diff --git a/_toc.yml b/_toc.yml index cceb3f61b..7cd6136c1 100644 --- a/_toc.yml +++ b/_toc.yml @@ -42,6 +42,8 @@ sections: - file: core/cartopy/01_Cartopy_Intro - file: core/datetime + sections: + - file: core/datetime/datetime - file: core/pandas sections: - file: core/pandas/pandas_fullNotebook diff --git a/core/datetime.md b/core/datetime.md index 55ffdd1ff..a121da735 100644 --- a/core/datetime.md +++ b/core/datetime.md @@ -4,4 +4,11 @@ This content is under construction! ``` -This section will contain tutorials on dealing with times and calendars in scientific Python, including use of the [datetime](https://docs.python.org/3/library/datetime.html) library. +This section contains tutorials on dealing with times and calendars in scientific Python, beginning with use of the [datetime](https://docs.python.org/3/library/datetime.html) standard library. + +When this chapter is fully built out, it will include a comprehensive guide to different time libraries and when/where to use them, including + +- [Numpy `datetime64`](https://numpy.org/doc/stable/reference/arrays.datetime.html) for efficient vectorized datetime operations +- [cftime library](https://unidata.github.io/cftime/) for dealing with non-standard calendars + +These will be cross-referenced with tutorials on dealing with timeseries data in [Pandas](pandas) and [Xarray](xarray). diff --git a/core/datetime/datetime.ipynb b/core/datetime/datetime.ipynb new file mode 100644 index 000000000..be7c45f90 --- /dev/null +++ b/core/datetime/datetime.ipynb @@ -0,0 +1,649 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Times and dates in Python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "Time is an essential component of nearly all geoscience data. Timescales span orders of magnitude from microseconds for lightning, hours for a supercell thunderstorm, days for a global weather model, millenia and beyond for the earth's climate. To properly analyze geoscience data, you must have a firm understanding of how to handle time in Python. \n", + "\n", + "In this notebook, we will:\n", + "\n", + "1. Introduce the [time](https://docs.python.org/3/library/time.html) and [datetime](https://docs.python.org/3/library/datetime.html) modules from the Python Standard Library\n", + "1. Take a brief aside into Object-Oriented programming and Python classes\n", + "1. Look at formatted input and output of dates and times\n", + "1. See how we can do simple arithmetic on date/time data, making use of the `timedelta` object\n", + "1. Briefly make use of the [pytz](https://pypi.org/project/pytz/) module to handle some thorny time zone issues in Python." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "Label the importance of each concept explicitly as **helpful/necessary**.\n", + "\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| Basic Python string formatting | Helpful | Try this [Real Python string formatting tutorial](https://realpython.com/python-string-formatting/) |\n", + "\n", + "- **Experience level**: **beginner**\n", + "- **Time to learn**: **medium**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Python Standard Library packages\n", + "# We'll discuss below WHY we alias the packages this way\n", + "import datetime as dt\n", + "import math\n", + "import time as tm\n", + "\n", + "# Third-party package for time zone handling, we'll discuss below!\n", + "import pytz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `Time` Versus `Datetime` modules \n", + "\n", + "### Some core terminology\n", + "\n", + "Python comes with [time](https://docs.python.org/3/library/time.html) and [datetime](https://docs.python.org/3/library/datetime.html) modules as part of the Standard Library included with every Python installation. Unfortunately, Python can be initially disorienting because of the heavily overlapping terminology concerning dates and times:\n", + "\n", + "- `datetime` **module** has a `datetime` **class**\n", + "- `datetime` **module** has a `time` **class**\n", + "- `datetime` **module** has a `date` **class**\n", + "- `time` **module** has a `time` function which returns (almost always) [Unix time](#What-is-Unix-Time?)\n", + "- `datetime` **class** has a `date` method which returns a `date` object\n", + "- `datetime` **class** has a `time` method which returns a `time` object\n", + "\n", + "This confusion can be partially alleviated by aliasing our imported modules, we did above:\n", + "\n", + "```\n", + "import datetime as dt\n", + "import time as tm\n", + "```\n", + "\n", + "We can now reference the `datetime` module (aliased to `dt`) and `datetime` object unambiguously." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pisecond = dt.datetime(2021, 3, 14, 15, 9, 26)\n", + "print(pisecond)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our variable `pisecond` now stores a particular date and time, which just happens to be $\\pi$-day 2021 down to the nearest second (3.1415926...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "now = tm.time()\n", + "print(now)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The variable `now` holds the current time in seconds since January 1, 1970 00:00 [UTC](#What-is-UTC?) (see [What is Unix Time](#What-is-Unix-Time?) below)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `time` module\n", + "\n", + "The `time` module is well-suited for measuring [Unix time](#What-is-Unix-Time?). For example, when you are calculating how long it takes a Python function to run (so-called \"benchmarking\"), you can employ the `time()` function from the `time` module to obtain Unix time before and after the function completes and take the difference of those two times." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "start = tm.time()\n", + "tm.sleep(1) # The sleep function will stop the program for n seconds\n", + "end = tm.time()\n", + "diff = end - start\n", + "print(f\"The benchmark took {diff} seconds\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "

Info

\n", + " For more accurate benchmarking, see the timeit module.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is Unix Time?\n", + "\n", + "Unix time is an example of system time which is the computer's notion of passing time. It is measured in seconds from the the start of the epoch which is January 1, 1970 00:00 [UTC](#What-is-UTC?). It is represented \"under the hood\" as a [floating point number](https://en.wikipedia.org/wiki/Floating_point) which is how computers represent real (ℝ) numbers ." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `datetime` module\n", + "\n", + "The `datetime` module handles time with the Gregorian calendar (the calendar we are all familiar with) and is independent of Unix time. The `datetime` module has an [object-oriented](#Thirty-second-introduction-to-Object-Oriented-programming) approach with the `date`, `time`, `datetime`, `timedelta`, and `tzinfo` classes.\n", + "\n", + "- `date` class represents the day, month and year\n", + "- `time` class represents the time of day\n", + "- `datetime` class is a combination of the `date` and `time` classes\n", + "- `timedelta` class represents a time duration\n", + "- `tzinfo` (abstract) class represents time zones\n", + "\n", + "The `datetime` module is effective for:\n", + "\n", + "- performing date and time arithmetic and calculating time duration\n", + "- reading and writing date and time strings with various formats\n", + "- handling time zones (with the help of third-party libraries)\n", + "\n", + "The `time` and `datetime` modules overlap in functionality, but in your geoscientific work, you will probably be using the `datetime` module more than the `time` module." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll delve into more details below, but here's a quick example of writing out our `pisecond` datetime object as a formatted string. Suppose we wanted to write out just the date, and write it in the _month/day/year_ format typically used in the US. We can use the `strftime()` method with a format specifier:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Pi day occurred on:', pisecond.strftime(format='%m/%d/%Y'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Thirty second introduction to Object-Oriented programming\n", + "\n", + "We have been talking about object-oriented (OO) programming by mentioning terms like \"class\", \"object\", and \"method\", but never really explaining what they mean. A class is a collection of related variables, similar to a [struct](https://en.wikipedia.org/wiki/Struct_(C_programming_language)), in the C programming language or even a tuple in Python) coupled with functions, or \"methods\" in OO parlance, that can act on those variables. An object is a concrete example of a class.\n", + "\n", + "For example, suppose we wanted a class to represent a location on Earth with latitude, and longitude, and along with those data we want a method to compute the [great-circle distance](https://en.wikipedia.org/wiki/Great-circle_distance) between this location and another point on the sphere:\n", + "\n", + "$$ d = R~\\arccos\\Big( \\sin(\\phi_1)\\sin(\\phi_2) + \\cos(\\phi_1)\\cos(\\phi_2)\\cos(\\lambda_1-\\lambda_2) \\Big) $$\n", + "\n", + "where $\\phi_1, \\lambda_1$ and $\\phi_2, \\lambda_2$ are the *latitude* and *longitude* of points 1 and 2 in *radians*, and $R$ is the radius of the Earth.\n", + "\n", + "In the next cell, we first *define* our custom class called `Coord` (including the code to implement the great-circle calculation). In subsequent cells we'll show how to use our new class by creating objects of type `Coord`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class Coord:\n", + " \"\"\"Earth location identified by (latitude, longitude) coordinates.\n", + " distancekm -- distance between two points in kilometers\n", + " \"\"\"\n", + "\n", + " def __init__(self, latitude=0.0, longitude=0.0):\n", + " self.lat = latitude\n", + " self.lon = longitude\n", + "\n", + " def distancekm(self, p):\n", + " \"\"\"Distance between two points in kilometers.\"\"\"\n", + " DEGREES_TO_RADIANS = math.pi / 180.0\n", + " EARTH_RADIUS = 6373 # in KMs\n", + " phi1 = self.lat * DEGREES_TO_RADIANS\n", + " phi2 = p.lat * DEGREES_TO_RADIANS\n", + " lambda1 = self.lon * DEGREES_TO_RADIANS\n", + " lambda2 = p.lon * DEGREES_TO_RADIANS\n", + " cosarc = math.sin(phi1) * math.sin(phi2) + math.cos(phi1) * math.cos(\n", + " phi2\n", + " ) * math.cos(lambda1 - lambda2)\n", + " arc = math.acos(cosarc)\n", + " return arc * EARTH_RADIUS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create a concrete example of a **class**, also known as an **object**, initialize the object with some data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "timbuktu = Coord(latitude=16.77, longitude=3.00)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, `timbuktu` is an **object** of the **class** `Coord` initialized with a latitude of `16.77` and a longitude of `3.00`. Great!\n", + "\n", + "Next, we create two `Coord` objects: `ny` and `paris`. We will invoke the `distancekm()` method on the `ny` object and pass the `paris` object as an argument to determine the distance between New York and Paris in kilometers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ny = Coord(\n", + " 40.71, 74.01\n", + ") # note that using the keywords for latitude and longitude is optional\n", + "paris = Coord(\n", + " latitude=48.86, longitude=2.35\n", + ") # but tends to make the code more readable!\n", + "distance = ny.distancekm(paris)\n", + "print(f\"The distance from New York to Paris is {distance:.1f} kilometers.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The old joke about OO programming is that they simply moved the struct that the function takes as an argument and put it first because it is special. So instead of having `distancekm(ny, paris)`, you have `ny.distancekm(paris)`. We have not talked about inheritance or polymorphism but that is OO in a nutshell: packaging together the data and the methods that operate on the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading and writing dates and times\n", + "\n", + "### Parsing lightning data timestamps with the `datetime.strptime` method\n", + "\n", + "Suppose you want to analyze [US NLDN lightning data](https://ghrc.nsstc.nasa.gov/uso/ds_docs/vaiconus/vaiconus_dataset.html). Here is a sample row of data:\n", + "\n", + " 06/27/07 16:18:21.898 18.739 -88.184 0.0 kA 0 1.0 0.4 2.5 8 1.2 13 G\n", + "\n", + "Part of the task involves parsing the `06/27/07 16:18:21.898` time string into a `datetime` object. (The full description of the data is [here](https://ghrc.nsstc.nasa.gov/uso/ds_docs/vaiconus/vaiconus_dataset.html#a6).) In order to parse this string or others that follow the same format, you will employ the [datetime.strptime()](https://docs.python.org/3/library/datetime.html#datetime.datetime.strptime) method from the `datetime` module. This method takes two arguments: \n", + "1. the date time string you wish to parse\n", + "2. the format which describes exactly how the date and time are arranged. \n", + "\n", + "[The full range of format options is described in the Python documentation](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior). In reality, the format will take some degree of experimentation to get right. This is a situation where Python shines as you can quickly try out different solutions in the IPython interpreter (or in a notebook). Beyond the official documentation, Google and [Stack Overflow](https://stackoverflow.com/) are your friends in this process. \n", + "\n", + "Eventually, after some trial and error, you will find the `'%m/%d/%y %H:%M:%S.%f'` format will properly parse the date and time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "strike_time = dt.datetime.strptime('06/27/07 16:18:21.898', '%m/%d/%y %H:%M:%S.%f')\n", + "# print strike_time to see if we have properly parsed our time\n", + "print(strike_time)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example usage of the `datetime` object\n", + "\n", + "Why did we bother doing this? It might look like all we've done here is take the string `06/27/07 16:18:21.898` and reformatted it to `2007-06-27 16:18:21.898000`.\n", + "\n", + "But in fact our variable `strike_time` is a `datetime` object that we can manipulate in many useful ways. \n", + "\n", + "A few quick examples:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Controlling the output format with `strftime()`\n", + "\n", + "Suppose we want to write out just the time (not date) in particular format like this:\n", + "```\n", + "16h 18m 21s\n", + "```\n", + "\n", + "We can do this with the [datetime.strftime()](https://docs.python.org/2/library/datetime.html#datetime.date.strftime) method, which takes a format identical to the one we employed for `strptime()`. After some trial and error from the IPython interpreter, we arrive at `'%Hh %Mm %Ss'`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(strike_time.strftime(format='%Hh %Mm %Ss'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### A simple query of just the year:\n", + "\n", + "Here's a useful shortcut that doesn't even need a format specifier:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "strike_time.year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works because the `datetime` object stores the data as individual attributes: \n", + "`year`, `month`, `day`, `hour`, `minute`, `second`, `microsecond`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### See how many days have elapsed since the strike:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(dt.datetime.now() - strike_time).days" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This last example actually shows how we can do simple arithmetic with `datetime` objects! We'll see more of this in the next section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating coastal tides with the `timedelta` class\n", + "\n", + "Let's suppose we are looking at coastal tide and current data perhaps in a [tropical cyclone storm surge scenario](http://www.nhc.noaa.gov/surge/). \n", + "\n", + "The [lunar day](http://oceanservice.noaa.gov/education/kits/tides/media/supp_tide05.html) is 24 hours, 50 minutes with two low tides and two high tides in that time duration. If we know the time of the current high tide, we can easily calculate the occurrence of the next low and high tides with the [timedelta class](https://docs.python.org/3/library/datetime.html#timedelta-objects). (In reality, the *exact time* of tides is influenced by local coastal effects, in addition to the laws of celestial mechanics, but we will ignore that fact for this exercise.)\n", + "\n", + "The `timedelta` class is initialized by supplying time duration usually supplied with [keyword arguments](https://docs.python.org/3/glossary.html#term-argument) to clearly express the length of time. Significantly, you can use the `timedelta` class with arithmetic operators (i.e., `+`, `-`, `*`, `/`) to obtain new dates and times as the next code sample illustrates. \n", + "\n", + "This convenient language feature is known as [operator overloading](https://en.wikipedia.org/wiki/Operator_overloading) and again illustrates Python's batteries-included philosophy of making life easier for the programmer. (In another language such as Java, you would have to call a method significantly obfuscating the code.) \n", + "\n", + "Another great feature is that the difference of two times (like we did above with the [lightning strike data](#See-how-many-days-have-elapsed-since-the-strike:)) will yield a `timedelta` object. Let's examine all these features in the following code block." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "high_tide = dt.datetime(2016, 6, 1, 4, 38, 0)\n", + "lunar_day = dt.timedelta(hours=24, minutes=50)\n", + "tide_duration = lunar_day / 4 # Here we do some arithmetic on the timedelta object!\n", + "next_low_tide = (\n", + " high_tide + tide_duration\n", + ") # Here we add a timedelta object to a datetime object\n", + "next_high_tide = high_tide + (2 * tide_duration) # and so on\n", + "tide_length = next_high_tide - high_tide\n", + "print(f\"The time between high and low tide is {tide_duration}.\")\n", + "print(f\"The current high tide is {high_tide}.\")\n", + "print(f\"The next low tide is {next_low_tide}.\")\n", + "print(f\"The next high tide {next_high_tide}.\")\n", + "print(f\"The tide length is {tide_length}.\")\n", + "print(\"The type of the 'tide_length' variable is {}.\".format(type(tide_length)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the last `print` statement, we use the [type()](https://docs.python.org/3/library/functions.html#type) built-in Python function to show that the difference between two times yields a `timedelta` object." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dealing with Time Zones\n", + "\n", + "Time zones can be a source of confusion and frustration in geoscientific data and in computer programming in general. Core date and time libraries in various programming languages inevitably have design flaws (Python is no different) leading to third-party libraries that attempt to fix the core library limitations. To avoid these issues, it is best to handle data in UTC, or at the very least operate in a consistent time zone, but that is not always possible. Users will expect their tornado alerts in local time.\n", + "\n", + "### What is UTC?\n", + "\n", + "[UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) is an abbreviation of Coordinated Universal Time and is equivalent to Greenwich Mean Time (GMT), in practice. (Greenwich at 0 degrees longitude, is a district of London, England.) In geoscientific data, times are often in UTC though you should always verify this assumption is actually true!\n", + "\n", + "### Time Zone Naive Versus Time Zone Aware `datetime` Objects\n", + "\n", + "When you create `datetime` objects in Python, they are so-called \"naive\" which means they are time zone unaware. In many situations, you can happily go forward without this detail getting in the way of your work. As the [Python documentation states](https://docs.python.org/3/library/datetime.html#aware-and-naive-objects):\n", + ">Naive objects are easy to understand and to work with, at the cost of ignoring some aspects of reality. \n", + "\n", + "However, if you wish to convey time zone information, you will have to make your `datetime` objects time zone aware. The `datetime` library is able to handle conversions to UTC:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "naive = dt.datetime.now()\n", + "aware = dt.datetime.now(dt.timezone.utc)\n", + "print(f\"I am time zone naive {naive}.\")\n", + "print(f\"I am time zone aware {aware}.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that `aware` has `+00:00` appended at the end, indicating zero hours offset from UTC.\n", + "\n", + "Our `naive` object shows the local time on whatever computer was used to run this code. If you're reading this online, then chances are it was executed on a cloud server that already uses UTC, so `naive` and `aware` will differ only at the microsecond level!\n", + "\n", + "In the code above, we used `dt.timezone.utc` to initialize the UTC timezone for our `aware` object. Unfortunately at this time the Python Standard Library does not fully support initializing `datetime` objects with arbitrary time zones, or conversions between different time zones. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Full time zone support with the `pytz` module\n", + "\n", + "For improved handling of time zones in Python, you will need the third-party [pytz](https://pypi.org/project/pytz/) module whose classes build upon, or \"inherit\" in OO terminology, from `datetime` classes. \n", + "\n", + "Here, we repeat the above exercise but initialize our `aware` object in a different time zone:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "naive = dt.datetime.now()\n", + "aware = dt.datetime.now(pytz.timezone('US/Mountain'))\n", + "print(f\"I am time zone naive: {naive}.\")\n", + "print(f\"I am time zone aware: {aware}.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `pytz.timezone()` method takes a time zone string and returns a `tzinfo` object which can be used to initialize the time zone. The `-06:00` denotes we are operating in a time zone six hours behind UTC.\n", + "\n", + "### Print Time with a Different Time Zone\n", + "\n", + "If you have data that are in UTC, and wish to convert them to another time zone, Mountain Time Zone for example, you will again make use of the `pytz` module. \n", + "\n", + "First, we will create a UTC time with the [utcnow()](https://docs.python.org/3/library/datetime.html#datetime.datetime.utcnow) method which inexplicably returns a time zone naive object so you must still specify the UTC time zone with the [replace()](https://docs.python.org/3/library/datetime.html#datetime.datetime.replace) method. We then create a \"US/Mountain\" `tzinfo` object as before, but this time we will use the [astimzone()](https://docs.python.org/3/library/datetime.html#datetime.datetime.astimezone) method to adjust the time to the specified time zone." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "utc = dt.datetime.utcnow().replace(tzinfo=pytz.utc)\n", + "print(\"The UTC time is {}.\".format(utc.strftime('%B %d, %Y, %-I:%M%p')))\n", + "mountaintz = pytz.timezone(\"US/Mountain\")\n", + "ny = utc.astimezone(mountaintz)\n", + "print(\"The 'US/Mountain' time is {}.\".format(ny.strftime('%B %d, %Y, %-I:%M%p')))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we've also used the `strftime()` method to format a human-friendly date and time string." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "The Python Standard Library contains several modules for dealing with date and time data. We saw how we can avoid some name ambiguities by aliasing the module names with `import datetime as dt` and `import time as tm`. The `tm.time()` method just returns the current [Unix time](#What-is-Unix-Time?) in seconds -- which can be useful for measuring elapsed time, but not all that useful for working with geophysical data.\n", + "\n", + "The `datetime` module contains various classes for storing, converting, comparing, and formatting date and time data on the Gregorian calendar. We saw how we can parse data files with date and time strings into `dt.datetime` objects using the `dt.datetime.strptime()` method. We also saw how we can do arithmetic on time and date data, making use of the `dt.timedelta` class to represent intervals of time.\n", + "\n", + "Finally, we looked at using the third-party [pytz](https://pypi.org/project/pytz/) module to handle timezone awareness and conversions.\n", + "\n", + "### What's Next?\n", + "\n", + "In subsequent tutorials, we will dig deeper into different time and date formats, and how they are handled by important Python modules such as Numpy, Pandas, and Xarray." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resources and References\n", + "\n", + "This notebook was adapted from material in [Unidata's Python Training](https://unidata.github.io/python-training/python/times_and_dates/).\n", + "\n", + "For further reading on these modules, take a look at the official documentation for:\n", + "- [time](https://docs.python.org/3/library/time.html)\n", + "- [datetime](https://docs.python.org/3/library/datetime.html)\n", + "- [pytz](https://pypi.org/project/pytz/)\n", + "\n", + "For more information on Python string formatting, try:\n", + "- [Python string documentation](https://docs.python.org/3/library/string.html)\n", + "- A nice tutorial from [RealPython](https://realpython.com/python-string-formatting/)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/core/pandas/enso_data.csv b/core/pandas/enso_data.csv deleted file mode 100644 index 1ada455af..000000000 --- a/core/pandas/enso_data.csv +++ /dev/null @@ -1,473 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-1993-03-01,27.17,0.65,27.54,0.4,28.6,0.41,27.66,0.44 -1993-04-01,26.44,0.97,28.45,0.95,28.93,0.43,28.59,0.81 -1993-05-01,25.15,0.95,28.16,1.08,29.13,0.34,28.82,0.97 -1993-06-01,23.76,0.94,27.11,0.68,29.17,0.33,28.28,0.63 -1993-07-01,22.06,0.46,25.77,0.15,29.2,0.4,27.55,0.33 -1993-08-01,21.05,0.4,24.93,-0.06,28.94,0.26,26.84,0.02 -1993-09-01,20.83,0.47,24.97,0.12,29.07,0.38,26.92,0.2 -1993-10-01,20.99,0.17,25.21,0.29,28.9,0.24,26.93,0.24 -1993-11-01,21.64,0.05,25.17,0.19,28.97,0.34,26.91,0.26 -1993-12-01,22.75,-0.04,25.32,0.18,28.9,0.41,26.76,0.19 -1994-01-01,24.32,-0.14,25.71,0.08,28.47,0.17,26.6,0.03 -1994-02-01,25.79,-0.28,26.07,-0.3,28.07,-0.03,26.59,-0.13 -1994-03-01,25.43,-1.09,26.89,-0.25,28.26,0.07,27.27,0.05 -1994-04-01,24.32,-1.15,27.06,-0.44,28.62,0.12,27.9,0.12 -1994-05-01,23.22,-0.98,26.97,-0.11,29.0,0.21,28.04,0.19 -1994-06-01,22.43,-0.39,26.5,0.07,29.18,0.34,27.99,0.34 -1994-07-01,21.21,-0.39,25.19,-0.43,29.4,0.6,27.35,0.13 -1994-08-01,19.7,-0.95,24.71,-0.28,29.46,0.78,27.35,0.53 -1994-09-01,20.16,-0.2,24.81,-0.04,29.23,0.54,27.0,0.28 -1994-10-01,21.53,0.71,25.53,0.61,29.45,0.79,27.49,0.8 -1994-11-01,22.41,0.82,25.87,0.89,29.63,1.0,27.87,1.22 -1994-12-01,23.61,0.82,26.07,0.93,29.5,1.01,27.87,1.3 -1995-01-01,25.33,0.87,26.34,0.71,29.2,0.9,27.55,0.98 -1995-02-01,26.43,0.36,26.87,0.5,29.01,0.91,27.45,0.73 -1995-03-01,26.12,-0.4,27.08,-0.06,28.96,0.77,27.63,0.41 -1995-04-01,24.47,-1.0,27.1,-0.4,28.89,0.39,27.93,0.15 -1995-05-01,23.1,-1.1,26.4,-0.68,29.15,0.36,27.73,-0.12 -1995-06-01,22.45,-0.37,26.2,-0.23,29.01,0.17,27.59,-0.06 -1995-07-01,21.23,-0.37,25.42,-0.2,28.78,-0.02,27.01,-0.21 -1995-08-01,20.01,-0.64,24.33,-0.66,28.43,-0.25,26.33,-0.49 -1995-09-01,20.17,-0.19,24.02,-0.83,28.25,-0.44,25.96,-0.76 -1995-10-01,20.15,-0.67,24.01,-0.91,28.07,-0.59,25.67,-1.02 -1995-11-01,21.2,-0.39,24.03,-0.95,27.97,-0.66,25.66,-0.99 -1995-12-01,22.02,-0.77,24.19,-0.95,28.07,-0.42,25.57,-1.0 -1996-01-01,23.84,-0.62,24.96,-0.67,27.92,-0.38,25.74,-0.83 -1996-02-01,25.71,-0.36,25.72,-0.65,27.57,-0.53,25.85,-0.87 -1996-03-01,26.09,-0.43,26.71,-0.43,27.71,-0.48,26.62,-0.6 -1996-04-01,23.85,-1.62,26.72,-0.78,28.07,-0.43,27.36,-0.42 -1996-05-01,22.89,-1.31,26.33,-0.75,28.43,-0.36,27.37,-0.48 -1996-06-01,21.56,-1.26,25.89,-0.54,28.55,-0.29,27.32,-0.33 -1996-07-01,20.02,-1.58,25.35,-0.27,28.5,-0.3,27.09,-0.13 -1996-08-01,19.53,-1.12,24.6,-0.39,28.54,-0.14,26.56,-0.26 -1996-09-01,19.24,-1.12,24.37,-0.48,28.43,-0.26,26.35,-0.37 -1996-10-01,19.95,-0.87,24.37,-0.55,28.42,-0.24,26.24,-0.45 -1996-11-01,20.26,-1.33,24.38,-0.6,28.33,-0.3,26.19,-0.46 -1996-12-01,21.61,-1.18,24.2,-0.94,28.44,-0.05,26.02,-0.55 -1997-01-01,23.67,-0.79,24.7,-0.93,28.41,0.11,25.96,-0.61 -1997-02-01,25.74,-0.33,25.75,-0.62,28.33,0.23,26.36,-0.36 -1997-03-01,26.95,0.43,26.98,-0.16,28.52,0.33,27.03,-0.19 -1997-04-01,26.64,1.17,27.59,0.09,29.32,0.82,28.03,0.25 -1997-05-01,26.71,2.51,28.06,0.98,29.45,0.66,28.6,0.75 -1997-06-01,26.27,3.45,28.14,1.71,29.4,0.56,28.94,1.29 -1997-07-01,25.59,3.99,28.01,2.39,29.5,0.7,28.92,1.7 -1997-08-01,24.8,4.15,27.84,2.85,29.26,0.58,28.84,2.02 -1997-09-01,24.4,4.04,27.84,2.99,29.32,0.63,28.93,2.21 -1997-10-01,24.58,3.76,28.17,3.25,29.32,0.66,29.23,2.54 -1997-11-01,25.63,4.04,28.55,3.57,29.49,0.86,29.32,2.67 -1997-12-01,26.92,4.13,28.76,3.62,29.32,0.83,29.26,2.69 -1998-01-01,28.22,3.76,28.94,3.31,29.01,0.71,29.1,2.53 -1998-02-01,28.98,2.91,28.93,2.56,28.87,0.77,28.86,2.14 -1998-03-01,29.15,2.63,29.14,2.0,28.65,0.46,28.67,1.45 -1998-04-01,28.61,3.14,29.09,1.59,28.53,0.03,28.56,0.78 -1998-05-01,27.69,3.49,28.17,1.09,28.71,-0.08,28.47,0.62 -1998-06-01,25.18,2.36,26.0,-0.43,28.61,-0.23,26.72,-0.93 -1998-07-01,23.43,1.83,25.24,-0.38,28.07,-0.73,25.94,-1.28 -1998-08-01,21.77,1.12,24.63,-0.36,27.77,-0.91,25.49,-1.33 -1998-09-01,20.87,0.51,24.19,-0.66,27.88,-0.81,25.61,-1.11 -1998-10-01,21.16,0.34,24.06,-0.86,27.33,-1.33,25.34,-1.35 -1998-11-01,21.43,-0.16,24.11,-0.87,27.23,-1.4,25.18,-1.47 -1998-12-01,22.56,-0.23,23.86,-1.28,27.11,-1.38,24.79,-1.78 -1999-01-01,23.73,-0.73,24.41,-1.22,26.59,-1.71,24.9,-1.67 -1999-02-01,25.64,-0.43,25.57,-0.8,26.52,-1.58,25.41,-1.31 -1999-03-01,26.62,0.1,26.67,-0.47,26.9,-1.29,26.25,-0.97 -1999-04-01,24.3,-1.17,26.66,-0.84,27.35,-1.15,26.84,-0.94 -1999-05-01,23.46,-0.74,26.44,-0.64,27.87,-0.92,26.97,-0.88 -1999-06-01,21.83,-0.99,25.59,-0.84,28.01,-0.83,26.6,-1.05 -1999-07-01,20.44,-1.16,24.85,-0.77,27.92,-0.88,26.35,-0.87 -1999-08-01,19.75,-0.9,24.02,-0.97,27.73,-0.95,25.59,-1.23 -1999-09-01,19.23,-1.13,23.72,-1.13,27.82,-0.87,25.71,-1.01 -1999-10-01,20.05,-0.77,23.75,-1.17,27.85,-0.81,25.64,-1.05 -1999-11-01,20.51,-1.08,23.46,-1.52,27.56,-1.07,25.12,-1.53 -1999-12-01,21.72,-1.07,23.54,-1.6,27.23,-1.26,24.9,-1.67 -2000-01-01,23.86,-0.6,23.88,-1.75,26.96,-1.34,24.65,-1.92 -2000-02-01,25.71,-0.36,25.31,-1.06,26.66,-1.44,25.19,-1.53 -2000-03-01,26.19,-0.33,26.61,-0.53,26.76,-1.43,26.08,-1.14 -2000-04-01,25.84,0.37,27.46,-0.04,27.37,-1.13,27.01,-0.77 -2000-05-01,24.1,-0.1,26.8,-0.28,27.81,-0.98,27.12,-0.73 -2000-06-01,22.25,-0.57,25.84,-0.59,28.11,-0.73,27.03,-0.62 -2000-07-01,20.59,-1.01,25.13,-0.49,28.2,-0.6,26.72,-0.5 -2000-08-01,20.1,-0.55,24.47,-0.52,28.32,-0.36,26.45,-0.37 -2000-09-01,19.94,-0.42,24.35,-0.5,28.44,-0.25,26.21,-0.51 -2000-10-01,20.37,-0.45,24.41,-0.51,28.17,-0.49,25.96,-0.73 -2000-11-01,20.6,-0.99,24.17,-0.81,28.09,-0.54,25.78,-0.87 -2000-12-01,22.22,-0.57,24.43,-0.71,27.6,-0.89,25.59,-0.98 -2001-01-01,23.88,-0.58,24.99,-0.64,27.5,-0.8,25.74,-0.83 -2001-02-01,25.91,-0.16,26.06,-0.31,27.27,-0.83,26.11,-0.61 -2001-03-01,27.44,0.92,27.23,0.09,27.62,-0.57,26.84,-0.38 -2001-04-01,26.69,1.22,27.52,0.02,28.19,-0.31,27.52,-0.26 -2001-05-01,23.77,-0.43,26.89,-0.19,28.64,-0.15,27.6,-0.25 -2001-06-01,21.74,-1.08,26.35,-0.08,28.83,-0.01,27.68,0.03 -2001-07-01,20.88,-0.72,25.43,-0.19,29.06,0.26,27.32,0.1 -2001-08-01,19.9,-0.75,24.72,-0.27,28.96,0.28,26.87,0.05 -2001-09-01,19.39,-0.97,24.27,-0.58,29.14,0.45,26.55,-0.17 -2001-10-01,19.52,-1.3,24.45,-0.47,29.01,0.35,26.59,-0.1 -2001-11-01,20.49,-1.1,24.35,-0.63,28.96,0.33,26.45,-0.2 -2001-12-01,21.96,-0.83,24.6,-0.54,28.6,0.11,26.17,-0.4 -2002-01-01,23.64,-0.82,25.09,-0.54,28.81,0.51,26.5,-0.07 -2002-02-01,26.06,-0.01,26.21,-0.16,28.76,0.66,26.95,0.23 -2002-03-01,27.53,1.01,27.22,0.08,28.68,0.49,27.32,0.1 -2002-04-01,26.53,1.06,27.56,0.06,29.09,0.59,27.94,0.16 -2002-05-01,24.8,0.6,27.24,0.16,29.45,0.66,28.15,0.3 -2002-06-01,22.67,-0.15,27.06,0.63,29.63,0.79,28.43,0.78 -2002-07-01,21.01,-0.59,26.03,0.41,29.49,0.69,27.98,0.76 -2002-08-01,19.94,-0.71,25.47,0.48,29.4,0.72,27.79,0.97 -2002-09-01,19.89,-0.47,25.54,0.69,29.44,0.75,27.83,1.11 -2002-10-01,21.16,0.34,25.85,0.93,29.56,0.9,28.05,1.36 -2002-11-01,22.25,0.66,26.37,1.39,29.83,1.2,28.27,1.62 -2002-12-01,23.44,0.65,26.48,1.34,29.49,1.0,28.09,1.52 -2003-01-01,24.38,-0.08,26.38,0.75,29.25,0.95,27.76,1.19 -2003-02-01,25.81,-0.26,26.7,0.33,29.03,0.93,27.49,0.77 -2003-03-01,25.97,-0.55,27.28,0.14,29.03,0.84,27.81,0.59 -2003-04-01,24.44,-1.03,27.15,-0.35,28.96,0.46,27.81,0.03 -2003-05-01,22.49,-1.71,26.14,-0.94,28.92,0.13,27.37,-0.48 -2003-06-01,21.58,-1.24,25.83,-0.6,29.09,0.25,27.48,-0.17 -2003-07-01,20.75,-0.85,25.75,0.13,29.11,0.31,27.43,0.21 -2003-08-01,20.14,-0.51,25.04,0.05,29.05,0.37,26.85,0.03 -2003-09-01,20.0,-0.36,24.97,0.12,29.02,0.33,26.96,0.24 -2003-10-01,20.99,0.17,25.33,0.41,29.22,0.56,27.19,0.5 -2003-11-01,21.92,0.33,25.4,0.42,29.31,0.68,27.05,0.4 -2003-12-01,22.99,0.2,25.56,0.42,29.02,0.53,26.89,0.32 -2004-01-01,24.6,0.14,25.92,0.29,28.83,0.53,26.74,0.17 -2004-02-01,25.81,-0.26,26.46,0.09,28.59,0.49,26.86,0.14 -2004-03-01,25.94,-0.58,27.16,0.02,28.43,0.24,27.1,-0.12 -2004-04-01,25.32,-0.15,27.37,-0.13,28.75,0.25,27.84,0.06 -2004-05-01,23.05,-1.15,26.72,-0.36,29.16,0.37,28.06,0.21 -2004-06-01,21.6,-1.22,26.27,-0.16,29.17,0.33,27.76,0.11 -2004-07-01,20.71,-0.89,25.41,-0.21,29.39,0.59,27.69,0.47 -2004-08-01,19.62,-1.03,25.05,0.06,29.31,0.63,27.54,0.72 -2004-09-01,20.07,-0.29,25.17,0.32,29.6,0.91,27.47,0.75 -2004-10-01,20.92,0.1,25.32,0.4,29.55,0.89,27.38,0.69 -2004-11-01,21.96,0.37,25.46,0.48,29.57,0.94,27.31,0.66 -2004-12-01,22.94,0.15,25.77,0.63,29.4,0.91,27.31,0.74 -2005-01-01,24.47,0.01,25.89,0.26,29.21,0.91,27.1,0.53 -2005-02-01,25.49,-0.58,26.2,-0.17,28.83,0.73,26.96,0.24 -2005-03-01,25.6,-0.92,27.01,-0.13,28.91,0.72,27.55,0.33 -2005-04-01,24.9,-0.57,27.77,0.27,28.96,0.46,28.07,0.29 -2005-05-01,24.4,0.2,27.48,0.4,29.18,0.39,28.2,0.35 -2005-06-01,22.47,-0.35,26.81,0.38,29.18,0.34,28.05,0.4 -2005-07-01,21.18,-0.42,25.93,0.31,29.05,0.25,27.47,0.25 -2005-08-01,20.61,-0.04,25.19,0.2,28.86,0.18,26.88,0.06 -2005-09-01,19.71,-0.65,24.57,-0.28,28.84,0.15,26.63,-0.09 -2005-10-01,19.72,-1.1,24.69,-0.23,28.89,0.23,26.75,0.06 -2005-11-01,20.62,-0.97,24.28,-0.7,28.67,0.04,26.34,-0.31 -2005-12-01,22.29,-0.5,24.28,-0.86,28.35,-0.14,25.89,-0.68 -2006-01-01,24.33,-0.13,25.0,-0.63,27.68,-0.62,25.64,-0.93 -2006-02-01,26.46,0.39,26.08,-0.29,27.39,-0.71,26.08,-0.64 -2006-03-01,26.77,0.25,26.54,-0.6,27.79,-0.4,26.57,-0.65 -2006-04-01,24.15,-1.32,27.25,-0.25,28.38,-0.12,27.59,-0.19 -2006-05-01,23.86,-0.34,27.04,-0.04,28.91,0.12,27.91,0.06 -2006-06-01,22.77,-0.05,26.44,0.01,29.15,0.31,27.85,0.2 -2006-07-01,22.19,0.59,25.8,0.18,29.07,0.27,27.35,0.13 -2006-08-01,21.59,0.94,25.45,0.46,29.22,0.54,27.22,0.4 -2006-09-01,21.43,1.07,25.74,0.89,29.4,0.71,27.34,0.62 -2006-10-01,22.22,1.4,25.96,1.04,29.44,0.78,27.47,0.78 -2006-11-01,22.69,1.1,26.07,1.09,29.63,1.0,27.73,1.08 -2006-12-01,23.45,0.66,26.36,1.22,29.47,0.98,27.76,1.19 -2007-01-01,24.99,0.53,26.5,0.87,28.93,0.63,27.26,0.69 -2007-02-01,26.24,0.17,26.45,0.08,28.62,0.52,26.81,0.09 -2007-03-01,25.74,-0.78,26.79,-0.35,28.57,0.38,27.18,-0.04 -2007-04-01,24.3,-1.17,27.13,-0.37,28.7,0.2,27.78,0.0 -2007-05-01,22.73,-1.47,26.35,-0.73,28.86,0.07,27.57,-0.28 -2007-06-01,21.59,-1.23,25.83,-0.6,28.98,0.14,27.55,-0.1 -2007-07-01,20.27,-1.33,24.79,-0.83,28.81,0.01,26.79,-0.43 -2007-08-01,19.16,-1.49,23.86,-1.13,28.58,-0.1,26.2,-0.62 -2007-09-01,18.57,-1.79,23.52,-1.33,28.12,-0.57,25.77,-0.95 -2007-10-01,18.8,-2.02,23.36,-1.56,27.86,-0.8,25.22,-1.47 -2007-11-01,19.49,-2.1,23.17,-1.81,27.42,-1.21,25.06,-1.59 -2007-12-01,21.02,-1.77,23.59,-1.55,27.3,-1.19,24.97,-1.6 -2008-01-01,23.86,-0.6,24.13,-1.5,26.62,-1.68,24.71,-1.86 -2008-02-01,26.32,0.25,25.05,-1.32,26.43,-1.67,24.83,-1.89 -2008-03-01,27.3,0.78,26.56,-0.58,26.84,-1.35,26.07,-1.15 -2008-04-01,25.89,0.42,27.18,-0.32,27.44,-1.06,26.83,-0.95 -2008-05-01,24.41,0.21,27.08,0.0,27.9,-0.89,27.18,-0.67 -2008-06-01,23.55,0.73,26.53,0.1,28.08,-0.76,27.17,-0.48 -2008-07-01,22.63,1.03,26.12,0.5,28.25,-0.55,27.19,-0.03 -2008-08-01,21.79,1.14,25.63,0.64,28.18,-0.5,26.85,0.03 -2008-09-01,21.19,0.83,25.09,0.24,28.14,-0.55,26.44,-0.28 -2008-10-01,20.75,-0.07,24.79,-0.13,28.29,-0.37,26.33,-0.36 -2008-11-01,21.44,-0.15,24.75,-0.23,28.08,-0.55,26.3,-0.35 -2008-12-01,22.43,-0.36,24.6,-0.54,27.72,-0.77,25.74,-0.83 -2009-01-01,24.42,-0.1,25.03,-0.6,27.42,-0.88,25.54,-1.03 -2009-02-01,26.03,-0.11,25.85,-0.52,27.37,-0.73,26.04,-0.68 -2009-03-01,26.38,-0.26,26.44,-0.7,27.79,-0.4,26.67,-0.55 -2009-04-01,25.98,0.37,27.39,-0.11,28.37,-0.13,27.5,-0.27 -2009-05-01,24.83,0.56,27.4,0.32,28.99,0.2,28.03,0.18 -2009-06-01,23.73,0.85,27.12,0.69,29.2,0.36,28.11,0.47 -2009-07-01,22.63,1.02,26.56,0.94,29.21,0.4,27.94,0.72 -2009-08-01,21.64,1.0,25.94,0.95,29.21,0.53,27.53,0.71 -2009-09-01,20.82,0.47,25.66,0.8,29.28,0.58,27.47,0.75 -2009-10-01,20.96,0.17,25.73,0.81,29.65,0.99,27.63,0.94 -2009-11-01,22.11,0.51,26.23,1.26,29.88,1.25,28.19,1.54 -2009-12-01,23.16,0.35,26.67,1.53,29.67,1.18,28.3,1.72 -2010-01-01,24.82,0.3,26.63,1.0,29.51,1.21,28.07,1.5 -2010-02-01,26.08,-0.06,27.12,0.75,29.1,1.0,27.94,1.22 -2010-03-01,26.24,-0.4,27.73,0.6,29.21,1.02,28.29,1.08 -2010-04-01,26.05,0.45,28.05,0.55,29.25,0.74,28.36,0.59 -2010-05-01,24.28,0.0,26.97,-0.11,29.03,0.24,27.68,-0.17 -2010-06-01,22.6,-0.27,25.75,-0.68,28.64,-0.21,27.0,-0.65 -2010-07-01,20.08,-1.54,24.53,-1.09,28.09,-0.71,26.09,-1.13 -2010-08-01,19.27,-1.37,23.87,-1.12,27.47,-1.2,25.5,-1.32 -2010-09-01,18.9,-1.44,23.59,-1.26,27.13,-1.56,25.07,-1.65 -2010-10-01,19.06,-1.73,23.25,-1.66,27.06,-1.6,25.01,-1.68 -2010-11-01,20.03,-1.56,23.4,-1.58,27.07,-1.57,25.07,-1.58 -2010-12-01,21.48,-1.34,23.5,-1.64,26.89,-1.6,24.95,-1.62 -2011-01-01,24.08,-0.44,24.31,-1.32,26.72,-1.58,24.93,-1.64 -2011-02-01,26.22,0.08,25.55,-0.82,26.95,-1.15,25.46,-1.27 -2011-03-01,26.21,-0.43,26.39,-0.75,27.42,-0.77,26.23,-0.98 -2011-04-01,25.76,0.16,27.18,-0.32,27.86,-0.64,27.02,-0.76 -2011-05-01,24.89,0.62,26.94,-0.14,28.28,-0.51,27.42,-0.43 -2011-06-01,23.72,0.85,26.54,0.1,28.47,-0.37,27.46,-0.18 -2011-07-01,22.07,0.45,25.61,-0.01,28.47,-0.33,26.96,-0.26 -2011-08-01,20.64,0.0,24.58,-0.42,28.32,-0.36,26.19,-0.64 -2011-09-01,19.75,-0.59,24.22,-0.63,28.05,-0.64,25.98,-0.74 -2011-10-01,20.19,-0.6,23.97,-0.95,27.94,-0.72,25.72,-0.97 -2011-11-01,20.79,-0.8,23.89,-1.09,27.87,-0.77,25.6,-1.05 -2011-12-01,21.85,-0.96,24.2,-0.94,27.39,-1.1,25.53,-1.04 -2012-01-01,23.88,-0.64,24.9,-0.73,27.09,-1.21,25.49,-1.08 -2012-02-01,26.3,0.16,26.19,-0.18,27.2,-0.9,26.03,-0.69 -2012-03-01,26.91,0.27,26.92,-0.21,27.53,-0.66,26.63,-0.58 -2012-04-01,26.9,1.3,27.58,0.08,28.16,-0.34,27.38,-0.39 -2012-05-01,25.48,1.2,27.23,0.15,28.53,-0.26,27.8,-0.05 -2012-06-01,24.47,1.59,27.09,0.66,28.73,-0.11,27.95,0.31 -2012-07-01,22.61,0.99,26.54,0.92,28.86,0.06,27.75,0.53 -2012-08-01,20.99,0.35,25.72,0.73,29.1,0.42,27.55,0.73 -2012-09-01,20.83,0.49,25.28,0.43,29.12,0.43,27.24,0.51 -2012-10-01,20.68,-0.11,24.93,0.01,29.16,0.5,26.98,0.29 -2012-11-01,21.21,-0.38,25.11,0.14,29.17,0.54,27.01,0.36 -2012-12-01,22.13,-0.68,24.91,-0.23,28.71,0.23,26.46,-0.11 -2013-01-01,24.0,-0.52,25.06,-0.57,28.28,-0.02,26.16,-0.41 -2013-02-01,25.74,-0.41,25.9,-0.46,28.06,-0.04,26.32,-0.4 -2013-03-01,26.71,0.07,27.21,0.07,27.95,-0.24,27.0,-0.22 -2013-04-01,24.74,-0.86,27.35,-0.15,28.47,-0.03,27.68,-0.1 -2013-05-01,22.89,-1.38,26.39,-0.69,28.71,-0.08,27.57,-0.27 -2013-06-01,21.48,-1.4,25.8,-0.64,28.76,-0.08,27.43,-0.21 -2013-07-01,20.29,-1.33,24.97,-0.66,28.76,-0.04,26.91,-0.31 -2013-08-01,19.66,-0.98,24.44,-0.55,28.71,0.03,26.54,-0.28 -2013-09-01,19.78,-0.57,24.72,-0.13,28.7,0.01,26.65,-0.07 -2013-10-01,20.16,-0.63,24.7,-0.21,28.7,0.04,26.36,-0.33 -2013-11-01,21.06,-0.54,24.81,-0.17,28.91,0.27,26.65,0.01 -2013-12-01,22.61,-0.2,25.1,-0.04,28.64,0.15,26.53,-0.04 -2014-01-01,24.79,0.27,25.26,-0.37,28.14,-0.17,26.06,-0.51 -2014-02-01,25.4,-0.75,25.56,-0.81,28.37,0.27,26.18,-0.55 -2014-03-01,25.86,-0.78,26.9,-0.24,28.71,0.52,26.99,-0.22 -2014-04-01,25.23,-0.37,27.73,0.23,29.13,0.63,28.01,0.24 -2014-05-01,25.57,1.3,27.69,0.61,29.56,0.77,28.31,0.46 -2014-06-01,24.51,1.64,27.32,0.89,29.43,0.59,28.11,0.46 -2014-07-01,22.98,1.36,26.27,0.65,29.09,0.29,27.4,0.18 -2014-08-01,21.91,1.27,25.51,0.52,29.14,0.46,27.02,0.2 -2014-09-01,21.3,0.96,25.31,0.45,29.34,0.65,27.17,0.45 -2014-10-01,21.54,0.75,25.58,0.66,29.31,0.64,27.17,0.49 -2014-11-01,22.33,0.74,25.88,0.91,29.52,0.88,27.5,0.85 -2014-12-01,22.9,0.08,25.94,0.8,29.4,0.91,27.35,0.78 -2015-01-01,24.13,-0.39,25.99,0.36,29.16,0.86,27.1,0.53 -2015-02-01,25.59,-0.55,26.55,0.18,29.12,1.02,27.29,0.56 -2015-03-01,26.69,0.06,27.29,0.15,29.32,1.13,27.79,0.58 -2015-04-01,26.95,1.35,28.17,0.67,29.73,1.23,28.56,0.78 -2015-05-01,26.71,2.43,28.28,1.19,29.88,1.09,28.88,1.03 -2015-06-01,25.42,2.54,28.1,1.66,29.93,1.09,28.96,1.32 -2015-07-01,24.48,2.87,27.79,2.17,29.8,1.0,28.82,1.6 -2015-08-01,22.88,2.24,27.33,2.34,29.66,0.98,28.89,2.07 -2015-09-01,22.91,2.57,27.48,2.63,29.73,1.04,29.0,2.28 -2015-10-01,23.31,2.52,27.58,2.66,29.79,1.12,29.15,2.46 -2015-11-01,23.83,2.24,27.91,2.93,30.3,1.67,29.6,2.95 -2015-12-01,25.01,2.19,27.99,2.85,30.11,1.63,29.39,2.82 -2016-01-01,25.93,1.41,28.21,2.58,29.65,1.35,29.17,2.6 -2016-02-01,26.81,0.67,28.36,1.99,29.55,1.45,29.12,2.4 -2016-03-01,27.57,0.93,28.7,1.57,29.53,1.34,28.9,1.68 -2016-04-01,25.83,0.23,28.34,0.84,29.39,0.89,28.87,1.09 -2016-05-01,24.55,0.27,27.11,0.03,29.39,0.6,28.15,0.3 -2016-06-01,23.17,0.29,26.31,-0.12,29.36,0.52,27.53,-0.12 -2016-07-01,21.79,0.17,25.14,-0.48,29.06,0.26,26.73,-0.49 -2016-08-01,21.03,0.39,24.53,-0.46,28.68,-0.0,26.28,-0.54 -2016-09-01,20.87,0.53,24.67,-0.18,28.48,-0.21,26.11,-0.61 -2016-10-01,21.18,0.39,24.47,-0.45,28.26,-0.4,25.96,-0.73 -2016-11-01,21.68,0.09,24.58,-0.4,28.27,-0.37,26.1,-0.55 -2016-12-01,23.35,0.53,24.78,-0.36,28.35,-0.14,26.16,-0.41 -2017-01-01,25.75,1.23,25.61,-0.02,28.18,-0.12,26.25,-0.32 -2017-02-01,27.76,1.62,27.0,0.63,28.03,-0.07,26.87,0.14 -2017-03-01,28.52,1.89,27.7,0.57,28.13,-0.06,27.34,0.13 -2017-04-01,26.53,0.93,28.09,0.59,28.65,0.15,28.1,0.32 -2017-05-01,25.06,0.78,27.6,0.51,29.08,0.29,28.3,0.46 -2017-06-01,22.98,0.1,26.73,0.3,29.39,0.55,28.19,0.55 -2017-07-01,21.54,-0.07,25.85,0.23,29.21,0.4,27.61,0.39 -2017-08-01,20.19,-0.45,24.82,-0.17,28.87,0.19,26.67,-0.15 -2017-09-01,19.67,-0.67,24.17,-0.68,28.69,0.0,26.29,-0.43 -2017-10-01,19.45,-1.34,24.28,-0.64,28.55,-0.11,26.23,-0.46 -2017-11-01,20.44,-1.15,23.92,-1.05,28.46,-0.18,25.79,-0.86 -2017-12-01,21.44,-1.38,24.05,-1.09,28.24,-0.25,25.8,-0.77 -2018-01-01,23.71,-0.81,24.48,-1.14,28.03,-0.27,25.82,-0.75 -2018-02-01,25.57,-0.57,25.36,-1.01,27.86,-0.24,25.83,-0.9 -2018-03-01,25.83,-0.8,26.37,-0.76,28.14,-0.05,26.48,-0.73 -2018-04-01,24.58,-1.02,27.12,-0.38,28.63,0.12,27.42,-0.36 -2018-05-01,23.73,-0.54,26.94,-0.15,29.01,0.22,27.72,-0.13 -2018-06-01,22.19,-0.69,26.72,0.29,29.16,0.32,27.85,0.2 -2018-07-01,21.43,-0.19,26.05,0.43,29.1,0.3,27.52,0.3 -2018-08-01,20.66,0.02,25.14,0.15,29.19,0.51,27.11,0.29 -2018-09-01,20.31,-0.03,25.2,0.35,29.17,0.48,27.1,0.38 -2018-10-01,21.23,0.43,25.78,0.86,29.61,0.95,27.55,0.86 -2018-11-01,22.27,0.68,26.02,1.05,29.59,0.95,27.64,0.99 -2018-12-01,23.6,0.78,26.12,0.98,29.52,1.03,27.53,0.96 -2019-01-01,25.1,0.57,26.17,0.55,29.0,0.65,27.08,0.52 -2019-02-01,26.45,0.24,26.91,0.55,29.06,0.86,27.41,0.68 -2019-03-01,26.8,0.07,27.89,0.71,29.1,0.77,28.22,0.95 -2019-04-01,25.68,-0.01,28.17,0.56,29.24,0.56,28.6,0.71 -2019-05-01,24.38,-0.08,27.69,0.51,29.58,0.63,28.57,0.62 -2019-06-01,22.62,-0.38,26.81,0.27,29.62,0.6,28.24,0.47 -2019-07-01,21.34,-0.33,25.68,-0.07,29.73,0.77,27.63,0.3 -2019-08-01,20.2,-0.46,24.89,-0.15,29.5,0.68,26.97,0.1 -2019-09-01,19.5,-0.83,24.61,-0.23,29.33,0.55,26.7,-0.03 -2019-10-01,20.02,-0.71,25.12,0.17,29.64,0.88,27.31,0.59 -2019-11-01,21.32,-0.22,25.46,0.41,29.47,0.7,27.26,0.51 -2019-12-01,23.16,0.4,25.47,0.26,29.5,0.91,27.07,0.43 -2020-01-01,24.55,0.02,25.81,0.2,29.28,0.93,27.09,0.53 -2020-02-01,26.56,0.35,26.61,0.24,29.17,0.98,27.14,0.41 -2020-03-01,27.11,0.38,27.43,0.24,29.22,0.89,27.82,0.55 -2020-04-01,26.0,0.32,28.01,0.4,29.29,0.61,28.32,0.44 -2020-05-01,24.24,-0.22,26.82,-0.36,28.94,-0.01,27.59,-0.36 -2020-06-01,22.13,-0.87,25.75,-0.79,29.07,0.05,27.3,-0.47 -2020-07-01,20.44,-1.23,25.08,-0.66,28.83,-0.13,26.89,-0.44 -2020-08-01,19.69,-0.97,24.42,-0.62,28.47,-0.35,26.18,-0.69 -2020-09-01,19.48,-0.85,23.58,-1.26,28.29,-0.49,25.77,-0.96 -2020-10-01,19.67,-1.07,23.61,-1.34,27.89,-0.87,25.3,-1.42 -2020-11-01,20.94,-0.61,23.82,-1.23,27.91,-0.86,25.34,-1.42 -2020-12-01,22.16,-0.6,24.38,-0.83,27.65,-0.95,25.53,-1.12 -2021-01-01,23.89,-0.64,25.06,-0.55,27.1,-1.25,25.58,-0.99 -2021-02-01,25.55,-0.66,25.8,-0.57,27.2,-1.0,25.81,-0.92 -2021-03-01,26.48,-0.26,26.8,-0.39,27.79,-0.55,26.75,-0.51 -2021-04-01,24.89,-0.8,26.96,-0.65,28.47,-0.21,27.4,-0.49 diff --git a/core/pandas/pandas_fullNotebook.ipynb b/core/pandas/pandas_fullNotebook.ipynb index 1781edbdc..666e1c0c4 100644 --- a/core/pandas/pandas_fullNotebook.ipynb +++ b/core/pandas/pandas_fullNotebook.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "1d7850d7", + "id": "9f59d3f1", "metadata": {}, "source": [ "
\"pandas
\n", @@ -24,7 +24,7 @@ }, { "cell_type": "markdown", - "id": "449d3086-2fa1-4520-a390-8ba043db7258", + "id": "b8627bb7", "metadata": {}, "source": [ "
\n", @@ -33,17 +33,18 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "0912eaec", + "execution_count": null, + "id": "daf58736", "metadata": {}, "outputs": [], "source": [ - "import pandas as pd" + "import pandas as pd\n", + "from pythia_datasets import DATASETS # Project Pythia's custom store of example data" ] }, { "cell_type": "markdown", - "id": "85f7c6a5", + "id": "dab3da7f", "metadata": {}, "source": [ "## The pandas [`DataFrame`](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe)...\n", @@ -56,19 +57,47 @@ "Let's take a look by reading in some `.csv` data, which comes from the NCDC teleconnections database, including various El Niño Southern Oscillation (ENSO) indices! [[ref](https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/)]." ] }, + { + "cell_type": "markdown", + "id": "1f2329c8", + "metadata": {}, + "source": [ + "
\n", + " Tip: Here we're getting the data from Project Pythia's custom library of example data, which we already imported above with from pythia_datasets import DATASETS. The DATASETS.fetch() method will automatically download and cache our example data file enso_data.csv locally.\n", + "
" + ] + }, { "cell_type": "code", - "execution_count": 2, - "id": "ed076336-6bb7-4008-ba1c-451f08b9b31f", + "execution_count": null, + "id": "0be820cd", "metadata": {}, "outputs": [], "source": [ - "df = pd.read_csv('enso_data.csv')" + "filepath = DATASETS.fetch('enso_data.csv')" ] }, { "cell_type": "markdown", - "id": "ecd8e8f4-2857-43e7-a6c3-8484f36e2d8f", + "id": "68316c6c", + "metadata": {}, + "source": [ + "Once we have a valid path to a data file that Pandas knows how to read, we can open it like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e99652d5", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(filepath)" + ] + }, + { + "cell_type": "markdown", + "id": "ae9dcbbd", "metadata": {}, "source": [ "If we print out our dataframe, you will notice that is text based, which is okay, but not the \"best\" looking output" @@ -76,51 +105,17 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "fd65e146-f213-4156-859c-1f3b30c5ea24", + "execution_count": null, + "id": "25a23571", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime Nino12 Nino12anom Nino3 Nino3anom Nino4 Nino4anom \\\n", - "0 1982-01-01 24.29 -0.17 25.87 0.24 28.30 0.00 \n", - "1 1982-02-01 25.49 -0.58 26.38 0.01 28.21 0.11 \n", - "2 1982-03-01 25.21 -1.31 26.98 -0.16 28.41 0.22 \n", - "3 1982-04-01 24.50 -0.97 27.68 0.18 28.92 0.42 \n", - "4 1982-05-01 23.97 -0.23 27.79 0.71 29.49 0.70 \n", - ".. ... ... ... ... ... ... ... \n", - "467 2020-12-01 22.16 -0.60 24.38 -0.83 27.65 -0.95 \n", - "468 2021-01-01 23.89 -0.64 25.06 -0.55 27.10 -1.25 \n", - "469 2021-02-01 25.55 -0.66 25.80 -0.57 27.20 -1.00 \n", - "470 2021-03-01 26.48 -0.26 26.80 -0.39 27.79 -0.55 \n", - "471 2021-04-01 24.89 -0.80 26.96 -0.65 28.47 -0.21 \n", - "\n", - " Nino34 Nino34anom \n", - "0 26.72 0.15 \n", - "1 26.70 -0.02 \n", - "2 27.20 -0.02 \n", - "3 28.02 0.24 \n", - "4 28.54 0.69 \n", - ".. ... ... \n", - "467 25.53 -1.12 \n", - "468 25.58 -0.99 \n", - "469 25.81 -0.92 \n", - "470 26.75 -0.51 \n", - "471 27.40 -0.49 \n", - "\n", - "[472 rows x 9 columns]\n" - ] - } - ], + "outputs": [], "source": [ "print(df)" ] }, { "cell_type": "markdown", - "id": "11e7d697-f5ae-4487-a74c-2406afcdfafb", + "id": "f22bb442", "metadata": {}, "source": [ "Instead, if we just use the pandas dataframe itself (without wrapping it in `print`), we have a nicely rendered table which is native to pandas and Jupyter Notebooks. See how much nicer that looks?" @@ -128,222 +123,17 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "4ff69abc", + "execution_count": null, + "id": "b8942e69", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimeNino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anom
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31982-04-0124.50-0.9727.680.1828.920.4228.020.24
41982-05-0123.97-0.2327.790.7129.490.7028.540.69
..............................
4672020-12-0122.16-0.6024.38-0.8327.65-0.9525.53-1.12
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472 rows × 9 columns

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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anom
datetime
1982-01-0124.29-0.1725.870.2428.300.0026.720.15
1982-02-0125.49-0.5826.380.0128.210.1126.70-0.02
1982-03-0125.21-1.3126.98-0.1628.410.2227.20-0.02
1982-04-0124.50-0.9727.680.1828.920.4228.020.24
1982-05-0123.97-0.2327.790.7129.490.7028.540.69
...........................
2020-12-0122.16-0.6024.38-0.8327.65-0.9525.53-1.12
2021-01-0123.89-0.6425.06-0.5527.10-1.2525.58-0.99
2021-02-0125.55-0.6625.80-0.5727.20-1.0025.81-0.92
2021-03-0126.48-0.2626.80-0.3927.79-0.5526.75-0.51
2021-04-0124.89-0.8026.96-0.6528.47-0.2127.40-0.49
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472 rows × 8 columns

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" - ], - "text/plain": [ - " Nino12 Nino12anom Nino3 Nino3anom Nino4 Nino4anom Nino34 \\\n", - "datetime \n", - "1982-01-01 24.29 -0.17 25.87 0.24 28.30 0.00 26.72 \n", - "1982-02-01 25.49 -0.58 26.38 0.01 28.21 0.11 26.70 \n", - "1982-03-01 25.21 -1.31 26.98 -0.16 28.41 0.22 27.20 \n", - "1982-04-01 24.50 -0.97 27.68 0.18 28.92 0.42 28.02 \n", - "1982-05-01 23.97 -0.23 27.79 0.71 29.49 0.70 28.54 \n", - "... ... ... ... ... ... ... ... \n", - "2020-12-01 22.16 -0.60 24.38 -0.83 27.65 -0.95 25.53 \n", - "2021-01-01 23.89 -0.64 25.06 -0.55 27.10 -1.25 25.58 \n", - "2021-02-01 25.55 -0.66 25.80 -0.57 27.20 -1.00 25.81 \n", - "2021-03-01 26.48 -0.26 26.80 -0.39 27.79 -0.55 26.75 \n", - "2021-04-01 24.89 -0.80 26.96 -0.65 28.47 -0.21 27.40 \n", - "\n", - " Nino34anom \n", - "datetime \n", - "1982-01-01 0.15 \n", - "1982-02-01 -0.02 \n", - "1982-03-01 -0.02 \n", - "1982-04-01 0.24 \n", - "1982-05-01 0.69 \n", - "... ... \n", - "2020-12-01 -1.12 \n", - "2021-01-01 -0.99 \n", - "2021-02-01 -0.92 \n", - "2021-03-01 -0.51 \n", - "2021-04-01 -0.49 \n", - "\n", - "[472 rows x 8 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df = pd.read_csv('enso_data.csv', index_col=0, parse_dates=True)\n", + "df = pd.read_csv(filepath, index_col=0, parse_dates=True)\n", "\n", "df" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "d648b36a", + "execution_count": null, + "id": "b0d3ae28", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DatetimeIndex(['1982-01-01', '1982-02-01', '1982-03-01', '1982-04-01',\n", - " '1982-05-01', '1982-06-01', '1982-07-01', '1982-08-01',\n", - " '1982-09-01', '1982-10-01',\n", - " ...\n", - " '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01',\n", - " '2020-11-01', '2020-12-01', '2021-01-01', '2021-02-01',\n", - " '2021-03-01', '2021-04-01'],\n", - " dtype='datetime64[ns]', name='datetime', length=472, freq=None)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.index" ] }, { "cell_type": "markdown", - "id": "1757f632", + "id": "8d26ed71", "metadata": {}, "source": [ "... now we have our data helpfully organized by a proper `datetime`-like object. Each of our multiple columns of data can now be referenced by their date! This sneak preview at the pandas `DatetimeIndex` also unlocks for us much of pandas most useful time series functionality. Don't worry, we'll get there. What are the actual columns of data we've read in here?" @@ -634,30 +189,17 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "33a95505", + "execution_count": null, + "id": "847347f8", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Nino12', 'Nino12anom', 'Nino3', 'Nino3anom', 'Nino4', 'Nino4anom',\n", - " 'Nino34', 'Nino34anom'],\n", - " dtype='object')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.columns" ] }, { "cell_type": "markdown", - "id": "c3ab938b", + "id": "08d3b2fb", "metadata": {}, "source": [ "## The pandas [`Series`](https://pandas.pydata.org/docs/user_guide/dsintro.html#series)...\n", @@ -671,40 +213,17 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "dc4d61cf", + "execution_count": null, + "id": "ee085815", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 26.72\n", - "1982-02-01 26.70\n", - "1982-03-01 27.20\n", - "1982-04-01 28.02\n", - "1982-05-01 28.54\n", - " ... \n", - "2020-12-01 25.53\n", - "2021-01-01 25.58\n", - "2021-02-01 25.81\n", - "2021-03-01 26.75\n", - "2021-04-01 27.40\n", - "Name: Nino34, Length: 472, dtype: float64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[\"Nino34\"]" ] }, { "cell_type": "markdown", - "id": "2b73e16d-f921-48c4-91f7-6eb2c8bd7b08", + "id": "2fcc97fa", "metadata": {}, "source": [ "
\n", @@ -713,40 +232,17 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "c4849ee1", + "execution_count": null, + "id": "7d46cbb9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 26.72\n", - "1982-02-01 26.70\n", - "1982-03-01 27.20\n", - "1982-04-01 28.02\n", - "1982-05-01 28.54\n", - " ... \n", - "2020-12-01 25.53\n", - "2021-01-01 25.58\n", - "2021-02-01 25.81\n", - "2021-03-01 26.75\n", - "2021-04-01 27.40\n", - "Name: Nino34, Length: 472, dtype: float64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.Nino34" ] }, { "cell_type": "markdown", - "id": "3bed7402", + "id": "c2a10249", "metadata": {}, "source": [ "## Slicing and Dicing the `DataFrame` and `Series`" @@ -754,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "9b0bdcaa", + "id": "e38faac9", "metadata": {}, "source": [ "We will expand on what you just saw, soon! Importantly,\n", @@ -768,33 +264,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "bdb5ebff", + "execution_count": null, + "id": "ec9ed333", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 26.72\n", - "1982-02-01 26.70\n", - "1982-03-01 27.20\n", - "1982-04-01 28.02\n", - "1982-05-01 28.54\n", - " ... \n", - "2020-12-01 25.53\n", - "2021-01-01 25.58\n", - "2021-02-01 25.81\n", - "2021-03-01 26.75\n", - "2021-04-01 27.40\n", - "Name: Nino34, Length: 472, dtype: float64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series = df[\"Nino34\"]\n", "\n", @@ -803,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "75bc2403", + "id": "c84a81b9", "metadata": {}, "source": [ "`Series` can be indexed, selected, and subset as both `ndarray`-like," @@ -811,28 +284,17 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "37d20e44", + "execution_count": null, + "id": "39bb0ae3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "28.02" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series[3]" ] }, { "cell_type": "markdown", - "id": "6e7ac685", + "id": "a1a55a7c", "metadata": {}, "source": [ "and `dict`-like, using labels" @@ -840,28 +302,17 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "f64e5695", + "execution_count": null, + "id": "62006988", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "28.02" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series[\"1982-04-01\"]" ] }, { "cell_type": "markdown", - "id": "686a3ecf", + "id": "cb17f245", "metadata": {}, "source": [ "and these can be extended in ways that you might expect, but perhaps also in unexpected ways:" @@ -869,7 +320,7 @@ }, { "cell_type": "markdown", - "id": "251cb40a-06cd-4bfa-b959-a434e0e601eb", + "id": "ae740b50", "metadata": {}, "source": [ "### Numpy-like Interval Slices" @@ -877,7 +328,7 @@ }, { "cell_type": "markdown", - "id": "ee4a8bfd-e63c-4bf6-9b43-addfeca9b4b2", + "id": "85123793", "metadata": {}, "source": [ "
\n", @@ -887,41 +338,17 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "2ddc17a0", + "execution_count": null, + "id": "221e798d", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 26.72\n", - "1982-02-01 26.70\n", - "1982-03-01 27.20\n", - "1982-04-01 28.02\n", - "1982-05-01 28.54\n", - "1982-06-01 28.75\n", - "1982-07-01 28.10\n", - "1982-08-01 27.93\n", - "1982-09-01 28.11\n", - "1982-10-01 28.64\n", - "1982-11-01 28.81\n", - "1982-12-01 29.21\n", - "Name: Nino34, dtype: float64" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series[0:12]" ] }, { "cell_type": "markdown", - "id": "606352a3-6596-4683-ab9c-c72a0bf609d1", + "id": "6281e3a6", "metadata": {}, "source": [ "### Label-based Slicing" @@ -929,7 +356,7 @@ }, { "cell_type": "markdown", - "id": "849645a7-d3a2-4a61-9cf9-52a765086049", + "id": "b6dceb40", "metadata": {}, "source": [ "
\n", @@ -938,41 +365,17 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "1c31803c", + "execution_count": null, + "id": "f7a06967", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 26.72\n", - "1982-02-01 26.70\n", - "1982-03-01 27.20\n", - "1982-04-01 28.02\n", - "1982-05-01 28.54\n", - "1982-06-01 28.75\n", - "1982-07-01 28.10\n", - "1982-08-01 27.93\n", - "1982-09-01 28.11\n", - "1982-10-01 28.64\n", - "1982-11-01 28.81\n", - "1982-12-01 29.21\n", - "Name: Nino34, dtype: float64" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series[\"1982-01-01\":\"1982-12-01\"]" ] }, { "cell_type": "markdown", - "id": "057795dd-ac38-4812-a04c-25a9516931bc", + "id": "84edfd6f", "metadata": {}, "source": [ "### Another Way of Slicing" @@ -980,41 +383,17 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "a629daf5", + "execution_count": null, + "id": "771d6f04", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 26.72\n", - "1982-02-01 26.70\n", - "1982-03-01 27.20\n", - "1982-04-01 28.02\n", - "1982-05-01 28.54\n", - "1982-06-01 28.75\n", - "1982-07-01 28.10\n", - "1982-08-01 27.93\n", - "1982-09-01 28.11\n", - "1982-10-01 28.64\n", - "1982-11-01 28.81\n", - "1982-12-01 29.21\n", - "Name: Nino34, dtype: float64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series[slice(\"1982-01-01\", \"1982-12-01\")]" ] }, { "cell_type": "markdown", - "id": "fab92608", + "id": "9798abf4", "metadata": {}, "source": [ "### Using `.iloc` and `.loc` to index\n", @@ -1024,117 +403,47 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "8828ebae", + "execution_count": null, + "id": "d5eb9de2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "28.02" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series.iloc[3]" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "d37ca35a", + "execution_count": null, + "id": "8d0bc3e8", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 26.72\n", - "1982-02-01 26.70\n", - "1982-03-01 27.20\n", - "1982-04-01 28.02\n", - "1982-05-01 28.54\n", - "1982-06-01 28.75\n", - "1982-07-01 28.10\n", - "1982-08-01 27.93\n", - "1982-09-01 28.11\n", - "1982-10-01 28.64\n", - "1982-11-01 28.81\n", - "1982-12-01 29.21\n", - "Name: Nino34, dtype: float64" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series.iloc[0:12]" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "1cd4b99d", + "execution_count": null, + "id": "59a10070", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "28.02" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series.loc[\"1982-04-01\"]" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "df53360c", + "execution_count": null, + "id": "3e2b3fc1", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 26.72\n", - "1982-02-01 26.70\n", - "1982-03-01 27.20\n", - "1982-04-01 28.02\n", - "1982-05-01 28.54\n", - "1982-06-01 28.75\n", - "1982-07-01 28.10\n", - "1982-08-01 27.93\n", - "1982-09-01 28.11\n", - "1982-10-01 28.64\n", - "1982-11-01 28.81\n", - "1982-12-01 29.21\n", - "Name: Nino34, dtype: float64" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series.loc[\"1982-01-01\":\"1982-12-01\"]" ] }, { "cell_type": "markdown", - "id": "67be0464", + "id": "44824b21", "metadata": {}, "source": [ "These capabilities extend back to our original `DataFrame`, as well! :" @@ -1142,7 +451,7 @@ }, { "cell_type": "markdown", - "id": "183e94a7-36dd-4f93-a8fe-e51b58ca0c15", + "id": "481cde44", "metadata": {}, "source": [ "
\n", @@ -1152,114 +461,47 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "6d0c9bd7", + "execution_count": null, + "id": "b8971371", "metadata": { "tags": [ "raises-exception" ] }, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'1982-01-01'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3079\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3081\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: '1982-01-01'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"1982-01-01\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3022\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3023\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3025\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3026\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3080\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3081\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3084\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: '1982-01-01'" - ] - } - ], + "outputs": [], "source": [ "df[\"1982-01-01\"]" ] }, { "cell_type": "code", - "execution_count": 22, - "id": "006ba58a", + "execution_count": null, + "id": "3c147c06", "metadata": { "tags": [ "raises-exception" ] }, - "outputs": [ - { - "ename": "KeyError", - "evalue": "0", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3079\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3081\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 0", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3022\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3023\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3025\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3026\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/pythia-book-dev/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3080\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3081\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3084\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 0" - ] - } - ], + "outputs": [], "source": [ "df[0]" ] }, { "cell_type": "code", - "execution_count": 23, - "id": "94bff713", + "execution_count": null, + "id": "0b05470d", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 26.72\n", - "1982-02-01 26.70\n", - "1982-03-01 27.20\n", - "1982-04-01 28.02\n", - "1982-05-01 28.54\n", - " ... \n", - "2020-12-01 25.53\n", - "2021-01-01 25.58\n", - "2021-02-01 25.81\n", - "2021-03-01 26.75\n", - "2021-04-01 27.40\n", - "Name: Nino34, Length: 472, dtype: float64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[\"Nino34\"]" ] }, { "cell_type": "markdown", - "id": "0d7f203e", + "id": "d4e6213d", "metadata": {}, "source": [ "With this plus our knowledge of the `Series` `df[\"Nino34\"]` gives us, we can chain our brackets to pull out any value from any of our columns in `df`." @@ -1267,49 +509,27 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "e8b04193", + "execution_count": null, + "id": "c61fa6d4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "28.02" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[\"Nino34\"][\"1982-04-01\"] # selecting the Series, then selecting a label within" ] }, { "cell_type": "code", - "execution_count": 25, - "id": "7ca8eadc", + "execution_count": null, + "id": "3bd7cf95", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "28.02" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[\"Nino34\"][3]" ] }, { "cell_type": "markdown", - "id": "09d33515", + "id": "afb0d6ef", "metadata": {}, "source": [ "However, this is not a pandas-preferred way to index and subset our data, and has limited capabilities for us. As we touched on before, `.loc` and `.iloc` give us more to work with, and their functionality grows further for `df`." @@ -1317,28 +537,17 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "6c9ac325", + "execution_count": null, + "id": "9fb5df7b", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "28.02" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc[\"1982-04-01\", \"Nino34\"] # note [, ] ordering" ] }, { "cell_type": "markdown", - "id": "e7cfdd38", + "id": "e710252f", "metadata": {}, "source": [ "These allow us to pull out entire rows of `df`," @@ -1346,517 +555,47 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "f51c7741", + "execution_count": null, + "id": "aad4fde6", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Nino12 24.50\n", - "Nino12anom -0.97\n", - "Nino3 27.68\n", - "Nino3anom 0.18\n", - "Nino4 28.92\n", - "Nino4anom 0.42\n", - "Nino34 28.02\n", - "Nino34anom 0.24\n", - "Name: 1982-04-01 00:00:00, dtype: float64" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc[\"1982-04-01\"]" ] }, { "cell_type": "code", - "execution_count": 28, - "id": "e42c3508", + "execution_count": null, + "id": "f93737ba", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anom
datetime
1982-01-0124.29-0.1725.870.2428.300.0026.720.15
1982-02-0125.49-0.5826.380.0128.210.1126.70-0.02
1982-03-0125.21-1.3126.98-0.1628.410.2227.20-0.02
1982-04-0124.50-0.9727.680.1828.920.4228.020.24
1982-05-0123.97-0.2327.790.7129.490.7028.540.69
1982-06-0122.890.0727.461.0329.760.9228.751.10
1982-07-0122.470.8726.440.8229.380.5828.100.88
1982-08-0121.751.1026.151.1629.040.3627.931.11
1982-09-0121.801.4426.521.6729.160.4728.111.39
1982-10-0122.942.1227.112.1929.380.7228.641.95
1982-11-0124.593.0027.622.6429.230.6028.812.16
1982-12-0126.133.3428.393.2529.150.6629.212.64
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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anom
datetime
1982-01-0124.29-0.1725.870.2428.300.0026.720.15
1982-02-0125.49-0.5826.380.0128.210.1126.70-0.02
1982-03-0125.21-1.3126.98-0.1628.410.2227.20-0.02
1982-04-0124.50-0.9727.680.1828.920.4228.020.24
1982-05-0123.97-0.2327.790.7129.490.7028.540.69
1982-06-0122.890.0727.461.0329.760.9228.751.10
1982-07-0122.470.8726.440.8229.380.5828.100.88
1982-08-0121.751.1026.151.1629.040.3627.931.11
1982-09-0121.801.4426.521.6729.160.4728.111.39
1982-10-0122.942.1227.112.1929.380.7228.641.95
1982-11-0124.593.0027.622.6429.230.6028.812.16
1982-12-0126.133.3428.393.2529.150.6629.212.64
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Nino12Nino3Nino4Nino34
datetime
1982-01-0124.2925.8728.3026.72
1982-02-0125.4926.3828.2126.70
1982-03-0125.2126.9828.4127.20
1982-04-0124.5027.6828.9228.02
1982-05-0123.9727.7929.4928.54
1982-06-0122.8927.4629.7628.75
1982-07-0122.4726.4429.3828.10
1982-08-0121.7526.1529.0427.93
1982-09-0121.8026.5229.1628.11
1982-10-0122.9427.1129.3828.64
1982-11-0124.5927.6229.2328.81
1982-12-0126.1328.3929.1529.21
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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anom
datetime
1982-01-0124.29-0.1725.870.2428.300.0026.720.15
1982-02-0125.49-0.5826.380.0128.210.1126.70-0.02
1982-03-0125.21-1.3126.98-0.1628.410.2227.20-0.02
1982-04-0124.50-0.9727.680.1828.920.4228.020.24
1982-05-0123.97-0.2327.790.7129.490.7028.540.69
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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anom
datetime
2020-12-0122.16-0.6024.38-0.8327.65-0.9525.53-1.12
2021-01-0123.89-0.6425.06-0.5527.10-1.2525.58-0.99
2021-02-0125.55-0.6625.80-0.5727.20-1.0025.81-0.92
2021-03-0126.48-0.2626.80-0.3927.79-0.5526.75-0.51
2021-04-0124.89-0.8026.96-0.6528.47-0.2127.40-0.49
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" - ], - "text/plain": [ - " Nino12 Nino12anom Nino3 Nino3anom Nino4 Nino4anom Nino34 \\\n", - "datetime \n", - "2020-12-01 22.16 -0.60 24.38 -0.83 27.65 -0.95 25.53 \n", - "2021-01-01 23.89 -0.64 25.06 -0.55 27.10 -1.25 25.58 \n", - "2021-02-01 25.55 -0.66 25.80 -0.57 27.20 -1.00 25.81 \n", - "2021-03-01 26.48 -0.26 26.80 -0.39 27.79 -0.55 26.75 \n", - "2021-04-01 24.89 -0.80 26.96 -0.65 28.47 -0.21 27.40 \n", - "\n", - " Nino34anom \n", - "datetime \n", - "2020-12-01 -1.12 \n", - "2021-01-01 -0.99 \n", - "2021-02-01 -0.92 \n", - "2021-03-01 -0.51 \n", - "2021-04-01 -0.49 " - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.tail()" ] }, { "cell_type": "markdown", - "id": "0da2b376-8964-4b7b-b313-fa2ffb8ef372", + "id": "cba9f221", "metadata": {}, "source": [ "### Quick Plots of Your Data\n", @@ -2321,30 +665,17 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "7b09fdb4", + "execution_count": null, + "id": "bf317171", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.Nino34.plot();" ] }, { "cell_type": "markdown", - "id": "92800a25", + "id": "99c2c7a3", "metadata": {}, "source": [ "Before, we called `.plot()` which generated a single line plot. This is helpful, but there are other plots which can also help with understanding your data! Let's try using a histogram to understand distributions...\n", @@ -2354,30 +685,17 @@ }, { "cell_type": "code", - "execution_count": 35, - "id": "c4db0c16", + "execution_count": null, + "id": "5f85e2dd", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df[['Nino12', 'Nino34']].plot.hist();" ] }, { "cell_type": "markdown", - "id": "db891ecb", + "id": "a4e07618", "metadata": {}, "source": [ "We can see some clear differences in the distributions, which is helpful! Another plot one might like to use would be a `boxplot`. Here, we replace `hist` with `box`" @@ -2385,30 +703,17 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "b353ed8b", + "execution_count": null, + "id": "6329d231", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df[['Nino12', 'Nino34']].plot.box();" ] }, { "cell_type": "markdown", - "id": "6346ecd3", + "id": "c338385b", "metadata": {}, "source": [ "Here, we again see a clear difference in the distributions. These are not the only plots you can use within pandas! For more examples of plotting choices, check out [the pandas plot documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html)" @@ -2416,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "fd10d71d-63df-44d5-bca3-6eb436cfd68d", + "id": "69fc4078", "metadata": {}, "source": [ "#### Customize your Plot\n", @@ -2425,23 +730,10 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "cadb301b-2f22-4cff-9c58-bd8394e9e5fc", + "execution_count": null, + "id": "da22f990", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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o3C+88EKURWKY1CIb6VkSbnbcmpgrbjfHnaVQubwJ1Jm2xo4di3w+jx07dmRqZSwzu3btQrFYxMiRI9HQ0DAsHLcX4ZYJam+88UakZWKYNFIsFlEsFgEA+Xzedft8Po9cLodSqWR8LwnYcWtSy6FyeWzquN9cLof29nYA2e7nVvu3ARjCXSwWHdfYzrJwewmVy210ZpJLG8uXL8eDDz6YdDGYDKO6bSLS+k4aXLfquDk5zQGrULnOzGlZEG67CTtqIVyu9m8DABEZLWsnsRouofJcrvwYlkqlSMsUBbNnz8YZZ5yBZcuWJV0UJqN4CZNL0iDc7Lg1cQqVW/Vxp+Hi6lLLwi1DwHIdbgBa/dxZdtxehFs2YpIM+wVl9erVSReBySiyftbJKJekoW5XHffevXtjbXhnSrhrOVQuhdvcAKkF4f7Zz34GADj99NON93T6ubMq3KVSyZOLyLLjlnhxSwyj4iWjXJIG4VYdNzB0AawoyZRwOzluq77ELAm3VR83MLjqmXk507RRLBZx77334je/+U3V+8uWLcNTTz2FkSNH4uKLLzbe13HcWQ2Vq/3bOn12WXXc6vVwylVggrFkyRL88Ic/zHTDzgk/ofI0jBiSQi2H8MbZz12I7ZdCwOy4u7q6IIRAXV2dZTZiloTbLlQuh4fFnfzglfPPPx/33Xcf8vk83v/+9xvHsWrVKgDA8ccfX5UxX8uO20uYHMiu41Ybk1lacjVrzJ8/H0B5XvtTTz014dKETxZD5aVSyaizJ0yYgM7Ozlj7uTPtuGXFYVdBJn1xvWAn3Em05vzw2GOPASi7RvUGtoskeOnjlt/NinB7ySgHsuu4d+3aZbyW9y8TLmrjqFbPcRZD5Wp9PWrUKADuCWorV64MLRck08ItKw67FZiy5LilwJmPRTruuLMWvaL276gVjF3yoI7jlqIuGy9ZEe7h6LhrVVSSQAhhLFqxcOFC4335zGSFXbt24fzzz8fDDz/suF0cWeUdHR2h6oDs325padGqo/v7+zF9+nQceOCBofx+poRbipt0a26OO0vCneVQeX9/f9UDpFbidsP1vDhuGWKvVeFmx82o3HDDDZg2bRp++ctf4oknnjDez0LkUOXb3/42fve73+G0005z3C5IqFynbl+3bh0mT56ME088UXv/bqgTZklj4STcqrEJIx8kU8ItK0QZmpBiphMqT3vyTJZD5ebsyrAct/xMnoOs9KN6DZVn1XGrwq1em3vuuQePP/54AiWqDb7xjW8AAL7+9a/jpZdeMt7PggFRkdMcuxF1qFzei88++2xoOqA6bp06Wi1nGBMtZSr2IiuHUaNGYdu2ba6OO5/PI5/Po1gsYmBgwFOLLm6yHCo3D4MIy3HLz6TjzorjGC6O2ypU/uabb+Kiiy4CwJnmQWlvb68Sg6zc/xLdhmvUoXLZMAbKjQk5g2MQvDputWHb19cXWIsy7bjdhBvITrg8y47bSbjDcNzynGSl4houfdxWoXJ1at6sNUTSgHrOxo4dW/Vspb0OM6Pe/06NuKizyuXMjUB5it4wsHLcTsKtdvOFUY/VhHDbJacByWcf6pLlPm6zcKuh8zD6uFtaWgCk/xpKhmNWubzO6n2qGyplBlGzjnt7e6vEICv3v0R9tp0mJ4k6VK4K92uvvab9G07I42lpaTHqJ3OXoYrZcQclU8KthsoBPcedlUrRbthUrYTKgzhu+WBkxXEMF8dtFSpXxXrTpk2xlGPRokW4+uqrayJBTnWE27dvz7TjVhtxToskyec8Kset/nZYjlutr3WiumELd6b6uP2EyqVApH3lJbspT7MQKtdJTgvSx501x+23jztrwm0VKt++fbvx3qZNmzB37tzIy3HMMccAKNcLX/3qVyP/vShRHaFZuLNy/0vUht2WLVtw8MEHW24XteOOQrjVMich3DXvuNUlJNOMXai8sbERhUIBvb29qX1wo3bcWevj9ptVnvZ71Iyb4+7o6Ii1PGFVykmyc+dO4/XWrVurnqWs3P8S1Wyo4WozcQq3en6DoEYJdKZf5T5uAKNHjwZQW47bLlRORFrJD0niJzmtlvu4h6Pjlvdv3KFyNekpaxOUWKFW8FaLKmUJs+O2I85QeVhzQQRx3E5mRZdMCreX5DSddZ/TgF2oHEh/gpqf4WBeZk6rdeHOquPesWOH8TqpPm71N9SGRFZxEpas3P8S3T5uP47by2gh1e2H1fixctwcKregv78fxWIR+XzecKWyost6qLy/vx8DAwPI5/OWrc60J6hJ4ZaNDrXPOwzHrYbKszA22G9WedYct1ohJhUqf/PNN43X69ati/z3wqRUKuFb3/oW/vnPfxrvOQl3rTtuP6Fyt3PS09NT1YAIy3F7Fe6wQ+WZiS2pLsZcIZrDyypZCJWrYXKrZSDTnqAmhXr8+PFYs2ZNaI5bfaALhQIGBgZSP5EOMDwcd6lUqkpEk9fZnJwWNXL1OSB7wn3HHXfg+uuvBzAY8q8lx60Kt3pfmPEzjlvXcZv71sNq/KhRAp1GxLB13GqSk1yjWjJx4kTb72VBuO0S0yRZCZWPHz8eQHh93PKzurq6zIzHB4ZHH/fOnTurymvluDdv3hx5OVTHvW3btsxMiwsATz755JD3asVx9/f3a4tVlKFyaSqkZiTluIetcKuV4eTJk6s+cxLuLPRxO/VvA4N9+nFOaLF161acccYZ+M1vfuO6rZNwh+G4C4WCVuZmWhgOWeXSycjpaPft2wchRNU96rdrRwiBSy65BNdcc43rtmvXrq36e/369b5+MwnUCIFsBMl7Rz5LKlm49yVmk+FUdj+hcl3hlr8ro5ZROO4kQuWRCTcRTSWix4hoGREtJaIvVN6fR0RPE9ESIlpERMfq7E91bpMmTar6TMdxp7lSdHPchx12GABg8eLFsZXpIx/5CB588EFccMEFrttG5bjVVq2u477//vtxxRVXJLqS2HBw3FK4DzjgAADla753796qystvhGjTpk24++678d3vftd1W3NiZFaEWwiBF1980fhbNnjkvTNjxgzjM3l/1Kpw+wmVy2dLV7hl1DJKx+10jFly3AMAviSEmA3gOACfJaI5AG4C8HUhxDwAX6v87Yrq3MxCbRZylSyEyu2GgkmOPbbctnnmmWdiKU9PTw8eeeQR7e2jctx+QuVnn302fvSjH+EHP/iBdvnDRlYOw8Fx77///gDK11m+JyNiXV1dvhojana4W0Vr/txp2sk0sWrVqqqsfJnIJ49n9uzZxmdjx44FkK1Qudq/DSQXKpf7lpGh/v7+UBrINRsqF0J0CCEWV153AlgGYAoAAWBkZbNRADbq7E8NP9bV1VWt8JL1ULl8gGU4x4ycGeqFF16IpdW9ZMkS47XOzFeyspTXRMdx6wixV8etZpw/8MADruWOCq+hvyw6bukQJ0+ejEKhgGKxaIjP+PHjjUaoHyFVE5ncXLt5gp+srNluTtbauLFcDcryz5kzx/hszJgxAGrXcUcZKlf3HeaCU8Ni5jQimgZgPoBnAHwRwHeIaB2A7wK43uY7l1VC6Yu2bt06pFUmW1Dm12ayECqX0xxOnz7d8vO2tjbMmDEDvb29VeG1qLByzE6YHbfOIiPyb6eKVu3j1hFu1aktWrQosXG9aqRAhyw77nHjxhmiKftsx40bF2gIo9pPbnZuZsyTMmXFlZrve7NwZ91xy+OQdbNT2aPMKvcqsLokPRwscuEmohEA7gPwRSHEHgCfAXClEGIqgCsB3G71PSHEbUKIo4UQR7e3tw+ZXUddY9WJLITKpXDPnDnTdptZs2YBiKcPz2n2JiukcJsdtxDCdspT+bfT/r2GytesWWO87u/vH5K4FBdqg0OHLDpuKdzt7e3GBDny/I8bNy7QbH+qcLs5brNwZ8Vxm8spoxWy8pe5A0B2VjhU8TJdcRyh8vr6ei2zoEtNDwcjojqURftXQog/Vt7+GAD5+vcAtJLTzBfXaryzFVkQbjnHshRnK3SWjgsLr8Itr40M6Unh7u/vhxACdXV1hjhJdITba6hcFW4gOYci7zVd4c6y425vb8e4ceMAAMuWLQMQrnC7OW55/9Sa41YbuvIcRHVsQghceeWV+PnPfx7aPuUzoCPccWSVJ+24MyPcVFbW2wEsE0LcrHy0EcBJldcnA1ihsz+z49YV7iz0cUvHnRbhVm8yL8Itx0pK4bYLkwPehFsNlTs9HF6Eu6urC9dffz1efvll22384jVUnkXHLcV13LhxmDBhAgBg6dKlAMqh3SCTBg0Hx22+783JaeozI7sdonLcixcvxg9+8ANceumloe3Ty3TFUYbKVd0I03HXcqj8bQAuAnByZejXEiI6A8ClAL5HRC8CuBHAZTo7Mwv3Jz/5SQDAueee6/i9tPdxd3Z2YsOGDWhoaKgKj5mRfUVJOG63aUbNQy66u7tRKpVsE9PU96IKlQPOD9J//dd/4Vvf+haOOOII22384jVUrjZCsyLe8j4cMWLEEOFOs+MWQuDOO+8ccq/EjXw25IgYeZyqcD/44IM444wzcMMNNwCITrjV2cXCuv/8CHeWHHfSyWmRTXkqhHgKgJ0tPsrr/swX96qrrsL8+fPx1re+1fF7aQ+Vy5mfDj744CHhZJWkQuWlUgn9/f2OD5W8No2NjWhqakJ3dzd6enpCc9xRCLcM60aB11A5UHbdxWIRpVJJO38jSeS5bWxsNIRbRlqCJqepGddhJ6ctWLAAH//4xwEg0XnvZbnHjh2Ljo4O47lWhxKefvrpOP30041pXaMKlatzyu/YscPo+giClz7uuELlUTtuuZaCVTQ4M6HysDE77kKhgFNPPdUQNDvSLtxyWkinsejAoHCbJ5yIAvONrWaZW6E+HOowICfHLR8ir6Fyp5teDquT0Ql12zfeeAN/+9vfjG10x1j7wWuoHMheP7c6PFMKtyTNyWlqcmeSi/aowg0Mzjwn71n1/ow6OU2dwc1pMRAveOnjjiOrXDekrYva2Mjn88jn8xBC2OpMlkLloeJnzVZgsP8wrRWirKTUcelWJOW4Afd+bivh3rdvn5bjdqpovYbK5QMp3Z78u6urC7NmzcKZZ56Js88+27ZMYeE1VA5kr587LuEOO1SuXpNHH33Uc9nCQpZbCrc661xDQ0OVawtTcKyIUriTDpWrAuvFcff19WHnzp2OnwODeuRWnlgdNxEdT0Q/IqKXiGgrEa0lor8R0WeJaFTgX/eAn3AKkH7HrY6HdSKtwq2Gd9UlV/ft22dEB6yiIlGEys197fIh2rJli3H933jjDWOf6jGEiZ9Qea067iiT0wYGBoylfuU95lYpq/vzMjtg2MhyypEYaoTK3KjMouM2h8qdBDaNWeVz587FmDFjbO8/s5FMjXAT0YMAPgngIQCnA5gEYA6ArwBoBHA/Eb0/cAk08RNOAbIj3Gly3GYxdRJXc2tZFW7ZVykrJ5UohFs+NOYFBdTuBfkgquF/pyUH/eAnVF5LjlvNKvfquIUQVRPnODluNaKjO3e1Wp6wRMoPUqRlg33fvn01JdxWoXK7nIIgoXK3hpqfPu5SqWQM0ZUjfpz2q/6fuHADuEgI8QkhxANCiI1CiAEhRJcQYrEQ4ntCiHcA+KfD90PFb6i8VoQ7qaxyILhwy3Cgipesct0+bjvhVs+ZnD9bFYSwK3A/ofJacdyzZ89GU1OTcQ2WLVuGgw46CG1tbVorzZnnrndy3KrQ6Vbk6v6SXALUyXGb8y9UUYgioS5K4W5oaHAdkhvHlKde+rjVZD27hrRXxx1nH/doInqb+U0iejsRHQwAQojY1pn0GypPex+3V8edRHKaF+GW5QxDuMMKlZvPWVdXV5Vwq8NhwiBIqDyLjltdgvIzn/kMgMFr8Mgjj2DVqlXYtWuX1vzx5uvr5LjV5Eddx+1FuLdv346VK1c6buMXdUrQ+vp6CCGMYzU77lwuF5kB6ezsrIpChPUsqM+A2+pZcU15quu41TXe7SJGXoRbTTpUyxQEJ+H+AQCrUndXPosVDpWns487SscdNFQutzWfsz179lRV4GE77iCh8rQ2MM2YF/059thjMXXqVFxyySUArBfM8TKZj0Q3VK7ruNWK2LytEKIqIWnKlCmYPn16lQMLC7Xs8tmWz4tV4mRUCWpmYQq7j1snWuZ1/Xp1W7cohJ8+bjn8DrAXbnO959Q4MUeRohbuaUKIl8xvCiEWAZgW+Jc9UquhcnUGKieyJtx79+5NLFTu5rg7OztTGyrPguMuFosoFosgIuMYFy5ciOXLlxuCbSXcbsMKgaHX1ymzVw2Vh+G4L7nkEowZM8ZYyEfua/Hixa7l9opadh3hjqqf23xNwg6V6zy7dusZOKEzBEv9Tb+OO4zkNPNxRy3cTuNl9M9wSAQNladRuP/2t78ZSRBpdNxShP06bjlmOgzHrbNYvV2onB13uFgNWyoUClXryY8aNXTQiRfhnjhxIlpaWrBx40Zbx6tW+H4ct3rvPfvss/jFL34BYOi691GM91bD/PK8yefFSbjDdtzy2ZANsLCFWyda5jRs1AkdB+2njzuI47bat/m9qIX7OSIaMnktEX0CwPOBf9kjQUPlaasQt2/fjjPPPNP420rcVGRyWhx93PJBknOPRxkq11nWM8yscqAcflWFe9OmTbb79EOt93HrhDblvaPiRbibm5vxtreVU2yefPJJy23DdNy33z64SKF55is/Q9rccHLcVudVp+HqB3lNpk2bBiCZULnTRE1O6AhxGvq443bcXwTwb0T0v0T0vcq/x1EeIvaFwL/skayHynfv3o0VKwbXUzFPz+k03SmQjOM2r/ZlhV/hliuGDQwMWF4bNQSm8/DLqVmBwXNl57g3bdpUJZBhL/8ZZAKWtDUwrdARbjkhiooX4a6vr8eJJ54IAHjiiScst1UrfF03pYqwWoHLGQyBoQ3VKBy3VR+3juOOSrinTJmCfD6PXbt2hfIbVs+uXeKWn1A54F24de8R9V7QFW6nY4zVcQshNgsh3grg6wBWV/59XQhxvBAiXIuiQdYnYJk1axZmzJhhtOY2bNhgfHbGGWe4fr+xsRFEhL6+vsiPRVZoXhy3vIF1hRtwnvZUClg+nwcRuVZcVjMk2Qm3OvwFAFavXm13eL4IMuVp0o67r68Pn/70p3H//ffbbqMj3C0tLUMao16F+6STyosILly40HJbq3HcfkPlal96d3d31TPm1M/uF9Vx64TKo0pOk89Ga2urkWejToCj8vTTT+OSSy4xyumE+gy4JW6VSiUUCgVPDV3Am3B7WR1MbdyFESqP23FLXgTwq8q/JUQ0prLOdqz4DZWnxcnIcOxzzz0HYHDO5A9/+MNaw2SIKDbX7Ue4zcPB3JLTAOd+bi8tWvX9hoaGIRWFOVQuz72cH37t2rWhjo/1u8gIkPx9+sgjj+CnP/0pbrzxRtttdISbiIa4bh3hVhtgc+bMAVAdulSxGsftVImXSiVb4VYnfenu7q7aT9jDBYHqaEEaktOam5uNYX124fK3v/3tuPvuu3HFFVe47lc3Wua3fxvw1sdt1aC3Q01czWJymmQxgK0AXkd57eytAFYR0WIi8rzKl1+yHCpXXZQUK+m4Z86c6Roml6RRuM2REOketmzZgv7+fjQ3N9s+lDrCLa+f7pASq5CYPF9SqKVw77fffmhra0NPT0+oCWpZziqXiVlO/bq6w3f8CLfaEGxra0Nzc/OQZEKJ1ThuJzelTsFLROjv7zcaSmbHHZdwe80qD9txy2vS0tLiKtyyDn388cdd96vbx+23fxvwHyp3ukf6+vqqPlcben/729/w0ksvoVQqGfeNfMadogrqSnp223hFR7j/DuAMIcQ4IcRYAO8B8DsAlwP4n8Al0CTLoXK1UpAXTQr3fvvtp72fuBLUgjhuKdyy39gp6c5JuM2rJLkJt7q9+YGW50sKtwyVjxo1ylgDPcz1mbOcVf7ss88CcL7Hggi37tru9fX1ICJMnToVwNDuDcB6HLdTJS4r4ZEjRw4RerNwq5W3Xeg4CGrZvYTKw3bcslGr47glGzdudN2vV8cdh3DrOG5zA1HeM6+88grOPPNMzJ07t8pEykRGHcdttWqhX3SE+2ghxEPyDyHEwwBOFEI8DSC6tRFNZNlxq8kO8saQwj1lyhTt/cTluMPIKpfHLIdmWRGmcFuFyu0ctxzuMWbMmNCFW02q042kAOlw3EKIUIXbKrPcy9zSAByF2yqr3Gn/8tkbOXJk1b3X399fdbz79u1LxHHLSJDVMxO1425ubjaGpNoJt3xWdNAdDhZ1qNxqWU+ne8Q84Y8U7hdeeGHINqoW6QwHM08MFQQd4d5BRNcR0QGVf9cC2ElEeQCx1TJZ7uO2Em75kPoR7rgct8wq9yPcTu5B4iTcauhb/V8nVG6u5OT5mjx5MoDBazBp0qTQhVveZ7lczhBjHdJwn65cudJwnl1dXbbu2K/jBryt7Q4MRqScHLc5q9yu3PK6t7a2Vt175so6qVC5FAmrOiFqx62Gyu2OV40Oul3HtDhuNVKrs728R+TzKP9+5ZVXjG2WLl1q7FOnLEk57gsB7AfgT5V/Uyvv5QGcF7gEmmQ5VG41vMCP45aTWkQxrlQihBgSKvczHEwKgI5wW7WA7Vbf8RIqN095Kh23ZOLEicb5D2sst58wOZAOx61OPDIwMBB4ikp1Ehbp5rwKt67jVufztiu3KlKqcJuzxs2h8h07doTeoLKagEVi1X2WhuQ0tUGkDm21Io193DpaIBtx8hrI+nrJkiXGNi+//DIAfcdtnhgqjMVtXIVbCLFNCPE5AG8XQswXQnxOCLFVCNEnhIhmBn4LailUvm/fPuzZswf19fWWS17aIStCp/mbg9Lf3w8hBOrq6owbzSk0byfcUoD8Om6vwu0UKjf3cUsmTpxolCGsEKSfxDQgHct6yjC5xC6yoyvc6rWX7ltXuOVzLoXbaqy9udJ368NUG1VqaN1KuNV9CCFw0003OZbbC8ViEf39/SAi1NXVDVmv3slxRxkqdxNu9bdff/11x/1aDQezKnucfdzynjLPHa4iTZG8Bp2dnRBCVIXKpXCrJlJnHLccbtfZ2Rn4OXcVbiJ6KxG9CuDVyt9ziSi2pDRJlmdOMwu3DCOPGzduyCxNTsjKTx26Ejaqi9FZU9lOuCVBQ+Ve+7idssrNFeLEiRO1p8rUxc9QMCAdy3qap/p0E263fkm1q0Bn+lzAm+OW+5LX0K0iV8XEynHLusUs3ADwjW98I7TGv/qMqcM8JVbCHZXjVqMQMipiFypXf9utayktw8FU3ZDXV8dxq8K9ffv2qnPi13E3NTVhxIgREEIEntRHJ1T+fQCnAdgOAEKIFwGcGOhXfZDlucrNoXI57MOL2wbicdxqpSIdt1No3m4ct8TJlYXpuJ2yymXlNG/evKrvTJw4UXtspy5+Q+VJO+7e3l4jHCgrraCOW03OUyfmccJ83WfNmgWgHKo0nxtzA8ItQU2NhlgJt4zIqMlpp5xyCpqamtDT0xOaaKrPGDDY9wmU7xurdQviSE5z64pTf9tNuHVD5XH2ccvGtI7jHjduHBobG1EqlYZEIF599VUA3pPTGhoaQqvDtbJnhBDm5m7stqCWQuXScXsV7jgct9oCjtpxO4WuwgqVCyEMAZo4cWJVZuykSZNCd9x+Q+VJO+6Ojg709fVhv/32q3IbVkQp3OYG+gEHHID99tsPO3bsMCpMiVkAdR13oVComrVPPk8yeVHt425sbAzd7ZpD/Mccc4zxWXt7u2VSYxzJaW4N9bAc93PPPYdPfOIT2L59e6A+bp3r4jVULgV15MiRRoPKLNzyubBKTrMqi1qGOIV7HRG9FYAgonoiuhrAskC/6oOgofKkhFsIYWQhAuEId1yOO0nh9hoqV8uhupO+vj4Ui0UjVDZjxgzjO7JVrf5eUPyGypN23OZwHmDvuOU94mU4mLwvrrvuOscsbfP9RETGnOXmiT/M94ib41avjZoYKR23Ktzm9cbVsgXFfP4OOugg4zO7MdJpcNx+hNtqONhb3vIW3HHHHbjqqqsCOW4dIVZ1w0ty2qhRo4zooRzHb56Twqvjjlu4Pw3gswCmAFgPYF7l71gJGipPysm8/vrrVXNhd3Z2+hZuedHT3MdtfgCTdNx9fX1VM0MB1cun5vP5yPq4/WaVJ3WfqufbTbh1HffnP/95vPvd78Y999xjCPfixYtxyimnaJVDIucsNy82Yg6VB+3jthPusB23VfRw5syZjt/x6ri7u7tx9913u45AUYVbXne75Cn1vLrN7+/kuGV2+uLFiwP1cUfhuOX5GjVqlHHP2gm31+FgsYbKK1nlHxFCTBBCjBdCfFQIsT3Qr/ogq6HyBx98EABw7LHHAqh23G5LeZqJ03E3NTWhqakJ+XzecK5WmCta9UEFkg+VmysGmTlrLt9wzyqPQrhbW1vx0EMP4SMf+UhVg+7ll1/Wvp8A4PjjjwcALFq0qGpbs3P10sethsrlnArS+aqhclW4nSp8L1gd44IFCzBnzhz88Y9/tPyO18bDu9/9blxyySX4zne+47idGirP5XKOjXX1t/fs2eNoINRzbdfo2LVrV+SOWzV8XkPlsrEvI0QyK9z8+4CecMfiuInoh0T033b/Av2qD7Iq3DK896EPfQhAsFB53I6biFxdt1UlpIbLkwiVWwm3rBi+9KUvYfTo0bjmmmuq9j/cs8rVcF5Ywq1i7kJ5+OGHLbezup9mzZqFhoYGvPnmm1UVnrkcXvq4Vcf9xhtvAAAOO+ww4z3VzcfhuGfPno2lS5fiAx/4gOV3vITK9+zZg6eeegrA0JECZlTHDcCxn1v+9oEHHgjAOVyuk1W+a9euWPu4dbRAddzmULm5vlavn85wsLgc9yIAzwNoBHAkyguMrEA5VB577WL1QOuQ9HAweSPI7NggWeVx93EDCCzcTpW7F8ftFiq06pMcGBgwHIWsGPbbbz9s3brVGJfLWeVl1HCevOZhCre5AadOaGFVDvV+qqurM0T1xRdfNN4336u6fdxqqLynp8cQbrkaWU9PT5XjDruP248J8RIqX7BggfF6+vTpjtvqCrcQwvjtadOmAXCe09yqj9v8jHV1dQUKlXvt4/YSKm9tbTXOiXTcI0aMqLrnvYbKvThuddlnK5zW475bCHE3gOkA3imE+KEQ4ocATkFZvGPFr+PWGQ62bds2XHfddbj22mtDXSUKqJ41p1AooLe3Fx0dHQDS6bjND1JSjtsuVK6zrCcRGQ+SfEDUFr3qhjmrvEwUoXIV2Vg178OpHCrz588HUC34YTjuTZs2Yfv27WhqasLkyZON+1U+Y3H1cbvhxXGrCW5u97UaKgfshVu9r+W2Tvu2Gg4my64Of5MNhyCO206Ii8UiSqUSiAj5fF5LuK2WW5WOu7m5uapu0xVuP8lp1157rePnOrXLZACtAOTq6SMq78VKlKHye+65x3BgY8eOxXXXXeezlENR+1hGjhyJHTt2GCGmWnXc6lhuJ+F2Glfpda5yK6Hv7e01KmC7ikHXcZdKJTz44IMYN24c3vKWt9hul3XHHZVwm2c+cxNu8/mTY/DVGazsxnHb7duqj1uO+DjooINARGhubkZPT4+lcEfZx+2GF8etznToNOGNEGKIcNoJt1pmNT/ADrtuCfm7EjmxTpA+brtzota9RKSlBWq5zcItp6eVyYzqYjA6w8EaGhqMc+dWh7vVRzpZ5d8C8AIR3UVEd6G8PveNGt8LlaChcqeLpTrYsEVRLbe80OrqVF5oaWlBPp/Hvn37Qh/PKTELtyxzUo5bPhDqQ2q1iISd+5I5BnYVg67jPvvss/He974Xp5xyimOWfVb7uKMWbpmcKXFLIDM/5/L7//znP4fsw3zNvQwHk4tHHHzwwQAG7xNZJ8TVx+2GF8etK9xyeuNCoWDcr16E2+mZURuw8n7au3cvisViVfnk1KlROG5z3eQlma2urm5IqLy5ubmqnNLUAOE77sDCLYS4E8BbACyo/Du+EkKPlaCO26lCVG/AsEKmEvXmkVnkspXrVbiJKPLZ08zJIvLmdBvbGXWoPJ/PI5/PQwhheS3NYiLL/b3vfa/qeMzoOG4hBP7yl78AKFc+DzzwgO22Wc0qjzo57Stf+Qp++MMf4oYbbqjahxmnUHlLSwtef/11Y0EYu3HcOsPB5D0qM8rNwi1dVVr6uL00HtRJbpzqM6tzbSfc6rl2mvFQYuVcu7q6htxTK1eWl7sI0sftFoWT2+kIt5PjNofKdYXbz3Awt+vslFU+Tb4WQmwSQtxf+bep8jkR0dBlbCIi6MQWTo47buGWeBVuYDBcvm7dOtxwww2hLn4ApCc5zRwqV1+7zU4EALfcckvV50Ecd7FYrHL5v/3tb223zfo47oaGhkiEe8SIEbjiiiuMxCa7820n3IVCAW9729sAAPfee29VOczjuHUct3k+cCfhToPjjiJU7kW4vTputQGrOm5zPSK3i2I4mPk+1Ym+qtfG3JevK9xCiKrz7ic5LYjj/g4R3UdEFxPRoUQ0noj2J6KTieg/ACwEMNtx7yEhK826ujpPi3IAehdLvQHDWHJNRW31qeMAm5ubh8zrrYPcxzHHHIOvf/3ruO6660JtbIQt3DqO2+ramEPl6m9YVV7mh/R973tf1SxpduXQqYTMvyeH2liR9ZnTVMdtd83N94gX3MTVqUtMzqD2pS99CbfeeqvtOG6dPm51tjJAL1SeZB93VKFyczm8OG6dULnZcdvdU1EMBzMPBVWF3m7NdqtQuVpGO+FWr89XvvIVjB492ph3QDUhunlKvoVbCPEhAF8FMBPAjwA8CeB+AJ8E8BqAk4UQjzjuPSRU4faKTqhcvbmjdNyqcE+cONFzIwQoj/cEqiv4MPu7zVnlSfdx6zpuKxeoZq/aVQyFQgH5fB6lUsm2cSd/r6WlBUSEXbt22d5PtZBV7raSlxQG9fzq4iauTqL20Y9+1Hj99NNPB3Lc0vlL7ISbHffQbb0kp5n7uMMUbjfHbU68y+VyruveWzU4JOY+bqvktN7eXtx4443o6+vDxRdfDKDahOgs3CT344RjH7cQ4lUhxJeFEO8QQsysrMd9oRDiHiFEuArnXA4AwYQ7qVC52qo1C7cf5HhWq98IgyQcdxShcrXsgHPF4Fbhq+G80aNHQwgxZA1nSS1klbv1YcoQuh/hDuK4DzjgAPz1r38FUF64xyyAXqY8HTFihOF+5L6BwXs3rX3cXh23TiRJLYebcKuZ0bqhctVx2wmWDCF7Qddxq/WR2wphVqFyiW6oXL5etmwZisViVV0m96leIyt893GniSDCnaY+7qiEO0zHbSfcXpLTdIeDeckqV38jTMetllHHBco8BfO4ZElWs8rVysVtJa8gwh3EcQODc83LCSrkuH113zqOG6heL9w817489igcd1qGg/lJTtNp2AHV51rHcU+YMMF2X3a4OW6r6VTdvuM3VK4K9+GHH268v3jx4qq6TFe4wxgOljhSuL0OBQOSd9x2wi3X/vXKoYceOuS9KB2321KiVjMf6SanObV+wwiVh+W4VWfiJtxZzSpXKxdd4faToxHEcQODwi2zwdX7zs1xm6+N1bm2WiQn7D7uqLPKwxBucx9skOFgVn3caqJuPp/3vG6DWm7dPm5ZHrV8duXWCZVbCXdfX19VedatW2fb6HF61mtKuKPq444jOc2cVe7Xce+339BE/iiEW95gsqK0W4pRPohqf09aQuVJOu6sZpWrlYudcEfZx+22CqC8H+V9pzbUvDpu2c+t3q/m+yQtfdxxJafZJU+pDWMvwi0nu8nlclUTIsmuCcB+DXI3dPu4owqVq3VeoVAAEaFYLFY9N5s3b656tnK5nFbEInConIg+Yfo7T0T/7va9MIk6VB5Vcpp5yr0wQuVEhO985zu48MILjczYKB23XE3LbipYdW5fSZaS09Tv6EwKUuuOWyc5Law+7vXr1+Oee+6xTLS0E+6Wlpaqa+nFcZsbVXfffTeOP/54PPTQQ8Y2VsLttY9748aNuOeee2zrnDiT03p6emwzqK3OtRRucw6HlxwIOXySiJDL5UBEhgjKMfj777+/sb2fMLlabj+OO+xQuTrNstodoAq3/FwnXO7WQNOpXU4hog8C+ASAsQDuBPC481fCRT7YXsaNSpIMlZtbtGEINwBcffXVAAb7u8MUbrtlMO2EOyrH7beP209yGvdx22cNl0qlKjfU19eH/v7+qsUjvKCe6xNOOAFr1qxBV1cXPv3pT1eVw0nUxo0bZ0yVaeW4dUPlRxxxRNVMbIBzqNxNNB955BF87GMfM9YiaGtrw5lnnjlkuyiHgwkhqgShVCqhv7/f8resytHW1gbAXrh1HLfVMzBixAh0dnbixz/+MYBqx+1XuP30cTvpQalUMrQmn897CpUD5XPT09NTFa3YvHnzkLrJrSsKCGfmtAsB3A3gZQB/A/BFIcTVbt8LE3ky/QwZSHLmNPODEUaoXCXsEB4w1HHLxsa2bduGuEG5TnddXV1VBZpkqNzOcTuVw0u/a1Sh8jQ57lwuZ1s5B+nfBqrPtZyzX65Zby6HHTJcru7PvG8rdBpVZpflpY/7T3/6kyHagP0KT1E67r6+PhSLRRQKBUNY7JyxVSNJFW7VqVv10+qswiaR94sMlavj6KU58IrbdbFawMSpzlHvDzVKIHETblke9bz4ddxhhMqnA/gCgPsArAZwERE1O34pZOQNFES4k5iAxVwJqZWMfECCoDOFn1fMwl1fX4+2tjYUi8UhrXAZJlfdNhDNsp7q67CT03Qdd5TJaUk7bjtXYH4egoTJAetzrc7Q5lW41QaZlylP7TDfJ62trdoNZPNMc3YVc5R93OpqX27jra3OdWNjIxobG9Hf31/lCL2M47Z6BtT7pbW1FRdeeKHxt596HXCf8tRqOJhTnWO+LuZGXHNzs9HABobWbVZ1nZXjlsJt57iLxaJrPaCTEfBnAF8TQnwKwEkor8n9nMb3QiOI446qj9uu30jF6sG45ZZbcPXVVxsTPgQhDuEG7MPlVmFyIFzhtgqVu03kLwmrjzuOUHlUjnvbtm246aabtMeNynNhl6AWJDFN3b96roMId9iO23yftLS0aPdxy+OYOXMmAPu5D6LMKpfXS80F8JrBbxUu95KcZnWeVfd63333VXUb+sldUssdVqjcXG6z425qanIUVKu6bvny5di2bRtyuZxRT8r60e6Z1ElA1BHuY4UQ/wAAUeZ7AM7W+F5oyMrMzxSLUfRx9/T0YM6cOfjUpz7luJ1VKOrzn/88vvOd77j+hg5RCrd6s8uK0izcVolpwOANL8OudjgNzQgjVB6W4/aSnJa2rPIPf/jDuO6663DppZc6bmeuxO364cJy3OpzpgqcjnCrlb4Xx60TDbESbl3RlMcxefLkqr/NRDmOW3Xcbklkdhn8VkNAvSSnWT0D6v0yderUqu395Eqo+7c7J15D5eYGlfleaGpqctQRK+HesWMHBgYGcPLJJxvnwC1UHpZwNxHR7UT0dwAgojkATtT4XmjE2cfd19fn6nqeeOIJLF++HLfddpvjdm5DW4LiVbj/8Ic/4Mgjj8Tq1attt/HiuN1C5W4NLa+hcqfKK8qsci+OO21Z5Y8++igA4He/+53jdnbCbRcq99vHrQ6bkchMY2DwGjg9M2qfaJSOu76+3vgHuD9n8tzIORrsFmnx47jdlrWVBA2VA9E4bjUKZx7SeuSRR9oejxO6yWl+Q+W5XM6YH3/kyJHI5/PajruxsbHq+p5//vnGa7fkNJ2cJR3hvgvAQwDkjCGvA/iixvdCI6xQudUNPzAwgIGBAeRyOdfhJBLdMYd+WtZe8Jqc9qEPfQgvvPACLr/8ctttrCZU8RsqdxNunQlYgmSVhz2Ou66uzpia0W4mubRmlbvt13yv2oXKgzpuIhpyX+zcuROdnZ3o7+/H3r17kc/nh0RxVFTH5iWrXCcaolby8hi99nG7OW4/wp3L5bQa6laOO4xQuZcJWKwar2rjQZ7XV155Bbfeeis+8pGP2B6PE36Gg3kJlQPlhu/dd9+NX//61wAwZFU5FXMjUr1O55xzjvE6DMetU7uME0L8joiuBwAhxAARxZpBIwXXT6iciFAoFAyBNj8s6kIF9fX16O3tRXd3t2NFr14Qp22jFm6/ofJXX33V9jMnx22ehMUuVD5p0iSMGjWqanUuK6LOKg975jS/GbU6uC1+4Aez6O7atatqfm4V3SErQYUbKJ9vswtcs2aNcZ+1tbU5LsCjDiWyGscdluOWx6jbxy2FWjruMIUbKF+b/v5+9Pb22tYpqnDL3/GSVQ64C7duqFw9z1Zz+x966KGWM0HqEvaUp1bXpVAoGIuFAMAFF1yAZcuW4bTTThvyfbNwn3XWWfjrX/+KBx54oGoJ57iEey8RjQUgAICIjgPgvCZZebupAH4BYCKAEoDbhBC3ENFvUV5xDABGA9glhJjntK8gjhsoP9xdXV3o7e0dcpOqDtMqnd8K9YHcuXNn5oRbThdphZVwq0PCVOxC5S0tLVi5cqVrODUtE7DoOgi/bkMHGRkK03GvWLGi6u8XX3wRJ510kuW25oaPnXAHTU4DrBvga9euNc6Z2zr1qnAHGcdthXqfqLkagL7jdhNuv/VCfX099u7d61gOeX2am5uN7ZIKlat17Y4dO2zL7Be36+J3OJhTg6pQKODGG290LA9QPk+/+93vsGvXriHD3eIKlV8F4AEABxPRQpTF+HMa3xsA8CUhxGwAxwH4LBHNEUKcL4SYVxHr+wD80W1HYQg3YH2jqclYOuvMAtVhUqcbUmcyiSB4FW5ZIer096uVq9Xi7x0dHbjnnnsADBVuoCz2btcrLOEWQrhOwKIzjltnAhY//Xs6ROG4ly9fXvX3smXLbLe1C5WH3ccNWCfxrF+/3sgbcBNuGYq2269bqNyr49bt447acet05YXRxy2jMj//+c+NKJvdcDC77keg+jxH0QXkp4/bKVTut9EtMTvu+vp6yzHqsSSnCSEWozwM7K0APgXgUCHESxrf66h8F0KITgDLABgdBFSOhZ0H4Ndu+wqSVa5+z0m45fhFu+1UVOG2W94RSJ/jVrNxrSoVtb9frVSshPu0007DU089BWBoqFwXnVC5Th+3nGIxn89XjbOMIjlNrTytKi2rcusQheN+4403qv42zz+tEldWOWD9HK9fv95oBLsJt1qxqnkXuqFy3XHcXvq4ZQg7l8sZlXUUoXK3cqj1mZsRsUuelY576dKl+PrXvw6g+r4uFAooFAoQQjiGnNXrdOedd2Ly5MnGsqxh4JawF0ao3AtW0R8rIh0ORkTnyH8A3o9yeHsGgPdV3tOGiKYBmA/gGeXttwPYLIRYYfOdy4hoEREtkgcYpeNWhdttEhZd4Y46q9xrcpp6/K+99tqQz9X+frWPUTrqJ554AmeffTZWrFiBl19+ecjnXgnLcduJZX19PQ466CBMmzZNy3HbnUc1cqLOSWx1P1n1tevgxXF3dnbaTkGrIu9jeZ7tEuqAeIVbPTfSnarC7WWlKDUjXTc5LYo+bnleWltbjYZsmFnlgH4DAiifY7e+aDtjoT4r999/v+W2TnWq1Xk+8cQTsWHDBpxxxhm2ZfdKPp93TOp0CpXrJqd5wWqEgxVuE7AEDZW/r/LvEwBuB/CRyr+fA/io654rENEIlEPiXxRCqDXHBXBw20KI24QQRwshjpY3SVzCHXaoPC2OW23hyekmVawyyoFqx33//ffjfe97X9XnYQu3EMKTcDud59deew0rVqxwTHbSFW5zGNnqPrEK2evgxXFPnjwZEyZMcJ1URVZEUgidhNtcbrdQeViOe+7cuQDKyx/qOm4VVbgLhQJyuRyKxaLvUKjfrHL1vEjhDruPW+d5VxuZusJtbkAcd9xxxuujjz7assxO+47asKg4XRunUHnUjltHuCNx3EKIfxNC/BvKSWlzhBAfFEJ8EIB2GiAR1aEs2r8SQvxReb8A4BwAv9XZT1h93FY3mRpO8dPHnaVQuXqjWDU45HGbbzqzMJvdunlqQF3syr93714IIdDU1FQV+vbquAEYYT0n3KaTNFdETg28qB13T0+PIRKqaFkhy60j3LrJaWH3ccsxvF5C5QCMRUkuu+yyqvedXLdXx21OTnN6zlTHLQW/q6vLMoTrVyB0JpPykkhpVz/Nnz8fP/vZz6q+a37GdJ6BqHJ7VJzqwLhD5Vbrc1sR18xp04QQHcrfm1EOmTtS6cO+HcAyIcTNpo/fBWC5EMI+vVkhzX3cWUlOKxaLVcdlVW6r9WuBQcdth5MgOKFWRGoFJ/tizUOXZAVz0003ob29Hd///vcB+O9Xlnh13E73SdSOW42UuD3gfoTbLVSuTqnpF/U5PvnkkwGUHbduchpQnjr4ySefxJe//OWq9526MaLs45buesSIEcYa1KVSyTIc6lcgvDputwaHk7GQUzLL8+jHccch3E7H6CTcUYTK1fUngoTKwxoO9r9E9BDKYW0B4MMAHtP43tsAXATgZSJaUnnv/woh/lbZh2tSmiSOrHIvw8Gy6LjNN4mVcKsZqSp2ofCGhgZMnDgRZ599tk5xh5DL5ZDL5VAqlYwVjYDBqRbNDQb1PG7btg1XXXUVDj30UBx44IFDPveCm+O2E26rSitqx71q1SrjtV0fqkTeF1IIvQi33QQsdo07L6gV47HHHouWlhbs3bvXODYd4a6vr8cJJ5ww5P2gjlu9bvIceO3jlv/39PSgs7NzyPMUR6hc3bdduZ1C2uY604/jjiNU7nRtrO7VKEPlqtEIEioPZTiYEOIKAD8FMBfAPJTHY7sOBxNCPCWEICHEEXL4V0W0IYS4RAjxE9fSDe4LQDqT05wcd5qS08w3iRfhbmhosLwRf/GLX2D16tW+19MFrCsjN8etcv/99wd23G7n0Rw5ibKP202433zzTeO1rnAHcdzmZ8HKxXhFDfG3trYaU2C+9FJ5sIqXPm4zTo5bp49bzYWQ23l13AAc+7mjdNzqvnXva6t71XwedSMy5jJEjV/HnaRwu43jDitUDiHEH4UQV1b+LdD5TpjEESr30setDqvJiuMOItyAtesOY01xq2PQcdyf//znAZT7RsMKlaehj9stVO7FcZuT05yGg+nOnBaG4zbPlS+Fe+PGjQCCCbdOtrNuxSwbU177uNX/ra5RHKFynTnWneonO8ctt3VyjXEmp9k57oGBAfT394OIqsrhdcpTL6RKuCtDwlYQ0W4i2kNEnUTkr1PTJ1GGymWrTJ0RKCzHXUvCbfVeEKct8eK4VVE766yzAJSFO+h59upMouzjTsJxW2Xx2yXQhCHcsrErf8u8WlQQ4ZYNTKtj9Voxm4Xbi+OW/4fpuL0mp3mNJKmY73Fzg1RNwPOy37CxqwNVQ6ZGUeJy3E4mU/e6OKHjuG8C8H4hxCghxEghRKsQwt/4H5/EIdxNTU3GxAkbNmxw3J9aKZjn71ZJU3KarIDlg2e1upWTcFsJSlTCbee41alaZ82aZbyXVHKaVQPPb1n8OG634WC6wq0KmiyHFECz8FiNjfWKHE4oF7sxrxZlNzOaDjJByKph6nVmLHku/PZxA9bCHddwMLdye3Hc5m2dhDuJULnuSJM0hMrdhDusUPlmIYT9XIkxEGSREfV7TsLd3NyM6dOnAwBWrlxpu69SqVT1MG7atMl2mb00Om7pbpyyyq2E2/wb+XzeNdtcByfhNjvuiy++GFOmTMH3vvc9TJgwAfl8Hlu2bDGuR9A+bp3VwQDnPm6/11zXcasNLl3HPXLkSORyOXR3d2tPdiOFxyz2VmNjvXLXXXfhV7/6Fb71rW8BqBbuKVOmBNq3dOtWXVhROm7zMDmnzOEsh8p1HHcSoXLzMdoJd5Shct2s8jCEW6eEiyoLg/wJgLFHdVx21MTluA855BAAQxdnUJFjjFtaWkBE6Orqwp49eyxFLGrh9pOcNnXqVKxcudJzqNx8ozc0NDhOaqKLl1D5tGnTqlz35MmTsW7dOsOFpilUHpXjVh9q3T7u+vp6jBw5Ert27UJnZ+eQULTVfermuIOK64UXXmj8rQq3fAb9YrVAhsRrH7dcwlGnj1ueQ3lv6PQBR52cJu+pIFnl8n6zc9xWxxdnqDyLjls3wueEjuMeCWAfgHdjcDa192p8LzSiFG419CcrjZUrV9q6aLVlLZOz7CbCiLrl6Wc42Pjx41FfX4/u7u4hoV6vwh0GXkLlZmSFL+fkTlNyWlSO24twqxWRU9+vVb+83fZhCLcZVbhl1MsvslFi1TDVdVT/+Mc/cM011+CCCy4A4B6RAYaKldMkG1H2cav3X5BQuZpVri7iI9+XdUTWHHfQ1cGcUCdgcbrHYnHcldnTEiUux93W1oaxY8di+/bt6OjosOxrU5MeJk6ciJUrV2LTpk2YOXPmkG3TGCpvaWnBmDFjsGnTpiFLkqpLApox/0ZYx2Q1rtLOcZsJS7j9Ou4k+rjVe1i3j9tNuHVD5aVSqWo++7CIw3GXSiWjHpHn2o5TTjkFp5xyivG3en8IISwjTeZz6BQqj7KPWxVNWU4/ofJ8Po+6ujr09/ejv7/fUx93Ghy33SyQUa4Opt5XTiOTIhVuIvohKmtwWyGE+Lzr3kMiaB+3U5+kuc/ukEMOwfbt27Fy5UpL4VYrLjfHnXbh3rFjR9UxOjlu880c1jEFcdyyv14Kt98yeVnWE0iuj1t1PkB4jttqCJsaCi0Wi8jn81WN3DC6SSRq6D5owqPc1w9+8AM899xzeOKJJ5DL5arctteyFwoF5PN5Yw50K0Eyu8ykQuWqaLqFyt0EtrGxEf39/ejp6UltclqYjjvMcjuNTDKvama+H4Mmpy0C8LzDv1ipq6urmrfaC7qhcmBwqj81e1dFbclJ4e7o6LDcNo1Z5S0tLbaZt07Cfd9996G9vd34Oyrh/s///E/8/e9/B5A+xy3LmlQfd39/f1UXjq5wFwoFz447l8sNqZyjCJMD1ZOeWEWuvKAmCC1cuNBYRS1oGFS3cecWKhdC+C6L1+S0IKFyoPo+T2tyWph93EHvERUn4ZbLDwshLJ93nfrc1nELIe7WLGMs6C6kYYXuIiPAoFjYDZ1RZ1qTSxIm5bj9JKe1tLTYJpY4CffJJ5+MzZs3o729Hdu3b8dRRx0VqOwS84P01a9+1fhMV7jVpQz94JYsotvHbbeqmQ46c5WbRUM3OU113FaTsNiVeeTIkejq6kJnZydGjRoVylAwO5588km88sorOP744wPtx5x4J89ZGEs27tu3D729vZYro+mGymU58vm8Z+fvNznNT6gcqL7P05qcZteg8pNVHjRUruI2F4jMM+rt7R3yezpapzVzWtbRHQ4GuE8Arwp3lpLT1P5ruzCek3ADZWf0+OOP4/LLL8ett97qu9wq6qT/5odJNzlNEvdc5eb7SS6WIlvUXnBa/EBiLp+XPm7ZCJLdEFb7tRJuYLARG8ZQMDtOOOEEY8WvIKiOGxh8joNWyl5X2rJz3EEa816T09wa9m71k2p47BomSTtuu8VOkg6Vu5kOp2vjdH0lmRDucePG4ZZbbvH9fd3kNEB/rVQ1VJ6FPm610vUr3ABw6KGH4kc/+lFV2DwI6jGoCUWFQgHjxo1z/K5ZuONKTrOrLIJcb51JPsz3r5c+bqdhUnblNieoRRUqDxM74Q7DcQP6Iw/snrEg4uA1OS2sULkcEqhO0JOWPm676UODhMqDOO5HHnkEZ599Nv793//dcTunKF+gUHmaOOCAA4y5qf3gpY/bTbi9OO40CbdaliiXbPSKegzbtm0DUL6pX3rpJddkxIkTJxqri8nv+cFrcprd/RRkBjedbg+voXKvwm23DrusuKMMlYeFOVRuFm6/YqLruOX+7SJ3UQu3VXJa0FC5bLip20nhfvbZZ/G///u/aG5uBhHhmGOOiTVU7le4nQQzSLnf9a534V3vepfrdk7Pe+jCTUT/TwhxspfvpIEwHbdVH3cWktPUhzSI4w4bK+E+6qijMGPGDK3vTpw40VicIuoJWNyS04I01HQm+ZC/N2rUKOzevdtTcpofxx1nqDwszI0KeU9H7bh1Q+VBGndeHXcYWeXAYF6E1aiDUqmEd77znVX7TFOo3Nz4j2JGOz8EDZU7DQd7yfwWgBnyfSHEER7KmSh++rjdQuWNjY1ob28HEWHr1q0YGBgYUimkKTlNfZjSJNyyAti9e7eRmCXn1tZhv/32M4Tbr+OWIcBSqWR5HXWT08KolHUc99ixY7F7927XPm7VZTpNTOIWKjc77jQLt5mw+7h1hdtOIKIW7rCHgwHOjtvMmjVrUu24nfrmwwiV6xLUcTv1ca8G8BKA8zA4Y9oW5XVmiCJU3tDQgEKhgPb2dgghLBcbSVNymvow2d3sSQi3nFZyw4YNxjzcbn3bKmo/d5DZ3JwepDj6uL04btmw6erqsp3hT92XW6jcLTlNJrRlRbjvvfde43XYfdx2oXLdPu4gwu03Oc3unnKbTMcs3GqZ7eqIlStXprqP2ykbPi2OO5BwCyHeD+A+ALcBmCuEWA2gXwixRgixxl9xk8FLqNxpqkJ1H3KfTv3caerjTqvjVoVbhsq9CPe8efOM10HOs1OySBx93F4cd3NzM5qamiCEcHTdQZPTpHBfeeWV+MUvfjHkWUkrF1xwAS699FIA4fVxhzWOO85QuVs4tlgsIpfL2TZmZBmtHLfdPaAKdxyhcnmedbPKo5gYxw+RZpULIRYAeA+AdxDRAwCivxIREFUfNwDHSVjSJNxqxWIVxiuVSokkHwUV7quvvhpXXnkl5s6di5NOOsl3OZyGhMXZx60j3I2NjY5CLAmanKbOvXzttddmxnEDQ51YUMedxVC5U2NQvZfsxpOb+7jNE/RYsWLFilhD5bKu8uq4kw6VOzUEQ0lOE0LsBXAVEc0FEGx2hIRwGnfb39+PXC6nNccwMDS85DQJS5qS09RWsJUbkA2YxsZG3zPU+UEK9/r16w2H50W4m5qacPPNNwcuh07oyhwqD7NS9hIqb2howJgxY7Bx40bs3LnTmPrVrtx+k9PUyVomTpyYaeG26w7QxS1UbifcaUhOs9rebi5vFadQuR0rV640RnnE6bi99nHXtOMmohwRyW2WAXiWiMY4fSeN2Am31dzLXvq4gWRD5V6S06wct3qMskJ3mzggbGQftV/HHRZ2LeBisYhisQgiMho0MtFLXRsbiH4ct1oZ2U1bq2KeOY2I0NnZOaRisCv3Bz/4QeP1tm3bMjEcTGKu0IM2Otwct7nSVxt36vzzQRoQXvq46+rqHOsHc+TQCqfkNDtWrFiRSB+3bqg8LePPI+vjJqKzAXQA2EBEZwF4EsB3AbxERJlKThsxYgTy+Tx2795ddYGt+uz8hsrT3sdtNRxMbaXKUL/VwipRokYsNm/eDCAZ4bZ7kKxWW5KTz2zdurUqOSwux93Y2Gg0HuxC5UKIIdNf2s2eZicmRx99NPbs2YNcLoeNGzcaDjwLjtv8HAftn/fquPP5vKVhiCtUrk7AYp7jXi2HjnBbDQcDgN///vdDvrNhw4ZUh8qlSevp6RkyvXCtZJX/O4C5AN4K4JcALq6M4X5b5bPMUF9fj9mzZ6NUKuHll1823g9TuK36uNOUVa5W4lahcjmkSgppXNTX12P8+PFV1yZJx21+kKwaX83NzWhubkZvb29Vy92ur1iHsB23rJRyuZwRMrULlzs1MFtbWzFlyhQIIfDaa68BGJ6O2+s4bsC6LolSuIvFouHu8/k8crmc7WIWfkLl5vvj3HPPxfe///2q96ymR40Sr6FyIopkVjuvRJ2ctkkIsQrAWiHEa5X31rh9L43I7OMlS5YY71k9zF7GcQOD7sscNgXS77ithDtuxw0MhsvluY278QDYJ6fZuQfZuJDhffW7cSSnuTluq3HLdmO53Roc06ZNAwC8+uqrAOzH8KYJc4Ue1HF7TU5Ty6A+Z2E07uyed6vokN195SVUbpWcJjGvJ6COdEhjqBywr+PDXB3MjSgdN5T+7Y8r7+WRwezy+fPnAwBeeOEF4z2rh7m+vh75fN5YQN6MuaXq5HyiDhmpLsBpHWegujWZplA5UD0We8SIEVXZzHGhEypXUcPlkjiT09yyyq0qIT+OGyhPOQyUJ9cAkMj18UrYwq07jls931bPWZSO2+pedVuv2ksft1WZrRYCchL6sPGTKGrXzx1niD9K4b4MFYEWQjyrvD8VwLc8lDEVWDluq4dZDaWsXr16yH7MLVUn5xO1487lcrahIqeypClUDgwKA5BMwwGwD4XaXUMr4Y4zOc1pJjTAWkiCCrdEZv+nmbiT0+IIlbslp1kJj5vjdiqHPFfyftFx3MCgcKdxAhYgmox/rzh1vQQKlQshnhNC9BBRIxEdRkSHElGjEGK1EOKeAGVOhFmzZgEA3njjDeM9uyxZeWFnzJiB5cuXV31mbqnqOO4oW55OWZIqVo5bvXGTdNyqMCTRcADsKzg34bYKlQeplNV+SjNqo9HNcXsRbrcQvznnYDg7bquKVl2H3ep8hxWVcXPcVvdqkFC5ea56Xcctt0+rcNvVmXEKd5RZ5QUiugnAegB3A7gHwDoiuomIor8iISPFSn3w7BZNUP++//77qz4zt1RHjhyJfD6Prq6uqhMuK2B1GFEU6Aq33epgMts0yT5u2Yea1O8DyTtuInKtmJNy3OYhglkU7rCS06xC5Wo2sjoxyfTp0wHASOoD4gmVq9fcLVTuVA6zKOs6bqftw0adflh3hIed444zqS7KUPl3AIwBcKAQ4ighxHwABwMYjfKwsExhNTTDrhWunrj999+/6jNzS5WIjIpNrRCtEkWiwI/jzufzaGhogBDCOJ7hHip36+O2S04Ly005lcG8fy+OW01O8yvc5so5C8Id9nAwp1C53T0io3xxCXdUjtu8LxX13jAffxyOu66uDoVCAcViseq8ZNlxB80qfy+AS4UQnfINIcQeAJ8BcIbHciaOeqJkKNLuQq1du9Z4bX5IrG54qwoxrtab06T5Kk4zO/X392Pr1q3I5XIYP358hKW1Jg2hci/DwYDwHbf6PbuK2TxzGmDvuP0kp9lVWGbHncU+7iiT0+yu+8yZMwGgqrstyj5uq+S0IMJtbrC5hcrl0Fjzb0eNVbg87X3cUTpuISyWHhJCFAHYL0mUUohoSOWsMzRDZ51XqwS1uDIUnZapUzG7AjXjdd26dRBCYMqUKbFMPmBG7UNNanIPt+FgXvq4/VZYbglqVuO4/fRx2w0H41C5PU6O2+78SccdlnDrOu44Q+Xq9+UzYf7tqLEaEhYkqzzpUHlQx/0qEV1sfpOIPgpgucX2qcccLte5UOYWtlU2plWFGLfjdhJuq/52NbNcDvMxZw/HhdqVkNQYYa/JadJdrF+/3ngvqDjoOu7GxkajUlXnE1exEm67ERBuDQ61AldXnUozcSan2TXSp06diqamJmzevNmYrS5I487PcLAwQ+V2Iv+Tn/wEN95445A58+MSbqshYVl13KVSyXVoL+As3J8F8Fki+l8i+h4RfZeIHgfweZTD5ZnDHO6ye+D+8pe/GK/Njtvqhndy3GkQbqv+dtnY2Lx5c+LCDQB33HEHzj77bJx33nmJ/L7bil/me2TGjBkAyv2X8kELuiyqF8edz+dBRCiVSkNmxQKiS07LgtsGqitzIYRtIqouTqFyu7H+uVwOhx12GADgN7/5DYD4HbedQOiM49bp4waAT33qU7j++uuHnNu0hsrtuheTGA6mm1Njxmk42AYhxFsAfAPAagBrAXxDCHGsEGJDgDInhjncZVdhnXnmmfja174GwH7NZas+btVxx7UmrU4ft9UDLce1P/fcc6kQ7n/7t3/DggULYnlorHCbBtF8Hdva2jB+/Hjs27cPGzZsqPquX+HWTU5raGgAETk69DCT09QKPAuzpgGoSsDs7e0NvECKn1A5AFxzzTUAgK997Wvo6emJPTnN7js647gLhUKVGMv7xw7zfBhxrTLoNVRu172YRKjcfD/pzt7mNBysmYjqhBD/TwjxQwAPATiCiD4QrMjJ4SVUrg4zUMm645a85S1vAQA8++yzxkQz6rCs4Yaffi9zH2ZYwq0TKnfbPszkNFX8oxwhETaqE4sjVG51j5x77rmYOnUqtm7dirVr1waa8jTu5DSguptELsFrh3kGyrjwGiqXDVE1sVQ28Oy+EzZuo1jcco2cQuV/BzANAIjoEAD/AnAQgCuI6Jv+ipss5ofPyRXbhU699nGnITnNqizHHnssAOAPf/gD7rrrLgDJOu6kCSLccriPrDjiCJW7bW8VcmttbUUulxsy54CXRqZO/1taUPM4wkpOc8oqt3rWicgQv97e3kyFyoHqaIsX4Y6rfxsYbFyoDVKn8yzrvkcffdQY+60KpjoWPyq8Dj8141TCNiHEisrrjwH4tRDicwDeg/JQscwR1HGXSiXLFrN03GEODdLFr+OeNWvWEPfBwu1NuM3DfeJy3PLe0xlSolYAdkt7ekmYsupPTyvqyIkoHbdbt5jaP56Wucp1QuVAteNW1xSwQm0UxSncskEhu6xKpZLj8c2bNw8TJ07Ehg0bsHTpUgDxhsnV3zE/u4FD5age8nUygEcAQAjRByA7zW4Fu+Q0XcetirYaMjz88MMBAE8++aTRgkvTOG6rlngul8N3v/tdQ6xHjBgxZLKZ4YSfpf4OPfRQAMA///nPqu9G5bjN95RX4QaCzzmQRccdpnB7GcctUeuStDhu3VC5Wk63OR6SCpVL4ZYjPFatWoVisYgJEyZYloOIcNpppwEAHnroIQDxJqYB0YbKX6pkkl8J4BAADwMAEY32W9iksUtOs6qUrRy3XStu/vz5GD9+PNatW2csfRh3cppXxw0Al19+OVavXo1XX30VTz31VCbWWY4KP477pJNOQmtrK1544QWsWLEi8uQ083X0mpwGBB+6mFXhjmMct51LUr8bxyIjOn3cuqFy9XlwCyEnFSo3O265kJRcEdIKmZy7bt06APELt11WeRiO+1IA21Du5363EEL2/M9BBqc8BfyFytUWtl0rNZfLGS24ww47DAsXLkxVqNytYpk9ezbmzp0bfuEyhB/hbmxsxFlnnQUA+O1vfxt5qNzsop0cul0F4JRIqVNpZSlUPtwct5e5ynVD5Z2dnY6fq6QlVC6XbnYSbvMQsrQ57iDDwbqFEN8SQnxBCPGi8v4/hRC/9F/k5DA/fDrJaVZDDKxaqRdccIHx+sc//nEqk9OyMHFGUvidTem97y2nezz33HORh8rNDzWHyp1Rk9OCCrfVkCOJW3Qt7lC5Wg67+kE3VO42I6NKUqFy2ffuxXGbhTupPm5zBCdwqJyIHiOi/2fz79GgBU8C3XHcgHOo3Opmf8973oMHHngAAPDMM8+kqo9btxU3nLE7j27XUeYFdHR0GN+Naua0MITbao5zneS0q666CgBw7bXXuhxFepDXYffu3SgWiygUCr6fAfOELipu94hVcpqfekGOi7abXcuqAWG1GA6gHyqX/do6E++owh3nYkFqH7cQAosXLwYwGA63ImnHbTfcWDdU7iTrV1u8dxyAawFs0S9ievASKrdKTnMLL51++ulobGzEypUrsWnTJtt9h4mXUDk7bnvsnIlbo0dWUOvWrUNvby+IyLer001OM4fKnfq4w3LcN910Ez72sY8ZM4FlAVk5y/nkg+RwFAoF1NfXo6+vD319fVV1gG4fd1DHLZd+7e/vR39//5B9WJXDajEcWRadctxzzz248sor8e1vf9u1fOr5lUuaxsHIkSPR0tKCvXv34l//+hc6Ojowfvx4HHzwwbbfSVq4/STDqjiFyp+X/wCMAPBtAB8G8GkhxDFBCp0UulOeAt4dt9zPUUcdBQBYuHAhgHQINztud/yGyuWc5bKh1tzc7HuSEr/JaU593OaQm9/JgvL5PI444ohYxriGhawcpXAHXcDGampNQL+PO2hyGuCcoGZVDjvHrRsqP/zww/GPf/zDqNecUM/vIYcc4rp9WBCR4bp//vOfAwBOO+00x3s16VC52o2jEkZWOYjoNCJ6CsBXAfyXEOLtQogH/Rc3WbyEyq0ct0546Zhjym2aZ5991nbfYcJ93OGgtoDVUKhOGHTs2LFD9uOHOELl5qzygYGBIQvQ1Aqycty+fTuAYI4b8D4trkStS4LMnAZ4j7JYrWIHROMwk3LcwGA/95133gmg3HXpRFoct7kRGDhUTkTPAWgH8B2UZ00DER0pPxdCLPZV4gQJOgGLTitVrictHVjULtcq+90MO2538vk8mpqa0N3dje7ubuPB1mn0TJ482RCHIMIdR3Ka2XGrs71laTpTHczCnZTjDmsCFsBZuK3K4RYqd3PcXlCFO07HDQyd1e0d73iH4/ZpEW6/oXInP74XQBeAcyv/VATKk7JkCi9TnjoNB3O6uDKJQ94QUbtcp6kYJey49RgxYgS6u7vR1dVlPNg6D9LkyZPx8ssvA4jOcculWYHBJKUgfdzScctITVYWD/FCmH3c6v7shDvqPm71e7ozuMUp3OrxO/UvR4Eq3E1NTUYXlh1Jh8obGxtBROjp6UGxWDSeabsuLjO2nwoh3hGkYEQ0FcAvAExEeaa124QQt1Q++xyAKwAMAPirECKWVFU/yWm6w8EkukvhhUWhUEA+n0exWMTAwIDlBWfHrUdLSwu2bt2Krq4uI5tW54GWURa5D7/ozj0unXEYjns4CHdYoXI74XYLb1qFyv3WC17XBR8zZgyICDt27KiqH6JwmGqXUdyTOanCvf/++7tGj8wLk8TtuIkIzc3NxlBF+fwFTk4jomuV1x8yfXajRtkGAHxJCDEb5Wz0zxLRHCJ6J4CzABwhhDgUMU7m4ke4e3t7jT5PP4vPx9GCc3Pd7Lj1sEpQ0w2VS8Jw3E5CrDMrFmDfch+OjjvqULk813Y5AvL53LNnD4Bypew3yc/pWbe6R/L5vNFYk+dB/X6Yjnv8+PFYuHAhXn/99dD2qYs6j7rOmgtJh8rVMqjh8jCS0z6svL7e9NnpboUSQnTIfnAhRCeAZQCmAPgMgG8JIXorn8U2tMzcWnVb1cecha5zs5vHO8Yhlm793HFNv5p1/Aq32tqPKlRu1RIPYwKW4SDcUYfK5WxydpWtrC/kOQ9SDq+OG7AOl+uO4/bKW9/61tgT04DqZ1BneWLzuPwkzI1VP3cYU56SzWurvx0homkA5gN4BsAMAG8nomeI6HEiim1omRfHDQxNUNPp406z4+ZQuTNWk7DoPNDHH3+88TqOULl5ey993M3Nzaivr0dPTw+6u7sDz/aWZuQxycowLMdtTihyc9yyvpDCHUQs/awLbs4sV9eerpXGvCrcOo5bjsuX5yIJx22VWR7Gsp7C5rXV37YQ0QgA9wH4ohBiD8r96m0oh8+vAfA7suiQIKLLiGgRES0yJ1b4xatwm7f308cdh1ha9cersOPWw8px6zxI6gxNq1at8v37cThuIqpy3cPBcUuSdtxyKdUg5fAaKgeGjuVWRTtL4/KdmDBhgvFazTlxQr2etRQqn0tEe4ioE8ARldfy78N1CkZEdSiL9q+EEH+svL0ewB9FmWdRTlwbZ/6uEOI2IcTRQoijZYsxKF6yygF7x522UDk77nCQLWB1YQUdx622O7ds8d/z4+S4vSzZCDhXAOq0pyzc+riNvXXr4w5DuP2EymXSmMxriCpMniTqudeNHqnzzyfpuEMNlQsh8kKIkUKIViFEofJa/u2qABUXfTuAZUKIm5WP/oTKUDIimgGgHuVVyCInqOPOaqicHbcestHlVbgBYNGiRZg1axZ+9rOf+f59r8lpflYHAwYd2LZt24aVcEeVnOa1jzuIYPpx3Oa8Bt3pTrPG9ddfj/nz5+PMM8/U2l69nkn0cVvdT2GM4w7K2wBcBOBlIlpSee//ArgDwB1E9AqAPgAfE+ZZ+yPCbspTXcet01JtbGxEoVAwKk6zkEcBO+5wGDVqFIDyohQS3Qf6qKOOwrJlywL9vtPSkU6hci993ED1NK1SuGuxjzuuUHnaHffo0aMBDBXuWnLcAHDjjTfixht1BjyVSTpUbuW4dUPlkQm3EOIp2CexfTSq33XCbspTO0EzZ2vr3PBEhNbWVuMhmTp1aggld4YddzhYCXec505n6cigfdzAYH/gpk2bjEqDHbf+/rw6bikGcruohdvOccuGQy2Gyv2QRuEOI6u85vAbKvfSxw1Uu+w0CLe8KdlxO+PkuOM4d3EJt3TcmzdvHlah8rAct9escnN9EXVymtsQwFoNlXuFQ+UZQQ1Flkol15ayneN2u+HVBzWOdWl1hXu4t7DdCBIqDwPzbE4qVg0IneFjVve26ril2NSicI8YMQJEZEyglHRWud3fXgjiuGs9VO4V9XlLm+MOtDpYraGupKWGQO2mxzM7bl0BVJOb4hwOZifc/KDqkbRw2wkD4DxzmlUft1PIbbg47nw+b1xTIPmZ0yRxDwcz93FzQ75M0qFyp+FgHCpXUCtmnQrZTx+33H+csOMOB7NwCyGM4V2y8ouSJPu4azE5DRgc+gakx3HHnZxm7uPmUHkZeT137tyJxx9/HEAyM6dxqNwFdbiPrBydLpTfPm5zH1jU6Dru4f6gumEW7g0bNmDv3r1ob2+vEoCocAqVR9XHLY+5Fh03MChaQHJZ5ebnLqnkNA6VVyOv52c+8xnjvYMOOii23+dQuSb5fN4QbznhvlPLxm44mK4AxlUZcqg8HMzCvXz5cgDAzJkzY/l9nVC51z5uq/tbrny2efNmY+GL4SDcQUPldhOwxNnH7Sc5TUaLdu3ahVKplEhYOI2oDaixY8fi0UcfrZq+OGqsQuVSa9zukWEl3MBg5Szn7Y0iVH7uueXlyy+77LJghdXEbcpTflD1kPeGDClK4Z41a1Ysv+8UKneaOc1pHLeVmDQ0NKCtrQ3FYhFr1qwBULvCHUeoPO2Ou66uDi0tLSiVSujs7OSGfAV1ze77778fJ598cqy/b9UQlIbSLcI3rELlQLlyXr9+vTFvbxSh8ttvvx0XXHCB9gw+QWHHHQ4yGtPV1YVisYjXXnsNQPzC7TU5zevMaUC50tq5c6dRUdRqH3eYjlsKrt1wMDvHncvlUF9fb1ynuJPTgPJ52Lt3L3bt2sX1QYXLL78cBxxwAE499dSq+yQurBy3fB7Vtc2tGLaOW0e4/Q4HGzlyJM4555zYHC4np4WD2pWyZ8+e2B23eh1LpVLVZ2GGyoHqRRkAdtw62C2fK0Pldo4bqH724k5OA6r7uTkCV6alpQXnnXdeIqINWEfYWLht8CLcfoeDxQ0np4WH2s/95ptvAgAOOeSQWH6biGzFwSk5bd++fYbrc9peRQ0TElEsU/MmQZjJaeaZFyU6CUVqRCPucdxAtXCz404HVsmoLNw2mIVbJznNax933HCoPDxU4e7o6AAQzyQ6ErtwuZNwL1y4EB/4wAdct1dRHffEiRNrdlY91XEHDZXbPWc6jlsmBALxz5wGDC4ss3nzZq4PUoI5+Rlg4bbFS3Ka3z7uuLFzaZK0RgrSiLw/1qxZg+7ubowYMWLIUq1RYjckzCk5DQD+8pe/GMs2Au4uUHXccUzLmxRROG7zc6bjuNWlieNOTgOAgw8+GACwcuVKDpWnBHOofGBgALt370Yul3OdN2LYCrefPu603vAcKg8PeX/cfHN5Jdo43TZgn1nutKynZOHChUO213HctSzc6sxpQSfXKBQKyOfzKBaLVV0TXh13FMPBisUihBDI5XKW5ZDdPStXrkytARlumB23HGff1taGXM5ZmoetcMsZsXSEu7u7G/39/ejp6UEul0udAHKoPDyOOeYYADBmUpo0aVKsv+8nVC6RZbbbXmW4OG71nreb2tjP/tRnTcdxhxUqt3PcbovhTJ8+HUC14+b6IFnM0TXdoWDAMBbu9evXA9ALlff09BitoTFjxri2huKGs8rD44YbbsBHPzq46mzcjtsuVK4j3Oy4hxJ2xrDVmuluE7AA0Qu32/KzVo47bQZkuGGOrun2bwPDWLjlSTrqqKNst1Udt+w/jGPqS684CbcQwnjIeT1ud4gIJ510kvF3UqHyr371q0ZUCNATbrWPmx13mdmzZ+PKK6/ErbfeGsr+nBy3U6g8rD5uu2fdzXFPmTIFjY2N2Lx5s9FNyA35ZFFHKZRKJRZuJ9Q+LwA4//zzbbdVk9O8hDHixkm4VdFOW6QgrcyfP994nVSo/LHHHsOll15qvO+0rKdE7Rd3C9+qQhJ34yROiAg333wzPvvZz4ayP6tnzavjjmI4mNuiSblczkhQe+WVVwKXgwkOEVlqDAu3BapwH3HEEZg9e7bttmpymnQzOic1bpymPE1rQl2aOfTQQ43XcawKpqIOWbLqs1YrZnPFq4qJm+NW399vv/0ClHh44ddxRz0cTGdVqcMPPxwAsGLFCgBcJ6QBNarLwu3A7NmzMXr0aMyZMwd33XWX47aqIGYhVG4l3JyY5h31XMXdUFMrddUVW1XMbW1t+O1vf4sFCxYA8CbcQNnV33vvvTjggAPCKfwwIAzHHWVymlN32Iknnlj1N9cJyaP2cz/11FMA9CZ8GnZzlbe3t2PTpk2or693zTK1ctxpFG45XWVXV9eQzzgxzR9PPvkknnjiCZx11lmx/q5aqctJMwB7IT7vvPOMSls23EqlkjFlqpMLfMc73hFKmYcTSTtuv8lpAAt3GpH3QkdHBx588EEQ0ZDJlKwYdsIN6IeIsuK41XXGzXAGqT9OOOEEnHDCCbH/rrrggCoETg66rq4ORISBgQEMDAxUOcAwhkAxg/h13OqUp0FmcPObnAaUo41jx441QrJcJySPFO4//OEP6Ovrwzvf+U6tvJphFyr3guq4056cls/n0dfXN2TBCQ6VZ4sNGzYYr+W64IBzxWye49xtZTDGP1bzles4biLC6tWrsWLFCkeBd0NdylVdiEYnVJ7L5fC+973P+JvrhOSRjbgXX3wRALTNAgu3A2pYSk6RmsbkNCKydd0cKs8WmzZtMl7LuQMA91Co6sR0+rcZf/h13ABwwAEHBF6whogsw+W61/yKK64wXvPw0OSRDe5Vq1YBGLpqnx0s3A6o6fpywYk0Om4AxupOe/bsqXqfQ+XZ4vvf/77xeteuXcZrt4qZhTse/PZxh4nV7Ho6jhsoz1vx3ve+F5MnT8aBBx4YXSEZLaRwr169GgALd2jIEytDmGkVbjvHzaHybHHqqadi3759yOVy2Lt3ryHCbmJsnp7XaVvGP0Ecd1hYCbeXa/6nP/0J69atq+p3Z5JBXkt5/Vi4Q0I+qFkR7m9+85t44YUXjPd5HHf2aGpqMsaPy35uP447LiEZTqTVcXtpoOfzeZ6MKSWYRxiwcIeEPLEyFBX23MdhIYX7N7/5DY488kjjfXbc2UQKtwyXu2UNq46bk9Oiw+8iI2ESVLiZ9MDCHRHmEyvHTKcN2cdthpPTsolZuLmPOx04hcrT4LiDrjnOxIt6vRoaGmzrcTMs3C6oYy7z+XxqK0PpuM1wclo2MQu3W8XMwh0PaXDcsm9aFW45+Q430LOFqi8TJkzQnneBhdsF9cQ2NzendkILN+HmBzpbyC4ZKdyyYrYTbk5OiwenZT3T4Lj5Oc8W6vOsGyYHWLhdCWvGo6ixE24OlWcT6bjlWG63ill1gnE7wOGE2XELIVi4Gd+wcEdE1oWbQ+XZRDpuOWMfO+50YBZuOXsZEcWWqS3rIXV6XO7jziaqpnhZpY+F2wVzqDytuCWn8SxJ2ULOV7xx40YA3hw3C3d0mIU7ieiGlePmPu5sokZpzj//fO3vsXC7kHXHrbNqEJM+ZOtbzh/g5rjVBXFYuKPDPFd53GFygEPltYT6jJ500kna3+NOMBey4rhV4VYT6HSnQmTSxZQpUwCUhVsI4Voxq4uMsHBHR1odNwt3NrnwwguxdOlSXHzxxZ4Sn1m4XVAdd5qnCFT719SHlyvxbCKFe/369ejr64MQAnV1dbbOjpPT4sEs3HFPdwpYDwfjPu5s0tTUhJtvvtnz9zhU7kJWQuVWKwWpr9lxZ4vJkycDKK8WJpOQnCplTk6LBzvHnXSonPu4hxcs3C5kJVT+nve8BwcffDCAcmUihADgPlUmk07q6+vR3t6OYrGINWvWAHCulDk5LR7S4Lg5VM6wcLuQFcc9YsQIrFixwqiszatKsePOHjJBbcWKFQCcHTcnp8WDHL0hx9enxXGzcA8vWLhdyIpwA+WkNLNws+POLrKfe+XKlQCcK2Wr5DTu4w4fc9Jgko6bx3EPX1i4XchKqFwinbUUbHZf2UWO5V69ejUAPcfd09NjXHt2X+HT2tqK1tZWdHd3Y+fOney4mURg4XYhS44bgK3j5lB59pAr0W3btg2AXh93d3c3JypFjOq605JVztd8eMHC7ULWHLcUbnbc2UdW0HLaU52s8p6eHnZfEaNOjsOOm0kCFm4XsjKOWyKdNTvu7CPvN+m4dYR73759XIlHjDrGnrPKmSRg4XYh66FydtzZRYbKpeN2qpTV1cS4Eo8WNVSeNsfNyWnDA047dSHroXJ23NnFi+MeN26csS0Ld7RYhcqTctwLFizAlClTuI97mMHC7ULWHLc5VM6OO7vIe0+GY50q5bFjxwIAduzYYTgxrsSjwSpUHqfjbmlpQS6Xw549e3DOOecAGGw48DUfHkQWKieiqUT0GBEtI6KlRPSFyvs3ENEGIlpS+XdGVGUIA9XlZOGhYMddO5hzKpwcd11dHUaNGoVSqYRNmza5bs/4R85QuHz58kQcd6FQwHHHHVf13sDAQNU8DkxtE2Uf9wCALwkhZgM4DsBniWhO5bPvCyHmVf79LcIyBEZdvMPL6i1JwX3ctYNZuN0ajjJcvn79eq3tGX/MnDkTDQ0NWLVqlZF/EKfjBspTHJtpamrKRB3FBCcy4RZCdAghFldedwJYBmBKVL8XB3L+7zRjFypnx509vDhugIU7LgqFAg4//HAAwOL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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anom
count472.000000472.000000472.000000472.000000472.000000472.000000472.000000472.000000
mean23.2096190.05972525.9365680.03942828.6250640.06381427.0767800.034894
std2.4315221.1575901.3496210.9654640.7554220.7094011.0630040.947936
min18.570000-2.10000023.030000-2.07000026.430000-1.87000024.270000-2.380000
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50%22.980000-0.16000025.885000-0.11500028.7600000.20500027.1000000.015000
75%25.3225000.51500026.9625000.51250029.1900000.63000027.7925000.565000
max29.1500004.62000029.1400003.62000030.3000001.67000029.6000002.950000
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" - ], - "text/plain": [ - " Nino12 Nino12anom Nino3 Nino3anom Nino4 Nino4anom \\\n", - "count 472.000000 472.000000 472.000000 472.000000 472.000000 472.000000 \n", - "mean 23.209619 0.059725 25.936568 0.039428 28.625064 0.063814 \n", - "std 2.431522 1.157590 1.349621 0.965464 0.755422 0.709401 \n", - "min 18.570000 -2.100000 23.030000 -2.070000 26.430000 -1.870000 \n", - "25% 21.152500 -0.712500 24.850000 -0.600000 28.140000 -0.430000 \n", - "50% 22.980000 -0.160000 25.885000 -0.115000 28.760000 0.205000 \n", - "75% 25.322500 0.515000 26.962500 0.512500 29.190000 0.630000 \n", - "max 29.150000 4.620000 29.140000 3.620000 30.300000 1.670000 \n", - "\n", - " Nino34 Nino34anom \n", - "count 472.000000 472.000000 \n", - "mean 27.076780 0.034894 \n", - "std 1.063004 0.947936 \n", - "min 24.270000 -2.380000 \n", - "25% 26.330000 -0.572500 \n", - "50% 27.100000 0.015000 \n", - "75% 27.792500 0.565000 \n", - "max 29.600000 2.950000 " - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.describe()" ] }, { "cell_type": "markdown", - "id": "a09a3ca9", + "id": "a92bb8b3", "metadata": {}, "source": [ "You can look at specific statistics too, such as mean! Notice how the output is a `Series` (column) now" @@ -2635,36 +776,17 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "2a4b8b6a", + "execution_count": null, + "id": "db9e4a16", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Nino12 23.209619\n", - "Nino12anom 0.059725\n", - "Nino3 25.936568\n", - "Nino3anom 0.039428\n", - "Nino4 28.625064\n", - "Nino4anom 0.063814\n", - "Nino34 27.076780\n", - "Nino34anom 0.034894\n", - "dtype: float64" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.mean()" ] }, { "cell_type": "markdown", - "id": "c3ead9af", + "id": "1ff5aec7", "metadata": {}, "source": [ "If you are interested in a single column mean, subset for that and use `.mean`" @@ -2672,28 +794,17 @@ }, { "cell_type": "code", - "execution_count": 40, - "id": "2123223e", + "execution_count": null, + "id": "9aa38e59", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "27.07677966101695" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.Nino34.mean()" ] }, { "cell_type": "markdown", - "id": "678fde2f", + "id": "7295e7b0", "metadata": {}, "source": [ "### Subsetting Using the Datetime Column\n", @@ -2705,590 +816,10 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "8a82e067", + "execution_count": null, + "id": "a506724a", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anom
datetime
1982-01-0124.29-0.1725.870.2428.300.0026.720.15
1983-01-0127.422.9628.923.2929.000.7029.362.79
1984-01-0124.18-0.2824.82-0.8127.64-0.6625.64-0.93
1985-01-0123.59-0.8724.51-1.1227.71-0.5925.43-1.14
1986-01-0124.610.1524.73-0.9028.11-0.1925.79-0.78
1987-01-0125.300.8426.691.0629.020.7227.911.34
1988-01-0124.640.1826.120.4929.130.8327.320.75
1989-01-0124.09-0.3724.15-1.4826.54-1.7624.53-2.04
1990-01-0124.02-0.4425.34-0.2928.560.2626.55-0.02
1991-01-0123.86-0.6025.650.0229.000.7027.010.44
1992-01-0124.830.3727.001.3729.060.7628.411.84
1993-01-0124.43-0.0325.56-0.0728.600.3026.690.12
1994-01-0124.32-0.1425.710.0828.470.1726.600.03
1995-01-0125.330.8726.340.7129.200.9027.550.98
1996-01-0123.84-0.6224.96-0.6727.92-0.3825.74-0.83
1997-01-0123.67-0.7924.70-0.9328.410.1125.96-0.61
1998-01-0128.223.7628.943.3129.010.7129.102.53
1999-01-0123.73-0.7324.41-1.2226.59-1.7124.90-1.67
2000-01-0123.86-0.6023.88-1.7526.96-1.3424.65-1.92
2001-01-0123.88-0.5824.99-0.6427.50-0.8025.74-0.83
2002-01-0123.64-0.8225.09-0.5428.810.5126.50-0.07
2003-01-0124.38-0.0826.380.7529.250.9527.761.19
2004-01-0124.600.1425.920.2928.830.5326.740.17
2005-01-0124.470.0125.890.2629.210.9127.100.53
2006-01-0124.33-0.1325.00-0.6327.68-0.6225.64-0.93
2007-01-0124.990.5326.500.8728.930.6327.260.69
2008-01-0123.86-0.6024.13-1.5026.62-1.6824.71-1.86
2009-01-0124.42-0.1025.03-0.6027.42-0.8825.54-1.03
2010-01-0124.820.3026.631.0029.511.2128.071.50
2011-01-0124.08-0.4424.31-1.3226.72-1.5824.93-1.64
2012-01-0123.88-0.6424.90-0.7327.09-1.2125.49-1.08
2013-01-0124.00-0.5225.06-0.5728.28-0.0226.16-0.41
2014-01-0124.790.2725.26-0.3728.14-0.1726.06-0.51
2015-01-0124.13-0.3925.990.3629.160.8627.100.53
2016-01-0125.931.4128.212.5829.651.3529.172.60
2017-01-0125.751.2325.61-0.0228.18-0.1226.25-0.32
2018-01-0123.71-0.8124.48-1.1428.03-0.2725.82-0.75
2019-01-0125.100.5726.170.5529.000.6527.080.52
2020-01-0124.550.0225.810.2029.280.9327.090.53
2021-01-0123.89-0.6425.06-0.5527.10-1.2525.58-0.99
\n", - "
" - ], - "text/plain": [ - " Nino12 Nino12anom Nino3 Nino3anom Nino4 Nino4anom Nino34 \\\n", - "datetime \n", - "1982-01-01 24.29 -0.17 25.87 0.24 28.30 0.00 26.72 \n", - "1983-01-01 27.42 2.96 28.92 3.29 29.00 0.70 29.36 \n", - "1984-01-01 24.18 -0.28 24.82 -0.81 27.64 -0.66 25.64 \n", - "1985-01-01 23.59 -0.87 24.51 -1.12 27.71 -0.59 25.43 \n", - "1986-01-01 24.61 0.15 24.73 -0.90 28.11 -0.19 25.79 \n", - "1987-01-01 25.30 0.84 26.69 1.06 29.02 0.72 27.91 \n", - "1988-01-01 24.64 0.18 26.12 0.49 29.13 0.83 27.32 \n", - "1989-01-01 24.09 -0.37 24.15 -1.48 26.54 -1.76 24.53 \n", - "1990-01-01 24.02 -0.44 25.34 -0.29 28.56 0.26 26.55 \n", - "1991-01-01 23.86 -0.60 25.65 0.02 29.00 0.70 27.01 \n", - "1992-01-01 24.83 0.37 27.00 1.37 29.06 0.76 28.41 \n", - "1993-01-01 24.43 -0.03 25.56 -0.07 28.60 0.30 26.69 \n", - "1994-01-01 24.32 -0.14 25.71 0.08 28.47 0.17 26.60 \n", - "1995-01-01 25.33 0.87 26.34 0.71 29.20 0.90 27.55 \n", - "1996-01-01 23.84 -0.62 24.96 -0.67 27.92 -0.38 25.74 \n", - "1997-01-01 23.67 -0.79 24.70 -0.93 28.41 0.11 25.96 \n", - "1998-01-01 28.22 3.76 28.94 3.31 29.01 0.71 29.10 \n", - "1999-01-01 23.73 -0.73 24.41 -1.22 26.59 -1.71 24.90 \n", - "2000-01-01 23.86 -0.60 23.88 -1.75 26.96 -1.34 24.65 \n", - "2001-01-01 23.88 -0.58 24.99 -0.64 27.50 -0.80 25.74 \n", - "2002-01-01 23.64 -0.82 25.09 -0.54 28.81 0.51 26.50 \n", - "2003-01-01 24.38 -0.08 26.38 0.75 29.25 0.95 27.76 \n", - "2004-01-01 24.60 0.14 25.92 0.29 28.83 0.53 26.74 \n", - "2005-01-01 24.47 0.01 25.89 0.26 29.21 0.91 27.10 \n", - "2006-01-01 24.33 -0.13 25.00 -0.63 27.68 -0.62 25.64 \n", - "2007-01-01 24.99 0.53 26.50 0.87 28.93 0.63 27.26 \n", - "2008-01-01 23.86 -0.60 24.13 -1.50 26.62 -1.68 24.71 \n", - "2009-01-01 24.42 -0.10 25.03 -0.60 27.42 -0.88 25.54 \n", - "2010-01-01 24.82 0.30 26.63 1.00 29.51 1.21 28.07 \n", - "2011-01-01 24.08 -0.44 24.31 -1.32 26.72 -1.58 24.93 \n", - "2012-01-01 23.88 -0.64 24.90 -0.73 27.09 -1.21 25.49 \n", - "2013-01-01 24.00 -0.52 25.06 -0.57 28.28 -0.02 26.16 \n", - "2014-01-01 24.79 0.27 25.26 -0.37 28.14 -0.17 26.06 \n", - "2015-01-01 24.13 -0.39 25.99 0.36 29.16 0.86 27.10 \n", - "2016-01-01 25.93 1.41 28.21 2.58 29.65 1.35 29.17 \n", - "2017-01-01 25.75 1.23 25.61 -0.02 28.18 -0.12 26.25 \n", - "2018-01-01 23.71 -0.81 24.48 -1.14 28.03 -0.27 25.82 \n", - "2019-01-01 25.10 0.57 26.17 0.55 29.00 0.65 27.08 \n", - "2020-01-01 24.55 0.02 25.81 0.20 29.28 0.93 27.09 \n", - "2021-01-01 23.89 -0.64 25.06 -0.55 27.10 -1.25 25.58 \n", - "\n", - " Nino34anom \n", - "datetime \n", - "1982-01-01 0.15 \n", - "1983-01-01 2.79 \n", - "1984-01-01 -0.93 \n", - "1985-01-01 -1.14 \n", - "1986-01-01 -0.78 \n", - "1987-01-01 1.34 \n", - "1988-01-01 0.75 \n", - "1989-01-01 -2.04 \n", - "1990-01-01 -0.02 \n", - "1991-01-01 0.44 \n", - "1992-01-01 1.84 \n", - "1993-01-01 0.12 \n", - "1994-01-01 0.03 \n", - "1995-01-01 0.98 \n", - "1996-01-01 -0.83 \n", - "1997-01-01 -0.61 \n", - "1998-01-01 2.53 \n", - "1999-01-01 -1.67 \n", - "2000-01-01 -1.92 \n", - "2001-01-01 -0.83 \n", - "2002-01-01 -0.07 \n", - "2003-01-01 1.19 \n", - "2004-01-01 0.17 \n", - "2005-01-01 0.53 \n", - "2006-01-01 -0.93 \n", - "2007-01-01 0.69 \n", - "2008-01-01 -1.86 \n", - "2009-01-01 -1.03 \n", - "2010-01-01 1.50 \n", - "2011-01-01 -1.64 \n", - "2012-01-01 -1.08 \n", - "2013-01-01 -0.41 \n", - "2014-01-01 -0.51 \n", - "2015-01-01 0.53 \n", - "2016-01-01 2.60 \n", - "2017-01-01 -0.32 \n", - "2018-01-01 -0.75 \n", - "2019-01-01 0.52 \n", - "2020-01-01 0.53 \n", - "2021-01-01 -0.99 " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Uses the datetime column\n", "df[df.index.month == 1]" @@ -3296,7 +827,7 @@ }, { "cell_type": "markdown", - "id": "373000f5", + "id": "16d4e0e7", "metadata": {}, "source": [ "You could even assign this month to a new column!" @@ -3304,8 +835,8 @@ }, { "cell_type": "code", - "execution_count": 42, - "id": "2d694ebc", + "execution_count": null, + "id": "fe5d5ee4", "metadata": {}, "outputs": [], "source": [ @@ -3314,7 +845,7 @@ }, { "cell_type": "markdown", - "id": "49100623", + "id": "8750443f", "metadata": {}, "source": [ "Now that it is its own column (`Series`), we can use `groupby` to group by the month, then taking the average, to determine average monthly values over the dataset" @@ -3322,30 +853,17 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "035b2a37", + "execution_count": null, + "id": "f94c91f9", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.groupby('month').mean().plot();" ] }, { "cell_type": "markdown", - "id": "b04c22fc", + "id": "a0c9481b", "metadata": {}, "source": [ "### Investigating Extreme Values" @@ -3353,7 +871,7 @@ }, { "cell_type": "markdown", - "id": "e3aea3cc", + "id": "fec15b77", "metadata": {}, "source": [ "You can also use ***conditional indexing***, such that you can search where rows meet a certain criteria. In this case, we are interested in where the Nino34 anomaly is greater than 2" @@ -3361,331 +879,17 @@ }, { "cell_type": "code", - "execution_count": 44, - "id": "4e723968", + "execution_count": null, + "id": "098fc88d", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anommonth
datetime
1982-11-0124.593.0027.622.6429.230.6028.812.1611
1982-12-0126.133.3428.393.2529.150.6629.212.6412
1983-01-0127.422.9628.923.2929.000.7029.362.791
1983-02-0128.092.0228.922.5528.790.6929.132.412
1997-08-0124.804.1527.842.8529.260.5828.842.028
1997-09-0124.404.0427.842.9929.320.6328.932.219
1997-10-0124.583.7628.173.2529.320.6629.232.5410
1997-11-0125.634.0428.553.5729.490.8629.322.6711
1997-12-0126.924.1328.763.6229.320.8329.262.6912
1998-01-0128.223.7628.943.3129.010.7129.102.531
1998-02-0128.982.9128.932.5628.870.7728.862.142
2015-08-0122.882.2427.332.3429.660.9828.892.078
2015-09-0122.912.5727.482.6329.731.0429.002.289
2015-10-0123.312.5227.582.6629.791.1229.152.4610
2015-11-0123.832.2427.912.9330.301.6729.602.9511
2015-12-0125.012.1927.992.8530.111.6329.392.8212
2016-01-0125.931.4128.212.5829.651.3529.172.601
2016-02-0126.810.6728.361.9929.551.4529.122.402
\n", - "
" - ], - "text/plain": [ - " Nino12 Nino12anom Nino3 Nino3anom Nino4 Nino4anom Nino34 \\\n", - "datetime \n", - "1982-11-01 24.59 3.00 27.62 2.64 29.23 0.60 28.81 \n", - "1982-12-01 26.13 3.34 28.39 3.25 29.15 0.66 29.21 \n", - "1983-01-01 27.42 2.96 28.92 3.29 29.00 0.70 29.36 \n", - "1983-02-01 28.09 2.02 28.92 2.55 28.79 0.69 29.13 \n", - "1997-08-01 24.80 4.15 27.84 2.85 29.26 0.58 28.84 \n", - "1997-09-01 24.40 4.04 27.84 2.99 29.32 0.63 28.93 \n", - "1997-10-01 24.58 3.76 28.17 3.25 29.32 0.66 29.23 \n", - "1997-11-01 25.63 4.04 28.55 3.57 29.49 0.86 29.32 \n", - "1997-12-01 26.92 4.13 28.76 3.62 29.32 0.83 29.26 \n", - "1998-01-01 28.22 3.76 28.94 3.31 29.01 0.71 29.10 \n", - "1998-02-01 28.98 2.91 28.93 2.56 28.87 0.77 28.86 \n", - "2015-08-01 22.88 2.24 27.33 2.34 29.66 0.98 28.89 \n", - "2015-09-01 22.91 2.57 27.48 2.63 29.73 1.04 29.00 \n", - "2015-10-01 23.31 2.52 27.58 2.66 29.79 1.12 29.15 \n", - "2015-11-01 23.83 2.24 27.91 2.93 30.30 1.67 29.60 \n", - "2015-12-01 25.01 2.19 27.99 2.85 30.11 1.63 29.39 \n", - "2016-01-01 25.93 1.41 28.21 2.58 29.65 1.35 29.17 \n", - "2016-02-01 26.81 0.67 28.36 1.99 29.55 1.45 29.12 \n", - "\n", - " Nino34anom month \n", - "datetime \n", - "1982-11-01 2.16 11 \n", - "1982-12-01 2.64 12 \n", - "1983-01-01 2.79 1 \n", - "1983-02-01 2.41 2 \n", - "1997-08-01 2.02 8 \n", - "1997-09-01 2.21 9 \n", - "1997-10-01 2.54 10 \n", - "1997-11-01 2.67 11 \n", - "1997-12-01 2.69 12 \n", - "1998-01-01 2.53 1 \n", - "1998-02-01 2.14 2 \n", - "2015-08-01 2.07 8 \n", - "2015-09-01 2.28 9 \n", - "2015-10-01 2.46 10 \n", - "2015-11-01 2.95 11 \n", - "2015-12-01 2.82 12 \n", - "2016-01-01 2.60 1 \n", - "2016-02-01 2.40 2 " - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[df.Nino34anom > 2]" ] }, { "cell_type": "markdown", - "id": "2bc0275e", + "id": "f26bc439", "metadata": {}, "source": [ "You can also sort columns based on the values!" @@ -3693,236 +897,17 @@ }, { "cell_type": "code", - "execution_count": 45, - "id": "a3cd6935", + "execution_count": null, + "id": "8051c4f6", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anommonth
datetime
1988-11-0120.55-1.0423.03-1.9526.76-1.8724.27-2.3811
1988-12-0121.80-0.9923.07-2.0726.75-1.7424.33-2.2412
1988-10-0119.50-1.3223.17-1.7527.06-1.6024.62-2.0710
1989-01-0124.09-0.3724.15-1.4826.54-1.7624.53-2.041
2000-01-0123.86-0.6023.88-1.7526.96-1.3424.65-1.921
..............................
1997-11-0125.634.0428.553.5729.490.8629.322.6711
1997-12-0126.924.1328.763.6229.320.8329.262.6912
1983-01-0127.422.9628.923.2929.000.7029.362.791
2015-12-0125.012.1927.992.8530.111.6329.392.8212
2015-11-0123.832.2427.912.9330.301.6729.602.9511
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472 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " Nino12 Nino12anom Nino3 Nino3anom Nino4 Nino4anom Nino34 \\\n", - "datetime \n", - "1988-11-01 20.55 -1.04 23.03 -1.95 26.76 -1.87 24.27 \n", - "1988-12-01 21.80 -0.99 23.07 -2.07 26.75 -1.74 24.33 \n", - "1988-10-01 19.50 -1.32 23.17 -1.75 27.06 -1.60 24.62 \n", - "1989-01-01 24.09 -0.37 24.15 -1.48 26.54 -1.76 24.53 \n", - "2000-01-01 23.86 -0.60 23.88 -1.75 26.96 -1.34 24.65 \n", - "... ... ... ... ... ... ... ... \n", - "1997-11-01 25.63 4.04 28.55 3.57 29.49 0.86 29.32 \n", - "1997-12-01 26.92 4.13 28.76 3.62 29.32 0.83 29.26 \n", - "1983-01-01 27.42 2.96 28.92 3.29 29.00 0.70 29.36 \n", - "2015-12-01 25.01 2.19 27.99 2.85 30.11 1.63 29.39 \n", - "2015-11-01 23.83 2.24 27.91 2.93 30.30 1.67 29.60 \n", - "\n", - " Nino34anom month \n", - "datetime \n", - "1988-11-01 -2.38 11 \n", - "1988-12-01 -2.24 12 \n", - "1988-10-01 -2.07 10 \n", - "1989-01-01 -2.04 1 \n", - "2000-01-01 -1.92 1 \n", - "... ... ... \n", - "1997-11-01 2.67 11 \n", - "1997-12-01 2.69 12 \n", - "1983-01-01 2.79 1 \n", - "2015-12-01 2.82 12 \n", - "2015-11-01 2.95 11 \n", - "\n", - "[472 rows x 9 columns]" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.sort_values('Nino34anom')" ] }, { "cell_type": "markdown", - "id": "9af53b83", + "id": "a293de79", "metadata": {}, "source": [ "Let's change the way that is ordered..." @@ -3930,236 +915,17 @@ }, { "cell_type": "code", - "execution_count": 46, - "id": "d89c47e8", + "execution_count": null, + "id": "be7ff8ce", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anommonth
datetime
2015-11-0123.832.2427.912.9330.301.6729.602.9511
2015-12-0125.012.1927.992.8530.111.6329.392.8212
1983-01-0127.422.9628.923.2929.000.7029.362.791
1997-12-0126.924.1328.763.6229.320.8329.262.6912
1997-11-0125.634.0428.553.5729.490.8629.322.6711
..............................
2000-01-0123.86-0.6023.88-1.7526.96-1.3424.65-1.921
1989-01-0124.09-0.3724.15-1.4826.54-1.7624.53-2.041
1988-10-0119.50-1.3223.17-1.7527.06-1.6024.62-2.0710
1988-12-0121.80-0.9923.07-2.0726.75-1.7424.33-2.2412
1988-11-0120.55-1.0423.03-1.9526.76-1.8724.27-2.3811
\n", - "

472 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " Nino12 Nino12anom Nino3 Nino3anom Nino4 Nino4anom Nino34 \\\n", - "datetime \n", - "2015-11-01 23.83 2.24 27.91 2.93 30.30 1.67 29.60 \n", - "2015-12-01 25.01 2.19 27.99 2.85 30.11 1.63 29.39 \n", - "1983-01-01 27.42 2.96 28.92 3.29 29.00 0.70 29.36 \n", - "1997-12-01 26.92 4.13 28.76 3.62 29.32 0.83 29.26 \n", - "1997-11-01 25.63 4.04 28.55 3.57 29.49 0.86 29.32 \n", - "... ... ... ... ... ... ... ... \n", - "2000-01-01 23.86 -0.60 23.88 -1.75 26.96 -1.34 24.65 \n", - "1989-01-01 24.09 -0.37 24.15 -1.48 26.54 -1.76 24.53 \n", - "1988-10-01 19.50 -1.32 23.17 -1.75 27.06 -1.60 24.62 \n", - "1988-12-01 21.80 -0.99 23.07 -2.07 26.75 -1.74 24.33 \n", - "1988-11-01 20.55 -1.04 23.03 -1.95 26.76 -1.87 24.27 \n", - "\n", - " Nino34anom month \n", - "datetime \n", - "2015-11-01 2.95 11 \n", - "2015-12-01 2.82 12 \n", - "1983-01-01 2.79 1 \n", - "1997-12-01 2.69 12 \n", - "1997-11-01 2.67 11 \n", - "... ... ... \n", - "2000-01-01 -1.92 1 \n", - "1989-01-01 -2.04 1 \n", - "1988-10-01 -2.07 10 \n", - "1988-12-01 -2.24 12 \n", - "1988-11-01 -2.38 11 \n", - "\n", - "[472 rows x 9 columns]" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.sort_values('Nino34anom', ascending=False)" ] }, { "cell_type": "markdown", - "id": "089d75db", + "id": "5504a0da", "metadata": {}, "source": [ "### Resampling\n", @@ -4168,53 +934,27 @@ }, { "cell_type": "code", - "execution_count": 47, - "id": "1a5c0bf6", + "execution_count": null, + "id": "597cfeac", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.Nino34.resample('1Y').mean().plot();" ] }, { "cell_type": "markdown", - "id": "f000116f", + "id": "16c80788", "metadata": {}, "source": [ "### Applying operations to a dataframe\n", @@ -4224,8 +964,8 @@ }, { "cell_type": "code", - "execution_count": 49, - "id": "dcee58a6", + "execution_count": null, + "id": "c8afa857", "metadata": {}, "outputs": [], "source": [ @@ -4239,7 +979,7 @@ }, { "cell_type": "markdown", - "id": "9c38ec17", + "id": "4fbef115", "metadata": {}, "source": [ "Now, this function accepts and returns a single value" @@ -4247,21 +987,10 @@ }, { "cell_type": "code", - "execution_count": 50, - "id": "735d982b", + "execution_count": null, + "id": "34892381", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "273.15" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Convert a single value\n", "convert_degc_to_kelvin(0)" @@ -4269,7 +998,7 @@ }, { "cell_type": "markdown", - "id": "434e497a", + "id": "384a0bdb", "metadata": {}, "source": [ "But what if we want to apply this to our dataframe? We can subset for Nino34, which is in degrees Celsius" @@ -4277,40 +1006,17 @@ }, { "cell_type": "code", - "execution_count": 51, - "id": "e406e427", + "execution_count": null, + "id": "f09ee7c6", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 26.72\n", - "1982-02-01 26.70\n", - "1982-03-01 27.20\n", - "1982-04-01 28.02\n", - "1982-05-01 28.54\n", - " ... \n", - "2020-12-01 25.53\n", - "2021-01-01 25.58\n", - "2021-02-01 25.81\n", - "2021-03-01 26.75\n", - "2021-04-01 27.40\n", - "Name: Nino34, Length: 472, dtype: float64" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nino34_series" ] }, { "cell_type": "markdown", - "id": "f42069a5", + "id": "28ac04e8", "metadata": {}, "source": [ "Notice how the object type is a pandas series" @@ -4318,28 +1024,17 @@ }, { "cell_type": "code", - "execution_count": 52, - "id": "7b000088", + "execution_count": null, + "id": "8718a43f", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pandas.core.series.Series" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(df.Nino12[0:10])" ] }, { "cell_type": "markdown", - "id": "68a8f033", + "id": "ff1f569f", "metadata": {}, "source": [ "If you call `.values`, the object type is now a numpy array. Pandas `Series` values include numpy arrays, and calling `.values` returns the series as a numpy array!" @@ -4347,28 +1042,17 @@ }, { "cell_type": "code", - "execution_count": 53, - "id": "1b6f337d", + "execution_count": null, + "id": "61a8255f", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(df.Nino12.values[0:10])" ] }, { "cell_type": "markdown", - "id": "fc00faa7", + "id": "2a5693fe", "metadata": {}, "source": [ "Let's apply this calculation to this `Series`; this returns another `Series` object." @@ -4376,40 +1060,17 @@ }, { "cell_type": "code", - "execution_count": 54, - "id": "c721039d", + "execution_count": null, + "id": "ae197a92", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 299.87\n", - "1982-02-01 299.85\n", - "1982-03-01 300.35\n", - "1982-04-01 301.17\n", - "1982-05-01 301.69\n", - " ... \n", - "2020-12-01 298.68\n", - "2021-01-01 298.73\n", - "2021-02-01 298.96\n", - "2021-03-01 299.90\n", - "2021-04-01 300.55\n", - "Name: Nino34, Length: 472, dtype: float64" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "convert_degc_to_kelvin(nino34_series)" ] }, { "cell_type": "markdown", - "id": "4435c540", + "id": "87871b82", "metadata": {}, "source": [ "If we include `.values`, it returns a `numpy array`" @@ -4417,7 +1078,7 @@ }, { "cell_type": "markdown", - "id": "7c7ea806-7efe-4798-bb9b-f8a395797e24", + "id": "a256f852", "metadata": {}, "source": [ "
\n", @@ -4426,86 +1087,17 @@ }, { "cell_type": "code", - "execution_count": 55, - "id": "9120d05e", + "execution_count": null, + "id": "52ae68ee", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([299.87, 299.85, 300.35, 301.17, 301.69, 301.9 , 301.25, 301.08,\n", - " 301.26, 301.79, 301.96, 302.36, 302.51, 302.28, 302.18, 302.06,\n", - " 302.04, 301.39, 300.22, 299.68, 299.59, 299.02, 298.73, 298.74,\n", - " 298.79, 299.54, 300.01, 300.54, 300.54, 300.01, 299.89, 299.49,\n", - " 299.58, 299.08, 298.56, 298.15, 298.58, 298.82, 299.38, 299.95,\n", - " 300.26, 300.01, 299.84, 299.65, 299.4 , 299.34, 299.34, 299.26,\n", - " 298.94, 299.09, 299.8 , 300.59, 300.65, 300.84, 300.52, 300.3 ,\n", - " 300.48, 300.72, 300.88, 300.85, 301.06, 301.17, 301.62, 301.95,\n", - " 301.9 , 302.18, 301.95, 301.73, 301.54, 301.22, 301.14, 300.75,\n", - " 300.47, 300.37, 300.46, 300.47, 299.63, 299.26, 298.72, 298.39,\n", - " 298.58, 297.77, 297.42, 297.48, 297.68, 298.48, 299.05, 299.84,\n", - " 300.24, 300.13, 299.89, 299.48, 299.4 , 299.41, 299.39, 299.53,\n", - " 299.7 , 300.1 , 300.61, 301.17, 301.21, 300.73, 300.4 , 300.2 ,\n", - " 299.9 , 300.13, 299.87, 300.06, 300.16, 300.08, 300.4 , 301.13,\n", - " 301.5 , 301.51, 301.07, 300.59, 300.22, 300.78, 301.01, 301.52,\n", - " 301.56, 301.78, 301.98, 302.29, 302.14, 301.17, 300.68, 299.79,\n", - " 299.63, 299.49, 299.66, 299.88, 299.84, 300.12, 300.81, 301.74,\n", - " 301.97, 301.43, 300.7 , 299.99, 300.07, 300.08, 300.06, 299.91,\n", - " 299.75, 299.74, 300.42, 301.05, 301.19, 301.14, 300.5 , 300.5 ,\n", - " 300.15, 300.64, 301.02, 301.02, 300.7 , 300.6 , 300.78, 301.08,\n", - " 300.88, 300.74, 300.16, 299.48, 299.11, 298.82, 298.81, 298.72,\n", - " 298.89, 299. , 299.77, 300.51, 300.52, 300.47, 300.24, 299.71,\n", - " 299.5 , 299.39, 299.34, 299.17, 299.11, 299.51, 300.18, 301.18,\n", - " 301.75, 302.09, 302.07, 301.99, 302.08, 302.38, 302.47, 302.41,\n", - " 302.25, 302.01, 301.82, 301.71, 301.62, 299.87, 299.09, 298.64,\n", - " 298.76, 298.49, 298.33, 297.94, 298.05, 298.56, 299.4 , 299.99,\n", - " 300.12, 299.75, 299.5 , 298.74, 298.86, 298.79, 298.27, 298.05,\n", - " 297.8 , 298.34, 299.23, 300.16, 300.27, 300.18, 299.87, 299.6 ,\n", - " 299.36, 299.11, 298.93, 298.74, 298.89, 299.26, 299.99, 300.67,\n", - " 300.75, 300.83, 300.47, 300.02, 299.7 , 299.74, 299.6 , 299.32,\n", - " 299.65, 300.1 , 300.47, 301.09, 301.3 , 301.58, 301.13, 300.94,\n", - " 300.98, 301.2 , 301.42, 301.24, 300.91, 300.64, 300.96, 300.96,\n", - " 300.52, 300.63, 300.58, 300. , 300.11, 300.34, 300.2 , 300.04,\n", - " 299.89, 300.01, 300.25, 300.99, 301.21, 300.91, 300.84, 300.69,\n", - " 300.62, 300.53, 300.46, 300.46, 300.25, 300.11, 300.7 , 301.22,\n", - " 301.35, 301.2 , 300.62, 300.03, 299.78, 299.9 , 299.49, 299.04,\n", - " 298.79, 299.23, 299.72, 300.74, 301.06, 301. , 300.5 , 300.37,\n", - " 300.49, 300.62, 300.88, 300.91, 300.41, 299.96, 300.33, 300.93,\n", - " 300.72, 300.7 , 299.94, 299.35, 298.92, 298.37, 298.21, 298.12,\n", - " 297.86, 297.98, 299.22, 299.98, 300.33, 300.32, 300.34, 300. ,\n", - " 299.59, 299.48, 299.45, 298.89, 298.69, 299.19, 299.82, 300.65,\n", - " 301.18, 301.26, 301.09, 300.68, 300.62, 300.78, 301.34, 301.45,\n", - " 301.22, 301.09, 301.44, 301.51, 300.83, 300.15, 299.24, 298.65,\n", - " 298.22, 298.16, 298.22, 298.1 , 298.08, 298.61, 299.38, 300.17,\n", - " 300.57, 300.61, 300.11, 299.34, 299.13, 298.87, 298.75, 298.68,\n", - " 298.64, 299.18, 299.78, 300.53, 300.95, 301.1 , 300.9 , 300.7 ,\n", - " 300.39, 300.13, 300.16, 299.61, 299.31, 299.47, 300.15, 300.83,\n", - " 300.72, 300.58, 300.06, 299.69, 299.8 , 299.51, 299.8 , 299.68,\n", - " 299.21, 299.33, 300.14, 301.16, 301.46, 301.26, 300.55, 300.17,\n", - " 300.32, 300.32, 300.65, 300.5 , 300.25, 300.44, 300.94, 301.71,\n", - " 302.03, 302.11, 301.97, 302.04, 302.15, 302.3 , 302.75, 302.54,\n", - " 302.32, 302.27, 302.05, 302.02, 301.3 , 300.68, 299.88, 299.43,\n", - " 299.26, 299.11, 299.25, 299.31, 299.4 , 300.02, 300.49, 301.25,\n", - " 301.45, 301.34, 300.76, 299.82, 299.44, 299.38, 298.94, 298.95,\n", - " 298.97, 298.98, 299.63, 300.57, 300.87, 301. , 300.67, 300.26,\n", - " 300.25, 300.7 , 300.79, 300.68, 300.23, 300.56, 301.37, 301.75,\n", - " 301.72, 301.39, 300.78, 300.12, 299.85, 300.46, 300.41, 300.22,\n", - " 300.24, 300.29, 300.97, 301.47, 300.74, 300.45, 300.04, 299.33,\n", - " 298.92, 298.45, 298.49, 298.68, 298.73, 298.96, 299.9 , 300.55])" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "convert_degc_to_kelvin(nino34_series.values)" ] }, { "cell_type": "markdown", - "id": "de2e5b57", + "id": "65b3cd56", "metadata": {}, "source": [ "We can now assign our pandas `Series` with the converted temperatures to a new column in our dataframe!" @@ -4513,8 +1105,8 @@ }, { "cell_type": "code", - "execution_count": 56, - "id": "1ca04877", + "execution_count": null, + "id": "2d84dfe1", "metadata": {}, "outputs": [], "source": [ @@ -4523,40 +1115,17 @@ }, { "cell_type": "code", - "execution_count": 57, - "id": "1d997829", + "execution_count": null, + "id": "dd9a0811", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime\n", - "1982-01-01 299.87\n", - "1982-02-01 299.85\n", - "1982-03-01 300.35\n", - "1982-04-01 301.17\n", - "1982-05-01 301.69\n", - " ... \n", - "2020-12-01 298.68\n", - "2021-01-01 298.73\n", - "2021-02-01 298.96\n", - "2021-03-01 299.90\n", - "2021-04-01 300.55\n", - "Name: Nino34_degK, Length: 472, dtype: float64" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.Nino34_degK" ] }, { "cell_type": "markdown", - "id": "1bcaf1cd", + "id": "a8c6dba3", "metadata": {}, "source": [ "Now that our analysis is done, we can save our data to a `csv` for later - or share with others!" @@ -4564,8 +1133,8 @@ }, { "cell_type": "code", - "execution_count": 58, - "id": "81b8d46b", + "execution_count": null, + "id": "054428db", "metadata": {}, "outputs": [], "source": [ @@ -4574,249 +1143,17 @@ }, { "cell_type": "code", - "execution_count": 59, - "id": "cd87abc5", + "execution_count": null, + "id": "3f7d378f", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Nino12Nino12anomNino3Nino3anomNino4Nino4anomNino34Nino34anommonthNino34_degK
datetime
1982-01-0124.29-0.1725.870.2428.300.0026.720.151299.87
1982-02-0125.49-0.5826.380.0128.210.1126.70-0.022299.85
1982-03-0125.21-1.3126.98-0.1628.410.2227.20-0.023300.35
1982-04-0124.50-0.9727.680.1828.920.4228.020.244301.17
1982-05-0123.97-0.2327.790.7129.490.7028.540.695301.69
.................................
2020-12-0122.16-0.6024.38-0.8327.65-0.9525.53-1.1212298.68
2021-01-0123.89-0.6425.06-0.5527.10-1.2525.58-0.991298.73
2021-02-0125.55-0.6625.80-0.5727.20-1.0025.81-0.922298.96
2021-03-0126.48-0.2626.80-0.3927.79-0.5526.75-0.513299.90
2021-04-0124.89-0.8026.96-0.6528.47-0.2127.40-0.494300.55
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472 rows × 10 columns

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" - ], - "text/plain": [ - " Nino12 Nino12anom Nino3 Nino3anom Nino4 Nino4anom Nino34 \\\n", - "datetime \n", - "1982-01-01 24.29 -0.17 25.87 0.24 28.30 0.00 26.72 \n", - "1982-02-01 25.49 -0.58 26.38 0.01 28.21 0.11 26.70 \n", - "1982-03-01 25.21 -1.31 26.98 -0.16 28.41 0.22 27.20 \n", - "1982-04-01 24.50 -0.97 27.68 0.18 28.92 0.42 28.02 \n", - "1982-05-01 23.97 -0.23 27.79 0.71 29.49 0.70 28.54 \n", - "... ... ... ... ... ... ... ... \n", - "2020-12-01 22.16 -0.60 24.38 -0.83 27.65 -0.95 25.53 \n", - "2021-01-01 23.89 -0.64 25.06 -0.55 27.10 -1.25 25.58 \n", - "2021-02-01 25.55 -0.66 25.80 -0.57 27.20 -1.00 25.81 \n", - "2021-03-01 26.48 -0.26 26.80 -0.39 27.79 -0.55 26.75 \n", - "2021-04-01 24.89 -0.80 26.96 -0.65 28.47 -0.21 27.40 \n", - "\n", - " Nino34anom month Nino34_degK \n", - "datetime \n", - "1982-01-01 0.15 1 299.87 \n", - "1982-02-01 -0.02 2 299.85 \n", - "1982-03-01 -0.02 3 300.35 \n", - "1982-04-01 0.24 4 301.17 \n", - "1982-05-01 0.69 5 301.69 \n", - "... ... ... ... \n", - "2020-12-01 -1.12 12 298.68 \n", - "2021-01-01 -0.99 1 298.73 \n", - "2021-02-01 -0.92 2 298.96 \n", - "2021-03-01 -0.51 3 299.90 \n", - "2021-04-01 -0.49 4 300.55 \n", - "\n", - "[472 rows x 10 columns]" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pd.read_csv('nino_analyzed_output.csv', index_col=0, parse_dates=True)" ] }, { "cell_type": "markdown", - "id": "33f433ed", + "id": "9327e958", "metadata": {}, "source": [ "---\n", diff --git a/environment.yml b/environment.yml index 2d195e064..f0e6daf2d 100644 --- a/environment.yml +++ b/environment.yml @@ -11,3 +11,7 @@ dependencies: - sqlalchemy<1.4 - cartopy - xarray + - pip + - pip: + # works for regular pip packages + - pythia-datasets