be an integer') - if store not in data.store_id.unique(): + if store not in data.store_id.unique(): raise ValueError(f'`store` {store} does not exist in the supplied DataFrame') - - + + # computation filt = (data['store_id'] == store) & (data['week'] == week) total_sales = data['sales_value'][filt].sum() @@ -544,8 +544,8 @@ def apply_discount(product, discount): price = round(product['price'] * (1.0 - discount), 2) assert 0 <= price <= product['price'], 'Invalid discount applied' return price - -apply_discount(milk, 2.00) + +apply_discount(milk, 2.00) # ### Try and except @@ -613,7 +613,7 @@ def apply_discount(product, discount): # ``` # ```{admonition} Video 🎥: -# +# # ``` # ## Scoping @@ -636,8 +636,8 @@ def apply_discount(product, discount): def func(x): return x + 1 - -func(x = 50) + +func(x = 50) # However, the function environment is only active when called and all function variables are removed from memory after being called. Consequently, you can continue using `x` that is contained in the global environment. @@ -657,8 +657,8 @@ def func(x): def func(x): return x + ' ' + y - -func(x = 'Brad') + +func(x = 'Brad') # The same will happen if we have nested functions. Python will search in enclosing functions in a hierarchical fashion until it finds the necessary variables. In this convoluted procedure... @@ -683,7 +683,7 @@ def my_name(sep): def my_paste(): return x + sep + y return my_paste() - + my_name(sep=' ') @@ -707,7 +707,7 @@ def convert(x): y = firstpart + '.' + secondpart return y -convert(8451) +convert(8451) # In[82]: