{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ce97q1ZcBiwj"
},
"source": [
"# Introduction to NumPy and Pandas\n",
"\n",
"## Introduction\n",
"\n",
"This notebook and accompanying webinar was developed and released by the [enDAQ team](https://endaq.com/). This is the second \"chapter\" of our series on *Python for Mechanical Engineers*:\n",
"\n",
"1. [Get Started with Python](https://colab.research.google.com/drive/1_pcGtgJleapV9tz5WfuRuqfWryjqhPHy#scrollTo=ikUJITDDIp19)\n",
" * Blog: [Get Started with Python: Why and How Mechanical Engineers Should Make the Switch](https://blog.endaq.com/get-started-with-python-why-how-mechanical-engineers-should-make-the-switch)\n",
"2. **Introduction to NumPy & Pandas**\n",
" * [Watch Recording of This](https://info.endaq.com/pandas-and-numpy-for-data-analysis-webinar)\n",
"3. [Introduction to Plotly](https://colab.research.google.com/drive/1pag2pKQQW5amWgRykAH8uMAPqHA2yUfU?usp=sharing)\n",
"4. [Introduction of the enDAQ Library](https://colab.research.google.com/drive/1WAtQ8JJC_ny0fki7eUABACMA-isZzKB6)\n",
"\n",
"To sign up for future webinars and watch previous ones, [visit our webinars page](https://endaq.com/pages/shock-vibration-webinars). "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lZPDu150sQbm"
},
"source": [
"### Recap of Python Introduction\n",
"\n",
"1. Python is popular for good reason\n",
"2. There are many ways to interact with Python\n",
" * Here we are in Google Colab based on Jupyter Notebooks\n",
"3. There are many open source libraries to use\n",
" * Today we are covering two of the most popular:\n",
" * Numpy\n",
" * Pandas\n",
" * And teasing a bit of:\n",
" * Plotly\n",
" * enDAQ\n",
"\n",
"<img src=\"https://info.endaq.com/hubfs/20210928-python-ide-libraries-inforgraphic-FINAL-2.png\" width=\"700\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OTdahAjBEIVj"
},
"source": [
"### SO MANY Other Resources\n",
"Python is incredibly popular and so there are a lot of resources you can tap into online. You can either:\n",
"\n",
"* Just start coding and google specific questions when you get stuck (my preference for learning)\n",
"* Follow a few online tutorials (like this first)\n",
"* Do both!\n",
"\n",
"**Here are a few resources:**\n",
"\n",
"* [Jake VanderPlas Python Data Science Handbook](https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb)\n",
" * [Buy the book](https://www.amazon.com/Python-Data-Science-Handbook-Essential-ebook-dp-B01N2JT3ST/dp/B01N2JT3ST/ref=mt_other?_encoding=UTF8&me=&qid=) for $35\n",
" * Go through a series of [well documented Colab Notebooks](https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb)\n",
" * **Highly recommended resource!**\n",
"* [Code Academy: Analyze Data with Python](https://www.codecademy.com/learn/paths/analyze-data-with-python)\n",
"* [Udemy Academy: Data Analysis with Pandas & NumPy in Python for Beginner](https://www.udemy.com/course/python-data-analysis-2020/)\n",
"* [Datacamp: Introduction to Python](https://www.datacamp.com/courses/intro-to-python-for-data-science)\n",
"\n",
"\n",
"<img src=\"https://info.endaq.com/hubfs/datacamp-numpy-cheat-sheet.png\" width=\"800\">\n",
"\n",
"<img src=\"https://info.endaq.com/hubfs/pandas-cheat-sheet1.png\" width=\"800\">\n",
"\n",
"<img src=\"https://info.endaq.com/hubfs/pandas-cheat-sheet2.png\" width=\"800\">\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WOyh91ABBlc3"
},
"source": [
"## NumPy\n",
"\n",
"NumPy provides an in depth overview of [what exactly NumPy is](https://numpy.org/doc/stable/user/whatisnumpy.html). Quoting them:\n",
">*At the core of the NumPy package, is the ndarray object. This encapsulates n-dimensional arrays of homogeneous data types, with many operations being performed in compiled code for performance.*\n",
"\n",
"Let's get started by importing it, typically shortened to `np` because of how frequently it's called. This will come standard in most Python distributions such as Anaconda. If you need to install it simply:\n",
"\n",
"```\n",
"!pip install numpy\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VLP6WeFY8UWo"
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WrQ0eAG8CBqG"
},
"source": [
"### Creating Arrays"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_S1VR75hIAUM"
},
"source": [
"#### Manually or from Lists\n",
"Manually create a list and demonstrate that operations on that list are difficult."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RFL-_fg5IDeg",
"outputId": "373c2282-92f1-4dab-b75f-55fcf5764575"
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 0, 1, 2, 3]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lst = [0, 1, 2, 3]\n",
"lst * 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nXhiXAjgIbdc"
},
"source": [
"That isn't what we expected! For lists, we need to loop through all elements to apply a function to them which can be incredibly time consuming. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zMQ03IPWIS7S",
"outputId": "f1ba686c-6261-408a-aae7-ea49e83e9141"
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 2, 4, 6]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[i * 2 for i in lst]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B-kxWUb6Ipf4"
},
"source": [
"Now lets make a numpy array and once in an array, let's show how intuitive operations are now that they are performed element by element."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qkFeoqrRIn-r",
"outputId": "c62abf15-989d-4a01-cdb3-8b8af8c8fd5e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3]\n",
"[0 2 4 6]\n",
"[2 3 4 5]\n"
]
}
],
"source": [
"array = np.array(lst)\n",
"print(array)\n",
"print(array * 2)\n",
"print(array + 2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iL3RZxhbIf4I"
},
"source": [
"Let's create a 2D matrix of 32 bit floats."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DLQY2HN_JdcB",
"outputId": "d4a0c081-676a-43a9-b08d-84f33a613170"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]], dtype=float32)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array([[1, 0, 0],\n",
" [0, 1, 0],\n",
" [0, 0, 1]], dtype=np.float32)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-Nuet-s2Jd3q"
},
"source": [
"#### Using Functions\n",
"\n",
"We'll go through a few here, but for more in depth examples and options see NumPy's [Array Creation Routines](https://numpy.org/doc/stable/reference/routines.array-creation.html). These first few examples are for very basic arrays/matrices."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CQyecbfVL3F9",
"outputId": "fb2c7665-6da1-4299-c57d-2cd26348b699"
},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0., 0., 0., 0.])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.zeros(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1TP42y0nL5iq",
"outputId": "49751d7a-31ca-4205-e2ca-1e4abd980a7a"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 0., 0.],\n",
" [0., 0., 0.]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.zeros([2, 3])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "22P2Xf2aL-T8",
"outputId": "797a529d-9e08-48e5-cddb-ada72817ed66"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.eye(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Dhn3g-dkxZY6"
},
"source": [
"Now let's start making sequences."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vVFX3uuD-E0G",
"outputId": "37e76848-9dee-4296-f453-e531243fd469"
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.arange(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-ZkTkdK5EtRg",
"outputId": "dc486237-4d77-4be8-f2b8-89fad6726b5b"
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.arange(0, #start\n",
" 10) #stop (not included in array)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a5OIfwPN-O48",
"outputId": "b4768962-0028-469d-c194-af02dd98f1a7"
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 2, 4, 6, 8])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.arange(0, #start\n",
" 10, #stop (not included in array)\n",
" 2) #step size"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yAO2opdCKMVE",
"outputId": "911b0441-5f17-4886-dfeb-f30150a133ac"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 2., 4., 6., 8., 10.])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.linspace(0, #start\n",
" 10, #stop (default to be included, can pass in endpoint=False)\n",
" 6) #number of data points evenly spaced"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "x0xigPrOK4Y1",
"outputId": "f2f023ee-94f7-44da-a7b1-7b7bc89dc776"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 10., 100., 1000.])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.logspace(0, #output starts being raised to this value\n",
" 3, #ending raised value\n",
" 4) #number of data points"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cv0nw1SnLb5x"
},
"source": [
"Logspace is the equivalent of raising a base by a linspaced array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "J3txdCX2LTjy",
"outputId": "de2c4c21-ea5d-4bb3-9b5f-ebc1b138357f"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 10., 100., 1000.])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10 ** np.linspace(0,3,4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ew2EuHuELa-A",
"outputId": "30ca9d00-eabb-4826-e6d0-e3d41f262c0d"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 2., 4., 8., 16.])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.logspace(0, 4, 5, base=2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wkVckjCtxl_A"
},
"source": [
"If you don't want to have to do the mental math to know what exponent to raise the values to, you can use geomspace but this only helps for base of 10."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ytK6IH_HLnXF",
"outputId": "bf3d4c46-b54a-4703-e32b-55c3f3d1d4ed"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 10., 100., 1000.])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.geomspace(1, 1000, 4)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DQT4Vdrax8IE"
},
"source": [
"Random numbers!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CNipvh8wMi5t",
"outputId": "28a12c88-6e3b-4d1f-d571-495db1fcc42b"
},
"outputs": [
{
"data": {
"text/plain": [
"array([0.05057259, 0.19699801, 0.46727495, 0.67357217, 0.60387782])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.rand(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YtcgKwEhMosI",
"outputId": "e632e6a6-5b74-4b64-a0b8-00c4e94fd72b"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.76299702, 0.06301467, 0.92031266],\n",
" [0.42219446, 0.73721929, 0.39450975]])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.rand(2, 3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NzxTMPNaCW86"
},
"source": [
"### Indexing\n",
"\n",
"Let's first create a simple array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qkWur-4ARuBa",
"outputId": "b2d0a3a6-2c5a-4e24-e849-5ca49ba53aff"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = np.arange(1, 11, 1)\n",
"array"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FFs20jVcjF2L"
},
"source": [
"Now index the first item."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OFCBTk5qTvLp",
"outputId": "a7f71372-c785-46f5-ea94-3c11437fe9b5"
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gJFlP9V2jH_A"
},
"source": [
"Index the last item."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7Ct2CXINT_HH",
"outputId": "5571a5f0-e08c-4a1f-cacf-a5158d5691d7"
},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array[-1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "an9Cc7rejJd-"
},
"source": [
"Grab every 2nd item."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ec6mWL-5UAok",
"outputId": "5177ffee-2c8d-4b76-b54a-8abdeebca2d3"
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 3, 5, 7, 9])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array[::2]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H-KRmA9hjMLu"
},
"source": [
"Grab every second item starting at index of 1 (the second value)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Kpwn_NT3UCdR",
"outputId": "1b19b929-d194-491d-c2c9-15f281b86683"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2, 4, 6, 8, 10])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array[1::2]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HgCo96qfjQJM"
},
"source": [
"Start from the second item, going to the 7th but skipping every other. (Our first time using the full `array[start (inclusive): stop (exclusive): step]` array 'slicing' notation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8q-r1nFgUF5Z",
"outputId": "cc8fd7bf-887b-4674-88ad-d2e5acde32b5"
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 4, 6])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array[1:6:2]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "O5NAi55AjpE-"
},
"source": [
"Reverse the order."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HLv2rpV5jna1",
"outputId": "9781f215-afc2-4d28-c1bc-83f106176a34"
},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array[::-1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IKIl4jfbk6LZ"
},
"source": [
"Boolean operations to index the array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vAbOU4ayUqDD",
"outputId": "ceece14d-1c76-4610-8dc4-4d652503e9c2"
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array[array < 5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kSxUAD5Vk8p9",
"outputId": "cd032815-3669-4a27-fee3-80f6bd5baf66"
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array[array * 2 < 5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MmlhYRGMhvZg"
},
"source": [
"Integer list can also index arrays."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1uUNfzc7huzw",
"outputId": "0a0aa077-08d3-4f75-a720-67d2073182d5"
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 4, 6])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array[[1,3,5]]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DlqWdsqqdJT0"
},
"source": [
"Now let's create a slightly more complicated, 2 dimensional array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4Vz5G3PNc2cZ",
"outputId": "16662c03-c015-4806-8ec3-6be75ea72c6c"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 1, 2, 3, 4],\n",
" [5, 6, 7, 8, 9]])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_2d = np.arange(10).reshape((2, 5))\n",
"array_2d"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "k688SAR_iAjf"
},
"source": [
"Indexing the second dimension."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WbYDyf-KiJzr",
"outputId": "139e6975-5656-49e4-9adf-3a17ca561e5d"
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 6])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_2d[:, 1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "euR2mj3RiRcl"
},
"source": [
"Any combination of these indexing methods works as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SW9G--6JiQfp",
"outputId": "001a5226-7cd0-43bd-a3a3-d39effd809f4"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 3]])"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_2d[[True, False], 1::2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aJqko5CBimsB",
"outputId": "cb1faf48-1bb1-4220-db95-df40e6972fa3"
},
"outputs": [
{
"data": {
"text/plain": [
"array([5, 6, 9])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_2d[1, [0,1,4]]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n2mS2VIsCzp8"
},
"source": [
"### Operations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_1Rs1BpJltMH",
"outputId": "a4a0f90f-df5a-4c50-f184-610bcd66bdb3"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oxc2cNL4lyKw",
"outputId": "b1e1b39a-64e6-438c-f35b-bbb076fae6c1"
},
"outputs": [
{
"data": {
"text/plain": [
"array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array + 100"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-N2RQG6Ilo2X",
"outputId": "1bea148b-72c4-413c-bf8c-5f2cc17d7a37"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array * 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EpuHnkodzQmv"
},
"source": [
"To raise all the elements in an array to an exponent we have to use the notation `**` not `^`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H0pp6Rt-ls7K",
"outputId": "912abeb5-d74f-4e31-c151-fd449f85feaa"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 4, 9, 16, 25, 36, 49, 64, 81, 100])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array ** 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ipJ0Ss5Rm2f7"
},
"source": [
"Use that shape to create a new array matching it to do operations with."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wX4Rudonl1Xb",
"outputId": "00f7b08f-7dfe-4748-dafb-0e7ede76d066"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 5, 10, 15, 20, 25, 30, 35, 40, 45])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array2 = np.arange(array.shape[0]) * 5\n",
"array2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IRvsl8qEl9V3",
"outputId": "d238cdb2-3630-4fb0-e651-712eb9a36c63"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 7, 13, 19, 25, 31, 37, 43, 49, 55])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array2 + array"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Qex1vdxJnFAC"
},
"source": [
"### Stats of an Array"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "m6KX660In-f2",
"outputId": "43020216-077c-4503-fec8-239dff49cff6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1 2 3 4 5 6 7 8 9 10]\n",
"[[0 1 2 3 4]\n",
" [5 6 7 8 9]]\n"
]
}
],
"source": [
"print(array)\n",
"print(array_2d)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9NkWPeaknIj1",
"outputId": "c51538dd-7c08-4926-8445-0b65e729ed30"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10,)\n",
"(2, 5)\n"
]
}
],
"source": [
"print(array.shape)\n",
"print(array_2d.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nPrYLzs_nxgh",
"outputId": "65d85450-b74a-4d90-8ac4-25bd7e9b71e7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"2\n"
]
}
],
"source": [
"print(len(array))\n",
"print(len(array_2d))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "z8bhAHwFnO_D",
"outputId": "99546b5c-3606-4713-e29b-7b2a6447e169"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"9\n"
]
}
],
"source": [
"print(array.max())\n",
"print(array_2d.max())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "n6SSOUXtnaXc",
"outputId": "89eb2d31-8edb-45b6-fd94-13ddc0944b20"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"0\n"
]
}
],
"source": [
"print(array.min())\n",
"print(array_2d.min())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zZSRKnF6nbas",
"outputId": "9cf0ecd3-7abe-4d94-8a64-36c623ff51da"
},
"outputs": [
{
"data": {
"text/plain": [
"2.8722813232690143"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array.std()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jdMsJndyndOQ",
"outputId": "6f3d707e-2963-4388-e8ce-f2297df4b6fb"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 3, 6, 10, 15, 21, 28, 36, 45, 55])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array.cumsum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VRpZYL4YnfUb",
"outputId": "dfdb683c-ed16-4f16-f9f8-05b173349f08"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 6, 24, 120, 720, 5040,\n",
" 40320, 362880, 3628800])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array.cumprod()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0nDm77jUm7St"
},
"source": [
"### Constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aMGMdwaqoXkn",
"outputId": "68025722-d17c-4c87-e958-40988b6f79ee"
},
"outputs": [
{
"data": {
"text/plain": [
"3.141592653589793"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.pi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jLhkYK-zoYo6",
"outputId": "21e3820a-bb77-41bf-e52e-d648cbc8ff3c"
},
"outputs": [
{
"data": {
"text/plain": [
"2.718281828459045"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.e"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0H-XpiGNoZgG",
"outputId": "88623e5a-fcee-4076-81da-b9efda2f3234"
},
"outputs": [
{
"data": {
"text/plain": [
"inf"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.inf"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jX-rL5P0ouD6"
},
"source": [
"### Functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eG1GKzVuovNY",
"outputId": "eecdf4ad-89b7-447a-e4a8-4a2758af972c"
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sin(np.pi / 2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tWQ3xUExo7Bn",
"outputId": "ffabcf03-ea44-4f68-dfdc-d3dc16c7ca4b"
},
"outputs": [
{
"data": {
"text/plain": [
"-1.0"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.cos(np.pi)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gk6bP-m-o9gC",
"outputId": "c1b67bd1-1cb5-436c-92db-1ca0263a734f"
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log(np.e)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WHZQnDRzpENN",
"outputId": "0ca8e0ff-f6aa-46ab-c019-057e555ab342"
},
"outputs": [
{
"data": {
"text/plain": [
"2.0"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log10(100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hAxm918epGJj",
"outputId": "d2a60bf9-123d-44ca-b875-a0ddd0a25013"
},
"outputs": [
{
"data": {
"text/plain": [
"6.0"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log2(64)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p8IvjWHkzvlm"
},
"source": [
"To demonstrate rounding, let's first make a new array with decimals. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VSJgi2IQpYXa",
"outputId": "e437c7a9-e4fe-4499-b7ee-b0db39da7eeb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 0.33333333 0.66666667 1. ]\n"
]
},
{
"data": {
"text/plain": [
"array([0. , 0.33, 0.67, 1. ])"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = np.arange(4) / 3\n",
"print(array)\n",
"np.around(array, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "88VwFdWBCFkC"
},
"source": [
"### Looping vs Vectorization\n",
"\n",
"As mentioned in the beginning, NumPy uses machine code with their ndarray objects which is what leads to the performance improvements. Let's demonstrate this by constructing a simple sine wave."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mX9awPKn-ZlU",
"outputId": "29c7994e-1b18-4885-ceea-4f5e363c2c3c"
},
"outputs": [
{
"data": {
"text/plain": [
"(1001,)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fs = 500 #sampling rate in Hz\n",
"d_t = 1 / fs #time steps in seconds\n",
"n_step = 1000 #number of steps (there will be n_step+1 data points)\n",
"\n",
"amp = 1 #amplitude of sine wave\n",
"f = 2 #frequency of sine wave\n",
"\n",
"time = np.linspace(0, #start time\n",
" n_step * d_t, #end time\n",
" num=n_step + 1) #number of data points, one more than the steps to include an endpoint\n",
"time.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CIKO7e0e0QvP"
},
"source": [
"First we make a function to loop through each element and calculate the amplitude."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wqVZJKtwTB8l"
},
"outputs": [],
"source": [
"def sine_wave_with_loop(time, amp, f, phase=0):\n",
" length = time.shape[0]\n",
" wave = np.zeros(length)\n",
"\n",
" for i in range(length-1):\n",
" wave[i] = np.sin(2 * np.pi * f * time[i] + phase * np.pi / 180)*amp\n",
" return wave"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8nkt7EDc0c3H"
},
"source": [
"Now let's time how quickly that executes for our time array of 1,001 data points."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CMwHGKrITpN4",
"outputId": "950cebd8-3bb5-4062-cbde-15b4b4727eb3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The slowest run took 7.52 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
"100 loops, best of 5: 2.55 ms per loop\n"
]
}
],
"source": [
"%timeit sine_wave_with_loop(time, amp, f)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g4s7-D_90nNH"
},
"source": [
"Now let's do the same using NumPy's sine function and vectorization."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "89xWQJpzAdal"
},
"outputs": [],
"source": [
"def sine_wave_with_numpy(time, amp, f, phase=0):\n",
" \"\"\"Takes in a time array and sine wave parameters, returns an array of the sine wave amplitude.\"\"\" \n",
" return np.sin(2 * np.pi * f * time + phase * np.pi / 180) * amp"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6rNJady-0sJp"
},
"source": [
"Notice my docstrings!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AsVkSRvtV3HI",
"outputId": "c2da90a6-e914-4964-c53b-2f1b2924a1e8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function sine_wave_with_numpy in module __main__:\n",
"\n",
"sine_wave_with_numpy(time, amp, f, phase=0)\n",
" Takes in a time array and sine wave parameters, returns an array of the sine wave amplitude.\n",
"\n"
]
}
],
"source": [
"help(sine_wave_with_numpy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Q5S5fTrw--Z3",
"outputId": "cb83ee47-de30-4215-9624-9c55571f30ce"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The slowest run took 4.34 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
"10000 loops, best of 5: 27.9 µs per loop\n"
]
}
],
"source": [
"%timeit sine_wave_with_numpy(time, amp, f)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jtS0wmIyppOW"
},
"source": [
"Using vectorization is about **100x faster!** And this increases the longer the loops are."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KQIddnfwp7t5"
},
"source": [
"#### Why Vectorization Works So Much Faster\n",
"The above example highlights that NumPy is much faster, but why? Because it is using compiled machine code under the hood for it's operations. \n",
"\n",
"Python has the [Numba](http://numba.pydata.org/) package which can be used to do this compilation which we will do to highlight just why NumPy is faster (and recommended!)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cX8PSvkRoJR3"
},
"outputs": [],
"source": [
"from numba import njit\n",
"\n",
"numba_sine_wave_with_loop = njit(sine_wave_with_loop)\n",
"numba_sine_wave_with_numpy = njit(sine_wave_with_numpy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "T_ckZKtlo1IJ",
"outputId": "da56b3e6-205c-4da7-8b89-b622903a94e1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The slowest run took 14426.53 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
"1 loop, best of 5: 22.8 µs per loop\n",
"The slowest run took 11783.25 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
"1 loop, best of 5: 22.1 µs per loop\n"
]
}
],
"source": [
"%timeit numba_sine_wave_with_loop(time, amp, f)\n",
"%timeit numba_sine_wave_with_numpy(time, amp, f)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gk0i33QcstPA"
},
"source": [
"Let's combine this into a DataFrame we'll discuss next in more detail, but here's a preview."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "U2y5ggCUpEHh",
"outputId": "b87bf24b-7621-4553-e384-5a0b14bd3035"
},
"outputs": [
{
"data": {
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" vertical-align: middle;\n",
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" <thead>\n",
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" <th>Time (us)</th>\n",
" <th>Method</th>\n",
" <th>Numba?</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <td>w</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>22.6</td>\n",
" <td>NumPy</td>\n",
" <td>w</td>\n",
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"text/plain": [
" Time (us) Method Numba?\n",
"0 277.0 Loop w/o\n",
"1 27.5 NumPy w/o\n",
"2 23.1 Loop w\n",
"3 22.6 NumPy w"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"time_data = pd.DataFrame({'Time (us)':[2.77*100, 27.5, 23.1, 22.6],\n",
" 'Method':['Loop','NumPy','Loop','NumPy'],\n",
" 'Numba?':['w/o','w/o','w','w']}\n",
")\n",
"time_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y4xT1NK3sqnQ"
},
"source": [
"Now let's plot it and preview Plotly!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 559
},
"id": "qxZGaIywpPBr",
"outputId": "a0fda4c9-0df4-4412-abe0-9e440f820206"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 23.9 MB 1.5 MB/s \n",
"\u001b[?25h"
]
},
{
"data": {
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"!pip install --upgrade -q plotly\n",
"import plotly.express as px\n",
"import plotly.io as pio; pio.renderers.default = \"iframe\"\n",
"\n",
"fig = px.bar(time_data, \n",
" x=\"Method\", \n",
" y=\"Time (us)\", \n",
" color=\"Numba?\", \n",
" title=\"Compute Time of a Sine Wave for 1000 Elements\",\n",
" barmode='group')\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q220YoGquB1h"
},
"source": [
"## Pandas\n",
"Pandas is built *on top of* NumPy meaning that the data is stored still as NumPy ndarray objects under the hood. But it exposes a much more intuitive labeling/indexing architecture and allows you to link arrays of different types (strings, floats, integers etc.) to one another.\n",
"\n",
"To quote Jake VanderPlas:\n",
">*At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.*\n",
"\n",
"To start, import pandas as `pd`, again this will come standard in virtually all Python distributions such as Anaconda. But to install is simply:\n",
"\n",
"```\n",
"!pip install pandas\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zigCIKBhNkNl"
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OtlqVHIwT6wx"
},
"source": [
"There are three types of Pandas objects, we'll only focus on the first two:\n",
"\n",
"1. **Series** – 1D labeled homogeneous array, size-immutable\n",
"2. **Data Frames** – 2D labeled, size-mutable tabular structure with heterogenic columns\n",
"3. **Panel** – 3D labeled size mutable array."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ReBZJAjGuFTx"
},
"source": [
"### Creating a Series\n",
"First let's create a few numpy arrays."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Slfe4gYJuKZL",
"outputId": "b14099a3-7fd2-4c23-b9fb-b5eccf322338"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 0.002 0.004 ... 1.996 1.998 2. ]\n",
"[ 1.22464680e-16 -2.51300954e-02 -5.02443182e-02 ... 5.02443182e-02\n",
" 2.51300954e-02 1.10218212e-15]\n"
]
}
],
"source": [
"amplitude = sine_wave_with_numpy(time, amp, f, 180)\n",
"print(time)\n",
"print(amplitude)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yH8v1NpUBr0X"
},
"source": [
"Now let's see what a series looks like made from one of the arrays."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ke_Vt_SmBrl0",
"outputId": "4e323c4a-ef2d-40a0-c44d-0511336ba3e5"
},
"outputs": [
{
"data": {
"text/plain": [
"0 1.224647e-16\n",
"1 -2.513010e-02\n",
"2 -5.024432e-02\n",
"3 -7.532681e-02\n",
"4 -1.003617e-01\n",
" ... \n",
"996 1.003617e-01\n",
"997 7.532681e-02\n",
"998 5.024432e-02\n",
"999 2.513010e-02\n",
"1000 1.102182e-15\n",
"Length: 1001, dtype: float64"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(amplitude)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jt0xL0cuB2Ai"
},
"source": [
"This type of series has some value, but you really start to see it when you add in an index."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OQ11y3klvvLj",
"outputId": "5dfe9f12-e4a2-4cc4-d234-dd048046d477"
},
"outputs": [
{
"data": {
"text/plain": [
"0.000 1.224647e-16\n",
"0.002 -2.513010e-02\n",
"0.004 -5.024432e-02\n",
"0.006 -7.532681e-02\n",
"0.008 -1.003617e-01\n",
" ... \n",
"1.992 1.003617e-01\n",
"1.994 7.532681e-02\n",
"1.996 5.024432e-02\n",
"1.998 2.513010e-02\n",
"2.000 1.102182e-15\n",
"Name: Amplitude, Length: 1001, dtype: float64"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series = pd.Series(data=amplitude,\n",
" index=time,\n",
" name='Amplitude')\n",
"series"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YxGmalSBl2_3"
},
"source": [
"Here's where Pandas shines - indexing is much more intuitive (and inclusive) to specify based on labels, not those confusing integer locations. We'll come back to this when we have the dataframe next too."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VTqpSZl9lyIl",
"outputId": "3a92856e-0677-4c80-f52f-7d0e2fd9e80a"
},
"outputs": [
{
"data": {
"text/plain": [
"0.000 1.224647e-16\n",
"0.002 -2.513010e-02\n",
"0.004 -5.024432e-02\n",
"0.006 -7.532681e-02\n",
"0.008 -1.003617e-01\n",
"0.010 -1.253332e-01\n",
"Name: Amplitude, dtype: float64"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series[0: 0.01]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s38flxoeB8n0"
},
"source": [
"Being able to plot quickly is also a plus!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"id": "YbluH1OXu-0f",
"outputId": "f1a137b5-b116-4ef8-e9de-4458e1510bcf"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f51188d8310>"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"series.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H8I5XnmU4Dp7"
},
"source": [
"Remember we never left the NumPy array, it is still here and can be accessed with the following."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xtmkmd2l5U4o",
"outputId": "278f9301-9f68-42b1-d0dd-11657f48e364"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.22464680e-16, -2.51300954e-02, -5.02443182e-02, ...,\n",
" 5.02443182e-02, 2.51300954e-02, 1.10218212e-15])"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series.values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-jvWYyW44JUH",
"outputId": "7ec67c4d-b14b-4dd3-ceda-5d42655636e4"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.22464680e-16, -2.51300954e-02, -5.02443182e-02, ...,\n",
" 5.02443182e-02, 2.51300954e-02, 1.10218212e-15])"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series.to_numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wzVCQDfNuIOP"
},
"source": [
"### Creating a DataFrame\n",
"\n",
"A DataFrame is basically a sequence of aligned series objects, and by aligned I mean they share a common index or label. This let's us mix and match types easily among other benefits.\n",
"\n",
"First we'll start creating dataframes using what is called a \"dictionary\" with keys and values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 423
},
"id": "Bty17IsDU6B3",
"outputId": "5434f563-2cdf-4618-ee07-a0056152f58f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Phase 0</th>\n",
" <th>Phase 90</th>\n",
" <th>Phase 180</th>\n",
" <th>Phase 270</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.000</th>\n",
" <td>0.000000e+00</td>\n",
" <td>1.000000</td>\n",
" <td>1.224647e-16</td>\n",
" <td>-1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.002</th>\n",
" <td>2.513010e-02</td>\n",
" <td>0.999684</td>\n",
" <td>-2.513010e-02</td>\n",
" <td>-0.999684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.004</th>\n",
" <td>5.024432e-02</td>\n",
" <td>0.998737</td>\n",
" <td>-5.024432e-02</td>\n",
" <td>-0.998737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.006</th>\n",
" <td>7.532681e-02</td>\n",
" <td>0.997159</td>\n",
" <td>-7.532681e-02</td>\n",
" <td>-0.997159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.008</th>\n",
" <td>1.003617e-01</td>\n",
" <td>0.994951</td>\n",
" <td>-1.003617e-01</td>\n",
" <td>-0.994951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.992</th>\n",
" <td>-1.003617e-01</td>\n",
" <td>0.994951</td>\n",
" <td>1.003617e-01</td>\n",
" <td>-0.994951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.994</th>\n",
" <td>-7.532681e-02</td>\n",
" <td>0.997159</td>\n",
" <td>7.532681e-02</td>\n",
" <td>-0.997159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.996</th>\n",
" <td>-5.024432e-02</td>\n",
" <td>0.998737</td>\n",
" <td>5.024432e-02</td>\n",
" <td>-0.998737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.998</th>\n",
" <td>-2.513010e-02</td>\n",
" <td>0.999684</td>\n",
" <td>2.513010e-02</td>\n",
" <td>-0.999684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.000</th>\n",
" <td>-9.797174e-16</td>\n",
" <td>1.000000</td>\n",
" <td>1.102182e-15</td>\n",
" <td>-1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1001 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" Phase 0 Phase 90 Phase 180 Phase 270\n",
"0.000 0.000000e+00 1.000000 1.224647e-16 -1.000000\n",
"0.002 2.513010e-02 0.999684 -2.513010e-02 -0.999684\n",
"0.004 5.024432e-02 0.998737 -5.024432e-02 -0.998737\n",
"0.006 7.532681e-02 0.997159 -7.532681e-02 -0.997159\n",
"0.008 1.003617e-01 0.994951 -1.003617e-01 -0.994951\n",
"... ... ... ... ...\n",
"1.992 -1.003617e-01 0.994951 1.003617e-01 -0.994951\n",
"1.994 -7.532681e-02 0.997159 7.532681e-02 -0.997159\n",
"1.996 -5.024432e-02 0.998737 5.024432e-02 -0.998737\n",
"1.998 -2.513010e-02 0.999684 2.513010e-02 -0.999684\n",
"2.000 -9.797174e-16 1.000000 1.102182e-15 -1.000000\n",
"\n",
"[1001 rows x 4 columns]"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({\"Phase 0\": sine_wave_with_numpy(time, amp, f, 00),\n",
" \"Phase 90\": sine_wave_with_numpy(time, amp, f, 90),\n",
" \"Phase 180\": sine_wave_with_numpy(time, amp, f, 180),\n",
" \"Phase 270\": sine_wave_with_numpy(time, amp, f, 270)},\n",
" index=time)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dGDtiYG-cU9V"
},
"source": [
"### Plotting (Preview)\n",
"\n",
"Dataframes also wrap around Matplotlib to allow for plotting directly from the dataframe object itself. This can also be done from the Pandas Series object too like we showed earlier."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
},
"id": "ZkC62XnKVd6X",
"outputId": "6e92b6eb-a66d-4582-9ea7-7fe80c6cf0f2"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f51187bd810>"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 423
},
"id": "goaaXKMXVjUq",
"outputId": "e3c5e802-dd8e-4fb3-8598-0dd8be598f66"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Phase 0</th>\n",
" <th>Phase 90</th>\n",
" <th>Phase 180</th>\n",
" <th>Phase 270</th>\n",
" <th>Max</th>\n",
" <th>Min</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.000</th>\n",
" <td>0.000000e+00</td>\n",
" <td>1.000000</td>\n",
" <td>1.224647e-16</td>\n",
" <td>-1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.002</th>\n",
" <td>2.513010e-02</td>\n",
" <td>0.999684</td>\n",
" <td>-2.513010e-02</td>\n",
" <td>-0.999684</td>\n",
" <td>0.999684</td>\n",
" <td>-0.999684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.004</th>\n",
" <td>5.024432e-02</td>\n",
" <td>0.998737</td>\n",
" <td>-5.024432e-02</td>\n",
" <td>-0.998737</td>\n",
" <td>0.998737</td>\n",
" <td>-0.998737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.006</th>\n",
" <td>7.532681e-02</td>\n",
" <td>0.997159</td>\n",
" <td>-7.532681e-02</td>\n",
" <td>-0.997159</td>\n",
" <td>0.997159</td>\n",
" <td>-0.997159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.008</th>\n",
" <td>1.003617e-01</td>\n",
" <td>0.994951</td>\n",
" <td>-1.003617e-01</td>\n",
" <td>-0.994951</td>\n",
" <td>0.994951</td>\n",
" <td>-0.994951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.992</th>\n",
" <td>-1.003617e-01</td>\n",
" <td>0.994951</td>\n",
" <td>1.003617e-01</td>\n",
" <td>-0.994951</td>\n",
" <td>0.994951</td>\n",
" <td>-0.994951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.994</th>\n",
" <td>-7.532681e-02</td>\n",
" <td>0.997159</td>\n",
" <td>7.532681e-02</td>\n",
" <td>-0.997159</td>\n",
" <td>0.997159</td>\n",
" <td>-0.997159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.996</th>\n",
" <td>-5.024432e-02</td>\n",
" <td>0.998737</td>\n",
" <td>5.024432e-02</td>\n",
" <td>-0.998737</td>\n",
" <td>0.998737</td>\n",
" <td>-0.998737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.998</th>\n",
" <td>-2.513010e-02</td>\n",
" <td>0.999684</td>\n",
" <td>2.513010e-02</td>\n",
" <td>-0.999684</td>\n",
" <td>0.999684</td>\n",
" <td>-0.999684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.000</th>\n",
" <td>-9.797174e-16</td>\n",
" <td>1.000000</td>\n",
" <td>1.102182e-15</td>\n",
" <td>-1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1001 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Phase 0 Phase 90 Phase 180 Phase 270 Max Min\n",
"0.000 0.000000e+00 1.000000 1.224647e-16 -1.000000 1.000000 -1.000000\n",
"0.002 2.513010e-02 0.999684 -2.513010e-02 -0.999684 0.999684 -0.999684\n",
"0.004 5.024432e-02 0.998737 -5.024432e-02 -0.998737 0.998737 -0.998737\n",
"0.006 7.532681e-02 0.997159 -7.532681e-02 -0.997159 0.997159 -0.997159\n",
"0.008 1.003617e-01 0.994951 -1.003617e-01 -0.994951 0.994951 -0.994951\n",
"... ... ... ... ... ... ...\n",
"1.992 -1.003617e-01 0.994951 1.003617e-01 -0.994951 0.994951 -0.994951\n",
"1.994 -7.532681e-02 0.997159 7.532681e-02 -0.997159 0.997159 -0.997159\n",
"1.996 -5.024432e-02 0.998737 5.024432e-02 -0.998737 0.998737 -0.998737\n",
"1.998 -2.513010e-02 0.999684 2.513010e-02 -0.999684 0.999684 -0.999684\n",
"2.000 -9.797174e-16 1.000000 1.102182e-15 -1.000000 1.000000 -1.000000\n",
"\n",
"[1001 rows x 6 columns]"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Max'] = df.max(axis=1)\n",
"df['Min'] = df.min(axis=1)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I7_Phn2UF7Yp"
},
"source": [
"This will be the topic of the next webinar, plotting with Plotly! \n",
"\n",
"Note that I need to install an upgraded version of Plotly in Colab because the default Plotly Express version doesn't work in Colab (but their more advanced graph objects does)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ilzy8VvZDgt0"
},
"outputs": [],
"source": [
"!pip install --upgrade -q plotly\n",
"import plotly.express as px"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "7XF4YcNIE1Hn",
"outputId": "6c694dac-a715-472b-a2cb-37b29b113858"
},
"outputs": [
{
"data": {
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"metadata": {},
"output_type": "display_data"
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"source": [
"px.line(df).show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0_45NbIUDvGI"
},
"source": [
"### Load from CSV\n",
"\n",
"This dataset was discussed in a blog on [vibration metrics and used bearing data as an example](https://blog.endaq.com/top-vibration-metrics-to-monitor-how-to-calculate-them).\n",
"\n",
"Note you don't *have* to use a CSV. They have a lot of other file formats natively supported ([see full list](https://pandas.pydata.org/docs/user_guide/io.html)):\n",
"\n",
"* hdf\n",
"* feather\n",
"* pickle\n",
"\n",
"But I know everyone likes CSVs!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 455
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"id": "8Yiph7IODh4E",
"outputId": "c87e5403-aeb5-4a33-a402-8971ecb52c85"
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" <td>0.145055</td>\n",
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" <th>9.999750</th>\n",
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" <td>0.092263</td>\n",
" <td>-0.067573</td>\n",
" <td>0.070721</td>\n",
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" <tr>\n",
" <th>9.999833</th>\n",
" <td>-0.036508</td>\n",
" <td>-0.168120</td>\n",
" <td>0.005685</td>\n",
" <td>0.103265</td>\n",
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" Fault_021 Fault_014 Fault_007 Normal\n",
"Time \n",
"0.000000 -0.105351 -0.074395 0.053116 0.046104\n",
"0.000083 0.132888 0.056365 0.116628 -0.037134\n",
"0.000167 -0.056535 0.201257 0.083654 -0.089496\n",
"0.000250 -0.193178 -0.024528 -0.026477 -0.084906\n",
"0.000333 0.064879 -0.072284 0.045319 -0.038594\n",
"... ... ... ... ...\n",
"9.999667 0.095754 0.145055 -0.098923 0.064254\n",
"9.999750 -0.123083 0.092263 -0.067573 0.070721\n",
"9.999833 -0.036508 -0.168120 0.005685 0.103265\n",
"9.999917 0.097006 -0.035898 0.093400 0.124335\n",
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"\n",
"[120000 rows x 4 columns]"
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},
"execution_count": 79,
"metadata": {},
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],
"source": [
"df = pd.read_csv('https://info.endaq.com/hubfs/Plots/bearing_data.csv', index_col=0)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SuYE7enn5-0T"
},
"source": [
"### Save CSV\n",
"\n",
"Like reading data, there are a host of native formats we can save data from a dataframe. [See documentation](https://pandas.pydata.org/docs/reference/io.html)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rB5uIDRt6CJe"
},
"outputs": [],
"source": [
"df.to_csv('bearing-data.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JrAWfnkOJZsk"
},
"source": [
"### Simple Analysis"
]
},
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" <td>0.013351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.132054</td>\n",
" <td>0.100872</td>\n",
" <td>0.080081</td>\n",
" <td>0.056535</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.917908</td>\n",
" <td>1.124376</td>\n",
" <td>0.594025</td>\n",
" <td>0.251382</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fault_021 Fault_014 Fault_007 Normal\n",
"count 120000.000000 120000.000000 120000.000000 120000.000000\n",
"mean 0.012251 0.002729 0.002953 0.010755\n",
"std 0.198383 0.157761 0.121272 0.065060\n",
"min -1.037862 -1.338628 -0.650390 -0.269114\n",
"25% -0.107020 -0.096649 -0.072284 -0.032544\n",
"50% 0.011682 0.001299 0.004548 0.013351\n",
"75% 0.132054 0.100872 0.080081 0.056535\n",
"max 0.917908 1.124376 0.594025 0.251382"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
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},
"id": "6fiVPnBNJb32",
"outputId": "df0b8b39-f9df-44bf-c236-7695d375cae4"
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"outputs": [
{
"data": {
"text/plain": [
"Fault_021 0.198383\n",
"Fault_014 0.157761\n",
"Fault_007 0.121272\n",
"Normal 0.065060\n",
"dtype: float64"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.std()"
]
},
{
"cell_type": "code",
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"base_uri": "https://localhost:8080/"
},
"id": "oQgXuQ9VJhTd",
"outputId": "537e189f-46b5-4945-808d-705f1932e4f0"
},
"outputs": [
{
"data": {
"text/plain": [
"Fault_021 0.917908\n",
"Fault_014 1.124376\n",
"Fault_007 0.594025\n",
"Normal 0.251382\n",
"dtype: float64"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.max()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KuwaUNJPth1r"
},
"source": [
"Note that these built in Pandas functions are using NumPy to process and are the equivalent of doing the following."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TUsz_I4ptfo3",
"outputId": "bd9f9729-ecf7-4899-8113-ca5a5cc4ffe0"
},
"outputs": [
{
"data": {
"text/plain": [
"Fault_021 0.917908\n",
"Fault_014 1.124376\n",
"Fault_007 0.594025\n",
"Normal 0.251382\n",
"dtype: float64"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.max(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cEG3SeRAJkyW",
"outputId": "33ef1359-42c7-41f7-d908-5a753168ac5f"
},
"outputs": [
{
"data": {
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"Fault_021 -0.107020\n",
"Fault_014 -0.096649\n",
"Fault_007 -0.072284\n",
"Normal -0.032544\n",
"Name: 0.25, dtype: float64"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.quantile(0.25)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 455
},
"id": "Shx-sdauJllV",
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" <td>0.114947</td>\n",
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" </tr>\n",
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],
"text/plain": [
" Fault_021 Fault_014 Fault_007 Normal abs(max)\n",
"Time \n",
"0.000000 -0.105351 -0.074395 0.053116 0.046104 0.105351\n",
"0.000083 0.132888 0.056365 0.116628 -0.037134 0.132888\n",
"0.000167 -0.056535 0.201257 0.083654 -0.089496 0.201257\n",
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"... ... ... ... ... ...\n",
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"\n",
"[120000 rows x 5 columns]"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['abs(max)'] = df.abs().max(axis=1)\n",
"df "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oilY0XL3MSA_"
},
"source": [
"### Indexing\n",
"Here is where indexing in Python gets a whole lot more intuitive! A dataframe with an index let's use index values (time in this case) to slice the dataframe, not rely on the nth element in the arrays."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 455
},
"id": "-u2YH6_WMxdX",
"outputId": "6b4dd652-7842-4961-ee70-2c8bb4970ca9"
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"outputs": [
{
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" Fault_021 Fault_014 Fault_007 Normal abs(max)\n",
"Time \n",
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"[600 rows x 5 columns]"
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},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[0: 0.05]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xTQkEsfJuDAR"
},
"source": [
"We can also use the same convention as before by adding in a step definition, in this case we'll grab every 100th point."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
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"height": 269
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"id": "aH5m-nHdM6MV",
"outputId": "97f1fc33-f5aa-42ca-83f5-d1e8b2919da6"
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{
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" Fault_021 Fault_014 Fault_007 Normal abs(max)\n",
"Time \n",
"0.000000 -0.105351 -0.074395 0.053116 0.046104 0.105351\n",
"0.008333 -0.481067 -0.137745 -0.010558 -0.012934 0.481067\n",
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"df[0: 0.05: 100]"
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{
"cell_type": "markdown",
"metadata": {
"id": "0xzown4qLWGb"
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"source": [
"There are ways to use the integer based indexing if you so desire."
]
},
{
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" <tr>\n",
" <th>0.000000</th>\n",
" <td>-0.105351</td>\n",
" <td>-0.074395</td>\n",
" <td>0.053116</td>\n",
" <td>0.046104</td>\n",
" <td>0.105351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.000083</th>\n",
" <td>0.132888</td>\n",
" <td>0.056365</td>\n",
" <td>0.116628</td>\n",
" <td>-0.037134</td>\n",
" <td>0.132888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.000167</th>\n",
" <td>-0.056535</td>\n",
" <td>0.201257</td>\n",
" <td>0.083654</td>\n",
" <td>-0.089496</td>\n",
" <td>0.201257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.000250</th>\n",
" <td>-0.193178</td>\n",
" <td>-0.024528</td>\n",
" <td>-0.026477</td>\n",
" <td>-0.084906</td>\n",
" <td>0.193178</td>\n",
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" <tr>\n",
" <th>0.000333</th>\n",
" <td>0.064879</td>\n",
" <td>-0.072284</td>\n",
" <td>0.045319</td>\n",
" <td>-0.038594</td>\n",
" <td>0.072284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.000417</th>\n",
" <td>0.214874</td>\n",
" <td>0.034761</td>\n",
" <td>0.060751</td>\n",
" <td>0.025451</td>\n",
" <td>0.214874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.000500</th>\n",
" <td>-0.076353</td>\n",
" <td>0.094212</td>\n",
" <td>-0.174130</td>\n",
" <td>0.040680</td>\n",
" <td>0.174130</td>\n",
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" <tr>\n",
" <th>0.000583</th>\n",
" <td>-0.065922</td>\n",
" <td>-0.070010</td>\n",
" <td>-0.229521</td>\n",
" <td>0.042558</td>\n",
" <td>0.229521</td>\n",
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" <tr>\n",
" <th>0.000667</th>\n",
" <td>0.206529</td>\n",
" <td>-0.079431</td>\n",
" <td>0.045482</td>\n",
" <td>0.038177</td>\n",
" <td>0.206529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.000750</th>\n",
" <td>0.021487</td>\n",
" <td>0.092426</td>\n",
" <td>0.027452</td>\n",
" <td>0.044018</td>\n",
" <td>0.092426</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fault_021 Fault_014 Fault_007 Normal abs(max)\n",
"Time \n",
"0.000000 -0.105351 -0.074395 0.053116 0.046104 0.105351\n",
"0.000083 0.132888 0.056365 0.116628 -0.037134 0.132888\n",
"0.000167 -0.056535 0.201257 0.083654 -0.089496 0.201257\n",
"0.000250 -0.193178 -0.024528 -0.026477 -0.084906 0.193178\n",
"0.000333 0.064879 -0.072284 0.045319 -0.038594 0.072284\n",
"0.000417 0.214874 0.034761 0.060751 0.025451 0.214874\n",
"0.000500 -0.076353 0.094212 -0.174130 0.040680 0.174130\n",
"0.000583 -0.065922 -0.070010 -0.229521 0.042558 0.229521\n",
"0.000667 0.206529 -0.079431 0.045482 0.038177 0.206529\n",
"0.000750 0.021487 0.092426 0.027452 0.044018 0.092426"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[0:10]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XxIMZ9-gMSfC"
},
"source": [
"### Rolling\n",
"I love the [rolling method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html) which allows for easy rolling window calculations, something you'll do frequently with time series data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 455
},
"id": "QeIgZF4WNo7K",
"outputId": "59361aae-3320-48b6-8716-e68971845511"
},
"outputs": [
{
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" <th>Normal</th>\n",
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" <td>0.768318</td>\n",
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" <td>0.18525</td>\n",
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" <th>9.999833</th>\n",
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"</div>"
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"text/plain": [
" Fault_021 Fault_014 Fault_007 Normal abs(max)\n",
"Time \n",
"0.000000 NaN NaN NaN NaN NaN\n",
"0.000083 NaN NaN NaN NaN NaN\n",
"0.000167 NaN NaN NaN NaN NaN\n",
"0.000250 NaN NaN NaN NaN NaN\n",
"0.000333 NaN NaN NaN NaN NaN\n",
"... ... ... ... ... ...\n",
"9.999667 0.748721 0.768318 0.408362 0.18525 0.933137\n",
"9.999750 0.748721 0.768318 0.408362 0.18525 0.933137\n",
"9.999833 0.748721 0.768318 0.408362 0.18525 0.933137\n",
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"\n",
"[120000 rows x 5 columns]"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n = int(df.shape[0] / 100)\n",
"df.rolling(n).max()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 455
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"id": "eke1fPqkN_NS",
"outputId": "e238d753-2fcb-4119-90b4-b8e34c98b900"
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"outputs": [
{
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" <th></th>\n",
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" <th>Normal</th>\n",
" <th>abs(max)</th>\n",
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" <td>0.641131</td>\n",
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" <th>0.300003</th>\n",
" <td>0.491289</td>\n",
" <td>0.846612</td>\n",
" <td>0.492178</td>\n",
" <td>0.172525</td>\n",
" <td>0.846612</td>\n",
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" <th>0.400003</th>\n",
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" <td>0.731823</td>\n",
" <td>0.500950</td>\n",
" <td>0.474798</td>\n",
" <td>0.206112</td>\n",
" <td>0.760820</td>\n",
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" <th>9.600080</th>\n",
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" <td>0.713253</td>\n",
" <td>0.470412</td>\n",
" <td>0.190466</td>\n",
" <td>0.713253</td>\n",
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" <td>0.547244</td>\n",
" <td>0.415996</td>\n",
" <td>0.229060</td>\n",
" <td>0.677791</td>\n",
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" <tr>\n",
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" <td>0.625220</td>\n",
" <td>0.569498</td>\n",
" <td>0.391469</td>\n",
" <td>0.176489</td>\n",
" <td>0.653801</td>\n",
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" <tr>\n",
" <th>9.900083</th>\n",
" <td>0.749555</td>\n",
" <td>0.949271</td>\n",
" <td>0.332342</td>\n",
" <td>0.176489</td>\n",
" <td>0.949271</td>\n",
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"<p>100 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" Fault_021 Fault_014 Fault_007 Normal abs(max)\n",
"Time \n",
"0.000000 NaN NaN NaN NaN NaN\n",
"0.100001 0.726190 0.710166 0.443448 0.184416 0.813809\n",
"0.200002 0.696150 0.641131 0.411773 0.204443 0.740376\n",
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"... ... ... ... ... ...\n",
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"9.600080 0.697818 0.713253 0.470412 0.190466 0.713253\n",
"9.700081 0.677791 0.547244 0.415996 0.229060 0.677791\n",
"9.800082 0.625220 0.569498 0.391469 0.176489 0.653801\n",
"9.900083 0.749555 0.949271 0.332342 0.176489 0.949271\n",
"\n",
"[100 rows x 5 columns]"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.rolling(n).max()[::n]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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{
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"source": [
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{
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"metadata": {
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},
"source": [
"### Datetime Data (Yay Finance!)\n",
"\n",
"Let's use Yahoo Finance and stock data as a relatable example of data with datetimes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"text": [
"\u001b[K |████████████████████████████████| 6.3 MB 56.8 MB/s \n",
"\u001b[?25h Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
],
"source": [
"!pip install -q yfinance\n",
"import yfinance as yf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"id": "NBd9jY74MOs_",
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"text": [
"[*********************100%***********************] 5 of 5 completed\n"
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" <th>2019-01-02</th>\n",
" <td>38.382229</td>\n",
" <td>1539.130005</td>\n",
" <td>1054.680054</td>\n",
" <td>97.961319</td>\n",
" <td>238.694550</td>\n",
" <td>39.480000</td>\n",
" <td>1539.130005</td>\n",
" <td>1054.680054</td>\n",
" <td>101.120003</td>\n",
" <td>250.179993</td>\n",
" <td>39.712502</td>\n",
" <td>1553.359985</td>\n",
" <td>1060.790039</td>\n",
" <td>101.750000</td>\n",
" <td>251.210007</td>\n",
" <td>38.557499</td>\n",
" <td>1460.930054</td>\n",
" <td>1025.280029</td>\n",
" <td>98.940002</td>\n",
" <td>245.949997</td>\n",
" <td>38.722500</td>\n",
" <td>1465.199951</td>\n",
" <td>1027.199951</td>\n",
" <td>99.550003</td>\n",
" <td>245.979996</td>\n",
" <td>148158800</td>\n",
" <td>7983100</td>\n",
" <td>1593400</td>\n",
" <td>35329300</td>\n",
" <td>126925200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-03</th>\n",
" <td>34.559078</td>\n",
" <td>1500.280029</td>\n",
" <td>1025.469971</td>\n",
" <td>94.357529</td>\n",
" <td>232.998627</td>\n",
" <td>35.547501</td>\n",
" <td>1500.280029</td>\n",
" <td>1025.469971</td>\n",
" <td>97.400002</td>\n",
" <td>244.210007</td>\n",
" <td>36.430000</td>\n",
" <td>1538.000000</td>\n",
" <td>1066.260010</td>\n",
" <td>100.190002</td>\n",
" <td>248.570007</td>\n",
" <td>35.500000</td>\n",
" <td>1497.109985</td>\n",
" <td>1022.369995</td>\n",
" <td>97.199997</td>\n",
" <td>243.669998</td>\n",
" <td>35.994999</td>\n",
" <td>1520.010010</td>\n",
" <td>1050.670044</td>\n",
" <td>100.099998</td>\n",
" <td>248.229996</td>\n",
" <td>365248800</td>\n",
" <td>6975600</td>\n",
" <td>2098000</td>\n",
" <td>42579100</td>\n",
" <td>144140700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-04</th>\n",
" <td>36.034370</td>\n",
" <td>1575.390015</td>\n",
" <td>1078.069946</td>\n",
" <td>98.746010</td>\n",
" <td>240.803070</td>\n",
" <td>37.064999</td>\n",
" <td>1575.390015</td>\n",
" <td>1078.069946</td>\n",
" <td>101.930000</td>\n",
" <td>252.389999</td>\n",
" <td>37.137501</td>\n",
" <td>1594.000000</td>\n",
" <td>1080.000000</td>\n",
" <td>102.510002</td>\n",
" <td>253.110001</td>\n",
" <td>35.950001</td>\n",
" <td>1518.310059</td>\n",
" <td>1036.859985</td>\n",
" <td>98.930000</td>\n",
" <td>247.169998</td>\n",
" <td>36.132500</td>\n",
" <td>1530.000000</td>\n",
" <td>1042.560059</td>\n",
" <td>99.720001</td>\n",
" <td>247.589996</td>\n",
" <td>234428400</td>\n",
" <td>9182600</td>\n",
" <td>2301100</td>\n",
" <td>44060600</td>\n",
" <td>142628800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-07</th>\n",
" <td>35.954170</td>\n",
" <td>1629.510010</td>\n",
" <td>1075.920044</td>\n",
" <td>98.871956</td>\n",
" <td>242.701706</td>\n",
" <td>36.982498</td>\n",
" <td>1629.510010</td>\n",
" <td>1075.920044</td>\n",
" <td>102.059998</td>\n",
" <td>254.380005</td>\n",
" <td>37.207500</td>\n",
" <td>1634.560059</td>\n",
" <td>1082.699951</td>\n",
" <td>103.269997</td>\n",
" <td>255.949997</td>\n",
" <td>36.474998</td>\n",
" <td>1589.189941</td>\n",
" <td>1062.640015</td>\n",
" <td>100.980003</td>\n",
" <td>251.690002</td>\n",
" <td>37.174999</td>\n",
" <td>1602.310059</td>\n",
" <td>1080.969971</td>\n",
" <td>101.639999</td>\n",
" <td>252.690002</td>\n",
" <td>219111200</td>\n",
" <td>7993200</td>\n",
" <td>2372300</td>\n",
" <td>35656100</td>\n",
" <td>103139100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-08</th>\n",
" <td>36.639565</td>\n",
" <td>1656.579956</td>\n",
" <td>1085.369995</td>\n",
" <td>99.588829</td>\n",
" <td>244.981995</td>\n",
" <td>37.687500</td>\n",
" <td>1656.579956</td>\n",
" <td>1085.369995</td>\n",
" <td>102.800003</td>\n",
" <td>256.769989</td>\n",
" <td>37.955002</td>\n",
" <td>1676.609985</td>\n",
" <td>1093.349976</td>\n",
" <td>103.970001</td>\n",
" <td>257.309998</td>\n",
" <td>37.130001</td>\n",
" <td>1616.609985</td>\n",
" <td>1068.349976</td>\n",
" <td>101.709999</td>\n",
" <td>254.000000</td>\n",
" <td>37.389999</td>\n",
" <td>1664.689941</td>\n",
" <td>1086.000000</td>\n",
" <td>103.040001</td>\n",
" <td>256.820007</td>\n",
" <td>164101200</td>\n",
" <td>8881400</td>\n",
" <td>1770700</td>\n",
" <td>31514400</td>\n",
" <td>102512600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-17</th>\n",
" <td>146.059998</td>\n",
" <td>3462.520020</td>\n",
" <td>2816.000000</td>\n",
" <td>299.869995</td>\n",
" <td>441.399994</td>\n",
" <td>146.059998</td>\n",
" <td>3462.520020</td>\n",
" <td>2816.000000</td>\n",
" <td>299.869995</td>\n",
" <td>441.399994</td>\n",
" <td>148.820007</td>\n",
" <td>3497.409912</td>\n",
" <td>2869.000000</td>\n",
" <td>304.500000</td>\n",
" <td>445.369995</td>\n",
" <td>145.759995</td>\n",
" <td>3452.129883</td>\n",
" <td>2809.399902</td>\n",
" <td>299.529999</td>\n",
" <td>441.019989</td>\n",
" <td>148.820007</td>\n",
" <td>3488.409912</td>\n",
" <td>2860.610107</td>\n",
" <td>304.170013</td>\n",
" <td>444.920013</td>\n",
" <td>129728700</td>\n",
" <td>4614100</td>\n",
" <td>2666800</td>\n",
" <td>41309300</td>\n",
" <td>118220200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-20</th>\n",
" <td>142.940002</td>\n",
" <td>3355.729980</td>\n",
" <td>2774.389893</td>\n",
" <td>294.299988</td>\n",
" <td>434.040009</td>\n",
" <td>142.940002</td>\n",
" <td>3355.729980</td>\n",
" <td>2774.389893</td>\n",
" <td>294.299988</td>\n",
" <td>434.040009</td>\n",
" <td>144.839996</td>\n",
" <td>3419.000000</td>\n",
" <td>2780.000000</td>\n",
" <td>298.720001</td>\n",
" <td>436.559998</td>\n",
" <td>141.270004</td>\n",
" <td>3305.010010</td>\n",
" <td>2726.439941</td>\n",
" <td>289.519989</td>\n",
" <td>428.859985</td>\n",
" <td>143.800003</td>\n",
" <td>3396.000000</td>\n",
" <td>2763.229980</td>\n",
" <td>296.329987</td>\n",
" <td>434.880005</td>\n",
" <td>123478900</td>\n",
" <td>4669100</td>\n",
" <td>2325900</td>\n",
" <td>38278700</td>\n",
" <td>166445500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-21</th>\n",
" <td>143.429993</td>\n",
" <td>3343.629883</td>\n",
" <td>2780.659912</td>\n",
" <td>294.799988</td>\n",
" <td>433.630005</td>\n",
" <td>143.429993</td>\n",
" <td>3343.629883</td>\n",
" <td>2780.659912</td>\n",
" <td>294.799988</td>\n",
" <td>433.630005</td>\n",
" <td>144.600006</td>\n",
" <td>3379.699951</td>\n",
" <td>2800.229980</td>\n",
" <td>297.540009</td>\n",
" <td>437.910004</td>\n",
" <td>142.779999</td>\n",
" <td>3332.389893</td>\n",
" <td>2765.560059</td>\n",
" <td>294.070007</td>\n",
" <td>433.070007</td>\n",
" <td>143.929993</td>\n",
" <td>3375.000000</td>\n",
" <td>2794.989990</td>\n",
" <td>295.690002</td>\n",
" <td>436.529999</td>\n",
" <td>75834000</td>\n",
" <td>2780900</td>\n",
" <td>1266600</td>\n",
" <td>22364100</td>\n",
" <td>92526100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-22</th>\n",
" <td>145.850006</td>\n",
" <td>3380.050049</td>\n",
" <td>2805.669922</td>\n",
" <td>298.579987</td>\n",
" <td>437.859985</td>\n",
" <td>145.850006</td>\n",
" <td>3380.050049</td>\n",
" <td>2805.669922</td>\n",
" <td>298.579987</td>\n",
" <td>437.859985</td>\n",
" <td>146.429993</td>\n",
" <td>3389.000000</td>\n",
" <td>2817.739990</td>\n",
" <td>300.220001</td>\n",
" <td>440.029999</td>\n",
" <td>143.699997</td>\n",
" <td>3341.050049</td>\n",
" <td>2771.030029</td>\n",
" <td>294.510010</td>\n",
" <td>433.750000</td>\n",
" <td>144.449997</td>\n",
" <td>3351.000000</td>\n",
" <td>2786.080078</td>\n",
" <td>296.730011</td>\n",
" <td>436.049988</td>\n",
" <td>76404300</td>\n",
" <td>2411400</td>\n",
" <td>1252800</td>\n",
" <td>26626300</td>\n",
" <td>102350100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-23</th>\n",
" <td>146.830002</td>\n",
" <td>3416.000000</td>\n",
" <td>2824.320068</td>\n",
" <td>299.559998</td>\n",
" <td>443.179993</td>\n",
" <td>146.830002</td>\n",
" <td>3416.000000</td>\n",
" <td>2824.320068</td>\n",
" <td>299.559998</td>\n",
" <td>443.179993</td>\n",
" <td>147.080002</td>\n",
" <td>3428.959961</td>\n",
" <td>2833.929932</td>\n",
" <td>300.899994</td>\n",
" <td>444.890015</td>\n",
" <td>145.639999</td>\n",
" <td>3380.050049</td>\n",
" <td>2808.030029</td>\n",
" <td>297.529999</td>\n",
" <td>439.600006</td>\n",
" <td>146.649994</td>\n",
" <td>3380.050049</td>\n",
" <td>2819.750000</td>\n",
" <td>298.850006</td>\n",
" <td>439.850006</td>\n",
" <td>64838200</td>\n",
" <td>2379400</td>\n",
" <td>1047600</td>\n",
" <td>18604600</td>\n",
" <td>76396000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>688 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" Adj Close ... Volume \n",
" AAPL AMZN GOOGL ... GOOGL MSFT SPY\n",
"Date ... \n",
"2019-01-02 38.382229 1539.130005 1054.680054 ... 1593400 35329300 126925200\n",
"2019-01-03 34.559078 1500.280029 1025.469971 ... 2098000 42579100 144140700\n",
"2019-01-04 36.034370 1575.390015 1078.069946 ... 2301100 44060600 142628800\n",
"2019-01-07 35.954170 1629.510010 1075.920044 ... 2372300 35656100 103139100\n",
"2019-01-08 36.639565 1656.579956 1085.369995 ... 1770700 31514400 102512600\n",
"... ... ... ... ... ... ... ...\n",
"2021-09-17 146.059998 3462.520020 2816.000000 ... 2666800 41309300 118220200\n",
"2021-09-20 142.940002 3355.729980 2774.389893 ... 2325900 38278700 166445500\n",
"2021-09-21 143.429993 3343.629883 2780.659912 ... 1266600 22364100 92526100\n",
"2021-09-22 145.850006 3380.050049 2805.669922 ... 1252800 26626300 102350100\n",
"2021-09-23 146.830002 3416.000000 2824.320068 ... 1047600 18604600 76396000\n",
"\n",
"[688 rows x 30 columns]"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = yf.download([\"SPY\", \"AAPL\", \"MSFT\", \"AMZN\", \"GOOGL\"],\n",
" start='2019-01-01',\n",
" end='2021-09-24') \n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DrzB4cQh7zvI"
},
"source": [
"Let's compare not the price, but the relative performance."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 455
},
"id": "KA6yCs-IPqpD",
"outputId": "5309f784-3917-4125-f1a9-f39b56fb3372"
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>AMZN</th>\n",
" <th>GOOGL</th>\n",
" <th>MSFT</th>\n",
" <th>SPY</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>2019-01-02</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-03</th>\n",
" <td>0.900393</td>\n",
" <td>0.974758</td>\n",
" <td>0.972304</td>\n",
" <td>0.963212</td>\n",
" <td>0.976137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-04</th>\n",
" <td>0.938830</td>\n",
" <td>1.023559</td>\n",
" <td>1.022177</td>\n",
" <td>1.008010</td>\n",
" <td>1.008834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-07</th>\n",
" <td>0.936740</td>\n",
" <td>1.058721</td>\n",
" <td>1.020139</td>\n",
" <td>1.009296</td>\n",
" <td>1.016788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-08</th>\n",
" <td>0.954597</td>\n",
" <td>1.076309</td>\n",
" <td>1.029099</td>\n",
" <td>1.016614</td>\n",
" <td>1.026341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-17</th>\n",
" <td>3.805407</td>\n",
" <td>2.249661</td>\n",
" <td>2.670004</td>\n",
" <td>3.061106</td>\n",
" <td>1.849225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-20</th>\n",
" <td>3.724119</td>\n",
" <td>2.180277</td>\n",
" <td>2.630551</td>\n",
" <td>3.004247</td>\n",
" <td>1.818391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-21</th>\n",
" <td>3.736885</td>\n",
" <td>2.172416</td>\n",
" <td>2.636496</td>\n",
" <td>3.009351</td>\n",
" <td>1.816673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-22</th>\n",
" <td>3.799936</td>\n",
" <td>2.196078</td>\n",
" <td>2.660210</td>\n",
" <td>3.047938</td>\n",
" <td>1.834395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-23</th>\n",
" <td>3.825468</td>\n",
" <td>2.219436</td>\n",
" <td>2.677893</td>\n",
" <td>3.057942</td>\n",
" <td>1.856682</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>688 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL AMZN GOOGL MSFT SPY\n",
"Date \n",
"2019-01-02 1.000000 1.000000 1.000000 1.000000 1.000000\n",
"2019-01-03 0.900393 0.974758 0.972304 0.963212 0.976137\n",
"2019-01-04 0.938830 1.023559 1.022177 1.008010 1.008834\n",
"2019-01-07 0.936740 1.058721 1.020139 1.009296 1.016788\n",
"2019-01-08 0.954597 1.076309 1.029099 1.016614 1.026341\n",
"... ... ... ... ... ...\n",
"2021-09-17 3.805407 2.249661 2.670004 3.061106 1.849225\n",
"2021-09-20 3.724119 2.180277 2.630551 3.004247 1.818391\n",
"2021-09-21 3.736885 2.172416 2.636496 3.009351 1.816673\n",
"2021-09-22 3.799936 2.196078 2.660210 3.047938 1.834395\n",
"2021-09-23 3.825468 2.219436 2.677893 3.057942 1.856682\n",
"\n",
"[688 rows x 5 columns]"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df['Adj Close']\n",
"df = df / df.iloc[0]\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "GwRWHNPUQj1I",
"outputId": "4bf45621-d8e6-4720-d389-3f17ebcd1383"
},
"outputs": [
{
"data": {
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"id": "0AN97x-7RGF9"
},
"source": [
"The [rolling function](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html) will play very nicely with datetime data as shown here when I get the moving average over a 40 day period. And this can handle unevenly sampled date easily."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "FL9eZOutQzso",
"outputId": "36661409-e636-4055-e6b7-7bd68c046329"
},
"outputs": [
{
"data": {
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{
"cell_type": "markdown",
"metadata": {
"id": "3XzrLgmRR02j"
},
"source": [
"Indexing with datetime data though will require a slightly extra step, but then it is easy."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZQpQ7EJIRAJP"
},
"outputs": [],
"source": [
"from datetime import date\n",
"start = date(2021, 4, 1)\n",
"end = date(2021, 4, 30)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 739
},
"id": "1YU6TiEcRzPa",
"outputId": "96ee05aa-e1a9-4e69-cf17-2d62f5e1c9b9"
},
"outputs": [
{
"data": {
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>AMZN</th>\n",
" <th>GOOGL</th>\n",
" <th>MSFT</th>\n",
" <th>SPY</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2021-04-01</th>\n",
" <td>3.194388</td>\n",
" <td>2.053758</td>\n",
" <td>2.019361</td>\n",
" <td>2.463520</td>\n",
" <td>1.667522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-05</th>\n",
" <td>3.269703</td>\n",
" <td>2.096464</td>\n",
" <td>2.103918</td>\n",
" <td>2.531830</td>\n",
" <td>1.691456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-06</th>\n",
" <td>3.277754</td>\n",
" <td>2.094573</td>\n",
" <td>2.094721</td>\n",
" <td>2.519530</td>\n",
" <td>1.690457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-07</th>\n",
" <td>3.321644</td>\n",
" <td>2.130678</td>\n",
" <td>2.122947</td>\n",
" <td>2.540267</td>\n",
" <td>1.692414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-08</th>\n",
" <td>3.385532</td>\n",
" <td>2.143614</td>\n",
" <td>2.133756</td>\n",
" <td>2.574320</td>\n",
" <td>1.700447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-09</th>\n",
" <td>3.454094</td>\n",
" <td>2.190978</td>\n",
" <td>2.152947</td>\n",
" <td>2.600749</td>\n",
" <td>1.712810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-12</th>\n",
" <td>3.408386</td>\n",
" <td>2.195649</td>\n",
" <td>2.128247</td>\n",
" <td>2.601359</td>\n",
" <td>1.713434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-13</th>\n",
" <td>3.491232</td>\n",
" <td>2.209040</td>\n",
" <td>2.137549</td>\n",
" <td>2.627585</td>\n",
" <td>1.718512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-14</th>\n",
" <td>3.428903</td>\n",
" <td>2.165509</td>\n",
" <td>2.125678</td>\n",
" <td>2.598106</td>\n",
" <td>1.712644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-15</th>\n",
" <td>3.493050</td>\n",
" <td>2.195455</td>\n",
" <td>2.166771</td>\n",
" <td>2.637852</td>\n",
" <td>1.731041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-16</th>\n",
" <td>3.484220</td>\n",
" <td>2.208676</td>\n",
" <td>2.164400</td>\n",
" <td>2.650457</td>\n",
" <td>1.736827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-19</th>\n",
" <td>3.501880</td>\n",
" <td>2.190855</td>\n",
" <td>2.171047</td>\n",
" <td>2.630127</td>\n",
" <td>1.728294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-20</th>\n",
" <td>3.456951</td>\n",
" <td>2.166607</td>\n",
" <td>2.160854</td>\n",
" <td>2.625247</td>\n",
" <td>1.715640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-21</th>\n",
" <td>3.467079</td>\n",
" <td>2.184364</td>\n",
" <td>2.160229</td>\n",
" <td>2.648830</td>\n",
" <td>1.731874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-22</th>\n",
" <td>3.426566</td>\n",
" <td>2.149942</td>\n",
" <td>2.135738</td>\n",
" <td>2.614167</td>\n",
" <td>1.716057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-23</th>\n",
" <td>3.488376</td>\n",
" <td>2.170629</td>\n",
" <td>2.180690</td>\n",
" <td>2.654624</td>\n",
" <td>1.734663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-26</th>\n",
" <td>3.498764</td>\n",
" <td>2.214888</td>\n",
" <td>2.190171</td>\n",
" <td>2.658690</td>\n",
" <td>1.738284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-27</th>\n",
" <td>3.490193</td>\n",
" <td>2.220365</td>\n",
" <td>2.172204</td>\n",
" <td>2.662960</td>\n",
" <td>1.737910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-28</th>\n",
" <td>3.469157</td>\n",
" <td>2.247049</td>\n",
" <td>2.236735</td>\n",
" <td>2.587636</td>\n",
" <td>1.737410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-29</th>\n",
" <td>3.466560</td>\n",
" <td>2.255372</td>\n",
" <td>2.268707</td>\n",
" <td>2.566798</td>\n",
" <td>1.748482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-30</th>\n",
" <td>3.414100</td>\n",
" <td>2.252844</td>\n",
" <td>2.231482</td>\n",
" <td>2.563443</td>\n",
" <td>1.736994</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AAPL AMZN GOOGL MSFT SPY\n",
"Date \n",
"2021-04-01 3.194388 2.053758 2.019361 2.463520 1.667522\n",
"2021-04-05 3.269703 2.096464 2.103918 2.531830 1.691456\n",
"2021-04-06 3.277754 2.094573 2.094721 2.519530 1.690457\n",
"2021-04-07 3.321644 2.130678 2.122947 2.540267 1.692414\n",
"2021-04-08 3.385532 2.143614 2.133756 2.574320 1.700447\n",
"2021-04-09 3.454094 2.190978 2.152947 2.600749 1.712810\n",
"2021-04-12 3.408386 2.195649 2.128247 2.601359 1.713434\n",
"2021-04-13 3.491232 2.209040 2.137549 2.627585 1.718512\n",
"2021-04-14 3.428903 2.165509 2.125678 2.598106 1.712644\n",
"2021-04-15 3.493050 2.195455 2.166771 2.637852 1.731041\n",
"2021-04-16 3.484220 2.208676 2.164400 2.650457 1.736827\n",
"2021-04-19 3.501880 2.190855 2.171047 2.630127 1.728294\n",
"2021-04-20 3.456951 2.166607 2.160854 2.625247 1.715640\n",
"2021-04-21 3.467079 2.184364 2.160229 2.648830 1.731874\n",
"2021-04-22 3.426566 2.149942 2.135738 2.614167 1.716057\n",
"2021-04-23 3.488376 2.170629 2.180690 2.654624 1.734663\n",
"2021-04-26 3.498764 2.214888 2.190171 2.658690 1.738284\n",
"2021-04-27 3.490193 2.220365 2.172204 2.662960 1.737910\n",
"2021-04-28 3.469157 2.247049 2.236735 2.587636 1.737410\n",
"2021-04-29 3.466560 2.255372 2.268707 2.566798 1.748482\n",
"2021-04-30 3.414100 2.252844 2.231482 2.563443 1.736994"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[start:end]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aJWvXHmlcGtV",
"outputId": "7564cf85-46c9-482b-e30c-895d888a17c3"
},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',\n",
" '2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',\n",
" '2019-09-30', '2019-10-31', '2019-11-30', '2019-12-31',\n",
" '2020-01-31', '2020-02-29', '2020-03-31', '2020-04-30',\n",
" '2020-05-31', '2020-06-30', '2020-07-31', '2020-08-31',\n",
" '2020-09-30', '2020-10-31', '2020-11-30', '2020-12-31',\n",
" '2021-01-31', '2021-02-28', '2021-03-31', '2021-04-30',\n",
" '2021-05-31', '2021-06-30', '2021-07-31', '2021-08-31'],\n",
" dtype='datetime64[ns]', freq='M')"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.date_range(start='1/1/2019', end='08/31/2021', freq='M')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 425
},
"id": "Q1KZmgV4cVGe",
"outputId": "6c89eb55-ca9f-4df1-ffce-dfb308d16ff4"
},
"outputs": [
{
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" <tbody>\n",
" <tr>\n",
" <th>2019-03-31</th>\n",
" <td>1.240671</td>\n",
" <td>1.182005</td>\n",
" <td>1.172043</td>\n",
" <td>1.193962</td>\n",
" <td>1.143113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-30</th>\n",
" <td>1.346619</td>\n",
" <td>1.275045</td>\n",
" <td>1.228998</td>\n",
" <td>1.373424</td>\n",
" <td>1.187797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-09-30</th>\n",
" <td>1.435250</td>\n",
" <td>1.313073</td>\n",
" <td>1.181344</td>\n",
" <td>1.410902</td>\n",
" <td>1.218385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-31</th>\n",
" <td>1.887424</td>\n",
" <td>1.214842</td>\n",
" <td>1.291833</td>\n",
" <td>1.595237</td>\n",
" <td>1.315273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-03-31</th>\n",
" <td>2.108057</td>\n",
" <td>1.410030</td>\n",
" <td>1.445813</td>\n",
" <td>1.893692</td>\n",
" <td>1.377995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-30</th>\n",
" <td>2.367841</td>\n",
" <td>1.796086</td>\n",
" <td>1.388762</td>\n",
" <td>2.053599</td>\n",
" <td>1.324073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-09-30</th>\n",
" <td>3.473547</td>\n",
" <td>2.294446</td>\n",
" <td>1.628352</td>\n",
" <td>2.343208</td>\n",
" <td>1.471860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-12-31</th>\n",
" <td>3.544629</td>\n",
" <td>2.237387</td>\n",
" <td>1.730354</td>\n",
" <td>2.281494</td>\n",
" <td>1.551179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-31</th>\n",
" <td>3.712409</td>\n",
" <td>2.196046</td>\n",
" <td>2.008780</td>\n",
" <td>2.484634</td>\n",
" <td>1.649707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-30</th>\n",
" <td>3.562980</td>\n",
" <td>2.277546</td>\n",
" <td>2.323662</td>\n",
" <td>2.765187</td>\n",
" <td>1.787611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-30</th>\n",
" <td>4.082358</td>\n",
" <td>2.424363</td>\n",
" <td>2.753736</td>\n",
" <td>3.115720</td>\n",
" <td>1.892556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AAPL AMZN GOOGL MSFT SPY\n",
"Date \n",
"2019-03-31 1.240671 1.182005 1.172043 1.193962 1.143113\n",
"2019-06-30 1.346619 1.275045 1.228998 1.373424 1.187797\n",
"2019-09-30 1.435250 1.313073 1.181344 1.410902 1.218385\n",
"2019-12-31 1.887424 1.214842 1.291833 1.595237 1.315273\n",
"2020-03-31 2.108057 1.410030 1.445813 1.893692 1.377995\n",
"2020-06-30 2.367841 1.796086 1.388762 2.053599 1.324073\n",
"2020-09-30 3.473547 2.294446 1.628352 2.343208 1.471860\n",
"2020-12-31 3.544629 2.237387 1.730354 2.281494 1.551179\n",
"2021-03-31 3.712409 2.196046 2.008780 2.484634 1.649707\n",
"2021-06-30 3.562980 2.277546 2.323662 2.765187 1.787611\n",
"2021-09-30 4.082358 2.424363 2.753736 3.115720 1.892556"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.resample(rule='Q').max()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "50HsfFt6TFSE"
},
"source": [
"### Sorting & Filtering on Tabular Data\n",
"To highlight filtering in DataFrames, we'll use a dataset with a bunch of different columns/series of different types. This data was pulled directly from the enDAQ cloud API off some example recording files."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "PI9EYy7JSgeo",
"outputId": "6936608c-279b-44e3-e0ad-f6faeff55be9"
},
"outputs": [
{
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" <th>samplePeakStartTime</th>\n",
" <th>displacementRMSFull</th>\n",
" <th>psuedoVelocityPeakFull</th>\n",
" <th>accelerationPeakFull</th>\n",
" <th>psdResultantOctave</th>\n",
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" <td>172.704559</td>\n",
" <td>1575800071</td>\n",
" <td>1632515146</td>\n",
" <td>1632515146</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>1.535</td>\n",
" <td>NaN</td>\n",
" <td>98.733</td>\n",
" <td>1265.964</td>\n",
" <td>0.040</td>\n",
" <td>86.595</td>\n",
" <td>1.105</td>\n",
" <td>[0. 0. 0. 0. 0. 0. 0. 0. ...</td>\n",
" <td>[0.035 0.024 0.007 ... 0.067 0.031 0.022]</td>\n",
" <td>20.133</td>\n",
" <td>3996.0</td>\n",
" <td>[0. 0. 0.001 0. 0.001 0. 0.001 0.0...</td>\n",
" <td>NaN</td>\n",
" <td>0.945</td>\n",
" <td>[53.648 76.9 43.779 44.217 39.351 11.396 9....</td>\n",
" <td>0.082</td>\n",
" <td>[ 0. 0. 0. ... nan nan nan]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>['Shock Severity: Very Low', 'Acceleration Sev...</td>\n",
" <td>5c5592b4-2dbc-3124-be8f-c7179eecda49</td>\n",
" <td>10118</td>\n",
" <td>Bolted-1632515144.ide</td>\n",
" <td>6149229</td>\n",
" <td>29.396118</td>\n",
" <td>1619041447</td>\n",
" <td>1632515144</td>\n",
" <td>1632515144</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>14.101</td>\n",
" <td>NaN</td>\n",
" <td>99.652</td>\n",
" <td>17.445</td>\n",
" <td>0.154</td>\n",
" <td>148.276</td>\n",
" <td>15.343</td>\n",
" <td>[0.001 0.003 0.004 0.064 0.106 0.102 0.103 0.1...</td>\n",
" <td>[5.283 5.944 5.19 ... 0.356 1.079 1.099]</td>\n",
" <td>23.172</td>\n",
" <td>20000.0</td>\n",
" <td>[0.001 0.004 0.004 0.128 0.125 0.119 0.14 0.1...</td>\n",
" <td>25.507</td>\n",
" <td>NaN</td>\n",
" <td>[ 26.338 27.636 64.097 102.733 107.863 124.6...</td>\n",
" <td>2.398</td>\n",
" <td>[0. 0.001 0.002 ... 0.002 0.001 0.001]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>['Shock Severity: Very Low', 'Acceleration Sev...</td>\n",
" <td>ef832e45-50fa-38b4-8f2c-91769f7cef6e</td>\n",
" <td>10309</td>\n",
" <td>RMI-2000-1632515143.ide</td>\n",
" <td>5909632</td>\n",
" <td>60.250855</td>\n",
" <td>24</td>\n",
" <td>1632515143</td>\n",
" <td>1632515143</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>1.247</td>\n",
" <td>NaN</td>\n",
" <td>100.467</td>\n",
" <td>40.383</td>\n",
" <td>0.005</td>\n",
" <td>17.287</td>\n",
" <td>0.332</td>\n",
" <td>[0. 0. 0. 0. 0.001 0.002 0.002 0.0...</td>\n",
" <td>[0.13 0.118 0.1 ... 0.105 0.1 0.066]</td>\n",
" <td>21.806</td>\n",
" <td>4012.0</td>\n",
" <td>[0. 0. 0. 0. 0.008 0.01 0.019 0.0...</td>\n",
" <td>1.754</td>\n",
" <td>1.557</td>\n",
" <td>[ 0.854 1.159 1.662 1.815 3.022 6.139 10....</td>\n",
" <td>0.079</td>\n",
" <td>[ 0. 0. 0. ... nan nan nan]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>['Shock Severity: Very Low', 'Acceleration Sev...</td>\n",
" <td>0396df6a-56b0-3e43-8e46-e1d0d7485ff5</td>\n",
" <td>9295</td>\n",
" <td>Seat-Base_21-1632515142.ide</td>\n",
" <td>5248836</td>\n",
" <td>83.092255</td>\n",
" <td>1575800210</td>\n",
" <td>1632515143</td>\n",
" <td>1632515143</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>7.318</td>\n",
" <td>NaN</td>\n",
" <td>98.930</td>\n",
" <td>1588.299</td>\n",
" <td>0.190</td>\n",
" <td>251.009</td>\n",
" <td>1.085</td>\n",
" <td>[0.002 0.007 0.008 0.005 0.002 0.005 0.002 0.0...</td>\n",
" <td>[0.084 0.137 0.178 ... 0.347 0.324 0.286]</td>\n",
" <td>17.820</td>\n",
" <td>4046.0</td>\n",
" <td>[0.002 0.014 0.016 0.013 0.003 0.031 0.003 0.0...</td>\n",
" <td>NaN</td>\n",
" <td>2.666</td>\n",
" <td>[ 74.73 79.453 101.006 151.429 73.92 53.4...</td>\n",
" <td>0.130</td>\n",
" <td>[0.001 0.002 0.002 ... nan nan nan]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>['Drive-Test', 'Vibration Severity: Very Low',...</td>\n",
" <td>5e293d65-c9e0-3aa1-9098-c9762e8fbc86</td>\n",
" <td>11046</td>\n",
" <td>Drive-Home_01-1632515142.ide</td>\n",
" <td>3632799</td>\n",
" <td>61.755371</td>\n",
" <td>1616178955</td>\n",
" <td>1632515142</td>\n",
" <td>1632515142</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>1.081</td>\n",
" <td>NaN</td>\n",
" <td>100.284</td>\n",
" <td>22.543</td>\n",
" <td>0.023</td>\n",
" <td>40.197</td>\n",
" <td>0.479</td>\n",
" <td>[0. 0. 0. 0. 0.001 0. 0. 0. ...</td>\n",
" <td>[0.001 0.003 0.002 ... 0.011 0.008 0.006]</td>\n",
" <td>29.061</td>\n",
" <td>4013.0</td>\n",
" <td>[0. 0. 0. 0. 0.007 0. 0. 0. ...</td>\n",
" <td>16.243</td>\n",
" <td>0.363</td>\n",
" <td>[ 2.336 7.82 19.078 10.384 16.975 38.326 18....</td>\n",
" <td>0.021</td>\n",
" <td>[ 0. 0. 0. ... nan nan nan]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>['Acceleration Severity: High', 'Shock Severit...</td>\n",
" <td>cd81c850-9e22-3b20-b570-7411e7a144cc</td>\n",
" <td>0</td>\n",
" <td>HiTest-Shock-1632515141.ide</td>\n",
" <td>2655894</td>\n",
" <td>20.331848</td>\n",
" <td>1543936974</td>\n",
" <td>1632515141</td>\n",
" <td>1632515141</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>167.835</td>\n",
" <td>NaN</td>\n",
" <td>101.126</td>\n",
" <td>327.392</td>\n",
" <td>4.055</td>\n",
" <td>6058.093</td>\n",
" <td>619.178</td>\n",
" <td>[0.304 0.486 0.399 0.454 0.347 0.178 0.185 0.1...</td>\n",
" <td>[ 3.088 3.019 2.893 ... 73.794 40.005 24.303]</td>\n",
" <td>9.538</td>\n",
" <td>19997.0</td>\n",
" <td>[ 0.304 0.537 0.479 0.811 0.624 0.554 0....</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[1378.444 2470.941 4368.78 5033.327 5814.49 ...</td>\n",
" <td>11.645</td>\n",
" <td>[0.157 0.304 0.42 ... 0.001 0.01 0.013]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>['Vibration Severity: High', 'Acceleration Sev...</td>\n",
" <td>0931a23d-3515-3d11-bf97-bb9b2c7863be</td>\n",
" <td>11162</td>\n",
" <td>Calibration-Shake-1632515140.IDE</td>\n",
" <td>2218130</td>\n",
" <td>27.882690</td>\n",
" <td>1621278970</td>\n",
" <td>1632515140</td>\n",
" <td>1632515140</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>46.346</td>\n",
" <td>NaN</td>\n",
" <td>102.251</td>\n",
" <td>8.460</td>\n",
" <td>0.617</td>\n",
" <td>1142.282</td>\n",
" <td>8.783</td>\n",
" <td>[2.100e-02 7.400e-02 7.500e-02 4.900e-02 5.500...</td>\n",
" <td>[7.486 7.496 7.137 ... 7.997 8.294 7.806]</td>\n",
" <td>24.545</td>\n",
" <td>5000.0</td>\n",
" <td>[2.10000e-02 9.00000e-02 8.60000e-02 6.30000e-...</td>\n",
" <td>NaN</td>\n",
" <td>0.166</td>\n",
" <td>[ 90.703 198.651 288.078 183.492 126.417 108.4...</td>\n",
" <td>2.712</td>\n",
" <td>[0.007 0.021 0.055 ... nan nan nan]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>['Shock Severity: Very Low', 'Acceleration Sev...</td>\n",
" <td>c1571d10-2329-3aea-aa5d-223733f6336b</td>\n",
" <td>10916</td>\n",
" <td>FUSE_HSTAB_000005-1632515139.ide</td>\n",
" <td>537562</td>\n",
" <td>18.491791</td>\n",
" <td>1619108004</td>\n",
" <td>1632515140</td>\n",
" <td>1632515140</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>1.504</td>\n",
" <td>NaN</td>\n",
" <td>90.706</td>\n",
" <td>12.519</td>\n",
" <td>0.036</td>\n",
" <td>53.375</td>\n",
" <td>0.202</td>\n",
" <td>[ 0. 0. 0. 0. 0. 0. 0. nan nan nan nan n...</td>\n",
" <td>[0.001 0.001 0.001 ... 0.002 0.006 0.006]</td>\n",
" <td>18.874</td>\n",
" <td>504.0</td>\n",
" <td>[0. 0. 0.001 0.001 0. 0. 0. n...</td>\n",
" <td>NaN</td>\n",
" <td>0.749</td>\n",
" <td>[ 2.617 6.761 17.326 34.067 28.721 9.469 15....</td>\n",
" <td>0.011</td>\n",
" <td>[ 0. 0. 0. ... nan nan nan]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>['Shock Severity: Medium', 'Acceleration Sever...</td>\n",
" <td>8ce137ff-904e-314b-81ff-ff2cea279db2</td>\n",
" <td>9874</td>\n",
" <td>Coffee_002-1631722736.IDE</td>\n",
" <td>60959516</td>\n",
" <td>769.299896</td>\n",
" <td>952113744</td>\n",
" <td>1631722736</td>\n",
" <td>1631722736</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>5.606</td>\n",
" <td>NaN</td>\n",
" <td>100.339</td>\n",
" <td>NaN</td>\n",
" <td>0.104</td>\n",
" <td>1338.396</td>\n",
" <td>2.698</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>24.540</td>\n",
" <td>5000.0</td>\n",
" <td>[]</td>\n",
" <td>NaN</td>\n",
" <td>0.082</td>\n",
" <td>[]</td>\n",
" <td>0.059</td>\n",
" <td>[]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1</td>\n",
" <td>['Vibration Severity: High', 'Shock Severity: ...</td>\n",
" <td>9d7383fb-40d6-34c1-9d0c-37f11c29bff5</td>\n",
" <td>11456</td>\n",
" <td>100_Joules_900_lbs-1629315313.ide</td>\n",
" <td>1596714</td>\n",
" <td>20.200623</td>\n",
" <td>1627327315</td>\n",
" <td>1629315313</td>\n",
" <td>1629315313</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>53.875</td>\n",
" <td>NaN</td>\n",
" <td>98.751</td>\n",
" <td>5.643</td>\n",
" <td>1.053</td>\n",
" <td>2961.256</td>\n",
" <td>218.634</td>\n",
" <td>[0.071 0.201 0.293 0.686 1.565 1.028 0.856 0.9...</td>\n",
" <td>[0.304 0.274 0.296 ... 1.513 1.313 0.652]</td>\n",
" <td>24.180</td>\n",
" <td>5000.0</td>\n",
" <td>[0.071 0.236 0.311 1.148 2.036 1.791 1.498 2.7...</td>\n",
" <td>NaN</td>\n",
" <td>24.471</td>\n",
" <td>[ 194.741 361.14 563.488 855.34 1712.229 ...</td>\n",
" <td>2.877</td>\n",
" <td>[0.02 0.071 0.138 ... nan nan nan]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2</td>\n",
" <td>['Acceleration Severity: High', 'Shock Severit...</td>\n",
" <td>5f35ed7f-ff55-36a3-896d-276f06a0e340</td>\n",
" <td>11456</td>\n",
" <td>50_Joules_900_lbs-1629315312.ide</td>\n",
" <td>1597750</td>\n",
" <td>20.201752</td>\n",
" <td>1627329399</td>\n",
" <td>1629315312</td>\n",
" <td>1629315312</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>54.507</td>\n",
" <td>NaN</td>\n",
" <td>98.745</td>\n",
" <td>9.875</td>\n",
" <td>1.066</td>\n",
" <td>2907.650</td>\n",
" <td>231.212</td>\n",
" <td>[0.074 0.196 0.327 0.874 1.521 0.917 0.478 0.7...</td>\n",
" <td>[0.304 0.21 0.098 ... 0.693 1.418 0.932]</td>\n",
" <td>24.175</td>\n",
" <td>5000.0</td>\n",
" <td>[0.074 0.232 0.392 1.258 2.045 2.049 1.143 2.3...</td>\n",
" <td>NaN</td>\n",
" <td>15.575</td>\n",
" <td>[ 242.141 388.517 736.981 1070.284 1843.722 ...</td>\n",
" <td>2.423</td>\n",
" <td>[0.021 0.074 0.137 ... nan nan nan]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>3</td>\n",
" <td>['Shock Severity: Very Low', 'Vibration Severi...</td>\n",
" <td>f7240576-47c6-34b8-bdce-3fe11bb5f1c6</td>\n",
" <td>11046</td>\n",
" <td>Drive-Home_07-1626805222.ide</td>\n",
" <td>36225758</td>\n",
" <td>634.732056</td>\n",
" <td>1616182557</td>\n",
" <td>1626805222</td>\n",
" <td>1626805222</td>\n",
" <td>NaN</td>\n",
" <td>42.4679965,-71.12926019999999</td>\n",
" <td>6.117</td>\n",
" <td>41.425</td>\n",
" <td>101.988</td>\n",
" <td>NaN</td>\n",
" <td>0.135</td>\n",
" <td>356.128</td>\n",
" <td>23.805</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>28.832</td>\n",
" <td>4012.0</td>\n",
" <td>[]</td>\n",
" <td>16.277</td>\n",
" <td>4.185</td>\n",
" <td>[]</td>\n",
" <td>0.097</td>\n",
" <td>[]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>4</td>\n",
" <td>['Big-Mining']</td>\n",
" <td>c2c234cc-8055-3fba-ad8b-446c4b6f85dc</td>\n",
" <td>5120</td>\n",
" <td>Mining-SSX28803_06-1626457584.IDE</td>\n",
" <td>402920686</td>\n",
" <td>3238.119202</td>\n",
" <td>1536953304</td>\n",
" <td>1626457585</td>\n",
" <td>1626457585</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[]</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[]</td>\n",
" <td>NaN</td>\n",
" <td>[]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>5</td>\n",
" <td>['Shock Severity: Medium', 'Acceleration Sever...</td>\n",
" <td>69fd99f8-3d85-38ec-9f5d-67e9d7b7e868</td>\n",
" <td>10030</td>\n",
" <td>200922_Moto_Max_Run5_Control_Larry-1626297441.ide</td>\n",
" <td>4780893</td>\n",
" <td>99.325134</td>\n",
" <td>1600818455</td>\n",
" <td>1626297442</td>\n",
" <td>1626297442</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>55.569</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.573</td>\n",
" <td>1.060</td>\n",
" <td>1280.349</td>\n",
" <td>29.864</td>\n",
" <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
" <td>[ 3.34 13.586 10.13 ... 7.737 8.978 8.672]</td>\n",
" <td>NaN</td>\n",
" <td>4014.0</td>\n",
" <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[165.983 278.352 717.331 950.513 697.421 358.6...</td>\n",
" <td>3.528</td>\n",
" <td>[0.006 0.016 0.04 ... nan nan nan]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>6</td>\n",
" <td>['Ford']</td>\n",
" <td>f38361de-30a7-3dda-aec4-e87a67d1ecbb</td>\n",
" <td>9695</td>\n",
" <td>ford_f150-1626296561.ide</td>\n",
" <td>96097059</td>\n",
" <td>1207.678344</td>\n",
" <td>1584142508</td>\n",
" <td>1626296561</td>\n",
" <td>1626296561</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[]</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[]</td>\n",
" <td>NaN</td>\n",
" <td>[]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>7</td>\n",
" <td>['Shock Severity: High', 'Vibration Severity: ...</td>\n",
" <td>40718e01-f422-3926-a22d-acf6daf1182b</td>\n",
" <td>7530</td>\n",
" <td>Motorcycle-Car-Crash-1626277852.ide</td>\n",
" <td>10489262</td>\n",
" <td>151.069336</td>\n",
" <td>1562173372</td>\n",
" <td>1626277852</td>\n",
" <td>1626277852</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>143.437</td>\n",
" <td>NaN</td>\n",
" <td>100.363</td>\n",
" <td>1137.342</td>\n",
" <td>3.988</td>\n",
" <td>12831.590</td>\n",
" <td>480.737</td>\n",
" <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
" <td>[3.371 3.641 3.649 ... 6.168 7.463 7.04 ]</td>\n",
" <td>26.989</td>\n",
" <td>10001.0</td>\n",
" <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
" <td>NaN</td>\n",
" <td>19.810</td>\n",
" <td>[ 2723.153 5977.212 11406.291 12031.397 7337...</td>\n",
" <td>1.732</td>\n",
" <td>[2.250e-01 8.390e-01 2.025e+00 ... 1.000e-03 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>8</td>\n",
" <td>['Shock Severity: Very Low', 'Surgical', 'Vibr...</td>\n",
" <td>ee6dc613-d422-347c-8119-f122a55ac81a</td>\n",
" <td>11071</td>\n",
" <td>surgical-instrument-1625829182.ide</td>\n",
" <td>541994</td>\n",
" <td>6.951172</td>\n",
" <td>1619110390</td>\n",
" <td>1625829183</td>\n",
" <td>1625829183</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>24.418</td>\n",
" <td>NaN</td>\n",
" <td>99.879</td>\n",
" <td>4.020</td>\n",
" <td>0.242</td>\n",
" <td>387.312</td>\n",
" <td>5.739</td>\n",
" <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
" <td>[0.667 0.549 1.05 ... 1.109 2.86 3.99 ]</td>\n",
" <td>21.889</td>\n",
" <td>5000.0</td>\n",
" <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
" <td>NaN</td>\n",
" <td>4.647</td>\n",
" <td>[ 88.953 171.73 104.303 71.184 58.883 72.4...</td>\n",
" <td>1.568</td>\n",
" <td>[0.001 0.001 0.003 ... nan nan nan]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>9</td>\n",
" <td>['Acceleration Severity: High', 'Shock Severit...</td>\n",
" <td>f642d81c-7fe7-37fd-ab75-d493fc0556db</td>\n",
" <td>9680</td>\n",
" <td>LOC__3__DAQ41551_11_01_02-1625170795.IDE</td>\n",
" <td>2343292</td>\n",
" <td>28.456818</td>\n",
" <td>1616645179</td>\n",
" <td>1625170795</td>\n",
" <td>1625170795</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>372.049</td>\n",
" <td>NaN</td>\n",
" <td>105.682</td>\n",
" <td>2045.370</td>\n",
" <td>9.580</td>\n",
" <td>8907.949</td>\n",
" <td>622.040</td>\n",
" <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
" <td>[ 32.679 24.925 30.95 ... 106.511 27.715 ...</td>\n",
" <td>33.452</td>\n",
" <td>20010.0</td>\n",
" <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
" <td>NaN</td>\n",
" <td>286.217</td>\n",
" <td>[2333.993 5629.021 6417.48 5802.895 3692.838 ...</td>\n",
" <td>94.197</td>\n",
" <td>[ 2.09 8.631 18.196 ... 69.565 59.225 60.801]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>10</td>\n",
" <td>['Vibration Severity: High', 'Shock Severity: ...</td>\n",
" <td>0c0e92ae-214a-32bd-a5bb-5e1801da06b2</td>\n",
" <td>9680</td>\n",
" <td>LOC__4__DAQ41551_15_05-1625170794.IDE</td>\n",
" <td>6927958</td>\n",
" <td>64.486054</td>\n",
" <td>1616646130</td>\n",
" <td>1625170794</td>\n",
" <td>1625170794</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>148.591</td>\n",
" <td>NaN</td>\n",
" <td>105.750</td>\n",
" <td>3004.357</td>\n",
" <td>2.615</td>\n",
" <td>2153.020</td>\n",
" <td>585.863</td>\n",
" <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
" <td>[14.223 14.903 24.949 ... 11.482 21.319 32.766]</td>\n",
" <td>32.202</td>\n",
" <td>19992.0</td>\n",
" <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
" <td>NaN</td>\n",
" <td>277.654</td>\n",
" <td>[ 378.188 748.336 1054.232 1115.369 756.905 ...</td>\n",
" <td>46.528</td>\n",
" <td>[ 0.072 0.302 0.816 ... 52.226 45.804 52.81 ]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>11</td>\n",
" <td>['Vibration Severity: High', 'Mining-Hammer', ...</td>\n",
" <td>99ac47a8-63c2-38a4-8d92-508698eb3752</td>\n",
" <td>9680</td>\n",
" <td>LOC__6__DAQ41551_25_01-1625170793.IDE</td>\n",
" <td>8664238</td>\n",
" <td>63.878937</td>\n",
" <td>1616648007</td>\n",
" <td>1625170793</td>\n",
" <td>1625170793</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>145.223</td>\n",
" <td>NaN</td>\n",
" <td>102.875</td>\n",
" <td>4867.941</td>\n",
" <td>3.088</td>\n",
" <td>2357.599</td>\n",
" <td>564.966</td>\n",
" <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
" <td>[ 66.83 54.723 63.536 ... 109.259 85.341 ...</td>\n",
" <td>26.031</td>\n",
" <td>19992.0</td>\n",
" <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
" <td>NaN</td>\n",
" <td>245.929</td>\n",
" <td>[ 780.577 1168.663 1851.417 1094.945 793.122 ...</td>\n",
" <td>54.408</td>\n",
" <td>[ 0.162 0.587 1.495 ... 45.639 43.86 40.177]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>12</td>\n",
" <td>['Mining-Hammer', 'Vibration Severity: High', ...</td>\n",
" <td>ebf31fb2-9d36-37de-999c-3edb01f17fb2</td>\n",
" <td>9680</td>\n",
" <td>LOC__2__DAQ38060_06_03_05-1625170793.IDE</td>\n",
" <td>1519172</td>\n",
" <td>27.057647</td>\n",
" <td>1616640862</td>\n",
" <td>1625170793</td>\n",
" <td>1625170793</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>323.287</td>\n",
" <td>NaN</td>\n",
" <td>104.473</td>\n",
" <td>1227.975</td>\n",
" <td>3.144</td>\n",
" <td>5845.241</td>\n",
" <td>995.670</td>\n",
" <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
" <td>[ 33.294 29.392 19.99 ... 175.123 162.043 ...</td>\n",
" <td>25.616</td>\n",
" <td>19998.0</td>\n",
" <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
" <td>NaN</td>\n",
" <td>266.815</td>\n",
" <td>[1073.684 940.873 1063.151 957.568 975.826 ...</td>\n",
" <td>131.087</td>\n",
" <td>[ 0.371 0.409 0.787 ... 27.577 24.778 37.822]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>13</td>\n",
" <td>['Shock Severity: Very Low', 'Vibration Severi...</td>\n",
" <td>e39fe506-f39a-3301-866c-9da6a30f9577</td>\n",
" <td>9695</td>\n",
" <td>Tilt_000000-1625156721.IDE</td>\n",
" <td>719403</td>\n",
" <td>23.355163</td>\n",
" <td>1625156461</td>\n",
" <td>1625156722</td>\n",
" <td>1625156722</td>\n",
" <td>NaN</td>\n",
" <td>nan,nan</td>\n",
" <td>11.042</td>\n",
" <td>NaN</td>\n",
" <td>99.510</td>\n",
" <td>22.966</td>\n",
" <td>0.345</td>\n",
" <td>330.946</td>\n",
" <td>0.378</td>\n",
" <td>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</td>\n",
" <td>[0.033 0.029 0.03 ... nan nan nan]</td>\n",
" <td>26.410</td>\n",
" <td>5000.0</td>\n",
" <td>[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n [0...</td>\n",
" <td>NaN</td>\n",
" <td>11.933</td>\n",
" <td>[142.046 273.02 216.774 129.288 54.011 24.0...</td>\n",
" <td>0.044</td>\n",
" <td>[0.008 0.02 0.03 ... nan nan nan]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 ... psdResultant1Hz\n",
"0 0 ... [0. 0. 0. ... 0. 0. 0.]\n",
"1 1 ... [ 0. 0. 0. ... nan nan nan]\n",
"2 2 ... [0. 0.001 0.002 ... 0.002 0.001 0.001]\n",
"3 3 ... [ 0. 0. 0. ... nan nan nan]\n",
"4 4 ... [0.001 0.002 0.002 ... nan nan nan]\n",
"5 5 ... [ 0. 0. 0. ... nan nan nan]\n",
"6 6 ... [0.157 0.304 0.42 ... 0.001 0.01 0.013]\n",
"7 7 ... [0.007 0.021 0.055 ... nan nan nan]\n",
"8 8 ... [ 0. 0. 0. ... nan nan nan]\n",
"9 0 ... []\n",
"10 1 ... [0.02 0.071 0.138 ... nan nan nan]\n",
"11 2 ... [0.021 0.074 0.137 ... nan nan nan]\n",
"12 3 ... []\n",
"13 4 ... []\n",
"14 5 ... [0.006 0.016 0.04 ... nan nan nan]\n",
"15 6 ... []\n",
"16 7 ... [2.250e-01 8.390e-01 2.025e+00 ... 1.000e-03 1...\n",
"17 8 ... [0.001 0.001 0.003 ... nan nan nan]\n",
"18 9 ... [ 2.09 8.631 18.196 ... 69.565 59.225 60.801]\n",
"19 10 ... [ 0.072 0.302 0.816 ... 52.226 45.804 52.81 ]\n",
"20 11 ... [ 0.162 0.587 1.495 ... 45.639 43.86 40.177]\n",
"21 12 ... [ 0.371 0.409 0.787 ... 27.577 24.778 37.822]\n",
"22 13 ... [0.008 0.02 0.03 ... nan nan nan]\n",
"\n",
"[23 rows x 29 columns]"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('https://info.endaq.com/hubfs/data/endaq-cloud-table.csv')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TrjLYbATgLDi",
"outputId": "7154d956-2df0-4a0b-a04c-b70ac0be95fb"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Unnamed: 0', 'tags', 'id', 'serial_number_id', 'file_name',\n",
" 'file_size', 'recording_length', 'recording_ts', 'created_ts',\n",
" 'modified_ts', 'device', 'gpsLocationFull', 'velocityRMSFull',\n",
" 'gpsSpeedFull', 'pressureMeanFull', 'samplePeakStartTime',\n",
" 'displacementRMSFull', 'psuedoVelocityPeakFull', 'accelerationPeakFull',\n",
" 'psdResultantOctave', 'samplePeakWindow', 'temperatureMeanFull',\n",
" 'accelerometerSampleRateFull', 'psdPeakOctaves', 'microphonoeRMSFull',\n",
" 'gyroscopeRMSFull', 'pvssResultantOctave', 'accelerationRMSFull',\n",
" 'psdResultant1Hz'],\n",
" dtype='object')"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8y82HS2h8c0G"
},
"source": [
"There's a lot of data here! So we'll focus on just a handful of columns and convert the time in seconds to a datetime object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "6BUQuL9kgQ6z",
"outputId": "72f73913-1a57-4804-f69d-c92882d80b0b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: middle;\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>serial_number_id</th>\n",
" <th>file_name</th>\n",
" <th>file_size</th>\n",
" <th>recording_length</th>\n",
" <th>recording_ts</th>\n",
" <th>accelerationPeakFull</th>\n",
" <th>psuedoVelocityPeakFull</th>\n",
" <th>accelerationRMSFull</th>\n",
" <th>velocityRMSFull</th>\n",
" <th>displacementRMSFull</th>\n",
" <th>pressureMeanFull</th>\n",
" <th>temperatureMeanFull</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>11456</td>\n",
" <td>50_Joules_900_lbs-1629315312.ide</td>\n",
" <td>1597750</td>\n",
" <td>20.201752</td>\n",
" <td>2021-07-26 19:56:39</td>\n",
" <td>231.212</td>\n",
" <td>2907.650</td>\n",
" <td>2.423</td>\n",
" <td>54.507</td>\n",
" <td>1.066</td>\n",
" <td>98.745</td>\n",
" <td>24.175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11456</td>\n",
" <td>100_Joules_900_lbs-1629315313.ide</td>\n",
" <td>1596714</td>\n",
" <td>20.200623</td>\n",
" <td>2021-07-26 19:21:55</td>\n",
" <td>218.634</td>\n",
" <td>2961.256</td>\n",
" <td>2.877</td>\n",
" <td>53.875</td>\n",
" <td>1.053</td>\n",
" <td>98.751</td>\n",
" <td>24.180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>9695</td>\n",
" <td>Tilt_000000-1625156721.IDE</td>\n",
" <td>719403</td>\n",
" <td>23.355163</td>\n",
" <td>2021-07-01 16:21:01</td>\n",
" <td>0.378</td>\n",
" <td>330.946</td>\n",
" <td>0.044</td>\n",
" <td>11.042</td>\n",
" <td>0.345</td>\n",
" <td>99.510</td>\n",
" <td>26.410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11162</td>\n",
" <td>Calibration-Shake-1632515140.IDE</td>\n",
" <td>2218130</td>\n",
" <td>27.882690</td>\n",
" <td>2021-05-17 19:16:10</td>\n",
" <td>8.783</td>\n",
" <td>1142.282</td>\n",
" <td>2.712</td>\n",
" <td>46.346</td>\n",
" <td>0.617</td>\n",
" <td>102.251</td>\n",
" <td>24.545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>11071</td>\n",
" <td>surgical-instrument-1625829182.ide</td>\n",
" <td>541994</td>\n",
" <td>6.951172</td>\n",
" <td>2021-04-22 16:53:10</td>\n",
" <td>5.739</td>\n",
" <td>387.312</td>\n",
" <td>1.568</td>\n",
" <td>24.418</td>\n",
" <td>0.242</td>\n",
" <td>99.879</td>\n",
" <td>21.889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10916</td>\n",
" <td>FUSE_HSTAB_000005-1632515139.ide</td>\n",
" <td>537562</td>\n",
" <td>18.491791</td>\n",
" <td>2021-04-22 16:13:24</td>\n",
" <td>0.202</td>\n",
" <td>53.375</td>\n",
" <td>0.011</td>\n",
" <td>1.504</td>\n",
" <td>0.036</td>\n",
" <td>90.706</td>\n",
" <td>18.874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10118</td>\n",
" <td>Bolted-1632515144.ide</td>\n",
" <td>6149229</td>\n",
" <td>29.396118</td>\n",
" <td>2021-04-21 21:44:07</td>\n",
" <td>15.343</td>\n",
" <td>148.276</td>\n",
" <td>2.398</td>\n",
" <td>14.101</td>\n",
" <td>0.154</td>\n",
" <td>99.652</td>\n",
" <td>23.172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>9680</td>\n",
" <td>LOC__6__DAQ41551_25_01-1625170793.IDE</td>\n",
" <td>8664238</td>\n",
" <td>63.878937</td>\n",
" <td>2021-03-25 04:53:27</td>\n",
" <td>564.966</td>\n",
" <td>2357.599</td>\n",
" <td>54.408</td>\n",
" <td>145.223</td>\n",
" <td>3.088</td>\n",
" <td>102.875</td>\n",
" <td>26.031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>9680</td>\n",
" <td>LOC__4__DAQ41551_15_05-1625170794.IDE</td>\n",
" <td>6927958</td>\n",
" <td>64.486054</td>\n",
" <td>2021-03-25 04:22:10</td>\n",
" <td>585.863</td>\n",
" <td>2153.020</td>\n",
" <td>46.528</td>\n",
" <td>148.591</td>\n",
" <td>2.615</td>\n",
" <td>105.750</td>\n",
" <td>32.202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>9680</td>\n",
" <td>LOC__3__DAQ41551_11_01_02-1625170795.IDE</td>\n",
" <td>2343292</td>\n",
" <td>28.456818</td>\n",
" <td>2021-03-25 04:06:19</td>\n",
" <td>622.040</td>\n",
" <td>8907.949</td>\n",
" <td>94.197</td>\n",
" <td>372.049</td>\n",
" <td>9.580</td>\n",
" <td>105.682</td>\n",
" <td>33.452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>9680</td>\n",
" <td>LOC__2__DAQ38060_06_03_05-1625170793.IDE</td>\n",
" <td>1519172</td>\n",
" <td>27.057647</td>\n",
" <td>2021-03-25 02:54:22</td>\n",
" <td>995.670</td>\n",
" <td>5845.241</td>\n",
" <td>131.087</td>\n",
" <td>323.287</td>\n",
" <td>3.144</td>\n",
" <td>104.473</td>\n",
" <td>25.616</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11046</td>\n",
" <td>Drive-Home_07-1626805222.ide</td>\n",
" <td>36225758</td>\n",
" <td>634.732056</td>\n",
" <td>2021-03-19 19:35:57</td>\n",
" <td>23.805</td>\n",
" <td>356.128</td>\n",
" <td>0.097</td>\n",
" <td>6.117</td>\n",
" <td>0.135</td>\n",
" <td>101.988</td>\n",
" <td>28.832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>11046</td>\n",
" <td>Drive-Home_01-1632515142.ide</td>\n",
" <td>3632799</td>\n",
" <td>61.755371</td>\n",
" <td>2021-03-19 18:35:55</td>\n",
" <td>0.479</td>\n",
" <td>40.197</td>\n",
" <td>0.021</td>\n",
" <td>1.081</td>\n",
" <td>0.023</td>\n",
" <td>100.284</td>\n",
" <td>29.061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>10030</td>\n",
" <td>200922_Moto_Max_Run5_Control_Larry-1626297441.ide</td>\n",
" <td>4780893</td>\n",
" <td>99.325134</td>\n",
" <td>2020-09-22 23:47:35</td>\n",
" <td>29.864</td>\n",
" <td>1280.349</td>\n",
" <td>3.528</td>\n",
" <td>55.569</td>\n",
" <td>1.060</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9695</td>\n",
" <td>train-passing-1632515146.ide</td>\n",
" <td>10492602</td>\n",
" <td>73.612335</td>\n",
" <td>2020-04-29 18:20:36</td>\n",
" <td>7.513</td>\n",
" <td>419.944</td>\n",
" <td>0.372</td>\n",
" <td>6.969</td>\n",
" <td>0.061</td>\n",
" <td>104.620</td>\n",
" <td>23.432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>9695</td>\n",
" <td>ford_f150-1626296561.ide</td>\n",
" <td>96097059</td>\n",
" <td>1207.678344</td>\n",
" <td>2020-03-13 23:35:08</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9295</td>\n",
" <td>Seat-Base_21-1632515142.ide</td>\n",
" <td>5248836</td>\n",
" <td>83.092255</td>\n",
" <td>2019-12-08 10:16:50</td>\n",
" <td>1.085</td>\n",
" <td>251.009</td>\n",
" <td>0.130</td>\n",
" <td>7.318</td>\n",
" <td>0.190</td>\n",
" <td>98.930</td>\n",
" <td>17.820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9316</td>\n",
" <td>Seat-Top_09-1632515145.ide</td>\n",
" <td>10491986</td>\n",
" <td>172.704559</td>\n",
" <td>2019-12-08 10:14:31</td>\n",
" <td>1.105</td>\n",
" <td>86.595</td>\n",
" <td>0.082</td>\n",
" <td>1.535</td>\n",
" <td>0.040</td>\n",
" <td>98.733</td>\n",
" <td>20.133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>7530</td>\n",
" <td>Motorcycle-Car-Crash-1626277852.ide</td>\n",
" <td>10489262</td>\n",
" <td>151.069336</td>\n",
" <td>2019-07-03 17:02:52</td>\n",
" <td>480.737</td>\n",
" <td>12831.590</td>\n",
" <td>1.732</td>\n",
" <td>143.437</td>\n",
" <td>3.988</td>\n",
" <td>100.363</td>\n",
" <td>26.989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>HiTest-Shock-1632515141.ide</td>\n",
" <td>2655894</td>\n",
" <td>20.331848</td>\n",
" <td>2018-12-04 15:22:54</td>\n",
" <td>619.178</td>\n",
" <td>6058.093</td>\n",
" <td>11.645</td>\n",
" <td>167.835</td>\n",
" <td>4.055</td>\n",
" <td>101.126</td>\n",
" <td>9.538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>5120</td>\n",
" <td>Mining-SSX28803_06-1626457584.IDE</td>\n",
" <td>402920686</td>\n",
" <td>3238.119202</td>\n",
" <td>2018-09-14 19:28:24</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9874</td>\n",
" <td>Coffee_002-1631722736.IDE</td>\n",
" <td>60959516</td>\n",
" <td>769.299896</td>\n",
" <td>2000-03-03 20:02:24</td>\n",
" <td>2.698</td>\n",
" <td>1338.396</td>\n",
" <td>0.059</td>\n",
" <td>5.606</td>\n",
" <td>0.104</td>\n",
" <td>100.339</td>\n",
" <td>24.540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10309</td>\n",
" <td>RMI-2000-1632515143.ide</td>\n",
" <td>5909632</td>\n",
" <td>60.250855</td>\n",
" <td>1970-01-01 00:00:24</td>\n",
" <td>0.332</td>\n",
" <td>17.287</td>\n",
" <td>0.079</td>\n",
" <td>1.247</td>\n",
" <td>0.005</td>\n",
" <td>100.467</td>\n",
" <td>21.806</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" serial_number_id ... temperatureMeanFull\n",
"11 11456 ... 24.175\n",
"10 11456 ... 24.180\n",
"22 9695 ... 26.410\n",
"7 11162 ... 24.545\n",
"17 11071 ... 21.889\n",
"8 10916 ... 18.874\n",
"2 10118 ... 23.172\n",
"20 9680 ... 26.031\n",
"19 9680 ... 32.202\n",
"18 9680 ... 33.452\n",
"21 9680 ... 25.616\n",
"12 11046 ... 28.832\n",
"5 11046 ... 29.061\n",
"14 10030 ... NaN\n",
"0 9695 ... 23.432\n",
"15 9695 ... NaN\n",
"4 9295 ... 17.820\n",
"1 9316 ... 20.133\n",
"16 7530 ... 26.989\n",
"6 0 ... 9.538\n",
"13 5120 ... NaN\n",
"9 9874 ... 24.540\n",
"3 10309 ... 21.806\n",
"\n",
"[23 rows x 12 columns]"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df[['serial_number_id', 'file_name', 'file_size', 'recording_length', 'recording_ts',\n",
" 'accelerationPeakFull', 'psuedoVelocityPeakFull', 'accelerationRMSFull',\n",
" 'velocityRMSFull', 'displacementRMSFull', 'pressureMeanFull', 'temperatureMeanFull']].copy()\n",
"\n",
"df['recording_ts'] = pd.to_datetime(df['recording_ts'], unit='s') \n",
"df = df.sort_values(by=['recording_ts'], ascending=False) \n",
"df "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5ZLSvcMZ8t6-"
},
"source": [
"Filtering is made simple with boolean expressions that can be combined. There is also a method to sort_values by columns/series."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 702
},
"id": "3oe0EgX0gyfz",
"outputId": "7008ef1a-9396-4183-c7c8-23f08a4b050f"
},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>serial_number_id</th>\n",
" <th>file_name</th>\n",
" <th>file_size</th>\n",
" <th>recording_length</th>\n",
" <th>recording_ts</th>\n",
" <th>accelerationPeakFull</th>\n",
" <th>psuedoVelocityPeakFull</th>\n",
" <th>accelerationRMSFull</th>\n",
" <th>velocityRMSFull</th>\n",
" <th>displacementRMSFull</th>\n",
" <th>pressureMeanFull</th>\n",
" <th>temperatureMeanFull</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>11456</td>\n",
" <td>50_Joules_900_lbs-1629315312.ide</td>\n",
" <td>1597750</td>\n",
" <td>20.201752</td>\n",
" <td>2021-07-26 19:56:39</td>\n",
" <td>231.212</td>\n",
" <td>2907.650</td>\n",
" <td>2.423</td>\n",
" <td>54.507</td>\n",
" <td>1.066</td>\n",
" <td>98.745</td>\n",
" <td>24.175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11456</td>\n",
" <td>100_Joules_900_lbs-1629315313.ide</td>\n",
" <td>1596714</td>\n",
" <td>20.200623</td>\n",
" <td>2021-07-26 19:21:55</td>\n",
" <td>218.634</td>\n",
" <td>2961.256</td>\n",
" <td>2.877</td>\n",
" <td>53.875</td>\n",
" <td>1.053</td>\n",
" <td>98.751</td>\n",
" <td>24.180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11162</td>\n",
" <td>Calibration-Shake-1632515140.IDE</td>\n",
" <td>2218130</td>\n",
" <td>27.882690</td>\n",
" <td>2021-05-17 19:16:10</td>\n",
" <td>8.783</td>\n",
" <td>1142.282</td>\n",
" <td>2.712</td>\n",
" <td>46.346</td>\n",
" <td>0.617</td>\n",
" <td>102.251</td>\n",
" <td>24.545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>11071</td>\n",
" <td>surgical-instrument-1625829182.ide</td>\n",
" <td>541994</td>\n",
" <td>6.951172</td>\n",
" <td>2021-04-22 16:53:10</td>\n",
" <td>5.739</td>\n",
" <td>387.312</td>\n",
" <td>1.568</td>\n",
" <td>24.418</td>\n",
" <td>0.242</td>\n",
" <td>99.879</td>\n",
" <td>21.889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11046</td>\n",
" <td>Drive-Home_07-1626805222.ide</td>\n",
" <td>36225758</td>\n",
" <td>634.732056</td>\n",
" <td>2021-03-19 19:35:57</td>\n",
" <td>23.805</td>\n",
" <td>356.128</td>\n",
" <td>0.097</td>\n",
" <td>6.117</td>\n",
" <td>0.135</td>\n",
" <td>101.988</td>\n",
" <td>28.832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>11046</td>\n",
" <td>Drive-Home_01-1632515142.ide</td>\n",
" <td>3632799</td>\n",
" <td>61.755371</td>\n",
" <td>2021-03-19 18:35:55</td>\n",
" <td>0.479</td>\n",
" <td>40.197</td>\n",
" <td>0.021</td>\n",
" <td>1.081</td>\n",
" <td>0.023</td>\n",
" <td>100.284</td>\n",
" <td>29.061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10916</td>\n",
" <td>FUSE_HSTAB_000005-1632515139.ide</td>\n",
" <td>537562</td>\n",
" <td>18.491791</td>\n",
" <td>2021-04-22 16:13:24</td>\n",
" <td>0.202</td>\n",
" <td>53.375</td>\n",
" <td>0.011</td>\n",
" <td>1.504</td>\n",
" <td>0.036</td>\n",
" <td>90.706</td>\n",
" <td>18.874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10118</td>\n",
" <td>Bolted-1632515144.ide</td>\n",
" <td>6149229</td>\n",
" <td>29.396118</td>\n",
" <td>2021-04-21 21:44:07</td>\n",
" <td>15.343</td>\n",
" <td>148.276</td>\n",
" <td>2.398</td>\n",
" <td>14.101</td>\n",
" <td>0.154</td>\n",
" <td>99.652</td>\n",
" <td>23.172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>9695</td>\n",
" <td>Tilt_000000-1625156721.IDE</td>\n",
" <td>719403</td>\n",
" <td>23.355163</td>\n",
" <td>2021-07-01 16:21:01</td>\n",
" <td>0.378</td>\n",
" <td>330.946</td>\n",
" <td>0.044</td>\n",
" <td>11.042</td>\n",
" <td>0.345</td>\n",
" <td>99.510</td>\n",
" <td>26.410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>9680</td>\n",
" <td>LOC__6__DAQ41551_25_01-1625170793.IDE</td>\n",
" <td>8664238</td>\n",
" <td>63.878937</td>\n",
" <td>2021-03-25 04:53:27</td>\n",
" <td>564.966</td>\n",
" <td>2357.599</td>\n",
" <td>54.408</td>\n",
" <td>145.223</td>\n",
" <td>3.088</td>\n",
" <td>102.875</td>\n",
" <td>26.031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>9680</td>\n",
" <td>LOC__4__DAQ41551_15_05-1625170794.IDE</td>\n",
" <td>6927958</td>\n",
" <td>64.486054</td>\n",
" <td>2021-03-25 04:22:10</td>\n",
" <td>585.863</td>\n",
" <td>2153.020</td>\n",
" <td>46.528</td>\n",
" <td>148.591</td>\n",
" <td>2.615</td>\n",
" <td>105.750</td>\n",
" <td>32.202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>9680</td>\n",
" <td>LOC__3__DAQ41551_11_01_02-1625170795.IDE</td>\n",
" <td>2343292</td>\n",
" <td>28.456818</td>\n",
" <td>2021-03-25 04:06:19</td>\n",
" <td>622.040</td>\n",
" <td>8907.949</td>\n",
" <td>94.197</td>\n",
" <td>372.049</td>\n",
" <td>9.580</td>\n",
" <td>105.682</td>\n",
" <td>33.452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>9680</td>\n",
" <td>LOC__2__DAQ38060_06_03_05-1625170793.IDE</td>\n",
" <td>1519172</td>\n",
" <td>27.057647</td>\n",
" <td>2021-03-25 02:54:22</td>\n",
" <td>995.670</td>\n",
" <td>5845.241</td>\n",
" <td>131.087</td>\n",
" <td>323.287</td>\n",
" <td>3.144</td>\n",
" <td>104.473</td>\n",
" <td>25.616</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" serial_number_id ... temperatureMeanFull\n",
"11 11456 ... 24.175\n",
"10 11456 ... 24.180\n",
"7 11162 ... 24.545\n",
"17 11071 ... 21.889\n",
"12 11046 ... 28.832\n",
"5 11046 ... 29.061\n",
"8 10916 ... 18.874\n",
"2 10118 ... 23.172\n",
"22 9695 ... 26.410\n",
"20 9680 ... 26.031\n",
"19 9680 ... 32.202\n",
"18 9680 ... 33.452\n",
"21 9680 ... 25.616\n",
"\n",
"[13 rows x 12 columns]"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mask = df.recording_ts > pd.to_datetime('2021-01-01')\n",
"df[mask].sort_values(by=['serial_number_id'], ascending=False) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 361
},
"id": "ug9gKCKQhW7K",
"outputId": "f8b91607-0276-4304-a49c-add75954c180"
},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>serial_number_id</th>\n",
" <th>file_name</th>\n",
" <th>file_size</th>\n",
" <th>recording_length</th>\n",
" <th>recording_ts</th>\n",
" <th>accelerationPeakFull</th>\n",
" <th>psuedoVelocityPeakFull</th>\n",
" <th>accelerationRMSFull</th>\n",
" <th>velocityRMSFull</th>\n",
" <th>displacementRMSFull</th>\n",
" <th>pressureMeanFull</th>\n",
" <th>temperatureMeanFull</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>9680</td>\n",
" <td>LOC__2__DAQ38060_06_03_05-1625170793.IDE</td>\n",
" <td>1519172</td>\n",
" <td>27.057647</td>\n",
" <td>2021-03-25 02:54:22</td>\n",
" <td>995.670</td>\n",
" <td>5845.241</td>\n",
" <td>131.087</td>\n",
" <td>323.287</td>\n",
" <td>3.144</td>\n",
" <td>104.473</td>\n",
" <td>25.616</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>9680</td>\n",
" <td>LOC__3__DAQ41551_11_01_02-1625170795.IDE</td>\n",
" <td>2343292</td>\n",
" <td>28.456818</td>\n",
" <td>2021-03-25 04:06:19</td>\n",
" <td>622.040</td>\n",
" <td>8907.949</td>\n",
" <td>94.197</td>\n",
" <td>372.049</td>\n",
" <td>9.580</td>\n",
" <td>105.682</td>\n",
" <td>33.452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>9680</td>\n",
" <td>LOC__4__DAQ41551_15_05-1625170794.IDE</td>\n",
" <td>6927958</td>\n",
" <td>64.486054</td>\n",
" <td>2021-03-25 04:22:10</td>\n",
" <td>585.863</td>\n",
" <td>2153.020</td>\n",
" <td>46.528</td>\n",
" <td>148.591</td>\n",
" <td>2.615</td>\n",
" <td>105.750</td>\n",
" <td>32.202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>9680</td>\n",
" <td>LOC__6__DAQ41551_25_01-1625170793.IDE</td>\n",
" <td>8664238</td>\n",
" <td>63.878937</td>\n",
" <td>2021-03-25 04:53:27</td>\n",
" <td>564.966</td>\n",
" <td>2357.599</td>\n",
" <td>54.408</td>\n",
" <td>145.223</td>\n",
" <td>3.088</td>\n",
" <td>102.875</td>\n",
" <td>26.031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>11456</td>\n",
" <td>50_Joules_900_lbs-1629315312.ide</td>\n",
" <td>1597750</td>\n",
" <td>20.201752</td>\n",
" <td>2021-07-26 19:56:39</td>\n",
" <td>231.212</td>\n",
" <td>2907.650</td>\n",
" <td>2.423</td>\n",
" <td>54.507</td>\n",
" <td>1.066</td>\n",
" <td>98.745</td>\n",
" <td>24.175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11456</td>\n",
" <td>100_Joules_900_lbs-1629315313.ide</td>\n",
" <td>1596714</td>\n",
" <td>20.200623</td>\n",
" <td>2021-07-26 19:21:55</td>\n",
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" <td>2961.256</td>\n",
" <td>2.877</td>\n",
" <td>53.875</td>\n",
" <td>1.053</td>\n",
" <td>98.751</td>\n",
" <td>24.180</td>\n",
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" serial_number_id ... temperatureMeanFull\n",
"21 9680 ... 25.616\n",
"18 9680 ... 33.452\n",
"19 9680 ... 32.202\n",
"20 9680 ... 26.031\n",
"11 11456 ... 24.175\n",
"10 11456 ... 24.180\n",
"\n",
"[6 rows x 12 columns]"
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},
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"mask = (df.recording_ts > pd.to_datetime('2021-01-01')) & (df.accelerationPeakFull > 100)\n",
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},
{
"cell_type": "markdown",
"metadata": {
"id": "8xY4Pn-Hcty-"
},
"source": [
"## Preview of enDAQ Library"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "siJuja8UvDKF"
},
"source": [
"### Installation\n",
"\n",
"The code is live on [GitHub](https://github.com/MideTechnology/endaq-python), [PyPI](https://pypi.org/project/endaq/), and cleaner documentation lives at [docs.endaq.com](https://docs.endaq.com/en/latest/).\n",
"\n",
"It can easily be installed with pip."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qVKyNUT9cvKA",
"outputId": "887fddf9-bab4-4953-adb9-1974705ca840"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[K |████████████████████████████████| 92 kB 1.1 MB/s \n",
"\u001b[K |████████████████████████████████| 81 kB 11.1 MB/s \n",
"\u001b[?25h Building wheel for endaq (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for endaq-calc (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for endaq-cloud (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for endaq-ide (PEP 517) ... \u001b[?25l\u001b[?25hdone\n"
]
}
],
"source": [
"!pip install -q endaq"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AOf2kh9nu6hY"
},
"source": [
"We are using newer versions of some of these libraries so we need to update them then reset the runtime. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "l-GXnXDQun1O",
"outputId": "19f6b56c-2e65-4ce6-ac10-dd353834be5e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 15.7 MB 417 kB/s \n",
"\u001b[K |████████████████████████████████| 28.5 MB 1.3 MB/s \n",
"\u001b[K |████████████████████████████████| 11.3 MB 28.8 MB/s \n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"tensorflow 2.6.0 requires numpy~=1.19.2, but you have numpy 1.21.2 which is incompatible.\n",
"google-colab 1.0.0 requires pandas~=1.1.0; python_version >= \"3.0\", but you have pandas 1.3.3 which is incompatible.\n",
"datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
"albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"! python -m pip install -U -q numpy scipy plotly pandas\n",
"exit()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xjTc9n3Ju5W1"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import scipy\n",
"\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"\n",
"import endaq\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9MPMD_K3vsI5"
},
"source": [
"### Access Data from IDE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2VB-DQoGv6dF"
},
"outputs": [],
"source": [
"def get_doc_and_table(file_location, display_table=True, display_recorder_info=True):\n",
" \"\"\"Given an IDE file location, return the object and table summary\"\"\"\n",
" dataset = endaq.ide.get_doc(file_location, quiet=True) \n",
" table = endaq.ide.get_channel_table(dataset)\n",
"\n",
" if display_recorder_info:\n",
" display(pd.Series(dataset.recorderInfo))\n",
" if display_table:\n",
" display(table)\n",
"\n",
" return dataset, table.data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 557
},
"id": "fUxT9tPOwgSc",
"outputId": "3376b560-7e5d-47fd-ba09-a6b9daf637b3"
},
"outputs": [
{
"data": {
"text/plain": [
"CalibrationDate 1583263964\n",
"CalibrationExpiry 1614799964\n",
"CalibrationSerialNumber 1999\n",
"RecorderTypeUID 1\n",
"RecorderSerial 10118\n",
"HwRev 1\n",
"FwRev 5\n",
"FwRevStr 2.0.36\n",
"ProductName W8-E100D40\n",
"PartNumber W8-E100D40\n",
"DateOfManufacture 1588954110\n",
"UniqueChipID 3782319969872279\n",
"McuType EFM32GG11B820F2048GL120\n",
"RecorderName Steve's Microphone\n",
"dtype: object"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_15029_\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th class=\"col_heading level0 col0\" >channel</th>\n",
" <th class=\"col_heading level0 col1\" >name</th>\n",
" <th class=\"col_heading level0 col2\" >type</th>\n",
" <th class=\"col_heading level0 col3\" >units</th>\n",
" <th class=\"col_heading level0 col4\" >start</th>\n",
" <th class=\"col_heading level0 col5\" >end</th>\n",
" <th class=\"col_heading level0 col6\" >duration</th>\n",
" <th class=\"col_heading level0 col7\" >samples</th>\n",
" <th class=\"col_heading level0 col8\" >rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_15029_row0_col0\" class=\"data row0 col0\" >8.0</td>\n",
" <td id=\"T_15029_row0_col1\" class=\"data row0 col1\" >X (100g)</td>\n",
" <td id=\"T_15029_row0_col2\" class=\"data row0 col2\" >Acceleration</td>\n",
" <td id=\"T_15029_row0_col3\" class=\"data row0 col3\" >g</td>\n",
" <td id=\"T_15029_row0_col4\" class=\"data row0 col4\" >00:00.0381</td>\n",
" <td id=\"T_15029_row0_col5\" class=\"data row0 col5\" >00:29.0667</td>\n",
" <td id=\"T_15029_row0_col6\" class=\"data row0 col6\" >00:29.0286</td>\n",
" <td id=\"T_15029_row0_col7\" class=\"data row0 col7\" >585728</td>\n",
" <td id=\"T_15029_row0_col8\" class=\"data row0 col8\" >19999.64 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_15029_row1_col0\" class=\"data row1 col0\" >8.1</td>\n",
" <td id=\"T_15029_row1_col1\" class=\"data row1 col1\" >Y (100g)</td>\n",
" <td id=\"T_15029_row1_col2\" class=\"data row1 col2\" >Acceleration</td>\n",
" <td id=\"T_15029_row1_col3\" class=\"data row1 col3\" >g</td>\n",
" <td id=\"T_15029_row1_col4\" class=\"data row1 col4\" >00:00.0381</td>\n",
" <td id=\"T_15029_row1_col5\" class=\"data row1 col5\" >00:29.0667</td>\n",
" <td id=\"T_15029_row1_col6\" class=\"data row1 col6\" >00:29.0286</td>\n",
" <td id=\"T_15029_row1_col7\" class=\"data row1 col7\" >585728</td>\n",
" <td id=\"T_15029_row1_col8\" class=\"data row1 col8\" >19999.64 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_15029_row2_col0\" class=\"data row2 col0\" >8.2</td>\n",
" <td id=\"T_15029_row2_col1\" class=\"data row2 col1\" >Z (100g)</td>\n",
" <td id=\"T_15029_row2_col2\" class=\"data row2 col2\" >Acceleration</td>\n",
" <td id=\"T_15029_row2_col3\" class=\"data row2 col3\" >g</td>\n",
" <td id=\"T_15029_row2_col4\" class=\"data row2 col4\" >00:00.0381</td>\n",
" <td id=\"T_15029_row2_col5\" class=\"data row2 col5\" >00:29.0667</td>\n",
" <td id=\"T_15029_row2_col6\" class=\"data row2 col6\" >00:29.0286</td>\n",
" <td id=\"T_15029_row2_col7\" class=\"data row2 col7\" >585728</td>\n",
" <td id=\"T_15029_row2_col8\" class=\"data row2 col8\" >19999.64 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_15029_row3_col0\" class=\"data row3 col0\" >8.3</td>\n",
" <td id=\"T_15029_row3_col1\" class=\"data row3 col1\" >Mic</td>\n",
" <td id=\"T_15029_row3_col2\" class=\"data row3 col2\" >Audio</td>\n",
" <td id=\"T_15029_row3_col3\" class=\"data row3 col3\" >A</td>\n",
" <td id=\"T_15029_row3_col4\" class=\"data row3 col4\" >00:00.0381</td>\n",
" <td id=\"T_15029_row3_col5\" class=\"data row3 col5\" >00:29.0667</td>\n",
" <td id=\"T_15029_row3_col6\" class=\"data row3 col6\" >00:29.0286</td>\n",
" <td id=\"T_15029_row3_col7\" class=\"data row3 col7\" >585728</td>\n",
" <td id=\"T_15029_row3_col8\" class=\"data row3 col8\" >19999.64 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_15029_row4_col0\" class=\"data row4 col0\" >80.0</td>\n",
" <td id=\"T_15029_row4_col1\" class=\"data row4 col1\" >X (40g)</td>\n",
" <td id=\"T_15029_row4_col2\" class=\"data row4 col2\" >Acceleration</td>\n",
" <td id=\"T_15029_row4_col3\" class=\"data row4 col3\" >g</td>\n",
" <td id=\"T_15029_row4_col4\" class=\"data row4 col4\" >00:00.0381</td>\n",
" <td id=\"T_15029_row4_col5\" class=\"data row4 col5\" >00:29.0669</td>\n",
" <td id=\"T_15029_row4_col6\" class=\"data row4 col6\" >00:29.0287</td>\n",
" <td id=\"T_15029_row4_col7\" class=\"data row4 col7\" >117760</td>\n",
" <td id=\"T_15029_row4_col8\" class=\"data row4 col8\" >4020.81 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
" <td id=\"T_15029_row5_col0\" class=\"data row5 col0\" >80.1</td>\n",
" <td id=\"T_15029_row5_col1\" class=\"data row5 col1\" >Y (40g)</td>\n",
" <td id=\"T_15029_row5_col2\" class=\"data row5 col2\" >Acceleration</td>\n",
" <td id=\"T_15029_row5_col3\" class=\"data row5 col3\" >g</td>\n",
" <td id=\"T_15029_row5_col4\" class=\"data row5 col4\" >00:00.0381</td>\n",
" <td id=\"T_15029_row5_col5\" class=\"data row5 col5\" >00:29.0669</td>\n",
" <td id=\"T_15029_row5_col6\" class=\"data row5 col6\" >00:29.0287</td>\n",
" <td id=\"T_15029_row5_col7\" class=\"data row5 col7\" >117760</td>\n",
" <td id=\"T_15029_row5_col8\" class=\"data row5 col8\" >4020.81 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
" <td id=\"T_15029_row6_col0\" class=\"data row6 col0\" >80.2</td>\n",
" <td id=\"T_15029_row6_col1\" class=\"data row6 col1\" >Z (40g)</td>\n",
" <td id=\"T_15029_row6_col2\" class=\"data row6 col2\" >Acceleration</td>\n",
" <td id=\"T_15029_row6_col3\" class=\"data row6 col3\" >g</td>\n",
" <td id=\"T_15029_row6_col4\" class=\"data row6 col4\" >00:00.0381</td>\n",
" <td id=\"T_15029_row6_col5\" class=\"data row6 col5\" >00:29.0669</td>\n",
" <td id=\"T_15029_row6_col6\" class=\"data row6 col6\" >00:29.0287</td>\n",
" <td id=\"T_15029_row6_col7\" class=\"data row6 col7\" >117760</td>\n",
" <td id=\"T_15029_row6_col8\" class=\"data row6 col8\" >4020.81 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
" <td id=\"T_15029_row7_col0\" class=\"data row7 col0\" >36.0</td>\n",
" <td id=\"T_15029_row7_col1\" class=\"data row7 col1\" >Pressure/Temperature:00</td>\n",
" <td id=\"T_15029_row7_col2\" class=\"data row7 col2\" >Pressure</td>\n",
" <td id=\"T_15029_row7_col3\" class=\"data row7 col3\" >Pa</td>\n",
" <td id=\"T_15029_row7_col4\" class=\"data row7 col4\" >00:00.0375</td>\n",
" <td id=\"T_15029_row7_col5\" class=\"data row7 col5\" >00:29.0771</td>\n",
" <td id=\"T_15029_row7_col6\" class=\"data row7 col6\" >00:29.0396</td>\n",
" <td id=\"T_15029_row7_col7\" class=\"data row7 col7\" >31</td>\n",
" <td id=\"T_15029_row7_col8\" class=\"data row7 col8\" >1.05 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
" <td id=\"T_15029_row8_col0\" class=\"data row8 col0\" >36.1</td>\n",
" <td id=\"T_15029_row8_col1\" class=\"data row8 col1\" >Pressure/Temperature:01</td>\n",
" <td id=\"T_15029_row8_col2\" class=\"data row8 col2\" >Temperature</td>\n",
" <td id=\"T_15029_row8_col3\" class=\"data row8 col3\" >°C</td>\n",
" <td id=\"T_15029_row8_col4\" class=\"data row8 col4\" >00:00.0375</td>\n",
" <td id=\"T_15029_row8_col5\" class=\"data row8 col5\" >00:29.0771</td>\n",
" <td id=\"T_15029_row8_col6\" class=\"data row8 col6\" >00:29.0396</td>\n",
" <td id=\"T_15029_row8_col7\" class=\"data row8 col7\" >31</td>\n",
" <td id=\"T_15029_row8_col8\" class=\"data row8 col8\" >1.05 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
" <td id=\"T_15029_row9_col0\" class=\"data row9 col0\" >59.0</td>\n",
" <td id=\"T_15029_row9_col1\" class=\"data row9 col1\" >Control Pad Pressure</td>\n",
" <td id=\"T_15029_row9_col2\" class=\"data row9 col2\" >Pressure</td>\n",
" <td id=\"T_15029_row9_col3\" class=\"data row9 col3\" >Pa</td>\n",
" <td id=\"T_15029_row9_col4\" class=\"data row9 col4\" >00:00.0414</td>\n",
" <td id=\"T_15029_row9_col5\" class=\"data row9 col5\" >00:29.0489</td>\n",
" <td id=\"T_15029_row9_col6\" class=\"data row9 col6\" >00:29.0075</td>\n",
" <td id=\"T_15029_row9_col7\" class=\"data row9 col7\" >291</td>\n",
" <td id=\"T_15029_row9_col8\" class=\"data row9 col8\" >10.01 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
" <td id=\"T_15029_row10_col0\" class=\"data row10 col0\" >59.1</td>\n",
" <td id=\"T_15029_row10_col1\" class=\"data row10 col1\" >Control Pad Temperature</td>\n",
" <td id=\"T_15029_row10_col2\" class=\"data row10 col2\" >Temperature</td>\n",
" <td id=\"T_15029_row10_col3\" class=\"data row10 col3\" >°C</td>\n",
" <td id=\"T_15029_row10_col4\" class=\"data row10 col4\" >00:00.0414</td>\n",
" <td id=\"T_15029_row10_col5\" class=\"data row10 col5\" >00:29.0489</td>\n",
" <td id=\"T_15029_row10_col6\" class=\"data row10 col6\" >00:29.0075</td>\n",
" <td id=\"T_15029_row10_col7\" class=\"data row10 col7\" >291</td>\n",
" <td id=\"T_15029_row10_col8\" class=\"data row10 col8\" >10.01 Hz</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_15029_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
" <td id=\"T_15029_row11_col0\" class=\"data row11 col0\" >59.2</td>\n",
" <td id=\"T_15029_row11_col1\" class=\"data row11 col1\" >Relative Humidity</td>\n",
" <td id=\"T_15029_row11_col2\" class=\"data row11 col2\" >Relative Humidity</td>\n",
" <td id=\"T_15029_row11_col3\" class=\"data row11 col3\" >RH</td>\n",
" <td id=\"T_15029_row11_col4\" class=\"data row11 col4\" >00:00.0414</td>\n",
" <td id=\"T_15029_row11_col5\" class=\"data row11 col5\" >00:29.0489</td>\n",
" <td id=\"T_15029_row11_col6\" class=\"data row11 col6\" >00:29.0075</td>\n",
" <td id=\"T_15029_row11_col7\" class=\"data row11 col7\" >291</td>\n",
" <td id=\"T_15029_row11_col8\" class=\"data row11 col8\" >10.01 Hz</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7feec8874750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"[doc,table] = get_doc_and_table('https://info.endaq.com/hubfs/data/Bolted.ide')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0FOIW0ri3Sqd"
},
"outputs": [],
"source": [
"def get_primary_accel(dataset, table, preferred_ch=None, time_mode='seconds'):\n",
" \"\"\"Given an IDE object and summary table, return a dataframe of the accelerometer with the most samples unless a channel number is explicitely defined.\"\"\"\n",
" if preferred_ch:\n",
" df = endaq.ide.to_pandas(doc.channels[preferred_ch],time_mode=time_mode)\n",
" else:\n",
" table = table.sort_values('samples',ascending=False)\n",
" channel = table.loc[table.type=='Acceleration','channel'].iloc[0].parent \n",
" df = endaq.ide.to_pandas(channel,time_mode=time_mode)\n",
"\n",
" return df.drop([\"Mic\"], axis=1, errors=\"ignore\") "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 455
},
"id": "XuU2PbWuwjlH",
"outputId": "280b68bf-bcaf-449e-d5db-f0f9e86c0f99"
},
"outputs": [
{
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"text/plain": [
" X (100g) Y (100g) Z (100g)\n",
"timestamp \n",
"0.381011 -1.268025 -2.824445 -2.262913\n",
"0.381061 -1.292439 -1.902800 -4.362554\n",
"0.381111 -1.438926 -1.164263 -4.740978\n",
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"... ... ... ...\n",
"29.667738 -1.036088 -1.817350 -2.207980\n",
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"29.667938 -0.865187 -3.526360 0.111391\n",
"\n",
"[585728 rows x 3 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = get_primary_accel(doc, table)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f8vaIGA03g8C"
},
"source": [
"### PSD\n",
"We created a wrapper to [SciPy's Welch's method](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.welch.html) to make the interface rely on dataframes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "v8WTbmq5xGpC",
"outputId": "6f29f012-a331-48c3-8eb7-2555506d89cb"
},
"outputs": [
{
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"source": [
"df_psd = endaq.calc.psd.welch(df, bin_width=10)\n",
"\n",
"fig = px.line(df_psd)\n",
"\n",
"fig.update_layout(\n",
" title='Power Spectral Density',\n",
" xaxis_title=\"Frequency (Hz)\",\n",
" yaxis_title=\"Acceleration (g^2/Hz)\",\n",
" xaxis_type=\"log\",\n",
" yaxis_type=\"log\",\n",
" legend=dict(\n",
" orientation=\"h\",\n",
" yanchor=\"top\",\n",
" y=-.2,\n",
" xanchor=\"right\",\n",
" x=1\n",
" )\n",
" )\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "thYYU5yLAXfI"
},
"source": [
"We also provide a function to convert a linear spaced PSD to an octave spaced one."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "97aXuVQPxiqZ",
"outputId": "240ada0e-881a-4b80-8667-eb7c04d1ad06"
},
"outputs": [
{
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"</body>\n",
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},
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"output_type": "display_data"
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],
"source": [
"df_psd_oct = endaq.calc.psd.to_octave(df_psd, fstart=4, octave_bins=3)\n",
"\n",
"fig = px.line(df_psd_oct)\n",
"\n",
"fig.update_layout(\n",
" title='Octave Spaced Power Spectral Density',\n",
" xaxis_title=\"Frequency (Hz)\",\n",
" yaxis_title=\"Acceleration (g^2/Hz)\",\n",
" xaxis_type=\"log\",\n",
" yaxis_type=\"log\",\n",
" legend=dict(\n",
" orientation=\"h\",\n",
" yanchor=\"top\",\n",
" y=-.2,\n",
" xanchor=\"right\",\n",
" x=1\n",
" )\n",
" )\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Cfk4pIK95Lbe"
},
"source": [
"### Shock\n",
"\n",
"For some shock data, we'll use the motorcycle crash test data discussed in our [blog post on pseudo velocity](https://blog.endaq.com/shock-analysis-response-spectrum-srs-pseudo-velocity-severity)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 455
},
"id": "0z3-ayAh4ytz",
"outputId": "514155ec-8fe9-41d6-c996-c2f08cad9ab4"
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"outputs": [
{
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" <th>X (500g)</th>\n",
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" <tr>\n",
" <th>1152.098232</th>\n",
" <td>0.796408</td>\n",
" <td>-5.452379</td>\n",
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" <th>1152.098532</th>\n",
" <td>1.145022</td>\n",
" <td>-5.936471</td>\n",
" <td>0.478006</td>\n",
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" <tr>\n",
" <th>1152.098632</th>\n",
" <td>1.280594</td>\n",
" <td>-5.859016</td>\n",
" <td>0.419654</td>\n",
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" X (500g) Y (500g) Z (500g)\n",
"timestamp \n",
"1024.088653 0.563998 -5.142559 0.575260\n",
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"1024.088853 0.389691 -5.142559 1.275489\n",
"1024.088953 0.234751 -5.007014 0.905924\n",
"1024.089053 0.196017 -5.181287 0.633613\n",
"... ... ... ...\n",
"1152.098232 0.796408 -5.452379 0.497457\n",
"1152.098332 0.835142 -5.607288 0.205695\n",
"1152.098432 1.048184 -5.549197 0.205695\n",
"1152.098532 1.145022 -5.936471 0.478006\n",
"1152.098632 1.280594 -5.859016 0.419654\n",
"\n",
"[1280087 rows x 3 columns]"
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},
"execution_count": 8,
"metadata": {},
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],
"source": [
"[doc,table] = get_doc_and_table('https://info.endaq.com/hubfs/data/Motorcycle-Car-Crash.ide',\n",
" display_recorder_info=False,\n",
" display_table=False)\n",
"df = get_primary_accel(doc, table)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "32e_tLm65_Ge"
},
"source": [
"#### Get Peak Time & Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nKt1eQtW5hyI"
},
"outputs": [],
"source": [
"def get_peak_time(df,num):\n",
" \"\"\"Get a subset of a dataframe around the peak value\"\"\"\n",
" peak_time = df.abs().max(axis=1).idxmax() #get the time at which the peak value occurs\n",
" d_t = (df.index[-1]-df.index[0])/(len(df.index)-1) #find the average time step\n",
" fs = 1/d_t #find the sampling rate\n",
"\n",
" num = num / 2 #total number of datapoints to plot (divide by 2 because it will be two sided)\n",
" df_peak = df[peak_time - num / fs : peak_time + num / fs ] #segment the dataframe to be around that peak value\n",
" \n",
" return df_peak"
]
},
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"id": "DryEV6a96bCI",
"outputId": "264ce923-11d8-4003-d074-2a3806e1320b"
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],
"source": [
"df_peak = get_peak_time(df,5000)\n",
"\n",
"fig = px.line(df_peak)\n",
"fig.update_layout(\n",
" title=\"Time History around Peak\",\n",
" xaxis_title=\"Time (s)\",\n",
" yaxis_title=\"Acceleration (g)\",\n",
" template=\"plotly_white\",\n",
" legend=dict(\n",
" orientation=\"h\",\n",
" yanchor=\"top\",\n",
" y=-.2,\n",
" xanchor=\"right\",\n",
" x=1\n",
" )\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TTkXtcr868k5"
},
"source": [
"#### Filter & Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "TtSWPMFX6ihW",
"outputId": "e7a56508-5a1f-4af4-de12-27798d030491"
},
"outputs": [
{
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"df_filtered = df - df.median() #For shock events it's sometimes best to avoid high pass filters because they can introduce weird artifacts\n",
"df_filtered = endaq.calc.filters.butterworth(df_filtered, low_cutoff=None, high_cutoff=150, half_order=3)\n",
"df_peak_filtered = get_peak_time(df_filtered,5000)\n",
"\n",
"fig = px.line(df_peak_filtered)\n",
"fig.update_layout(\n",
" title=\"Filtered Time History around Peak\",\n",
" xaxis_title=\"Time (s)\",\n",
" yaxis_title=\"Acceleration (g)\",\n",
" template=\"plotly_white\",\n",
" legend=dict(\n",
" orientation=\"h\",\n",
" yanchor=\"top\",\n",
" y=-.2,\n",
" xanchor=\"right\",\n",
" x=1\n",
" )\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "A7eyZ2sx803f"
},
"source": [
"#### Integrate to Velocity & Displacement"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tAVITAR07FJy"
},
"outputs": [],
"source": [
"[df_accel, df_vel, df_disp] = endaq.calc.integrate.integrals(df_peak_filtered, n=2, highpass_cutoff=None, tukey_percent=0)\n",
"\n",
"df_vel = df_vel-df_vel.iloc[0] #forced the starting velocity to 0\n",
"df_vel = df_vel*9.81*39.37 #convert to in/s"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "6NQrZXmJ87TE",
"outputId": "630f45ed-723c-446a-fdf9-ce949e54766e"
},
"outputs": [
{
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Velocity Time History\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Time (s)\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Velocity (in/s)\"}}}, {\"responsive\": true} ).then(function(){\n",
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],
"source": [
"fig = px.line(df_vel)\n",
"fig.update_layout(\n",
" title=\"Integrated Velocity Time History\",\n",
" xaxis_title=\"Time (s)\",\n",
" yaxis_title=\"Velocity (in/s)\",\n",
" template=\"plotly_dark\",\n",
" legend=dict(\n",
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{
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"metadata": {
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"source": [
"#### Shock Response Spectrum"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TaZLW-jS9bsg"
},
"outputs": [],
"source": [
"freqs=2 ** np.arange(0, 12, 1/12)\n",
"\n",
"pvss = endaq.calc.shock.pseudo_velocity(df_peak-df_peak.median(), freqs=freqs, damp=0.05)\n",
"pvss = pvss*9.81*39.37 #convert to in/s"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "N_Ggrhq_nKFc",
"outputId": "c2623b81-0edb-4576-9ca5-65645109cc3a"
},
"outputs": [
{
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"fig = px.line(pvss)\n",
"fig.update_layout(\n",
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" xaxis_title=\"Natural Frequency (Hz)\",\n",
" yaxis_title=\"Psuedo Velocity (in/s)\",\n",
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" y=-.2,\n",
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" x=1\n",
" )\n",
" )"
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