diff --git a/02 - The Pythonic Way/README.md b/02 - The Pythonic Way/README.md index 2b8c3d5..bdef6b1 100644 --- a/02 - The Pythonic Way/README.md +++ b/02 - The Pythonic Way/README.md @@ -36,13 +36,13 @@ You may well use Python as a calculator: But this is not very useful. To create more complex programs, you need to store values in variables. A variable is a storage location (identified by a memory address) paired with an associated symbolic name (an identifier), which contains some data referred to as a value. -![img.png](pictures/variable.png) +![Variables](pictures/variable.png) However, if everything in memory is represented as bits, how can Python know what a variable is? The answer is in types. Each variable is associated to a type, which tells Python how to interpret the bits in memory. For example, an integer is represented as a sequence of bits, and Python knows how to interpret them as a number. As we shall see, Python has many different types. -![img.png](pictures/types.png) +![Types](pictures/types.png) Python is dynamically typed, so you don't need to declare the type of a variable explicitly. @@ -430,6 +430,7 @@ from math import sqrt as square_root, sin as sine, cos as cosine ## 9. Complex Types: Lists, Tuples, and Dictionaries We covered a lot, but the types we talked about until now were pretty simple. Let's look at some more complex, and useful ones. +![Data Structures](pictures/data-structures.png) ### Lists diff --git a/02 - The Pythonic Way/pictures/data-structures.png b/02 - The Pythonic Way/pictures/data-structures.png new file mode 100644 index 0000000..e25ebb7 Binary files /dev/null and b/02 - The Pythonic Way/pictures/data-structures.png differ diff --git a/03 - Python for Data Science/README.md b/03 - Python for Data Science/README.md new file mode 100644 index 0000000..bf31f42 --- /dev/null +++ b/03 - Python for Data Science/README.md @@ -0,0 +1,572 @@ +# Python for Data Science + +## The Python Data Science Ecosystem + +The academic and industrial community has developed a rich ecosystem of libraries and tools for data science, machine +learning, and scientific computing in Python. These libraries provide a wide range of functionalities, from data +manipulation and visualization to statistical modeling and deep learning. +Here are some of the most popular libraries and frameworks in the Python data science ecosystem. + +### Natural Language Processing (NLP) + +1. **spaCy**: A fast and efficient NLP library for tasks like tokenization, part-of-speech tagging, named entity + recognition, and dependency parsing. +2. **Gensim**: A library specialized in topic modeling and document similarity analysis using techniques like word + embeddings (e.g., Word2Vec). + +### Machine Learning and Deep Learning + +3. **PyTorch**: A powerful deep learning framework providing tools for building and training neural networks, with a + focus on dynamic computational graphs. Built by Meta. +4. **TensorFlow**: Same as PyTorch, but supported by Google. + +### Statistical Modeling + +5. **scikit-learn**: A library for general machine learning tasks, including classification, regression, clustering, and + preprocessing. +6. **Statsmodels**: A library focused on statistical modeling, hypothesis testing, and linear regression analysis. + +### Data Visualization + +7. **Matplotlib**: A popular library for creating static, animated, and interactive visualizations in Python. +8. **Plotly**: A library for creating interactive visualizations and dashboards with support for web applications. + +### Data Manipulation and Computation + +9. **pandas**: A library offering data manipulation and analysis tools, particularly for working with structured data + like tables. +10. **Polars**: A high-performance DataFrame library designed for efficient handling of large datasets, often seen as a + faster alternative to pandas. + +### Scientific Computing + +11. **NumPy**: The foundational library for numerical computing in Python, providing support for arrays, matrices, and a + suite of mathematical operations. +12. **SciPy**: A library building on NumPy, offering more advanced mathematical, scientific, and engineering functions. + +At the center is **Python**, serving as the base language connecting all these libraries, showcasing its versatility in +data science, machine learning, and visualization. + +![Python Data Science Ecosystem](pictures/python-data-science-ecosystem.png) + +## NumPy + +NumPy (Numeric Python) is a fundamental library for numerical computing in Python. +It provides support for multidimensional arrays, matrices, and a wide range of mathematical functions to operate on +these arrays. +NumPy is the foundation for many other libraries in the Python data science ecosystem, such as pandas and scikit-learn. + +NumPy provides: + +- efficient multi-dimensional arrays +- slicing and dicing capabilities +- tools to perform array operations +- linear algebra primitives + +### Multidimensional Arrays + +NumPy's primary data structure is the `ndarray`, a multidimensional array that can store elements of the same data type. +The `ndarray` is similar to Python's built-in list, but with additional capabilities and optimizations for numerical +computing. +Example: + +```python +import numpy as np + +array = np.array([[1, 2], [3, 4]]) +print(array) +``` + +Output: + +```text +[[1 2] +[3 4]] +``` + +### Arrays vs Lists + +NumPy arrays and Python lists may look similar, but they have different characteristics and use cases. + +1. **Type and Purpose** + +- **Python Lists**: General-purpose containers for storing collections of objects. They can hold elements of different + types (e.g., integers, strings, floats, or even other lists). Python lists are versatile and part of Python's standard + library. +- **NumPy Arrays**: Designed specifically for numerical computation. They are part of the **NumPy library** and can only + hold elements of a single data type (e.g., integers or floats). They are optimized for **mathematical operations** and + scientific computations. + +2. **Speed and Efficiency** + +- **Python Lists**: Not optimized for numerical computations. When performing operations like addition, Python iterates + through each element manually, making these operations slower. Example: Adding two lists `[1, 2] + [3, 4]` results in + `[1, 2, 3, 4]` (concatenation, not element-wise addition). +- **NumPy Arrays**: Highly optimized for numerical operations thanks to their underlying **C implementation**. + Operations are **vectorized**, meaning they process entire arrays at once, which is much faster. Example: Adding two + arrays `np.array([1, 2]) + np.array([3, 4])` results in `array([4, 6])` (element-wise addition). + +3. **Memory Usage** + +- **Python Lists**: Store references to the objects they contain, so they consume more memory, especially for large + datasets. +- **NumPy Arrays**: Store data in contiguous memory blocks, leading to significantly better memory efficiency. + +4. **Functionality** + +- **Python Lists**: Provide general methods for manipulation, such as appending, slicing, or sorting. +- **NumPy Arrays**: Extend functionality with a wide range of **mathematical operations** (e.g., matrix multiplication, + element-wise computations, broadcasting, etc.), making them indispensable for data science tasks. + +5. **Type Constraints** + +- **Python Lists**: Can mix data types (e.g., `[1, "string", 3.5]` is valid). +- **NumPy Arrays**: Enforce a single data type. Mixing types results in implicit conversions to a common type (e.g., an + array of `[1, "string", 3.5]` becomes an array of strings). + +**When to Use Each?** + +- Use **Python lists** for general-purpose programming or when handling heterogeneous data. +- Use **NumPy arrays** when working with numerical data or requiring fast, efficient computations (e.g., in data + science, machine learning, and scientific applications). + +### Creating Arrays + +#### **From Lists** + +The simplest way to create a NumPy array is by converting a Python list or nested list into a NumPy array using the +`np.array` function. + +```python +import numpy as np + +# 1D array from a list +arr1 = np.array([1, 2, 3]) + +# 2D array from a nested list +arr2 = np.array([[1, 2, 3], [4, 5, 6]]) + +# 3D array from a nested list of lists +arr3 = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) + +print("1D Array:\n", arr1) +print("2D Array:\n", arr2) +print("3D Array:\n", arr3) +``` + +#### **Using Zeros, Ones, and Empty** + +NumPy provides utility functions for creating arrays prefilled with specific values: + +- **`np.zeros`**: Creates an array filled with zeros. +- **`np.ones`**: Creates an array filled with ones. +- **`np.empty`**: Creates an uninitialized array, where the values are random. + +```python +# Array of zeros +zeros_arr = np.zeros((3, 4)) # 3 rows, 4 columns + +# Array of ones +ones_arr = np.ones((2, 3)) # 2 rows, 3 columns + +# Uninitialized array (values are random, not zero) +empty_arr = np.empty((2, 2)) + +print("Zeros Array:\n", zeros_arr) +print("Ones Array:\n", ones_arr) +print("Empty Array:\n", empty_arr) +``` + +#### **Using `arange`** + +`np.arange` works similarly to Python's `range`, but returns a NumPy array instead of a list. + +```python +# Array of numbers from 0 to 9 +arr_range = np.arange(10) + +# Array with a step +arr_step = np.arange(0, 10, 2) + +print("Range Array:\n", arr_range) +print("Step Array:\n", arr_step) +``` + +#### **Using `linspace`** + +`np.linspace` creates an array of evenly spaced numbers between two values. Unlike `arange`, it specifies the number of +elements instead of the step size. + +```python +# 5 numbers between 0 and 1, inclusive +linspace_arr = np.linspace(0, 1, 5) + +print("Linspace Array:\n", linspace_arr) +``` + +#### **Using Identity Matrices** + +- **`np.eye`**: Creates a 2D array with ones on the diagonal (identity matrix). +- **`np.identity`**: Similar to `eye`, but only for square matrices. + +```python +# Identity matrix +identity = np.eye(3) # 3x3 identity matrix + +print("Identity Matrix:\n", identity) +``` + +### Array Attributes + +NumPy arrays come with a rich set of attributes that provide crucial information about their structure and contents. +These attributes allow you to understand and manipulate arrays effectively. Let’s explore these attributes in detail. + +#### **1. `ndim`: Number of Dimensions** + +The `ndim` attribute provides the number of dimensions (also called the rank) of the array. For instance: + +- A 1D array has `ndim = 1` +- A 2D array has `ndim = 2` +- A 3D array has `ndim = 3`, and so on. + +```python +arr1 = np.array([1, 2, 3]) # 1D array +arr2 = np.array([[1, 2, 3], [4, 5, 6]]) # 2D array +arr3 = np.array([[[1], [2]], [[3], [4]]]) # 3D array + +print(arr1.ndim) # Output: 1 +print(arr2.ndim) # Output: 2 +print(arr3.ndim) # Output: 3 +``` + +#### **2. `shape`: Dimensions of the Array** + +The `shape` attribute returns a tuple indicating the size of the array in each dimension. It is useful to understand the +layout of your data. + +```python +print(arr1.shape) # Output: (3,) -> 1D array with 3 elements +print(arr2.shape) # Output: (2, 3) -> 2 rows, 3 columns +print(arr3.shape) # Output: (2, 2, 1) -> 2 blocks, 2 rows, 1 column +``` + +The shape can also be reassigned to reshape an array: + +```python +reshaped = np.arange(12) +reshaped.shape = (3, 4) +print(reshaped) +# Output: +# [[ 0 1 2 3] +# [ 4 5 6 7] +# [ 8 9 10 11]] +``` + +#### **3. `size`: Total Number of Elements** + +The `size` attribute gives the total count of elements in the array, regardless of its dimensions. It is equivalent to +the product of the dimensions. + +```python +print(arr1.size) # Output: 3 +print(arr2.size) # Output: 6 (2 rows × 3 columns) +print(arr3.size) # Output: 4 (2 × 2 × 1) +``` + +#### **4. `dtype`: Data Type of Elements** + +The `dtype` attribute specifies the type of data stored in the array. NumPy supports various data types, such as +`int32`, `float64`, and `complex128`. + +```python +arr_int = np.array([1, 2, 3]) +arr_float = np.array([1.1, 2.2, 3.3]) +arr_complex = np.array([1 + 2j, 3 + 4j]) + +print(arr_int.dtype) # Output: int64 (or platform-specific integer type) +print(arr_float.dtype) # Output: float64 +print(arr_complex.dtype) # Output: complex128 +``` + +You can specify the data type explicitly when creating an array: + +```python +arr = np.array([1, 2, 3], dtype=np.float32) +print(arr.dtype) # Output: float32 +``` + +#### **5. `itemsize`: Size of Each Element in Bytes** + +The `itemsize` attribute indicates the size (in bytes) of each element in the array. This depends on the `dtype`. + +```python +print(arr_int.itemsize) # Output: 8 (on most systems for int64) +print(arr_float.itemsize) # Output: 8 (for float64) +print(arr_complex.itemsize) # Output: 16 (for complex128) +``` + +#### **6. `nbytes`: Total Memory Used** + +The `nbytes` attribute calculates the total memory used by the array in bytes. It is equivalent to `size * itemsize`. + +```python +print(arr_int.nbytes) # Output: 24 (3 elements × 8 bytes each) +print(arr_float.nbytes) # Output: 24 +``` + +#### **7. `T`: Transpose of the Array** + +The `T` attribute transposes the array (swaps rows and columns). This is particularly useful for matrices. + +```python +matrix = np.array([[1, 2, 3], [4, 5, 6]]) +print(matrix.T) +# Output: +# [[1 4] +# [2 5] +# [3 6]] +``` + +#### **Practical Example: Inspecting an Array** + +Let’s combine these attributes to inspect an array: + +```python +arr = np.arange(12).reshape(3, 4) + +print("Array:\n", arr) +print("Dimensions (ndim):", arr.ndim) # Output: 2 +print("Shape:", arr.shape) # Output: (3, 4) +print("Size:", arr.size) # Output: 12 +print("Data Type (dtype):", arr.dtype) # Output: int64 +print("Item Size (bytes):", arr.itemsize) # Output: 8 +print("Total Memory (nbytes):", arr.nbytes) # Output: 96 +``` + +### Indexing + +NumPy allows you to access elements using indices. +Indexing starts from 0, and arrays can be indexed along each dimension. + +### Examples + +```python +arr1 = np.array([1, 2, 3]) # 1D array +arr2 = np.array([[1, 2, 3], [4, 5, 6]]) # 2D array +arr3 = np.array([[[1], [2]], [[3], [4]]]) # 3D array + +# Accessing elements +print(arr2[0, 1]) # Output: 2 (Row 0, Column 1) +print(arr3[1, 0, 1]) # Output: 6 (Block 1, Row 0, Column 1) +``` + +Negative indexing is also supported: + +```python +print(arr2[-1, -1]) # Output: 6 (Last row, last column) +``` + +--- + +### Slicing and Dicing + +Slicing allows you to extract subarrays. +The syntax is `start:stop:step`. + +![Indexing in NumPy](pictures/indexing.png) + +Logical indexing is also possible, where you use a boolean array to filter elements. +![Fancy Indexing](pictures/fancy-indexing.png) + +### Examples + +```python +arr2 = np.array([[1, 2, 3], [4, 5, 6]]) # 2D array + +# Slicing rows and columns +print(arr2[:, 1]) # Output: [2 5] (All rows, column 1) +print(arr2[0, :]) # Output: [1 2 3] (Row 0, all columns) + +# Stepped slicing +print(arr2[:, ::2]) # Output: [[1 3] [4 6]] (Every second column) +``` + +### Broadcasting + +Broadcasting is a powerful feature that allows operations between arrays of different shapes. + +#### Rules of Broadcasting + +1. If the arrays differ in their number of dimensions, the smaller array is padded with ones on its left side. +2. Arrays with a size of 1 in a dimension are stretched to match the size of the other array. + +More details on the [NumPy documentation](https://numpy.org/doc/stable/user/basics.broadcasting.html). + +#### Examples + +```python +arr4 = np.array([[1, 2, 3], [4, 5, 6]]) +arr5 = np.array([1, 2, 3]) + +# Broadcasting +result = arr4 + arr5 # Add each row of arr4 to arr5 +print(result) +# Output: +# [[ 2 4 6] +# [ 5 7 9]] +``` + +### Array Operations + +#### Elementwise Operations + +```python +a = np.array([1, 2, 3]) +b = np.array([4, 5, 6]) + +# Addition +print(a + b) # Output: [5 7 9] + +# Multiplication +print(a * b) # Output: [4 10 18] + +# Exponentiation +print(a ** 2) # Output: [1 4 9] +``` + +#### Aggregate Functions + +```python +print(np.sum(arr2)) # Sum of all elements: 21 +print(np.mean(arr2)) # Mean: 3.5 +print(np.max(arr2)) # Maximum value: 6 +``` + +### Linear Algebra + +NumPy includes functions for performing common linear algebra operations, such as dot products, matrix multiplications, +and decompositions. + +#### Examples + +```python +A = np.array([[1, 2], [3, 4]]) +B = np.array([[5, 6], [7, 8]]) + +# Matrix multiplication +C = np.dot(A, B) +print(C) +# Output: +# [[19 22] +# [43 50]] + +# Transpose +print(A.T) +# Output: +# [[1 3] +# [2 4]] +``` + +NumPy also supports: + +- **Eigenvalues and eigenvectors**: `np.linalg.eig` +- **Inverse of a matrix**: `np.linalg.inv` + +### Random Numbers +Generating random numbers is a challenging problem in computer science because computers are inherently deterministic systems. +They operate by following precise rules and instructions, which makes it difficult to produce outcomes that are genuinely unpredictable. + +Randomness, by its nature, lacks order or predictability, while a computer’s processes are entirely governed by logic and algorithms. +Traditional methods for generating random numbers, known as pseudo-random number generators (PRNGs), rely on mathematical formulas or predefined rules to create sequences of numbers that mimic randomness. +However, these sequences are not truly random; they are determined by an initial value called a "seed." +If the seed is known, the entire sequence can be reproduced, highlighting the deterministic foundation of the process. + +This reliance on algorithms to simulate randomness reveals the underlying difficulty: achieving numbers that appear random while being produced by a predictable system. +True randomness, in contrast, often depends on unpredictable physical phenomena, like atmospheric noise or radioactive decay, which are outside the realm of standard computational processes. + +### Random Numbers in Numpy + +NumPy’s random number generation has been updated in version 1.17.0 to introduce the `numpy.random.Generator` framework, +which provides more control, better performance, and reproducibility compared to the older random number APIs. + +The modern way to generate random numbers is by using the `numpy.random.default_rng()` method to create a random number +generator instance. This approach ensures consistent and thread-safe results. + +#### **Creating a Random Generator** + +The `default_rng()` function initializes a new generator: + +```python +import numpy as np + +# Create a random number generator +rng = np.random.default_rng(seed=42) +``` + +#### Random Number Generation + +#### Uniform Distribution + +Generates random numbers between 0 and 1, uniformly distributed: + +```python +# Generate an array of 3 random numbers +uniform_random = rng.random(3) + +print("Uniform Random Numbers:\n", uniform_random) +``` + +#### Random Integers + +Generates random integers within a specified range: + +```python +# Generate a 2x3 array of random integers between 0 and 9 +rand_integers = rng.integers(0, 10, size=(2, 3)) + +print("Random Integers:\n", rand_integers) +``` + +#### Normal Distribution + +Generates numbers following a Gaussian distribution with specified mean and standard deviation: + +```python +# Generate a 3x3 array of normally distributed random numbers +rand_normal = rng.normal(loc=0, scale=1, size=(3, 3)) + +print("Random Numbers (Normal Distribution):\n", rand_normal) +``` + +#### Random Choices + +Selects random samples from a specified array: + +```python +# Randomly select 5 elements from a given array +rand_choices = rng.choice([10, 20, 30, 40, 50], size=5, replace=True) + +print("Random Choices:\n", rand_choices) +``` + +## Polars +Polars is a high-performance DataFrame library for Python and Rust, designed to handle large datasets efficiently. It offers a fast, multi-threaded engine that outperforms traditional libraries like pandas in both speed and memory usage. Polars achieves this by utilizing Apache Arrow's columnar memory format, enabling efficient data processing and interoperability with other data tools. + +Key features of Polars include: + +- **Lazy Evaluation**: Polars delays computation until necessary, optimizing query execution and reducing redundant calculations. + +- **Multi-threaded Execution**: It leverages multiple CPU cores to parallelize operations, significantly speeding up data processing tasks. + +- **Expressive Query Language**: Polars provides a user-friendly API for complex data manipulations, including filtering, aggregations, and joins. + +- **Interoperability**: Its compatibility with Apache Arrow facilitates seamless data exchange with other data processing systems. + +For professionals with a computer science background, Polars offers a robust alternative to pandas, especially when working with large datasets or requiring high-performance data processing. Its design principles align with modern data engineering practices, making it a valuable tool in the data science ecosystem. + +### Polars Tutorial +Go to the notebook folder and run +```bash +jupyter lab +``` +Then, open the `Polars Tutorial.ipynb` notebook to learn more about Polars and its functionalities. \ No newline at end of file diff --git a/03 - Python for Data Science/data/power_generation.xlsx b/03 - Python for Data Science/data/power_generation.xlsx new file mode 100644 index 0000000..aeac4f1 Binary files /dev/null and b/03 - Python for Data Science/data/power_generation.xlsx differ diff --git a/03 - Python for Data Science/notebooks/Polars Tutorial.ipynb b/03 - Python for Data Science/notebooks/Polars Tutorial.ipynb new file mode 100644 index 0000000..35ba81b --- /dev/null +++ b/03 - Python for Data Science/notebooks/Polars Tutorial.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Polars Tutorial\n", + "In this notebook, we'll walk through some of the most useful features of Polars.\n", + "\n", + "To do so, we'll use a [dataset by Terna](https://www.terna.it/en/electric-system/transparency-report/actual-generation), describing the total generation in Italy in the last year.\n", + "\n", + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import polars as pl\n", + "import plotly.express as px\n", + "import plotly.io as pio\n", + "\n", + "pio.templates.default = \"plotly_white\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = Path(\"..\") / \"data\" / \"power_generation.xlsx\"\n", + "df = pl.read_excel(data_path)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "# Anatomy of a Polars DataFrame\n", + "A Polars DataFrame is composed of several key elements.\n", + "\n", + "Columns form the backbone of a DataFrame. Each column represents a single variable and contains data of a specific type. Polars uses Arrow arrays for efficient memory usage and fast operations.\n", + "\n", + "Rows in Polars are conceptual rather than physical objects. They represent individual records but are accessed through column operations.\n", + "\n", + "Data types (dtypes) in Polars include common types like integers, floats, strings, and booleans, as well as specialized types for dates, times, and categorical data. The dtype determines how data is stored and manipulated.\n", + "\n", + "Unlike some other libraries, Polars doesn't use a separate index. Instead, the position of data in columns serves as an implicit index.\n", + "\n", + "The schema defines the structure of the DataFrame, specifying column names and their data types. It's crucial for understanding the layout of your data.\n", + "\n", + "Polars uses a chunked data structure, allowing for efficient parallel processing and lazy evaluation. This is key to its performance with large datasets.\n", + "\n", + "Expressions are a powerful feature in Polars for data manipulation. They allow you to define complex operations on columns efficiently.\n", + "\n", + "To work with Polars effectively, it's important to understand how these components interact. For example, you might select columns, apply expressions to transform data, or use the schema to understand your dataset's structure. The dtype of a column influences what operations you can perform, while the chunked structure enables Polars to process large datasets efficiently." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "print(df.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "df.schema" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "# Basic Manipulation\n", + "We'll now demonstrate some data manipulation using Polars. For the full reference, please refer to the [official documentation](https://docs.pola.rs/)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "df.select([\"Date\", \"Actual Generation (GWh)\", \"Primary Source\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "df.rename({\"Actual Generation (GWh)\": \"Generation\", \"Primary Source\": \"Source\"})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "df.with_columns((pl.col(\"Actual Generation (GWh)\") * 1000).alias(\"Generation_MWh\"))" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "# Filtering and Sorting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "df.filter(pl.col(\"Primary Source\") == \"Thermal\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "df.filter(pl.col(\"Actual Generation (GWh)\") > 10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "df.sort(\"Actual Generation (GWh)\", descending=True)" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "# Grouping and Aggregations\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [], + "source": [ + "df.group_by(\"Primary Source\").agg(\n", + " pl.col(\"Actual Generation (GWh)\").sum().alias(\"Total_Generation\"),\n", + " pl.col(\"Actual Generation (GWh)\").mean().alias(\"Avg_Generation\"),\n", + " pl.col(\"Actual Generation (GWh)\").count().alias(\"Count\"),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "df.group_by(\"Primary Source\").agg(\n", + " pl.col(\"Actual Generation (GWh)\").max().alias(\"Max Gen\")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [ + "# Time series operations\n", + "Many datasets in energy are time series, and our dataset is no exception. \n", + "\n", + "Therefore, we'll show some operations that can be performed on dates and time series." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [], + "source": [ + "df.with_columns(pl.col(\"Date\").dt.hour().alias(\"Hour\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": {}, + "outputs": [], + "source": [ + "generation_df = (\n", + " df.with_columns(pl.col(\"Date\").dt.hour().alias(\"Hour\"))\n", + " .group_by(\"Hour\")\n", + " .agg(pl.col(\"Actual Generation (GWh)\").mean().alias(\"Hourly Generation\"))\n", + " .sort(\"Hour\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [], + "source": [ + "px.line(generation_df, x=\"Hour\", y=\"Hourly Generation\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": {}, + "outputs": [], + "source": [ + "generation_by_hour_source_df = (\n", + " df.with_columns(pl.col(\"Date\").dt.hour().alias(\"Hour\"))\n", + " .with_columns(\n", + " pl.col(\"Actual Generation (GWh)\")\n", + " .mean()\n", + " .over(\"Hour\", \"Primary Source\")\n", + " .alias(\"Hourly Generation\")\n", + " )\n", + " .select(\"Hour\", \"Primary Source\", \"Hourly Generation\")\n", + " .unique()\n", + " .sort(\"Hour\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "metadata": {}, + "outputs": [], + "source": [ + "px.area(\n", + " generation_by_hour_source_df,\n", + " x=\"Hour\",\n", + " y=\"Hourly Generation\",\n", + " color=\"Primary Source\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "25", + "metadata": {}, + "source": [ + "# Advanced Data Manipulation\n", + "We'll now showcase some operations such as pivot and rolling means." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26", + "metadata": {}, + "outputs": [], + "source": [ + "df.pivot(\n", + " values=\"Actual Generation (GWh)\",\n", + " index=\"Date\",\n", + " on=\"Primary Source\",\n", + " aggregate_function=\"mean\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27", + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " df.select(pl.col(\"Date\"), pl.col(\"Actual Generation (GWh)\").sum().over(\"Date\"))\n", + " .unique()\n", + " .sort(\"Date\")\n", + " .select(pl.col(\"Date\"), pl.col(\"Actual Generation (GWh)\").rolling_mean(24))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "28", + "metadata": {}, + "source": [ + "# Exporting Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29", + "metadata": {}, + "outputs": [], + "source": [ + "output_df = (\n", + " df.with_columns(date=pl.col(\"Date\").dt.date())\n", + " .group_by(\"date\", \"Primary Source\")\n", + " .agg(pl.col(\"Actual Generation (GWh)\").sum().alias(\"generation_gwh\"))\n", + " .select(\n", + " pl.col(\"date\"),\n", + " pl.col(\"generation_gwh\"),\n", + " pl.col(\"Primary Source\").alias(\"source\"),\n", + " )\n", + " .sort(\"date\", \"source\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30", + "metadata": {}, + "outputs": [], + "source": [ + "output_df.write_csv(\"output.csv\")\n", + "output_df.write_excel(\"output.xlsx\")\n", + "output_df.write_parquet(\"output.parquet\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": 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