From e2f360caaa828bfce48d18b824298ae22bb66388 Mon Sep 17 00:00:00 2001 From: Dave Whipp Date: Wed, 17 Oct 2018 06:55:54 +0300 Subject: [PATCH] This is why you never use git add . --- .../advanced-plotting-checkpoint.ipynb | 636 ------------------ .../matplotlib-checkpoint.ipynb | 447 ------------ notebooks/L7/.nbgrader.log | 41 -- .../matplotlib-gallery-checkpoint.png | Bin 154885 -> 0 bytes .../subplots-checkpoint.png | Bin 636758 -> 0 bytes 5 files changed, 1124 deletions(-) delete mode 100644 notebooks/L7/.ipynb_checkpoints/advanced-plotting-checkpoint.ipynb delete mode 100644 notebooks/L7/.ipynb_checkpoints/matplotlib-checkpoint.ipynb delete mode 100644 notebooks/L7/.nbgrader.log delete mode 100644 notebooks/L7/img/.ipynb_checkpoints/matplotlib-gallery-checkpoint.png delete mode 100644 notebooks/L7/img/.ipynb_checkpoints/subplots-checkpoint.png diff --git a/notebooks/L7/.ipynb_checkpoints/advanced-plotting-checkpoint.ipynb b/notebooks/L7/.ipynb_checkpoints/advanced-plotting-checkpoint.ipynb deleted file mode 100644 index 7e889f5..0000000 --- a/notebooks/L7/.ipynb_checkpoints/advanced-plotting-checkpoint.ipynb +++ /dev/null @@ -1,636 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# More advanced plotting with Matplotlib\n", - "\n", - "At this point you should know the basics of making plots with Matplotlib module.\n", - "Now we will expand on our basic plotting skills to learn how to create more advanced plots.\n", - "In this part, we will show how to visualize data using Matplotlib and create plots such as the one below.\n", - "\n", - "![Subplot example in Matplotlib](img/subplots.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The dataset\n", - "\n", - "For this lesson we will use hourly weather data from Helsinki.\n", - "\n", - "- The data file (`1924927457196dat.txt`) is in the `data` subdirectory.\n", - "- It contains observed hourly temperatures, windspeeds, etc. covering years 2012 and 2013. Observations were recorded at the Malmi airport weather station in Helsinki. It is derived from a data file of daily temperature measurments downloaded from the [US National Oceanographic and Atmospheric Administration’s National Centers for Environmental Information climate database](https://www7.ncdc.noaa.gov/CDO/cdopoemain.cmd?datasetabbv=DS3505&countryabbv=&georegionabbv=&resolution=40).\n", - "- There should be around 16.5 thousand rows in the data.\n", - "\n", - "The first rows of the data looks like:\n", - "\n", - "```\n", - " USAF WBAN YR--MODAHRMN DIR SPD GUS CLG SKC L M H VSB MW MW MW MW AW AW AW AW W TEMP DEWP SLP ALT STP MAX MIN PCP01 PCP06 PCP24 PCPXX SD\n", - "029750 99999 201201010050 280 3 *** 89 BKN * * * 7.0 ** ** ** ** ** ** ** ** * 28 25 ****** 29.74 ****** *** *** ***** ***** ***** ***** ** \n", - "029750 99999 201201010150 310 3 *** 89 OVC * * * 7.0 ** ** ** ** ** ** ** ** * 27 25 ****** 29.77 ****** *** *** ***** ***** ***** ***** ** \n", - "029750 99999 201201010250 280 1 *** *** *** * * * 6.2 ** ** ** ** ** ** ** ** * 25 21 ****** 29.77 ****** *** *** ***** ***** ***** ***** ** \n", - "029750 99999 201201010350 200 1 *** *** *** * * * 6.2 ** ** ** ** ** ** ** ** * 21 21 ****** 29.80 ****** *** *** ***** ***** ***** ***** ** \n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Getting started\n", - "\n", - "Let's start again by importing the pyplot submodule of Matplotlib." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the data using NumPy\n", - "\n", - "As always, let's first import NumPy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now load our data using a similar approach to that used in the previous lessons.\n", - "Note that in this case, we only want the `YR--MODAHRMN` and `TEMP` columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fp = 'data/1924927457196dat.txt'\n", - "data = np.genfromtxt(fp, skip_header=1, usecols=(2, 21))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our data loaded, we can again split the data into separate column arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "date = data[:,0]\n", - "temp = data[:,1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also go ahead and convert `date` to integer values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "date = date.astype(int)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the data using Pandas\n", - "\n", - "As always, let's first import Pandas." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now load our data using a similar approach to that used in the previous lessons." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fp = 'data/1924927457196dat.txt'\n", - "dataFrame = pd.read_csv(fp, sep='\\s+', na_values=['*', '**', '***', '****', '*****', '******'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As in the last section of this week's lesson, let's separate `dataFrame` into separate value arrays for the `YR--MODAHRMN`, `SPD`, and `TEMP` columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "date = dataFrame['YR--MODAHRMN'].values\n", - "temp = dataFrame['TEMP'].values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preparing the data\n", - "\n", - "A quick check of the `temp` data will reveal there are a handful of missing data values.\n", - "We can check for the existence of `nan` values, for example, by running the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Number of date nans: ', np.isnan(date).sum())\n", - "print('Number of temp nans: ', np.isnan(temp).sum())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ouch, 26 bad values to cut.\n", - "Since we're working with value arrays, the easiest way to drop the `nan` values from `temp` is save only the values that are not `nan`.\n", - "We can do this, for example, using the following." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This command simply uses the NumPy `isnan()` function to find all `nan` values, then takes the logical opposite (not) of those values using the `~` character.\n", - "\n", - "For those using Pandas, you could possibly use `dropna()` as an alternative to this approach, but only for the dataframe, not the value arrays.\n", - "\n", - "Lastly, since we've cut `nan` values from `temp` we should do the same in `date`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting temperatures to Celsius\n", - "\n", - "Now that we have loaded our data, we can convert the values of temperature in Fahrenheit to Celsius, much like we have in earlier lessons." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We should now have a value array `temp_celsius` with all of the temperatures from the data file in Celsius." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using subplots\n", - "\n", - "Let's continue working with the weather data and learn how to do subplots, i.e. Figures where you have multiple plots in different panels as was shown at the start of the lesson." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extracting seasonal temperatures\n", - "\n", - "Let's first divide our data into different seasons: Winter (December-February), Spring (March-May), Summer (June-August), and Fall (Septempber-November)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n", - "\n", - "spring_mask = (date >= 201303010000) & (date < 201306010000)\n", - "spring_dates = date[spring_mask]\n", - "spring_temps = temp_celsius[spring_mask]\n", - "\n", - "summer_mask = (date >= 201306010000) & (date < 201309010000)\n", - "summer_dates = date[summer_mask]\n", - "summer_temps = temp_celsius[summer_mask]\n", - "\n", - "fall_mask = (date >= 201309010000) & (date < 201401010000)\n", - "fall_dates = date[fall_mask]\n", - "fall_temps = temp_celsius[fall_mask]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can plot our data to see how the different seasons look separately." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(spring_temps)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(summer_temps)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(fall_temps)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OK, so from these plots we can already see that the temperatures in different seasons are quite different, which is rather obvious of course.\n", - "It is important to also notice that the scale of the *y*-axis changes in these different plots.\n", - "If we would like to compare different seasons to each other we need to make sure that the temperature scale is similar in the plots of the different seasons.\n", - "\n", - "### Finding data bounds\n", - "\n", - "Let's set our *y*-axis limits so that the upper limit is the maximum temperature + 5 degrees in our data (full year), and the lowest is the minimum temperature - 5 degrees." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "print(\"Min:\", min_temp, \"Max:\", max_temp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OK, so now we can see that the minimum temperature in our data is approximately -29 degrees and the maximum is +29 degrees.\n", - "We can now use those values to standardize the y-axis scale of our plot.\n", - "\n", - "### Creating our first set of subplots\n", - "\n", - "Let's now continue and see how we can plot all these different plots into the same figure.\n", - "We can create a 2x2 panel for our visualization using matplotlib’s `subplots()` function where we specify how many rows and columns we want to have in our figure.\n", - "We can also specify the size of our figure with `figsize()` parameter that takes the `width` and `height` values (in inches) as input." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that as a result we have now a list containing two nested lists where the first one contains the axis for column 1 and 2 on **row 1** and the second list contains the axis for columns 1 and 2 for **row 2**.\n", - "We can parse these axes into own variables so it is easier to work with them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have four different axis variables for different panels in our figure.\n", - "Next we can use them to plot the seasonal data into them.\n", - "Let's first plot the seasons and give different colors for the lines, and specify the *y*-scale limits to be the same with all subplots.\n", - "With parameter `c` it is possible to specify the color of the line.\n", - "You can find an extensive list of possible colors and RGB-color codes from [this link](http://www.rapidtables.com/web/color/RGB_Color.htm).\n", - "With `lw` parameter you can specify the width of the line." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set plot line width\n", - "\n", - "\n", - "# Plot data\n", - "\n", - "ax12.plot(spring_temps, c='orange', lw=line_width)\n", - "ax21.plot(summer_temps, c='green', lw=line_width)\n", - "ax22.plot(fall_temps, c='brown', lw=line_width)\n", - "\n", - "# Set y-axis limits\n", - "\n", - "ax12.set_ylim(min_temp, max_temp)\n", - "ax21.set_ylim(min_temp, max_temp)\n", - "ax22.set_ylim(min_temp, max_temp)\n", - "\n", - "# Display figure\n", - "fig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great, now we have all the plots in same figure!\n", - "However, we can see that there are some problems with our *x*-axis as we haven't linked that axis to the dates of the observations.\n", - "We can change that with a bit of `datetime` module magic.\n", - "\n", - "### Converting our date string to dates for Matplotlib plotting\n", - "\n", - "We can start by importing the `datetime` submodule from `datetime`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With `datetime` imported, we can now create a function that will convert all of our dates in `date` to a special representation of that date data that Matplotlib understands as a date for plotting.\n", - "Before we can define and use our function, however, we'll need our dates as a character string array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "spring_str = spring_dates.astype(str)\n", - "summer_str = summer_dates.astype(str)\n", - "fall_str = fall_dates.astype(str)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OK, so now we have our dates as strings in `_str`.\n", - "Now we can create our date conversion function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`datetime.strptime()` is a special function in `datetime` used to convert strings to `datetime` objects.\n", - "In our case, we will convert each value from the format `YYYYMMDDhhmm` to `datetime`, where `YYYY` is the year, `MM` is the month, `DD` is the day, `hh` is the hour of the day, and `mm` is the minute in the hour.\n", - "\n", - "Let's put our function to work." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n", - "spring_dt = [convert(date) for date in spring_str]\n", - "spring_dt = np.array(spring_dt)\n", - "\n", - "summer_dt = [convert(date) for date in summer_str]\n", - "summer_dt = np.array(summer_dt)\n", - "\n", - "fall_dt = [convert(date) for date in fall_str]\n", - "fall_dt = np.array(fall_dt)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What has happened here is that we have created a new list of `datetime` values using list comprehension, then converted the output to NumPy arrays for each season.\n", - "If you don't understand everything at this point, don't worry.\n", - "The important thing is we're ready to plot again!\n", - "\n", - "We can set things up the way we did previously." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the figure and subplots\n", - "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12,8))\n", - "\n", - "# Rename the axes for ease of use\n", - "ax11 = axes[0][0]\n", - "ax12 = axes[0][1]\n", - "ax21 = axes[1][0]\n", - "ax22 = axes[1][1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we'll add our seasonal temperatures to the plot commands for each time period." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the plotted line width\n", - "line_width = 1.5\n", - "\n", - "# Plot data\n", - "ax11.plot(winter_dt, winter_temps, c='blue', lw=line_width)\n", - "ax12.plot(spring_dt, spring_temps, c='orange', lw=line_width)\n", - "ax21.plot(summer_dt, summer_temps, c='green', lw=line_width)\n", - "ax22.plot(fall_dt, fall_temps, c='brown', lw=line_width)\n", - "\n", - "# Set y-axis limits\n", - "ax11.set_ylim(min_temp, max_temp)\n", - "ax12.set_ylim(min_temp, max_temp)\n", - "ax21.set_ylim(min_temp, max_temp)\n", - "ax22.set_ylim(min_temp, max_temp)\n", - "\n", - "# Turn plot grids on\n", - "\n", - "ax12.grid()\n", - "ax21.grid()\n", - "ax22.grid()\n", - "\n", - "# Figure title\n", - "\n", - "\n", - "# Rotate the x-axis labels so they don't overlap\n", - "\n", - "plt.setp(ax12.xaxis.get_majorticklabels(), rotation=20)\n", - "plt.setp(ax21.xaxis.get_majorticklabels(), rotation=20)\n", - "plt.setp(ax22.xaxis.get_majorticklabels(), rotation=20)\n", - "\n", - "# Axis labels\n", - "\n", - "ax22.set_xlabel('Date')\n", - "\n", - "ax21.set_ylabel('Temperature [deg. C]')\n", - "\n", - "# Season label text\n", - "\n", - "ax12.text(datetime(2013, 5, 15), -25, 'Spring')\n", - "ax21.text(datetime(2013, 8, 15), -25, 'Summer')\n", - "ax22.text(datetime(2013, 11, 15), -25, 'Fall')\n", - "\n", - "fig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Not bad." - ] - } - ], - "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": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/L7/.ipynb_checkpoints/matplotlib-checkpoint.ipynb b/notebooks/L7/.ipynb_checkpoints/matplotlib-checkpoint.ipynb deleted file mode 100644 index e0c04c9..0000000 --- a/notebooks/L7/.ipynb_checkpoints/matplotlib-checkpoint.ipynb +++ /dev/null @@ -1,447 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plotting with Matplotlib\n", - "\n", - "Though there are many options for plotting data in Python, we will be using [Matplotlib](https://matplotlib.org/).\n", - "In particular, we will be using the `pyplot` module in Matplotlib, which provides MATLAB-like plotting.\n", - "The reason for this is simple: Matplotlib is the most common module used for plotting in Python and many examples of plotting you may find online will be using Matplotlib." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Our dataset\n", - "\n", - "For our first lesson plotting data using Matplotlib we will again be using the weather data file from Lesson 5.\n", - "\n", - "- The data file (`Kumpula-June-2016-w-metadata.txt`) is in the `data` subdirectory.\n", - "- It contains observed daily mean, minimum, and maximum temperatures from June 2016 recorded from the Kumpula weather observation station in Helsinki. It is derived from a data file of daily temperature measurments downloaded from the [US National Oceanographic and Atmospheric Administration’s National Centers for Environmental Information climate database](https://www.ncdc.noaa.gov/cdo-web/).\n", - "\n", - "## Getting started\n", - "\n", - "Let's start by importing the pyplot submodule of Matplotlib." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note again that we are renaming the Matplotlib pyplot submodule when we import it.\n", - "Perhaps now it is more clear why you might want to rename a module on import.\n", - "Having to type `matplotlib.pyplot` every time you use one of its methods would be a pain.\n", - "\n", - "### Loading the data with NumPy\n", - "\n", - "Those who have learned to use NumPy should load their data as follows.\n", - "\n", - "To start, we will need to import NumPy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can read in the data file in the same way we have for Lesson 5." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fp = 'data/Kumpula-June-2016-w-metadata.txt'\n", - "data = np.genfromtxt(fp, skip_header=9, delimiter=',')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you may recall, we will now have a data file with 4 columns.\n", - "Let's rename each of those below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "date = data[:, 0]\n", - "temp = data[:, 1]\n", - "temp_max = data[:, 2]\n", - "temp_min = data[:, 3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the data with Pandas\n", - "\n", - "Those who have learned to use Pandas should load their data as follows.\n", - "\n", - "To start, we will need to import Pandas." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can read in the data file in the same way we have for Lesson 5." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataFrame = pd.read_csv('data/Kumpula-June-2016-w-metadata.txt', skiprows=8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OK, great.\n", - "One thing we'll do a bit differently this week is that we're going to split the data from `dataFrame` into separate Pandas value arrays so we can plot things in the same way as with NumPy.\n", - "We can split the data into separate series as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "date = dataFrame['YEARMODA'].values\n", - "temp = dataFrame['TEMP'].values\n", - "temp_max = dataFrame['MAX'].values\n", - "temp_min = dataFrame['MIN'].values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `.values` attribute of a Pandas series returns only the numerical values of the given series, not the index list." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Our first plot\n", - "\n", - "OK, so let’s get to plotting! We can start by using the Matplotlib plt.plot() function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If all goes well, you should see the plot above.\n", - "\n", - "OK, so what happened here?\n", - "Well, first we assigned the values we would like to plot, the year and temperature, to the variables `x` and `y`.\n", - "This isn’t necessary, *per se*, but does make it easier to see what is plotted.\n", - "Next, it is perhaps pretty obvious that `plt.plot()` is a function in pyplot that produces a simple *x*-*y* plot.\n", - "Conveniently, plots are automatically displayed in Jupyter notebooks, so there is no need for the additional `plt.show()` function you might see in examples you can find online." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Basic plot formatting\n", - "\n", - "We can make our plot look a bit nicer and provide more information by using a few additional pyplot options." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This should produce the plot above.\n", - "\n", - "Now we see our temperature data as a red dashed line with circles showing the data points.\n", - "This comes from the additional `ro--` used with `plt.plot()`.\n", - "In this case, `r` tells the `plt.plot()` function to use red color, `o` tells it to show circles at the points, and `--` says to use a dashed line.\n", - "You can use `help(plt.plot)` to find out more about formatting plots.\n", - "Better yet, check out the [documentation for `plt.plot()` online](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html).\n", - "We have also added a title and axis labels, but their use is straightforward." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Embiggening\\* the plot\n", - "\n", - "While the plot sizes we're working with are OK, it would be nice to have them displayed a bit larger.\n", - "Fortunately, there is an easy way to make the plots larger in Jupyter notebooks.\n", - "To set the default plot size to be larger, simply run the Python cell below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams['figure.figsize'] = [12, 6]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The cell above sets the default plot size to be 12 inches wide by 6 inches tall.\n", - "Feel free to change these values if you prefer.\n", - "\n", - "To test whether this is working as expected, simply re-run one of the earlier cells that generated a plot.\n", - "\n", - "\\* To *[embiggen](https://en.oxforddictionaries.com/definition/embiggen)* means to enlarge.\n", - "It's a perfectly [cromulent](https://en.oxforddictionaries.com/definition/cromulent) word." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adding text labels to a plot\n", - "\n", - "Adding text to plots can be done using `plt.text()`.\n", - "\n", - "```python\n", - "plt.text(20160604.0, 68.0, 'High temperature in early June')\n", - "```\n", - "\n", - "This will display the text \"High temperature in early June\" at the location `x = 20160604.0` (i.e., June 4, 2016), `y = 68.0` on the plot.\n", - "We'll see how to do this in a live example in just a second.\n", - "With our approach to plotting thus far, the commands related to an individual plot should all be in the same Python cell." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Changing the axis ranges\n", - "\n", - "Changing the plot axes can be done using the `plt.axis()` function.\n", - "\n", - "```python\n", - "plt.axis([20160601, 20160615, 55.0, 70.0])\n", - "```\n", - "\n", - "The format for `plt.axis()` is `[xmin, xmax, ymin, ymax]` enclosed in square brackets (i.e., a Python list).\n", - "Here, the *x* range would be changed to the equivalents of June 1, 2016 to June 15, 2016 and the *y* range would be 55.0-70.0.\n", - "The complete set of commands to plot would thus be:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(x, y, 'ro--')\n", - "plt.title('Kumpula temperatures in June 2016')\n", - "plt.xlabel('Date')\n", - "plt.ylabel('Temperature [°F]')\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bar plots in Matplotlib\n", - "\n", - "In addition to line plots, there are many other options for plotting in Matplotlib.\n", - "Bar plots are one option, which can be used quite similarly to line plots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "plt.title('Kumpula temperatures in June 2016')\n", - "plt.xlabel('Date')\n", - "plt.ylabel('Temperature [°F]')\n", - "plt.text(20160604.0, 68.0, 'High temperature in early June')\n", - "plt.axis([20160601, 20160615, 55.0, 70.0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can find more about how to format bar charts on the [Matplotlib documentation website](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.bar.html?highlight=matplotlib%20pyplot%20bar#matplotlib.pyplot.bar)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Saving your plots as image files\n", - "\n", - "Saving plots created using Matplotlib done several ways.\n", - "The recommendation for use outside of Jupyter notebooks is to use the `plt.savefig()` function.\n", - "When using `plt.savefig()`, you simply give a list of commands to generate a plot and list `plt.savefig()` with some parameters as the last command.\n", - "The file name is required, and the image format will be determined based on the listed file extension.\n", - "\n", - "Matplotlib plots can be saved in a number of useful file formats, including PNG, PDF, and EPS.\n", - "PNG is a nice format for raster images, and EPS is probably easiest to use for vector graphics.\n", - "Let's check out an example and save our lovely bar plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.bar(x, y)\n", - "plt.title('Kumpula temperatures in June 2016')\n", - "plt.xlabel('Date')\n", - "plt.ylabel('Temperature [°F]')\n", - "plt.text(20160604.0, 68.0, 'High temperature in early June')\n", - "plt.axis([20160601, 20160615, 55.0, 70.0])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you refresh your **Files** tab on the left side of the JupyterLab window you should now see `bar-plot.png` listed.\n", - "We could try to save another version in higher resolution with a minor change to our plot commands above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.bar(x, y)\n", - "plt.title('Kumpula temperatures in June 2016')\n", - "plt.xlabel('Date')\n", - "plt.ylabel('Temperature [°F]')\n", - "plt.text(20160604.0, 68.0, 'High temperature in early June')\n", - "plt.axis([20160601, 20160615, 55.0, 70.0])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "#### Task 1: Plotting like the \"pros\"\n", - "\n", - "We’re only introducing a tiny amount of what can be done with pyplot.\n", - "In most cases, when we would like to create some more complicated type of plot, we would search using [Google](https://www.google.fi/) or visit the [Matplotlib plot gallery](http://matplotlib.org/gallery.html).\n", - "The great thing about the [Matplotlib plot gallery](http://matplotlib.org/gallery.html) is that not only can you find example plots there, but you can also find the Python commands used to create the plots.\n", - "This makes it easy to take a working example from the gallery and modify it for your use.\n", - "\n", - "![The Matplotlib plot gallery](img/matplotlib-gallery.png)\n", - "\n", - "Your job in this task is to:\n", - "\n", - "1. Visit the [Matplotlib plot gallery](http://matplotlib.org/gallery.html)\n", - "2. Find an interesting plot and click on it\n", - "3. Copy the code you find listed beneath the plot on the page that loads\n", - "4. Paste that into an Python cell in this notebook and run it to reproduce the plot\n", - "\n", - "After you have reproduced the plot, you are welcome to try to make a small change to the plot commands and see what happens.\n", - "For this, you can simply edit the Python cell contents and re-run.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Task 2: Plotting only part of a dataset\n", - "\n", - "For this task, you should use the values for arrays `x` and `y` calculated earlier in this part of the lesson, and use `plt.axis()` to limit the plot to the following *x* and *y* ranges: *x = June 7-14*, *y = 45.0-65.0*.\n", - "\n", - "- What do you expect to see in this case?\n", - "\n", - "**Note**: In order to get the plot to display properly, you will need to first type in the `plt.plot()` command, then `plt.axis()`." - ] - } - ], - "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": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git 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