From 6875b4e9be6f96825196a8afc86cfe2980c8900a Mon Sep 17 00:00:00 2001 From: agstephens Date: Tue, 28 Feb 2023 14:41:45 +0000 Subject: [PATCH 1/6] Added pandas Data Carpentry exercise --- python-data/solutions/ex05_pandas.ipynb | 1680 +++++++++++++++++++++++ 1 file changed, 1680 insertions(+) create mode 100644 python-data/solutions/ex05_pandas.ipynb diff --git a/python-data/solutions/ex05_pandas.ipynb b/python-data/solutions/ex05_pandas.ipynb new file mode 100644 index 0000000..47c1975 --- /dev/null +++ b/python-data/solutions/ex05_pandas.ipynb @@ -0,0 +1,1680 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1a4fb627-2190-43ac-b4b5-93352cd23aa4", + "metadata": {}, + "source": [ + "# Working with Pandas DataFrames in Python\n", + "\n", + "Part of: Data Analysis and Visualization in Python for EcologistsData Analysis and Visualization in Python for Ecologists (**Data Carpentry**)\n", + "\n", + "From: https://datacarpentry.org/python-ecology-lesson/02-starting-with-data/index.htmlhttps://datacarpentry.org/python-ecology-lesson/02-starting-with-data/index.html\n", + "\n", + "teaching: 30 mins\n", + "exercises: 30 mins\n", + "\n", + "Questions:\n", + "- \"How can I import data in Python?\"\n", + "- \"What is Pandas?\"\n", + "- \"Why should I use Pandas to work with data?\"\n", + " \n", + "Objectives:\n", + "- \"Navigate the workshop directory and download a dataset.\"\n", + "- \"Explain what a library is and what libraries are used for.\"\n", + "- \"Describe what the Python Data Analysis Library (Pandas) is.\"\n", + "- \"Load the Python Data Analysis Library (Pandas).\"\n", + "- \"Use `read_csv` to read tabular data into Python.\"\n", + "- \"Describe what a DataFrame is in Python.\"\n", + "- \"Access and summarize data stored in a DataFrame.\"\n", + "- \"Define indexing as it relates to data structures.\"\n", + "- \"Perform basic mathematical operations and summary statistics on data in a Pandas DataFrame.\"\n", + "- \"Create simple plots.\"\n", + " \n", + "Key points:\n", + "- \"Libraries enable us to extend the functionality of Python.\"\n", + "- \"Pandas is a popular library for working with data.\"\n", + "- \"A Dataframe is a Pandas data structure that allows one to access data by column (name or index) or row.\"\n", + "- \"Aggregating data using the `groupby()` function enables you to generate useful summaries of data quickly.\"\n", + "- \"Plots can be created from DataFrames or subsets of data that have been generated with `groupby()`.\"\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "3c62fdbe-6cd7-473a-b1d8-288bd5dec187", + "metadata": {}, + "source": [ + "We can automate the process of performing data manipulations in Python. It's efficient to spend time\n", + "building the code to perform these tasks because once it's built, we can use it\n", + "over and over on different datasets that use a similar format. This makes our\n", + "methods easily reproducible. We can also easily share our code with colleagues\n", + "and they can replicate the same analysis.\n", + "\n", + "### Starting in the same spot\n", + "\n", + "To help the lesson run smoothly, let's ensure everyone is in the same directory.\n", + "This should help us avoid path and file name issues. At this time please\n", + "navigate to the workshop directory. If you are working in Jupyter Notebook be sure\n", + "that you start your notebook in the workshop directory.\n", + "\n", + "A quick aside that there are Python libraries like [OS Library][os-lib] that can work with our\n", + "directory structure, however, that is not our focus today." + ] + }, + { + "cell_type": "markdown", + "id": "d3fd3a90-3455-4df0-a4f4-9a327d81b177", + "metadata": {}, + "source": [ + "### Our Data\n", + "\n", + "For this lesson, we will be using the Portal Teaching data, a subset of the data\n", + "from Ernst et al\n", + "[Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal,\n", + "Arizona, USA][ernst].\n", + "\n", + "We will be using files from the [Portal Project Teaching Database][pptd].\n", + "This section will use the `surveys.csv` file that can be downloaded here:\n", + "[https://ndownloader.figshare.com/files/2292172][figshare-ndownloader]\n", + "\n", + "We are studying the species and weight of animals caught in sites in our study\n", + "area. The dataset is stored as a `.csv` file: each row holds information for a\n", + "single animal, and the columns represent:\n", + "\n", + "| Column | Description |\n", + "|------------------|------------------------------------|\n", + "| record_id | Unique id for the observation |\n", + "| month | month of observation |\n", + "| day | day of observation |\n", + "| year | year of observation |\n", + "| plot_id | ID of a particular site |\n", + "| species_id | 2-letter code |\n", + "| sex | sex of animal (\"M\", \"F\") |\n", + "| hindfoot_length | length of the hindfoot in mm |\n", + "| weight | weight of the animal in grams |\n", + "\n", + "\n", + "The first few rows of our first file look like this:\n", + "\n", + "```\n", + "record_id,month,day,year,plot_id,species_id,sex,hindfoot_length,weight\n", + "1,7,16,1977,2,NL,M,32,\n", + "2,7,16,1977,3,NL,M,33,\n", + "3,7,16,1977,2,DM,F,37,\n", + "4,7,16,1977,7,DM,M,36,\n", + "5,7,16,1977,3,DM,M,35,\n", + "6,7,16,1977,1,PF,M,14,\n", + "7,7,16,1977,2,PE,F,,\n", + "8,7,16,1977,1,DM,M,37,\n", + "9,7,16,1977,1,DM,F,34,\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0e8a0333-7d52-4739-a9d7-ee2388a07033", + "metadata": {}, + "outputs": [], + "source": [ + "# Download the file\n", + "import requests\n", + "url = \"https://ndownloader.figshare.com/files/2292172\"\n", + "content = requests.get(url).text\n", + "\n", + "datafile = \"surveys.csv\"\n", + "with open(datafile, \"w\") as csv:\n", + " csv.write(content)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f73c654d-65fc-40bc-a02f-b708eec455dd", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "6c9ee6ef-0b55-404e-8757-678e7a760dd1", + "metadata": {}, + "source": [ + "Each time we call a function that's in a library, we use the syntax\n", + "`LibraryName.FunctionName`. Adding the library name with a `.` before the\n", + "function name tells Python where to find the function. In the example above, we\n", + "have imported Pandas as `pd`. This means we don't have to type out `pandas` each\n", + "time we call a Pandas function.\n", + "\n", + "\n", + "# Reading CSV Data Using Pandas\n", + "\n", + "We will begin by locating and reading our survey data which are in CSV format. CSV stands for\n", + "Comma-Separated Values and is a common way to store formatted data. Other symbols may also be used, so\n", + "you might see tab-separated, colon-separated or space separated files. It is quite easy to replace\n", + "one separator with another, to match your application. The first line in the file often has headers\n", + "to explain what is in each column. CSV (and other separators) make it easy to share data, and can be\n", + "imported and exported from many applications, including Microsoft Excel. For more details on CSV\n", + "files, see the [Data Organisation in Spreadsheets][spreadsheet-lesson5] lesson.\n", + "We can use Pandas' `read_csv` function to pull the file directly into a [DataFrame][pd-dataframe].\n", + "\n", + "## So What's a DataFrame?\n", + "\n", + "A DataFrame is a 2-dimensional data structure that can store data of different\n", + "types (including characters, integers, floating point values, factors and more)\n", + "in columns. It is similar to a spreadsheet or an SQL table or the `data.frame` in\n", + "R. A DataFrame always has an index (0-based). An index refers to the position of\n", + "an element in the data structure." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "eb36110d-ebc1-4376-8e13-abe486c128f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " record_id month day year plot_id species_id sex hindfoot_length \\\n", + "0 1 7 16 1977 2 NL M 32.0 \n", + "1 2 7 16 1977 3 NL M 33.0 \n", + "2 3 7 16 1977 2 DM F 37.0 \n", + "3 4 7 16 1977 7 DM M 36.0 \n", + "4 5 7 16 1977 3 DM M 35.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "35544 35545 12 31 2002 15 AH NaN NaN \n", + "35545 35546 12 31 2002 15 AH NaN NaN \n", + "35546 35547 12 31 2002 10 RM F 15.0 \n", + "35547 35548 12 31 2002 7 DO M 36.0 \n", + "35548 35549 12 31 2002 5 NaN NaN NaN \n", + "\n", + " weight \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "35544 NaN \n", + "35545 NaN \n", + "35546 14.0 \n", + "35547 51.0 \n", + "35548 NaN \n", + "\n", + "[35549 rows x 9 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Note that pd.read_csv is used because we imported pandas as pd\n", + "pd.read_csv(datafile)" + ] + }, + { + "cell_type": "markdown", + "id": "3686eebc-e266-4496-99ff-475f69ec72ce", + "metadata": {}, + "source": [ + "We can see that there were 35,549 rows parsed. Each row has 9\n", + "columns. The first column is the index of the DataFrame. The index is used to\n", + "identify the position of the data, but it is not an actual column of the DataFrame.\n", + "It looks like the `read_csv` function in Pandas read our file properly. However,\n", + "we haven't saved any data to memory so we can work with it. We need to assign the\n", + "DataFrame to a variable. Remember that a variable is a name for a value, such as `x`,\n", + "or `data`. We can create a new object with a variable name by assigning a value to it using `=`.\n", + "\n", + "Let's call the imported survey data `surveys_df`:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9c692fd0-32cf-4dad-be80-64458c563ab1", + "metadata": {}, + "outputs": [], + "source": [ + "surveys_df = pd.read_csv(datafile)" + ] + }, + { + "cell_type": "markdown", + "id": "dcefc541-b4ac-4ebc-93ed-8348180f7779", + "metadata": {}, + "source": [ + "Notice when you assign the imported DataFrame to a variable, Python does not\n", + "produce any output on the screen. We can view the value of the `surveys_df`\n", + "object by typing its name into the Python command prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5429befb-4dba-4251-9071-a259e96738e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " record_id month day year plot_id species_id sex hindfoot_length \\\n", + "0 1 7 16 1977 2 NL M 32.0 \n", + "1 2 7 16 1977 3 NL M 33.0 \n", + "2 3 7 16 1977 2 DM F 37.0 \n", + "3 4 7 16 1977 7 DM M 36.0 \n", + "4 5 7 16 1977 3 DM M 35.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "35544 35545 12 31 2002 15 AH NaN NaN \n", + "35545 35546 12 31 2002 15 AH NaN NaN \n", + "35546 35547 12 31 2002 10 RM F 15.0 \n", + "35547 35548 12 31 2002 7 DO M 36.0 \n", + "35548 35549 12 31 2002 5 NaN NaN NaN \n", + "\n", + " weight \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "35544 NaN \n", + "35545 NaN \n", + "35546 14.0 \n", + "35547 51.0 \n", + "35548 NaN \n", + "\n", + "[35549 rows x 9 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "surveys_df" + ] + }, + { + "cell_type": "markdown", + "id": "3c8b9fbf-5b0a-44b2-ae9b-2cbc094f2eeb", + "metadata": {}, + "source": [ + "Note: if the output is too wide to print on your narrow terminal window, you may see something\n", + "slightly different as the large set of data scrolls past. You may see simply the last column\n", + "of data.\n", + "\n", + "Never fear, all the data is there, if you scroll up. Selecting just a few rows, so it is\n", + "easier to fit on one window, you can see that pandas has neatly formatted the data to fit\n", + "our screen:Never fear, all the data is there, if you scroll up. Selecting just a few rows, so it is\n", + "easier to fit on one window, you can see that pandas has neatly formatted the data to fit\n", + "our screen:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7fa897f3-1237-434e-a4dd-44c1654578bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " record_id month day year plot_id species_id sex hindfoot_length \\\n", + "0 1 7 16 1977 2 NL M 32.0 \n", + "1 2 7 16 1977 3 NL M 33.0 \n", + "2 3 7 16 1977 2 DM F 37.0 \n", + "3 4 7 16 1977 7 DM M 36.0 \n", + "4 5 7 16 1977 3 DM M 35.0 \n", + "\n", + " weight \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "surveys_df.head() # The head() method displays the first several lines of a file. It\n", + " # is discussed below." + ] + }, + { + "cell_type": "markdown", + "id": "e1a68826-2a82-4700-8f3f-b543fb1c3c13", + "metadata": {}, + "source": [ + "## Exploring our Species Survey Data\n", + "\n", + "Again, we can use the `type` function to see what kind of thing `surveys_df` is:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "86fe32ef-6617-479c-9776-c0efa316e65f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(surveys_df)" + ] + }, + { + "cell_type": "markdown", + "id": "7002ed40-3eca-49f9-8bb9-541e37c332e8", + "metadata": {}, + "source": [ + "As expected, it's a DataFrame (or, to use the full name that Python uses to refer\n", + "to it internally, a `pandas.core.frame.DataFrame`).\n", + "\n", + "What kind of things does `surveys_df` contain? DataFrames have an attribute\n", + "called `dtypes` that answers this:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c586df32-ef8e-45c3-ad46-21c7f6aab133", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "record_id int64\n", + "month int64\n", + "day int64\n", + "year int64\n", + "plot_id int64\n", + "species_id object\n", + "sex object\n", + "hindfoot_length float64\n", + "weight float64\n", + "dtype: object" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "surveys_df.dtypes" + ] + }, + { + "cell_type": "markdown", + "id": "38184683-6906-4893-8289-3d756b1fbf08", + "metadata": {}, + "source": [ + "All the values in a column have the same type. For example, months have type\n", + "`int64`, which is a kind of integer. Cells in the month column cannot have\n", + "fractional values, but the weight and hindfoot_length columns can, because they\n", + "have type `float64`. The `object` type doesn't have a very helpful name, but in\n", + "this case it represents strings (such as 'M' and 'F' in the case of sex).\n", + "\n", + "We'll talk a bit more about what the different formats mean in a different lesson.\n", + "\n", + "### Useful Ways to View DataFrame objects in Python\n", + "\n", + "There are many ways to summarize and access the data stored in DataFrames,\n", + "using attributes and methods provided by the DataFrame object.\n", + "\n", + "To access an attribute, use the DataFrame object name followed by the attribute\n", + "name `df_object.attribute`. Using the DataFrame `surveys_df` and attribute\n", + "`columns`, an index of all the column names in the DataFrame can be accessed\n", + "with `surveys_df.columns`.\n", + "\n", + "Methods are called in a similar fashion using the syntax `df_object.method()`.\n", + "As an example, `surveys_df.head()` gets the first few rows in the DataFrame\n", + "`surveys_df` using **the `head()` method**. With a method, we can supply extra\n", + "information in the parens to control behaviour.\n", + "\n", + "Let's look at the data using these.\n", + "\n", + "> ## Challenge - DataFrames\n", + ">\n", + "> Using our DataFrame `surveys_df`, try out the attributes & methods below to see\n", + "> what they return.\n", + ">\n", + "> 1. `surveys_df.columns`\n", + "> 2. `surveys_df.shape` Take note of the output of `shape` - what format does it\n", + "> return the shape of the DataFrame in?\n", + ">\n", + "> HINT: [More on tuples, here][python-datastructures].\n", + "> 3. `surveys_df.head()` Also, what does `surveys_df.head(15)` do?\n", + "> 4. `surveys_df.tail()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65d9b5c5-187b-495c-af05-1d29ae10e20a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "e52d475d-1ab0-4d63-b40f-79d4c101d6eb", + "metadata": {}, + "source": [ + "## Calculating Statistics From Data In A Pandas DataFrame\n", + "\n", + "We've read our data into Python. Next, let's perform some quick summary\n", + "statistics to learn more about the data that we're working with. We might want\n", + "to know how many animals were collected in each site, or how many of each\n", + "species were caught. We can perform summary stats quickly using groups. But\n", + "first we need to figure out what we want to group by.\n", + "\n", + "Let's begin by exploring our data:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9a898fab-d2cb-42c2-bf7d-231b1c674c1f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['record_id', 'month', 'day', 'year', 'plot_id', 'species_id', 'sex',\n", + " 'hindfoot_length', 'weight'],\n", + " dtype='object')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Look at the column names\n", + "surveys_df.columns" + ] + }, + { + "cell_type": "markdown", + "id": "f62f48b9-2915-49a4-8b8d-2249f5f61698", + "metadata": {}, + "source": [ + "Let's get a list of all the species. The `pd.unique` function tells us all of\n", + "the unique values in the `species_id` column." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "026c7b71-f64a-4dd8-9c52-8b539c15f36c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['NL', 'DM', 'PF', 'PE', 'DS', 'PP', 'SH', 'OT', 'DO', 'OX', 'SS',\n", + " 'OL', 'RM', nan, 'SA', 'PM', 'AH', 'DX', 'AB', 'CB', 'CM', 'CQ',\n", + " 'RF', 'PC', 'PG', 'PH', 'PU', 'CV', 'UR', 'UP', 'ZL', 'UL', 'CS',\n", + " 'SC', 'BA', 'SF', 'RO', 'AS', 'SO', 'PI', 'ST', 'CU', 'SU', 'RX',\n", + " 'PB', 'PL', 'PX', 'CT', 'US'], dtype=object)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.unique(surveys_df['species_id'])" + ] + }, + { + "cell_type": "markdown", + "id": "5aa04d1b-5743-4f76-b6e9-b72dbbec3db8", + "metadata": {}, + "source": [ + "> ## Challenge - Statistics\n", + ">\n", + "> 1. Create a list of unique site ID's (\"plot_id\") found in the surveys data. Call it\n", + "> `site_names`. How many unique sites are there in the data? How many unique\n", + "> species are in the data?\n", + ">\n", + "> 2. What is the difference between `len(site_names)` and `surveys_df['plot_id'].nunique()`?" + ] + }, + { + "cell_type": "markdown", + "id": "77564088-9128-4d94-ace5-110e1518f86b", + "metadata": {}, + "source": [ + "# Groups in Pandas\n", + "\n", + "We often want to calculate summary statistics grouped by subsets or attributes\n", + "within fields of our data. For example, we might want to calculate the average\n", + "weight of all individuals per site.\n", + "\n", + "We can calculate basic statistics for all records in a single column using the\n", + "syntax below:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9f78f9f4-e387-405f-9dfa-1549eea21769", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 32283.000000\n", + "mean 42.672428\n", + "std 36.631259\n", + "min 4.000000\n", + "25% 20.000000\n", + "50% 37.000000\n", + "75% 48.000000\n", + "max 280.000000\n", + "Name: weight, dtype: float64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "surveys_df['weight'].describe()" + ] + }, + { + "cell_type": "markdown", + "id": "3f9cccd0-5861-42c8-ac47-94d241760afe", + "metadata": {}, + "source": [ + "We can also extract one specific metric if we wish:\n", + "\n", + "```\n", + "surveys_df['weight'].min()\n", + "surveys_df['weight'].max()\n", + "surveys_df['weight'].mean()\n", + "surveys_df['weight'].std()\n", + "surveys_df['weight'].count()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "2546bf29-ae70-4897-ba4f-3892122b2d3b", + "metadata": {}, + "source": [ + "But if we want to summarize by one or more variables, for example sex, we can\n", + "use **Pandas' `.groupby` method**. Once we've created a groupby DataFrame, we\n", + "can quickly calculate summary statistics by a group of our choice." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "00fc49b2-c1a3-4db5-ba2c-0f6b2627c927", + "metadata": {}, + "outputs": [], + "source": [ + "# Group data by sex\n", + "grouped_data = surveys_df.groupby('sex')" + ] + }, + { + "cell_type": "markdown", + "id": "612c8906-b9a7-491e-8fb4-1a2388277119", + "metadata": {}, + "source": [ + "The **pandas function `describe`** will return descriptive stats including: mean,\n", + "median, max, min, std and count for a particular column in the data. Pandas'\n", + "`describe` function will only return summary values for columns containing\n", + "numeric data." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "421e6cc7-2f04-4732-93d3-c65eb844ec10", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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record_idmonthdayyearplot_idhindfoot_lengthweight
sex
F18036.4120466.58304716.0071381990.64499711.44085428.83678042.170555
M17754.8356016.39266816.1842861990.48040111.09828229.70957842.995379
\n", + "
" + ], + "text/plain": [ + " record_id month day year plot_id \\\n", + "sex \n", + "F 18036.412046 6.583047 16.007138 1990.644997 11.440854 \n", + "M 17754.835601 6.392668 16.184286 1990.480401 11.098282 \n", + "\n", + " hindfoot_length weight \n", + "sex \n", + "F 28.836780 42.170555 \n", + "M 29.709578 42.995379 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Summary statistics for all numeric columns by sex\n", + "grouped_data.describe()\n", + "# Provide the mean for each numeric column by sex\n", + "grouped_data.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "f32f9aeb-3a0e-45cf-a58d-e1e855a4f142", + "metadata": {}, + "source": [ + "The `groupby` command is powerful in that it allows us to quickly generate\n", + "summary stats.\n", + "\n", + "> ## Challenge - Summary Data\n", + ">\n", + "> 1. How many recorded individuals are female `F` and how many male `M`?\n", + "> 2. What happens when you group by two columns using the following syntax and\n", + "> then calculate mean values?\n", + "> - `grouped_data2 = surveys_df.groupby(['plot_id', 'sex'])`\n", + "> - `grouped_data2.mean()`\n", + "> 3. Summarize weight values for each site in your data. HINT: you can use the\n", + "> following syntax to only create summary statistics for one column in your data.\n", + "> `by_site['weight'].describe()`\n", + ">\n", + ">\n", + ">> ## Did you get #3 right?\n", + ">> **A Snippet of the Output from challenge 3 looks like:**\n", + ">>\n", + ">> ```\n", + ">> site\n", + ">> 1 count 1903.000000\n", + ">> mean 51.822911\n", + ">> std 38.176670\n", + ">> min 4.000000\n", + ">> 25% 30.000000\n", + ">> 50% 44.000000\n", + ">> 75% 53.000000\n", + ">> max 231.000000\n", + ">> ...\n", + ">> ```" + ] + }, + { + "cell_type": "markdown", + "id": "00512ec3-e3be-4fd9-a3b0-f99633a0737e", + "metadata": {}, + "source": [ + "## Quickly Creating Summary Counts in Pandas\n", + "\n", + "Let's next count the number of samples for each species. We can do this in a few\n", + "ways, but we'll use `groupby` combined with **a `count()` method**." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "56735fff-81c5-47bc-abe3-f57a206a2b8e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "species_id\n", + "AB 303\n", + "AH 437\n", + "AS 2\n", + "BA 46\n", + "CB 50\n", + "CM 13\n", + "CQ 16\n", + "CS 1\n", + "CT 1\n", + "CU 1\n", + "CV 1\n", + "DM 10596\n", + "DO 3027\n", + "DS 2504\n", + "DX 40\n", + "NL 1252\n", + "OL 1006\n", + "OT 2249\n", + "OX 12\n", + "PB 2891\n", + "PC 39\n", + "PE 1299\n", + "PF 1597\n", + "PG 8\n", + "PH 32\n", + "PI 9\n", + "PL 36\n", + "PM 899\n", + "PP 3123\n", + "PU 5\n", + "PX 6\n", + "RF 75\n", + "RM 2609\n", + "RO 8\n", + "RX 2\n", + "SA 75\n", + "SC 1\n", + "SF 43\n", + "SH 147\n", + "SO 43\n", + "SS 248\n", + "ST 1\n", + "SU 5\n", + "UL 4\n", + "UP 8\n", + "UR 10\n", + "US 4\n", + "ZL 2\n", + "Name: record_id, dtype: int64\n" + ] + } + ], + "source": [ + "# Count the number of samples by species\n", + "species_counts = surveys_df.groupby('species_id')['record_id'].count()\n", + "print(species_counts)" + ] + }, + { + "cell_type": "markdown", + "id": "91527637-b894-4ab7-89d3-3baaf6c7c564", + "metadata": {}, + "source": [ + "Or, we can also count just the rows that have the species \"DO\":" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "014171dd-62da-4bcb-95e0-98b4818085a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3027" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "surveys_df.groupby('species_id')['record_id'].count()['DO']" + ] + }, + { + "cell_type": "markdown", + "id": "d402cd18-b262-4f1c-9cf9-d95fd68de038", + "metadata": {}, + "source": [ + "> ## Challenge - Make a list\n", + ">\n", + "> What's another way to create a list of species and associated `count` of the\n", + "> records in the data? Hint: you can perform `count`, `min`, etc. functions on\n", + "> groupby DataFrames in the same way you can perform them on regular DataFrames." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7e8ae98-dd6c-4e70-b6ed-866d67160f8d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "2e4f40b1-6568-4db2-acfe-affbf491cfc1", + "metadata": {}, + "source": [ + "## Basic Math Functions\n", + "\n", + "If we wanted to, we could perform math on an entire column of our data. For\n", + "example let's multiply all weight values by 2. A more practical use of this might\n", + "be to normalize the data according to a mean, area, or some other value\n", + "calculated from our data." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a5acc742-0c2b-42d9-b91b-189e01f7f5bf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 NaN\n", + "1 NaN\n", + "2 NaN\n", + "3 NaN\n", + "4 NaN\n", + " ... \n", + "35544 NaN\n", + "35545 NaN\n", + "35546 28.0\n", + "35547 102.0\n", + "35548 NaN\n", + "Name: weight, Length: 35549, dtype: float64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Multiply all weight values by 2\n", + "surveys_df['weight']*2" + ] + }, + { + "cell_type": "markdown", + "id": "d5e6fb78-a9b1-45b1-8205-08b93ab3c9fd", + "metadata": {}, + "source": [ + "# Quick & Easy Plotting Data Using Pandas\n", + "\n", + "We can plot our summary stats using Pandas, too." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "da1e26aa-ce14-42a5-854b-945c98b7c000", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make sure figures appear inline in Ipython Notebook\n", + "%matplotlib inline\n", + "# Create a quick bar chart\n", + "species_counts.plot(kind='bar');" + ] + }, + { + "cell_type": "markdown", + "id": "0e4c591a-ca11-4535-aadf-d74e50d5fe1b", + "metadata": {}, + "source": [ + "Count per species site\n", + "\n", + "We can also look at how many animals were captured in each site:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f6ec232f-9ec7-4ca0-b01f-9fc7c3947489", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "total_count = surveys_df.groupby('plot_id')['record_id'].nunique()\n", + "# Let's plot that too\n", + "total_count.plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "id": "f2aa1252-3611-4c2a-aafa-12c0c892e43a", + "metadata": {}, + "source": [ + "> ## Challenge - Plots\n", + ">\n", + "> 1. Create a plot of average weight across all species per site.\n", + "> 2. Create a plot of total males versus total females for the entire dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "778470a0-3584-4b48-87c3-1d5f631bbe57", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "5a2945e0-d51f-487d-81a6-44395601df1c", + "metadata": {}, + "source": [ + "> ## Summary Plotting Challenge\n", + ">\n", + "> Create a stacked bar plot, with weight on the Y axis, and the stacked variable\n", + "> being sex. The plot should show total weight by sex for each site. Some\n", + "> tips are below to help you solve this challenge:\n", + ">\n", + "> * For more information on pandas plots, see [pandas' documentation page on visualization][pandas-plot].\n", + "> * You can use the code that follows to create a stacked bar plot but the data to stack\n", + "> need to be in individual columns. Here's a simple example with some data where\n", + "> 'a', 'b', and 'c' are the groups, and 'one' and 'two' are the subgroups.\n", + ">\n", + "> ```\n", + "> d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']), 'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}\n", + "> pd.DataFrame(d)\n", + "> ```\n", + ">\n", + "> shows the following data\n", + ">\n", + "> ```\n", + "> one two\n", + "> a 1 1\n", + "> b 2 2\n", + "> c 3 3\n", + "> d NaN 4\n", + "> ```\n", + ">\n", + "> We can plot the above with\n", + ">\n", + "> ```\n", + "> # Plot stacked data so columns 'one' and 'two' are stacked\n", + "> my_df = pd.DataFrame(d)\n", + "> my_df.plot(kind='bar', stacked=True, title=\"The title of my graph\")\n", + "> ```\n", + ">\n", + "> * You can use the `.unstack()` method to transform grouped data into columns\n", + "> for each plotting. Try running `.unstack()` on some DataFrames above and see\n", + "> what it yields.\n", + ">\n", + "> Start by transforming the grouped data (by site and sex) into an unstacked layout, then create a stacked plot.\n", + ">" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1c9d455-6187-40ac-9953-b4ec0ac4b95d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "fb555c44-643c-4287-b80a-608ee0344b55", + "metadata": {}, + "source": [ + ">> ## Solution to Summary Challenge\n", + ">>\n", + ">> First we group data by site and by sex, and then calculate a total for each site.\n", + ">>\n", + ">> ```\n", + ">> by_site_sex = surveys_df.groupby(['plot_id', 'sex'])\n", + ">> site_sex_count = by_site_sex['weight'].sum()\n", + ">> ```\n", + ">>\n", + ">> This calculates the sums of weights for each sex within each site as a table\n", + ">>\n", + ">> ```\n", + ">> site sex\n", + ">> plot_id sex\n", + ">> 1 F 38253\n", + ">> M 59979\n", + ">> 2 F 50144\n", + ">> M 57250\n", + ">> 3 F 27251\n", + ">> M 28253\n", + ">> 4 F 39796\n", + ">> M 49377\n", + ">> \n", + ">> ```\n", + ">>\n", + ">> Below we'll use `.unstack()` on our grouped data to figure out the total weight that each sex contributed to each site.\n", + ">>\n", + ">> ```\n", + ">> by_site_sex = surveys_df.groupby(['plot_id', 'sex'])\n", + ">> site_sex_count = by_site_sex['weight'].sum()\n", + ">> site_sex_count.unstack()\n", + ">> ```\n", + ">>\n", + ">> The `unstack` method above will display the following output:\n", + ">>\n", + ">> ```\n", + ">> sex F M\n", + ">> plot_id\n", + ">> 1 38253 59979\n", + ">> 2 50144 57250\n", + ">> 3 27251 28253\n", + ">> 4 39796 49377\n", + ">> \n", + ">> ```\n", + ">>\n", + ">> Now, create a stacked bar plot with that data where the weights for each sex are stacked by site.\n", + ">>\n", + ">> Rather than display it as a table, we can plot the above data by stacking the values of each sex as follows:\n", + ">>\n", + ">> ```\n", + ">> by_site_sex = surveys_df.groupby(['plot_id', 'sex'])\n", + ">> site_sex_count = by_site_sex['weight'].sum()\n", + ">> spc = site_sex_count.unstack()\n", + ">> s_plot = spc.plot(kind='bar', stacked=True, title=\"Total weight by site and sex\")\n", + ">> s_plot.set_ylabel(\"Weight\")\n", + ">> s_plot.set_xlabel(\"Plot\")\n", + ">> ```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa3cc636-053b-4b92-9ed3-be349d4d1285", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9a864b9b-918d-4d08-98b6-7a140ce679ca", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "- ernst: http://www.esapubs.org/archive/ecol/E090/118/default.htm\n", + "- figshare-ndownloader: https://ndownloader.figshare.com/files/2292172\n", + "- os-lib: https://docs.python.org/3/library/os.html\n", + "- matplotlib: https://matplotlib.org\n", + "- numpy: https://www.numpy.org/\n", + "- pandas: https://pandas.pydata.org\n", + "- pandas-plot: http://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#basic-plotting-plot\n", + "- pd-dataframe: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html\n", + "- pptd: https://figshare.com/articles/Portal_Project_Teaching_Database/1314459\n", + "- python-datastructures: https://docs.python.org/3/tutorial/datastructures.html#tuples-and-sequences\n", + "- spreadsheet-lesson5: http://www.datacarpentry.org/spreadsheet-ecology-lesson/05-exporting-data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e4c4a30-de40-4ffd-befd-9b8dda7da968", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7c6d9e59-e673-4ae3-9ca9-0a1f5b6b7509", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 5054b89372215e7407a8f8d5df07a1e960ec5f40 Mon Sep 17 00:00:00 2001 From: Ag Stephens Date: Tue, 28 Feb 2023 15:34:56 +0000 Subject: [PATCH 2/6] Updated slides for python read/write data section --- python-data/slides/02_python_text_formats.pdf | Bin 831924 -> 424337 bytes .../slides/02_python_text_formats.pptx | Bin 613894 -> 705785 bytes python-data/slides/04_binary_formats.pdf | Bin 745885 -> 230962 bytes python-data/slides/04_binary_formats.pptx | Bin 178297 -> 178191 bytes python-data/slides/05_netcdf_overview.pdf | Bin 679306 -> 228167 bytes python-data/slides/05_netcdf_overview.pptx | Bin 182677 -> 182400 bytes python-data/slides/07_ncgen_ncdump_cdl.pdf | Bin 705485 -> 287033 bytes python-data/slides/07_ncgen_ncdump_cdl.pptx | Bin 204151 -> 203570 bytes 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