{ "cells": [ { "cell_type": "markdown", "id": "64d0f59c-8588-4f78-8bd5-078627729d92", "metadata": {}, "source": [ "## Introduction to Data Science\n", "\n", "#### University of Redlands - DATA 101\n", "#### Prof: Joanna Bieri [joanna_bieri@redlands.edu](mailto:joanna_bieri@redlands.edu)\n", "#### [Class Website: data101.joannabieri.com](https://data101.joannabieri.com)\n", "\n", "---------------------------------------\n", "# Homework Day 3\n", "---------------------------------------\n", "\n", "GOALS:\n", "\n", "1. Answer all the questions from the lecture.\n", "2. Practice making graphs with data.\n", "\n", "**IMPORTANT NOTE: For most people doing the HW problems while watching the lecture is really helpful. You can watch the section of the lecture, pause it, and then work on the problems.**\n", "\n", "----------------------------------------------------------\n", "\n", "For this homework you will load the Star Wars data and practice making different kinds of plots!\n", "\n", "This homework has **8 questions** from the lecture and **2 Problems**" ] }, { "cell_type": "code", "execution_count": 1, "id": "85d851b3-4123-484c-a51a-561490abcc68", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:24.139822Z", "iopub.status.busy": "2026-09-02T20:59:24.139189Z", "iopub.status.idle": "2026-09-02T20:59:25.793536Z", "shell.execute_reply": "2026-09-02T20:59:25.793020Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "from plotly.subplots import make_subplots\n", "import plotly.io as pio\n", "pio.renderers.default = 'notebook_connected'\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "0d9997ae-4d12-48cc-8229-9ec76d59e9a5", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:25.795171Z", "iopub.status.busy": "2026-09-02T20:59:25.794998Z", "iopub.status.idle": "2026-09-02T20:59:26.034963Z", "shell.execute_reply": "2026-09-02T20:59:26.033515Z" } }, "outputs": [], "source": [ "file_location = 'https://joannabieri.com/introdatascience/data/starwars.csv'\n", "DF = pd.read_csv(file_location)\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "5bd623a5-72ac-4f8b-96e3-1518e1e1f46c", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.039327Z", "iopub.status.busy": "2026-09-02T20:59:26.038840Z", "iopub.status.idle": "2026-09-02T20:59:26.070379Z", "shell.execute_reply": "2026-09-02T20:59:26.069862Z" } }, "outputs": [ { "data": { "text/html": [ "
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nameheightmasshair_colorskin_coloreye_colorbirth_yearsexgenderhomeworldspeciesfilmsvehiclesstarships
0Luke Skywalker172.077.0blondfairblue19.0malemasculineTatooineHumanA New Hope, The Empire Strikes Back, Return of...Snowspeeder, Imperial Speeder BikeX-wing, Imperial shuttle
1C-3PO167.075.0NaNgoldyellow112.0nonemasculineTatooineDroidA New Hope, The Empire Strikes Back, Return of...NaNNaN
2R2-D296.032.0NaNwhite, bluered33.0nonemasculineNabooDroidA New Hope, The Empire Strikes Back, Return of...NaNNaN
3Darth Vader202.0136.0nonewhiteyellow41.9malemasculineTatooineHumanA New Hope, The Empire Strikes Back, Return of...NaNTIE Advanced x1
4Leia Organa150.049.0brownlightbrown19.0femalefeminineAlderaanHumanA New Hope, The Empire Strikes Back, Return of...Imperial Speeder BikeNaN
.............................................
82FinnNaNNaNblackdarkdarkNaNmalemasculineNaNHumanThe Force AwakensNaNNaN
83ReyNaNNaNbrownlighthazelNaNfemalefeminineNaNHumanThe Force AwakensNaNNaN
84Poe DameronNaNNaNbrownlightbrownNaNmalemasculineNaNHumanThe Force AwakensNaNX-wing
85BB8NaNNaNnonenoneblackNaNnonemasculineNaNDroidThe Force AwakensNaNNaN
86Captain PhasmaNaNNaNnonenoneunknownNaNfemalefeminineNaNHumanThe Force AwakensNaNNaN
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" ], "text/plain": [ " name height mass hair_color skin_color eye_color \\\n", "0 Luke Skywalker 172.0 77.0 blond fair blue \n", "1 C-3PO 167.0 75.0 NaN gold yellow \n", "2 R2-D2 96.0 32.0 NaN white, blue red \n", "3 Darth Vader 202.0 136.0 none white yellow \n", "4 Leia Organa 150.0 49.0 brown light brown \n", ".. ... ... ... ... ... ... \n", "82 Finn NaN NaN black dark dark \n", "83 Rey NaN NaN brown light hazel \n", "84 Poe Dameron NaN NaN brown light brown \n", "85 BB8 NaN NaN none none black \n", "86 Captain Phasma NaN NaN none none unknown \n", "\n", " birth_year sex gender homeworld species \\\n", "0 19.0 male masculine Tatooine Human \n", "1 112.0 none masculine Tatooine Droid \n", "2 33.0 none masculine Naboo Droid \n", "3 41.9 male masculine Tatooine Human \n", "4 19.0 female feminine Alderaan Human \n", ".. ... ... ... ... ... \n", "82 NaN male masculine NaN Human \n", "83 NaN female feminine NaN Human \n", "84 NaN male masculine NaN Human \n", "85 NaN none masculine NaN Droid \n", "86 NaN female feminine NaN Human \n", "\n", " films \\\n", "0 A New Hope, The Empire Strikes Back, Return of... \n", "1 A New Hope, The Empire Strikes Back, Return of... \n", "2 A New Hope, The Empire Strikes Back, Return of... \n", "3 A New Hope, The Empire Strikes Back, Return of... \n", "4 A New Hope, The Empire Strikes Back, Return of... \n", ".. ... \n", "82 The Force Awakens \n", "83 The Force Awakens \n", "84 The Force Awakens \n", "85 The Force Awakens \n", "86 The Force Awakens \n", "\n", " vehicles starships \n", "0 Snowspeeder, Imperial Speeder Bike X-wing, Imperial shuttle \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN TIE Advanced x1 \n", "4 Imperial Speeder Bike NaN \n", ".. ... ... \n", "82 NaN NaN \n", "83 NaN NaN \n", "84 NaN X-wing \n", "85 NaN NaN \n", "86 NaN NaN \n", "\n", "[87 rows x 14 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(DF)\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "b8ce2965-1253-4593-85c5-eb187fd2d028", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.072219Z", "iopub.status.busy": "2026-09-02T20:59:26.072045Z", "iopub.status.idle": "2026-09-02T20:59:26.080905Z", "shell.execute_reply": "2026-09-02T20:59:26.080534Z" } }, "outputs": [ { "data": { "text/html": [ "
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nameheightmasshair_colorskin_coloreye_colorbirth_yearsexgenderhomeworldspeciesfilmsvehiclesstarships
0Luke Skywalker172.077.0blondfairblue19.0malemasculineTatooineHumanA New Hope, The Empire Strikes Back, Return of...Snowspeeder, Imperial Speeder BikeX-wing, Imperial shuttle
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" ], "text/plain": [ " name height mass hair_color skin_color eye_color birth_year \\\n", "0 Luke Skywalker 172.0 77.0 blond fair blue 19.0 \n", "\n", " sex gender homeworld species \\\n", "0 male masculine Tatooine Human \n", "\n", " films \\\n", "0 A New Hope, The Empire Strikes Back, Return of... \n", "\n", " vehicles starships \n", "0 Snowspeeder, Imperial Speeder Bike X-wing, Imperial shuttle " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observation_name = 'Luke Skywalker'\n", "# Select just the row where the name equals Luke Skywalker\n", "DF[DF['name']==observation_name]\n" ] }, { "cell_type": "markdown", "id": "c048f99a-1aa0-4746-a163-9ce27a746a55", "metadata": {}, "source": [ "**Q1** How would you update the cell above to search for a different character? Make a change to the code above to find information about C-3PO." ] }, { "cell_type": "code", "execution_count": 5, "id": "26747497-aee3-4c19-bd39-fbcadae31504", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.082361Z", "iopub.status.busy": "2026-09-02T20:59:26.082199Z", "iopub.status.idle": "2026-09-02T20:59:26.087922Z", "shell.execute_reply": "2026-09-02T20:59:26.087549Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "dc718850-25a9-45eb-8c24-a993782747fb", "metadata": {}, "source": [ "**Q2** In the data for C-3PO one of the entries says NaN. What does this mean?\n", "\n", "[Who is C-3PO?](https://www.starwars.com/databank/c-3po)" ] }, { "cell_type": "markdown", "id": "00567e47", "metadata": {}, "source": [ "**(Double Click Here)** to answer the question." ] }, { "cell_type": "markdown", "id": "8ec6d294-4f57-408f-bcd2-273d252ee757", "metadata": {}, "source": [ "**Q3** Look at some observations for other characters. Just choose a few different names and see what their data says." ] }, { "cell_type": "code", "execution_count": 6, "id": "75b788ec-3580-45ac-a08b-05e0fb1dc678", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.089472Z", "iopub.status.busy": "2026-09-02T20:59:26.089311Z", "iopub.status.idle": "2026-09-02T20:59:26.096096Z", "shell.execute_reply": "2026-09-02T20:59:26.095758Z" } }, "outputs": [], "source": [ "# Your code here - or you can just rerun the code cell above.\n" ] }, { "cell_type": "markdown", "id": "1cc4bd94-18db-4006-8e15-75cb1caf711e", "metadata": {}, "source": [ "**Q4** What happens if you spell a name wrong or forget to capitalize?" ] }, { "cell_type": "markdown", "id": "0f448205-202b-4131-98cb-de6c37100667", "metadata": {}, "source": [ "**(Double Click Here)**" ] }, { "cell_type": "markdown", "id": "bd8255c0-9d90-402e-9017-64a382e2b5be", "metadata": {}, "source": [ "**Q5** Check out each of the variables (columns):\n", "\n", "1. What does each column tell you?\n", "2. Is the data a word or a number or something else?\n", "3. If the data is a number, what are the units?\n", "\n", "-----------------------------------------" ] }, { "cell_type": "markdown", "id": "88a25285-8c62-48c4-8ef4-3031b97c93a9", "metadata": {}, "source": [ "**(Double Click Here)**" ] }, { "cell_type": "markdown", "id": "08f9ffc9-e880-448b-bc5f-30d7c7f973d0", "metadata": {}, "source": [ "### Exploratory Data Analysis (EDA)\n", "\n", "1. Summary Statistics\n", "2. Visualization (Today's Class)\n", "3. Data Wrangling (Future Class)\n", "\n", "#### Graph Mass vs. Height\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "6a2d9c12-6ba9-4c3a-bc6a-1971c01c263b", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.097563Z", "iopub.status.busy": "2026-09-02T20:59:26.097410Z", "iopub.status.idle": "2026-09-02T20:59:26.258977Z", "shell.execute_reply": "2026-09-02T20:59:26.258527Z" } }, "outputs": [ { "data": { "text/html": [ " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(DF,\n", " x='height',\n", " y='mass',\n", " title='Mass vs. Height of Starwars Characters')\n", "fig.show()\n" ] }, { "cell_type": "markdown", "id": "69e2e95d-f2b6-478a-8abf-199182396a99", "metadata": {}, "source": [ "**Q6** What do you notice about this graph? Are there any strange data points?" ] }, { "cell_type": "markdown", "id": "eaa97e0d-0691-40f3-9cde-d1acd4536efa", "metadata": {}, "source": [ "**(Double Click Here)**" ] }, { "cell_type": "code", "execution_count": 8, "id": "40945f6e-1a32-4c95-9a56-686a09db83f2", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.260463Z", "iopub.status.busy": "2026-09-02T20:59:26.260297Z", "iopub.status.idle": "2026-09-02T20:59:26.313506Z", "shell.execute_reply": "2026-09-02T20:59:26.313068Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Let's add some data so when we hover over the point we can see the characters name:\n", "fig = px.scatter(DF,\n", " x='height',\n", " y='mass',\n", " title='Mass vs. Height of Starwars Characters.',\n", " hover_data='name')\n", "fig.show()\n" ] }, { "cell_type": "markdown", "id": "4e1fc345-9c53-4fd9-8c85-20aacb27eb28", "metadata": {}, "source": [ "#### You try birth_year vs mass\n", "\n", "**Q7** See if you can figure out how to make a plot of the birth_year vs mass with the hover data being the name?" ] }, { "cell_type": "code", "execution_count": 9, "id": "8c796598-5da7-454b-b1c8-f0d7342aae70", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.314966Z", "iopub.status.busy": "2026-09-02T20:59:26.314805Z", "iopub.status.idle": "2026-09-02T20:59:26.316961Z", "shell.execute_reply": "2026-09-02T20:59:26.316546Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "0548a2d8-0708-4b97-8150-f3be3f1f0f6d", "metadata": {}, "source": [ "---------------------------\n", "--------------------------\n", "\n", "## Why Visualize?!?!\n", "\n", "Load the data for Anscombe's Quartet" ] }, { "cell_type": "code", "execution_count": 10, "id": "b9bc6271-6f86-4a1c-8e72-447da749a226", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.318326Z", "iopub.status.busy": "2026-09-02T20:59:26.318175Z", "iopub.status.idle": "2026-09-02T20:59:26.520008Z", "shell.execute_reply": "2026-09-02T20:59:26.518117Z" } }, "outputs": [], "source": [ "# Load the data\n", "#| label: Download data\n", "#| warning: false\n", "\n", "file_location = 'https://joannabieri.com/introdatascience/data/Anscombe_quartet_data.csv'\n", "DF_new = pd.read_csv(file_location)\n", "\n", "# Some code to make the data frame look nicer\n", "DF_new = DF_new.drop(['x4'], axis=1)\n", "DF_new.rename(columns={'x123':'x'}, inplace=True)\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "ba7e5222-17ca-4963-8d9a-13c65afdacef", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.525748Z", "iopub.status.busy": "2026-09-02T20:59:26.525053Z", "iopub.status.idle": "2026-09-02T20:59:26.549395Z", "shell.execute_reply": "2026-09-02T20:59:26.548547Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x y1 y2 y3 y4\n", "0 10.0 8.04 9.14 7.46 6.58\n", "1 8.0 6.95 8.14 6.77 5.76\n", "2 13.0 7.58 8.74 12.74 7.71\n", "3 9.0 8.81 8.77 7.11 8.84\n", "4 11.0 8.33 9.26 7.81 8.47\n", "5 14.0 9.96 8.10 8.84 7.04\n", "6 6.0 7.24 6.13 6.08 5.25\n", "7 4.0 4.26 3.10 5.39 12.50\n", "8 12.0 10.84 9.13 8.15 5.56\n", "9 7.0 4.82 7.26 6.42 7.91\n", "10 5.0 5.68 4.74 5.73 6.89" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(DF_new)\n" ] }, { "cell_type": "markdown", "id": "23bdb68f-b1f3-4a37-8ac9-7c04bfeaddf1", "metadata": {}, "source": [ "#### Summary Statistics\n", "\n", "Pandas can do all sorts of statistics for us really quickly using the .describe() function!" ] }, { "cell_type": "code", "execution_count": 12, "id": "fb0a1b3b-7b5f-4dc4-82d5-8d747ff5fbff", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.551935Z", "iopub.status.busy": "2026-09-02T20:59:26.551666Z", "iopub.status.idle": "2026-09-02T20:59:26.578775Z", "shell.execute_reply": "2026-09-02T20:59:26.578249Z" } }, "outputs": [ { "data": { "text/html": [ "
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max14.00000010.8400009.26000012.74000012.500000
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" ], "text/plain": [ " x y1 y2 y3 y4\n", "count 11.000000 11.000000 11.000000 11.000000 11.000000\n", "mean 9.000000 7.500909 7.500909 7.500000 7.500909\n", "std 3.316625 2.031568 2.031657 2.030424 2.030579\n", "min 4.000000 4.260000 3.100000 5.390000 5.250000\n", "25% 6.500000 6.315000 6.695000 6.250000 6.170000\n", "50% 9.000000 7.580000 8.140000 7.110000 7.040000\n", "75% 11.500000 8.570000 8.950000 7.980000 8.190000\n", "max 14.000000 10.840000 9.260000 12.740000 12.500000" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DF_new.describe()\n" ] }, { "cell_type": "markdown", "id": "76a59090-a87f-4684-a2f3-020712926be9", "metadata": {}, "source": [ "#### Visualization\n", "\n", "Make a scatter plot for each of the y-values. Here is the first one:" ] }, { "cell_type": "code", "execution_count": 13, "id": "45f2d0eb-2144-41d9-81b9-2f21c2b8f66e", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.580519Z", "iopub.status.busy": "2026-09-02T20:59:26.580303Z", "iopub.status.idle": "2026-09-02T20:59:26.629948Z", "shell.execute_reply": "2026-09-02T20:59:26.629504Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(DF_new,x='x',y='y1',title='x vs y1')\n", "fig.show()\n" ] }, { "cell_type": "markdown", "id": "31da2430-beba-43f4-8443-336201ed777b", "metadata": {}, "source": [ "**Q8** Now you make the other three plots! What do you notice? Are they all the same?" ] }, { "cell_type": "code", "execution_count": 14, "id": "06399ea8-da0c-47ce-a945-6b5448c66933", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.631399Z", "iopub.status.busy": "2026-09-02T20:59:26.631239Z", "iopub.status.idle": "2026-09-02T20:59:26.633691Z", "shell.execute_reply": "2026-09-02T20:59:26.633216Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "97f1144f-71a4-470d-8baa-6eec5531088c", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.635007Z", "iopub.status.busy": "2026-09-02T20:59:26.634858Z", "iopub.status.idle": "2026-09-02T20:59:26.642629Z", "shell.execute_reply": "2026-09-02T20:59:26.642226Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "5246cc9e-a2e2-4b08-bcb5-8606a55de6cc", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.643924Z", "iopub.status.busy": "2026-09-02T20:59:26.643777Z", "iopub.status.idle": "2026-09-02T20:59:26.651716Z", "shell.execute_reply": "2026-09-02T20:59:26.651309Z" } }, "outputs": [], "source": [ "# Your code here:\n" ] }, { "cell_type": "markdown", "id": "e7d3cdce-d876-43ea-ba33-8c58479a4623", "metadata": {}, "source": [ "**(Double Click Here)** to answer the questions about the graphs" ] }, { "cell_type": "markdown", "id": "95998a5e-8f7b-442c-bfe4-9281e4b01fe3", "metadata": {}, "source": [ "### Problem 1\n", "\n", "Let's try to make an even fancier graph of the Star Wars height vs mass data. \n", "\n", "Below you will find code that changes our Star Wars graph in the following ways:\n", "\n", "1. It changes the marker size to 15\n", "2. It changes the marker color to red\n", "3. It changes the marker symbol to be a star\n", "4. It makes the markers see through (opacity)\n", "5. It puts a line around the marker with a width and color given.\n", "\n", "Now your job is to make this plot your own. Change the colors, markers, etc.\n", "\n", "Below you will see a list of all possible colors and markers that are automatically part of plotly." ] }, { "cell_type": "code", "execution_count": 17, "id": "079045c1-84fe-4412-b273-db55e441790f", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.653125Z", "iopub.status.busy": "2026-09-02T20:59:26.652977Z", "iopub.status.idle": "2026-09-02T20:59:26.711775Z", "shell.execute_reply": "2026-09-02T20:59:26.711337Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(DF,\n", " x='height',\n", " y='mass',\n", " title='Mass vs. Height of Starwars Characters.',\n", " hover_data='name')\n", "fig.update_traces(marker={'size': 15,\n", " 'color' : 'red', \n", " 'symbol' : 'star',\n", " 'opacity' : 0.5,\n", " 'line':{'width':2,'color':'DarkSlateGrey'}})\n", "fig.show()\n" ] }, { "cell_type": "markdown", "id": "b9fea077-a50f-4ef0-b61f-7a7443e719b6", "metadata": {}, "source": [ "![Plotly Available Colors](images/colors.png)\n", "\n", "![Plotly Available Symbols](images/symbols.png)" ] }, { "cell_type": "markdown", "id": "85734f55-c856-41bc-8279-fdf7057df538", "metadata": {}, "source": [ "### Problem 2\n", "\n", "Interacting with new types of plots! Over the course of the semester we will learn how to make all sorts of fancy plots. Below is code that makes a histogram of the different hair colors for the Star Wars characters. \n", "\n", "A. Figure out what the code does.\n", "\n", "B. Change the code:\n", "* New variable (x)\n", "* New color\n", "* New Title" ] }, { "cell_type": "code", "execution_count": 18, "id": "f7377484-3bce-4632-86cd-a48692a37684", "metadata": { "execution": { "iopub.execute_input": "2026-09-02T20:59:26.713318Z", "iopub.status.busy": "2026-09-02T20:59:26.713148Z", "iopub.status.idle": "2026-09-02T20:59:26.758817Z", "shell.execute_reply": "2026-09-02T20:59:26.758390Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.histogram(DF,\n", " x='hair_color',\n", " labels={'hair_color':'Hair Color'},\n", " opacity=0.8,\n", " color_discrete_sequence=['lightseagreen'])\n", "fig.update_layout(bargap=0.1, title=\n", " 'Histogram Hair Color in the Star Wars Universe',\n", " title_x=0.5)\n", "fig.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7ca844d4-d44b-4052-9da6-4250ebd46bdc", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "83faf564-3892-46f3-a7a4-8009e1c78a78", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }