Add question numbers

python-data/solutions/ex05_matplotlib.ipynb

@@ -53,7 +53,7 @@
    "id": "6bc7192f-0962-4168-b6c6-5fd44daa24b2",
    "metadata": {},
    "source": [
-    "1. Let's create some sample data to plot. Create an array called `xaxis` with the value `[1,2,3,4,5]` and an array called `yaxis` with the value `[2, 16, 4, 8, 7]`. Plot this data on a single axes. Don't forget to import matplotlib!"
+    "Q1. Let's create some sample data to plot. Create an array called `xaxis` with the value `[1,2,3,4,5]` and an array called `yaxis` with the value `[2, 16, 4, 8, 7]`. Plot this data on a single axes. Don't forget to import matplotlib!"
    ]
   },
   {
@@ -109,7 +109,7 @@
    "id": "9cfacc0a-5af7-4912-b79e-0ac0743e20e5",
    "metadata": {},
    "source": [
-    "2. Create 6 empty plots in a 2x3 grid."
+    "Q2. Create 6 empty plots in a 2x3 grid."
    ]
   },
   {
@@ -170,7 +170,7 @@
    "id": "97d9019c-750c-4a23-b023-2c9ed472f154",
    "metadata": {},
    "source": [
-    "3. Some inputs won't work as intended. Create a pandas dataframe as follows: \n",
+    "Q3. Some inputs won't work as intended. Create a pandas dataframe as follows: \n",
     "\n",
     "```\n",
     "df = pd.DataFrame({\n",
@@ -258,7 +258,7 @@
    "id": "8d0a5420-bd47-4771-aebb-125ab192f458",
    "metadata": {},
    "source": [
-    "4. We need to extract only the numeric values to plot. Let's extract them as a numpy array and try again. Use `np.asarray(df[['A', 'B']])` to create a numpy array from the numeric data. Try plotting it now."
+    "Q4. We need to extract only the numeric values to plot. Let's extract them as a numpy array and try again. Use `np.asarray(df[['A', 'B']])` to create a numpy array from the numeric data. Try plotting it now."
    ]
   },
   {
@@ -316,7 +316,7 @@
    "id": "43bcbb06-d568-4091-a7a3-1e6ab4db0358",
    "metadata": {},
    "source": [
-    "5. Let's unpack the example given in the tutorial of using matplotlib with string-indexable objects. Instead of passing numpy arrays directly, we'll pass the names of the variables as strings.\n",
+    "Q5. Let's unpack the example given in the tutorial of using matplotlib with string-indexable objects. Instead of passing numpy arrays directly, we'll pass the names of the variables as strings.\n",
     "\n",
     "- Let's start by creating the dictionary. Create a variable called data using the following: \n",
     "    ```\n",
@@ -398,7 +398,7 @@
    "id": "ce3d4439-773f-4a61-8556-9967942f7300",
    "metadata": {},
    "source": [
-    "6. So far, we've been creating plots in the object oriented way: explicitly creating figures and axes. The pyplot-style is very subtly different - we just don't need to create the axis or subplots.\n",
+    "Q6. So far, we've been creating plots in the object oriented way: explicitly creating figures and axes. The pyplot-style is very subtly different - we just don't need to create the axis or subplots.\n",
     "- Create x axis data using `np.linspace(min, max, num)`. Create 10 values between 0 and 10.\n",
     "- Create y axis data using `np.linspace` to create 10 values between 0 and 100.\n",
     "- Plot this data on the implicit axes using `plt.plot()` - don't worry about seeting the figsize and layout."
@@ -464,7 +464,7 @@
    "id": "1d18ed69-464a-4f6f-af12-4b43de5fb010",
    "metadata": {},
    "source": [
-    "7. Let's use the x and y values from before and create some new y values to practice styling plots.\n",
+    "Q7. Let's use the x and y values from before and create some new y values to practice styling plots.\n",
     "- Create `y2 = np.linspace(0, -100, 10)`.\n",
     "- Plot both of these sets of data on the same axes using the [Styling Artists example](https://matplotlib.org/stable/users/explain/quick_start.html#styling-artists).\n",
     "- Plot the original y data in purple with the `--` linestyle and the new y data in green with the `:` linestyle.\n",
@@ -525,7 +525,7 @@
    "id": "d3bba7b2-208b-4927-a246-b1ff18cadbf9",
    "metadata": {},
    "source": [
-    "8. There are lots of different customisation options in matplotlib for colour! You can even have different colours for the markers and outlines in a scatter plot. Use the following to generate some data for a scatter plot:\n",
+    "Q8. There are lots of different customisation options in matplotlib for colour! You can even have different colours for the markers and outlines in a scatter plot. Use the following to generate some data for a scatter plot:\n",
     "```\n",
     "data1, data2 = np.random.randn(2,100)\n",
     "```\n",
@@ -584,7 +584,7 @@
    "id": "e1308dc3-fc3c-4a49-a762-3fdb0a54eff4",
    "metadata": {},
    "source": [
-    "9. Generate two more scatter plot datasets as we did above then plot all 4 on one graph. Give each dataset a label and a different marker style - e.g. stars (`*`), plus (`P`) or diamonds (`D`). You can see more options for markers [in the documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/marker_reference.html)."
+    "Q9. Generate two more scatter plot datasets as we did above then plot all 4 on one graph. Give each dataset a label and a different marker style - e.g. stars (`*`), plus (`P`) or diamonds (`D`). You can see more options for markers [in the documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/marker_reference.html)."
    ]
   },
   {
@@ -652,7 +652,7 @@
    "id": "a912305e-861a-428b-8223-9d245004baf7",
    "metadata": {},
    "source": [
-    "10. Take the plot we just created in the previous question and give it `xlabel`, `ylabel` and a `title` of your choice. Add some text to the plot saying `some text` at `50, 0`. Add an annotation at top saying `some annotation` with a black arrow pointing to some data using `xy=(40,2)` and `xytext=(3,1.5)`. Also add a legend identifying each data set."
+    "Q10. Take the plot we just created in the previous question and give it `xlabel`, `ylabel` and a `title` of your choice. Add some text to the plot saying `some text` at `50, 0`. Add an annotation at top saying `some annotation` with a black arrow pointing to some data using `xy=(40,2)` and `xytext=(3,1.5)`. Also add a legend identifying each data set."
    ]
   },
   {
@@ -726,7 +726,7 @@
    "id": "27c4fc57-9ca3-442b-a62b-469a5f7d73c5",
    "metadata": {},
    "source": [
-    "11. Let's practice plotting some log scale data.\n",
+    "Q11. Let's practice plotting some log scale data.\n",
     "- Create the xdata using `xdata = np.arange(5)`.\n",
     "- Create the ydata using `ydata = np.array([0.1, 0.5, 1, 5, 10])`.\n",
     "- Transform the y data by raising it to the power of 10 using `ydata = 10**ydata`.\n",
@@ -792,7 +792,7 @@
    "id": "447fc1f2-81e1-4034-bbcb-b00d57ab88b3",
    "metadata": {},
    "source": [
-    "12. To demonstrate the difference between automatic and manual ticks, let's create two subplots. Follow the following steps:\n",
+    "Q12. To demonstrate the difference between automatic and manual ticks, let's create two subplots. Follow the following steps:\n",
     "\n",
     "- Create some data using `xdata = np.linspace(0, 99, 100)`, and `ydata = np.sin(xdata / 10)`\n",
     "- Create a figure with 2 subplots arranged vertically.\n",
@@ -866,7 +866,7 @@
    "id": "ecc8e915-c9ad-44a3-9964-2d047516a3bb",
    "metadata": {},
    "source": [
-    "13. Let's see how matplotlib handles plotting dates. We'll create a time series plot using an array of dates and random cumulative data:\n",
+    "Q13. Let's see how matplotlib handles plotting dates. We'll create a time series plot using an array of dates and random cumulative data:\n",
     "- Generate a numpy array of dates starting from `2022-01-01` to `2022-01-10` at intervals of 3 hours using `dates=np.arange(np.datetime64('2022-01-01'), np.datetime64('2022-01-10'), np.timedelta64(3, 'h'))`\n",
     "- Creative a cumulative sum of random numbers for the same length of the array of dates using `data=np.cumsum(np.random.randn(len(dates)))`\n",
     "- Plot the data with the dates on the x axis and `data` on the y axis.\n",
@@ -929,7 +929,7 @@
    "id": "0f022a40-f65d-4649-a63e-bcdbd4cdf4a1",
    "metadata": {},
    "source": [
-    "14. Let's have a go at plotting some categorical data. We'll create a bar chart using a list of categories and random values:\n",
+    "Q14. Let's have a go at plotting some categorical data. We'll create a bar chart using a list of categories and random values:\n",
     "- Define a list of four fruit names `['apple', 'banana', 'cherry', 'date']`\n",
     "- Generate random data for these categories using `np.random.rand()`\n",
     "- Create a bar plot using these categories and their corresponding random values using `ax.bar()`"
@@ -993,7 +993,7 @@
    "id": "9d86f4d0-fd73-40b4-aa36-beb40ffbab10",
    "metadata": {},
    "source": [
-    "15. Let's create a plot that demonstrates the use of both a secondary y-axis and a secondary x-axis with different scales:\n",
+    "Q15. Let's create a plot that demonstrates the use of both a secondary y-axis and a secondary x-axis with different scales:\n",
     "- Generate a time series `t` ranging from 0 to 2π with 100 points (hint: use `np.linspace` with `np.pi`)\n",
     "- Create two datasets: `s` for a sine wave and `l` for a linearly increasing dataset between 0 and the length of `t` (hint: use `np.sin()` and `np.arange()` with `len(t)`)\n",
     "- Plot both datasets on the same figure\n",
@@ -1083,7 +1083,7 @@
    "id": "4a4d9f48-0dbc-4b5e-a54a-f440c6c9f46b",
    "metadata": {},
    "source": [
-    "16. Let's create a series of suplots to practice visualizing data with colormaps:\n",
+    "Q16. Let's create a series of suplots to practice visualizing data with colormaps:\n",
     "- Generate x and y data using `x, y = np.meshgrid(np.linspace(-3, 3, 128), np.linspace(-3, 3, 128))`\n",
     "- Generate Z data using `z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)`\n",
     "- Generate 2 datsets to use for our scatter plot using `np.random.randn(100)` and generate a third dataset to use for the colors using `np.random.rand(100)`\n",
@@ -1189,7 +1189,7 @@
    "id": "4d431ef9-295d-4c21-b9ef-2f4b118b11c6",
    "metadata": {},
    "source": [
-    "17. Let's create a figure with multiple subplots using the `suplot_mosiac` method. Each subplot should have its own distinct data and be customized with titles, labels, and a legend. You will also need to manipulate different axes in a single figure and work with multiple figures in a single program:\n",
+    "Q17. Let's create a figure with multiple subplots using the `suplot_mosiac` method. Each subplot should have its own distinct data and be customized with titles, labels, and a legend. You will also need to manipulate different axes in a single figure and work with multiple figures in a single program:\n",
     "- Create data for the plots using:\n",
     "  ```\n",
     "  x = np.linspace(0,2 * np.pi, 100)\n",