diff --git a/data/comments_temp.csv b/data/week1/comments_temp.csv similarity index 100% rename from data/comments_temp.csv rename to data/week1/comments_temp.csv diff --git a/data/submissions_temp.csv b/data/week1/submissions_temp.csv similarity index 100% rename from data/submissions_temp.csv rename to data/week1/submissions_temp.csv diff --git a/personal_albert/week2/.ipynb_checkpoints/Week2-checkpoint.ipynb b/personal_albert/week2/.ipynb_checkpoints/Week2-checkpoint.ipynb new file mode 100644 index 0000000..228e676 --- /dev/null +++ b/personal_albert/week2/.ipynb_checkpoints/Week2-checkpoint.ipynb @@ -0,0 +1,914 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Overview" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This week we are going to learn a bit about __Data Visualization__, which is an important aspect in Computational Social Science. Why is it so important to make nice plots if we can use stats and modelling? I hope I will convince that it is _very_ important to make meaningful visualizations. In the second part of the class, we will learn how to produce some beautiful figures using the data you downloaded last week. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the plan:\n", + "\n", + "* __Part 1__: Some talking from me on __why do we even care about visualizing data__. \n", + "* __Part 2__: Here is where you convince yourself that data visualization is useful by doing a __little visualization exercise__.\n", + "* __Part 3__: We will look at the relation between the attention to GME on Reddit and the evolution of the GME market indicators.\n", + "* __Part 4__: We will visualize the activity of Redditors posting about GME.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1: Intro to visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start by watching this short introduction video to Data Visualization.\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> * _Video Lecture_: [Intro to Data Visualization](https://www.youtube.com/watch?v=oLSdlg3PUO0)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo(\"oLSdlg3PUO0\",width=800, height=450)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2: A little visualization exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ok, but is data visualization really so necessary? Let's see if I can convince you of that with this little visualization exercise." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "> *Exercise 1: Visualization vs stats*\n", + "> \n", + "> Start by downloading these four datasets: [Data 1](https://raw.githubusercontent.com/suneman/socialdataanalysis2020/master/files/data1.tsv), [Data 2](https://raw.githubusercontent.com/suneman/socialdataanalysis2020/master/files/data2.tsv), [Data 3](https://raw.githubusercontent.com/suneman/socialdataanalysis2020/master/files/data3.tsv), and [Data 4](https://raw.githubusercontent.com/suneman/socialdataanalysis2020/master/files/data4.tsv). The format is `.tsv`, which stands for _tab separated values_. \n", + "> Each file has two columns (separated using the tab character). The first column is $x$-values, and the second column is $y$-values. \n", + "> \n", + "> * Using the `numpy` function `mean`, calculate the mean of both $x$-values and $y$-values for each dataset. \n", + "> * Use python string formatting to print precisely two decimal places of these results to the output cell. Check out [this _stackoverflow_ page](http://stackoverflow.com/questions/8885663/how-to-format-a-floating-number-to-fixed-width-in-python) for help with the string formatting. \n", + "> * Now calculate the variance for all of the various sets of $x$- and $y$-values (to three decimal places).\n", + "> * Use [`scipy.stats.pearsonr`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html) to calculate the [Pearson correlation](https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient) between $x$- and $y$-values for all four data sets (also to three decimal places).\n", + "> * The next step is use _linear regression_ to fit a straight line $f(x) = a x + b$ through each dataset and report $a$ and $b$ (to two decimal places). An easy way to fit a straight line in Python is using `scipy`'s `linregress`. It works like this\n", + "> ```\n", + "> from scipy import stats\n", + "> slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)\n", + ">```\n", + "> * Finally, it's time to plot the four datasets using `matplotlib.pyplot`. Use a two-by-two [`subplot`](http://matplotlib.org/examples/pylab_examples/subplot_demo.html) to put all of the plots nicely in a grid and use the same $x$ and $y$ range for all four plots. And include the linear fit in all four plots. (To get a sense of what I think the plot should look like, you can take a look at my version [here](https://raw.githubusercontent.com/suneman/socialdataanalysis2017/master/files/anscombe.png).)\n", + "> * Explain - in your own words - what you think my point with this exercise is.\n", + "\n", + "\n", + "Get more insight in the ideas behind this exercise by reading [here](https://en.wikipedia.org/wiki/Anscombe%27s_quartet).\n", + "\n", + "And the [video below](https://www.youtube.com/watch?v=DbJyPELmhJc) generalizes in the coolest way imaginable. It's a treat, but don't watch it until **after** you've done the exercises.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 y1 x2 y2 x3 y3 x4 y4\n", + "0 10 8.04 10 9.14 10 7.46 8 6.58\n", + "1 8 6.95 8 8.14 8 6.77 8 5.76\n", + "2 13 7.58 13 8.74 13 12.74 8 7.71\n", + "3 9 8.81 9 8.77 9 7.11 8 8.84\n", + "4 11 8.33 11 9.26 11 7.81 8 8.47\n", + "5 14 9.96 14 8.10 14 8.84 8 7.04\n", + "6 6 7.24 6 6.13 6 6.08 8 5.25\n", + "7 4 4.26 4 3.10 4 5.39 19 12.50\n", + "8 12 10.84 12 9.13 12 8.15 8 5.56\n", + "9 7 4.82 7 7.26 7 6.42 8 7.91\n", + "10 5 5.68 5 4.74 5 5.73 8 6.89" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# exercise 1\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from tqdm.notebook import tqdm\n", + "\n", + "# specify url for retrieval of data\n", + "data = ['data1', 'data2', 'data3', 'data4']\n", + "url_path = 'https://raw.githubusercontent.com/suneman/socialdataanalysis2020/master/files/'\n", + "\n", + "# retrieved data using pandas + url\n", + "df = pd.DataFrame()\n", + "for i, data_name in enumerate(data):\n", + " data_i = pd.read_csv(url_path+data_name+'.tsv', sep='\\t', header=None, names=[f'x{i+1}', f'y{i+1}'])\n", + " df[[f'x{i+1}',f'y{i+1}']] = data_i\n", + "\n", + "# show dataframe\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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x1y1x2y2x3y3x4y4
count11.00011.00011.00011.00011.00011.0011.00011.000
mean9.0007.5019.0007.5019.0007.509.0007.501
std3.3172.0323.3172.0323.3172.033.3172.031
min4.0004.2604.0003.1004.0005.398.0005.250
25%6.5006.3156.5006.6956.5006.258.0006.170
50%9.0007.5809.0008.1409.0007.118.0007.040
75%11.5008.57011.5008.95011.5007.988.0008.190
max14.00010.84014.0009.26014.00012.7419.00012.500
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" + ], + "text/plain": [ + " x1 y1 x2 y2 x3 y3 x4 y4\n", + "count 11.000 11.000 11.000 11.000 11.000 11.00 11.000 11.000\n", + "mean 9.000 7.501 9.000 7.501 9.000 7.50 9.000 7.501\n", + "std 3.317 2.032 3.317 2.032 3.317 2.03 3.317 2.031\n", + "min 4.000 4.260 4.000 3.100 4.000 5.39 8.000 5.250\n", + "25% 6.500 6.315 6.500 6.695 6.500 6.25 8.000 6.170\n", + "50% 9.000 7.580 9.000 8.140 9.000 7.11 8.000 7.040\n", + "75% 11.500 8.570 11.500 8.950 11.500 7.98 8.000 8.190\n", + "max 14.000 10.840 14.000 9.260 14.000 12.74 19.000 12.500" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# describing the dataframe with 3 digits\n", + "np.round(df.describe(), 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean value of x1 = 9.00\n", + "Mean value of y1 = 7.50\n", + "\n", + "Mean value of x2 = 9.00\n", + "Mean value of y2 = 7.50\n", + "\n", + "Mean value of x3 = 9.00\n", + "Mean value of y3 = 7.50\n", + "\n", + "Mean value of x4 = 9.00\n", + "Mean value of y4 = 7.50\n" + ] + } + ], + "source": [ + "# loop through columns in dataframe\n", + "for i, column in enumerate(df):\n", + " if np.logical_and(i > 0, i % 2 == 0): # simply for better overview\n", + " print(\"\")\n", + " \n", + " # compute and print mean value with 2 decimal places \n", + " print('Mean value of {:s} = {:.2f}'.format(column, df[column].mean())) " + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean value of x1 = 11.000\n", + "Mean value of y1 = 4.127\n", + "\n", + "Mean value of x2 = 11.000\n", + "Mean value of y2 = 4.128\n", + "\n", + "Mean value of x3 = 11.000\n", + "Mean value of y3 = 4.123\n", + "\n", + "Mean value of x4 = 11.000\n", + "Mean value of y4 = 4.123\n" + ] + } + ], + "source": [ + "# loop through columns in dataframe\n", + "for i, column in enumerate(df):\n", + " if np.logical_and(i > 0, i % 2 == 0): # simply for better overview\n", + " print(\"\")\n", + " \n", + " # compute and print variance value with 3 decimal places \n", + " print('Mean value of {:s} = {:.3f}'.format(column, df[column].var())) " + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import pearsonr\n", + "from scipy import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation between x0 and y0 = 0.816 (p-value = 0.002)\n", + "Characteristics from linear regression: a = 0.500, b= 3.000\n", + "\n", + "Correlation between x1 and y1 = 0.816 (p-value = 0.002)\n", + "Characteristics from linear regression: a = 0.500, b= 3.001\n", + "\n", + "Correlation between x2 and y2 = 0.816 (p-value = 0.002)\n", + "Characteristics from linear regression: a = 0.500, b= 3.002\n", + "\n", + "Correlation between x3 and y3 = 0.817 (p-value = 0.002)\n", + "Characteristics from linear regression: a = 0.500, b= 3.002\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# setup figure\n", + "fig, axs = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True, figsize=((10,8)))\n", + "\n", + "# loop through x/y-pairs\n", + "for i in range(4):\n", + " # compute correlation coef.\n", + " x, y = df.iloc[:, 2*i], df.iloc[:, 2*i+1]\n", + " corr, p = pearsonr(x, y)\n", + " print('Correlation between x{:d} and y{:d} = {:.3f} (p-value = {:.3f})'.format(i, i, corr, p)) \n", + " \n", + " # fit linear regression\n", + " res = stats.linregress(x,y)\n", + " slope, intercept, r_value, p_value, std_err = res\n", + " \n", + " print('Characteristics from linear regression: a = {:.3f}, b= {:.3f}\\n'.format(slope, intercept)) \n", + "\n", + " \n", + " # add subplot to figure\n", + " plot_x = np.linspace(x.min()-3, x.max()+2, x.__len__()) # for a longer regression line\n", + " axs[i//2, i%2].scatter(x,y)\n", + " axs[i//2, i%2].plot(plot_x, intercept + slope*plot_x, 'k', label='fitted line')\n", + " axs[i//2, i%2].set_title(f'(x{i+1}, y{i+1})')\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Comment:**\n", + "\n", + "The idea of the exercise is to show that visualizations *do* in fact have the ability to tell a different story about the data than statistics. This is because all four data sets have identical statistical properties (like mean, variance and correlation) yet look completely different when visualizing the data. This is known as Anscombe's Quartet." + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo(\"DbJyPELmhJc\",width=800, height=450)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prelude to Part 3: Some tips to make nicer figures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before even starting visualizing some cool data, I just want to give a few tips for making nice plots in matplotlib. Unless you are already a pro-visualizer, those should be pretty useful to make your plots look much nicer. \n", + "Paying attention to details can make an incredible difference when we present our work to others." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> *Video Lecture*: [How to improve your plots](https://www.youtube.com/watch?v=sdszHGaP_ag)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo(\"sdszHGaP_ag\",width=800, height=450)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 3: Visualizing trends: activity on Reddit and GME prices." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's really time to put into practice what we learnt by plotting some data! We will start by looking at the time series describing the number of comments about GME in wallstreetbets over time. Using exploratory data visualization, we will try to answer the folling research question:\n", + "\n", + "*Is the activity on wallstreetbet related to the price of the GME stock?*\n", + "\n", + " We will use two datasets today: \n", + " * the *GME market data*, that you can download from [here](https://finance.yahoo.com/quote/GME/history/). Make sure to select the same time period as the reddit data you downloaded in Week1.\n", + " * the dataset you downloaded in Week1, Exercise 2. We will refer to this as the _comments dataset_." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> *Reading*: [Section 14.1 of the Data Visualization book](https://clauswilke.com/dataviz/visualizing-trends.html). Start by reading about \"visualizing trends\" in the Data Visualization Book. We will use moving averages, so you can skip the part on LOESS. \n", + ">\n", + "> *Reading*: [Sections 3.1 and 3.2 of the Data Visualization book](https://clauswilke.com/dataviz/coordinate-systems-axes.html#cartesian-coordinates). Learn about non-linear axes to better visualize hetereogeneous data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> _Exercise 2 : Plotting prices and comments using line-graphs._\n", + "> 1. Plot the daily volume of the GME stock over time using the _GME market data_. On top of the daily data, plot the rolling average, using a 7 days window (you can use the function [``pd.rolling``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html)). Use a [log-scale on the y-axis](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.yscale.html).\n", + "> 2. Now make a second plot where you plot the total number of comments on Reddit per day. Follow the same steps you followed in step 1.\n", + "> 3. What is the advantage of using the log-scale on the y-axis? What is the advantage of using a rolling-window?\n", + "> 3. Now take a minute to __look at these two figures__. Then write in a couple of lines: What are the three most important observations you can draw by looking at the figures?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 4: Visualizing associations: GME returns vs volume of comments" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will continue by studying more in detail the association between GME market indicators and the attention to the topic on Reddit. \n", + "First, we will create the time-series of daily [returns](https://en.wikipedia.org/wiki/Price_return). Returns measure the change in price given two given points in time (in our case we will focus on consecutive days). They constitute a quantity of interest when it comes to stock time-series, because they tell us how much _profit_ one would make if he/she bought the stock on a given day and sold it at a later time. For consistency, we will also compute returns (corresponding to daily changes) for the number of Reddit comments over time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "> *Reading*: [Sections 12.1 and 12.2 of the Data Visualization book](https://clauswilke.com/dataviz/visualizing-associations.html). Learn about visualizing and measuring associations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> _Exercise 3 : Returns vs number of comments using scatter-plots_.\n", + "> In this exercise, we will look at the association between GME market indicators and the volume of comments on Reddit. \n", + "> 1. Compute the daily log-returns as ``np.log(Close_price(t)/Close_price(t-1))``, where ``Close_price(t)`` is the Close Price of GME on day t. You can use the function [pd.Series.shift](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.shift.html). Working with log-returns instead of regular returns is a standard thing to do in economics, if you are interested in why, check out [this blog post](https://quantivity.wordpress.com/2011/02/21/why-log-returns/).\n", + "> 2. Compute the daily log-change in number of new comments as ``np.log(comments(t)/comments(t-1))`` where ``comments(t)`` is the number of comments on day t. \n", + "> 3. Compute the correlation coefficient (find the formula in the Data Visualization book, section 12.2) between the series computed in step 1 and step 2 (note that you need to first remove days without any comments from the time-series). Is the correlation statistically significant? **Hint:** check the [Wikipedia page](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) of the Pearson correlation and its [scipy implementation](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.pearsonr.html).\n", + "> 4. Make a [scatter plot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html) of the daily log-return on investment for the GME stock against the daily log-change in number of comments. Color the markers for 2020 and 2021 in different colors, and make the marker size proportional to the Close price. \n", + "> 5. Now take a minute to __look at the figure you just prepared__. Then write in a couple of lines: What are the three most salient observations you can draw by looking at it? \n", + "> 6. Based on the exploratory data visualization in Exercises 2 and 3, what can you conclude on the research question: *Is the activity on wallstreetbet related to the price of the GME stock?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 4 : Visualizing distributions: the activity of redditors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*But do all redditors played the same role in the GME saga?*\n", + "It is time to start looking at the activity of different redditors over time, and study the differences between them. First, I will show some tips and tricks to visualize distributions, then we will put things into practice by visualizing the distribution of key quantities describing redditors on wallstreetbets. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> *Video Lecture*: [Plotting histograms and distributions](https://www.youtube.com/watch?v=UpwEsguMtY4)\n", + ">\n", + "> *Reading*: [Section 7.1 of the Data Visualization book](https://clauswilke.com/dataviz/histograms-density-plots.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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u6TVhTN6vOzKvdJGSvXZKmnMfXutp1q1i3avWHV4t7cLbEMEQaN+PqnsJcTyJI25bnX1M5Ux+a1DlLNB1e3V09jbl8ssuldI50RBqiM/VB7TBEwPB2O2525k7fL9GNmGy61gMq/Dvs1a9W7H6NDaimZVjEMEzWyj6qw2MBvEO/Yd3PfOj6NmST5KS7afaZmsLUxdppZwSE1mSMdZEgcfrHl/F3ciPauUV8PEZaTEb31pvfpvZiKsOI/u/RGn9JeoK9aldvYSpFUy12jDA+K898leO5K1o9JjLN+Msdu1w2G4AIHEFuOjj/wAp9Y/7fC//AGxi1juijKzQ06tvUMlipiLdSejAaMTC5lSRrmNtSNdxzPDG8IJOw33IJ22nGm9K+h5TM5HrusGdfSf1PV8PUeiVhX24+I8fFtv3DZMTEwIoqii15jpf9VMxr2iSqrDimYjz3jdGOmvSNy7Pi8lTq1sk7Cus9bh7ruGK0yy2MF0bj6rZ2GIbcXLmO/bZ2kwevcbjsZK7E4Z9bJXM0KEuCd9Q5uWmj4vrXgFog6qPcOaACGgbN57TPpC0bbu2K2QxmSkxuRpRSQNkMYnrT15XBzop67/VPrAEO2/uaRHq3Q+59Oz6XkpZMxbyUOV+l4Y2xmC7XYY4HRQ/Z6prHObw8uTuW2w2uFi4M4VNOLOnTPeZz6THXdaKqOSIqn6+rfNpukO3fnqY8ajxFVr49S4tlJ1LITcAM7Zw6YFvrCWPhI4XbtPWAjuX3rnptkq5C5TpRY4x4lxjnfk7zq0tuZg3khpsYw7cJ9Xif3kHltzO+sdHWUuQRsymcN2avlqGQieKMUMTG0hKDAyKItDTJ1u5fzO7R3pqPoytuu3LWKyjcezLvEl6CahXuAT8IY6xVfMOKCRwHPblvz922qMThsortNr/AKrdO1/ksVYelXnZiO6VLt2bGQYXHw2HZvEyXmG7ZdCKr4rBhmbOY2u42MLHN9XmXFu2wXjW6W7tmtRgqYyOXN5C5kKjqjrJFSA4x4ZasOm4eJ0XrM2by73czwjilmK0MYMlj8h6W6U43DyY1zZImNfO6SVkpsvfHs0PJadwG8y7fdR09EckcTJKmTfVydPK5K/RyDK7HNZHkn8U1WaB7i2aLYAbn+r3cyFmmrhJyt9f3a9tGYnB2+vf7MTV/SrkcTXpwX6NGvlr89hoEl530bFWg6v/AAt8waX7PMoaI+/dr9zyG8g6HekducFuCRkDLmMcwTGnObFSeKUO6uevKWh3Du1wLSNwQOfPlgZHozv2Yak0+ZfJmsdPYkgyb6UDoDDaEYkpyUiOrfX+raR3EHcjvKkvR3pi1jmWHXbwv2LcoeXtqw1ooWhob1UEcTRwx8gdt9t9z3kk4xauG9jamI5vHfplpbvfslc4XJlr4+rM/UusMXjHMZkMhVpumY58TbU7Ii9jSA5zQ48wCR+q3DnBzOIEFrmbgjmCCNwR7wsLL4KlcLTbqVrJjBDDYgjlLQ7YkNLwdgdgs7qwG8DQGgN4QAOQG2wAHuC8c8totr1ccrRZwfod6N8NldK0ZLdOFtmaKyTkIwI7MTmWZ2xyiYbHdga3v5eqN1ptQcGU0EMtehisZOo2KnFk3xgzyQwZdldjxK71jxxl2/vL3n2qXYfobyUVNmKk1JZ+io2uY6lUpwVnPje9z5I3WBvKWPLnbgkjZxHdyU01boGC3gX4Kq4U4OrrsheGdZwCvYin5t4hxFxj5nfvcSvq1cXRGLzc9/8AK/XKM7/Hr0yeqcaIrve+d/dCH69wdOhkNIRUq0NWJ+ekkdHBG2NhldT4S8gfvENaN/uWz6Yv8s6Q/wDbM/8A9KVJekTRzcvVgjbYkqW8fZiuY+9E0OdXtQghryx3KSMhzgWHv3+5R/A6AyUmTq5POZVmQfi2yDH161VtWGJ8oDZJnNH2pCAP0C81GLRNMVVVZxFUWzvN72+u7nTXFomZ0ifnf+X3+0lclh05cETzH6VLUqyyNOxbBZtRRTc/YHMc5p+55UV6TtL0cEzTd7Gwx1rdTNUafWQtDX2a9lkjbEcxHOXcNJ3Pdu73rresNP18rRs4+00mC7EY38JAc07hzJGEjYPY9rXDf2tC59hui/Ivt0ZczmTk6uDcH46sKrID1jQGxy2XN/nZGtAG5JPI8+Z3vD41FGHETNrXvG94iPVzDriKc50v45PvpC0/p7FDKZjKxenS5hzY2wWQyaWSTqhFFToN4QWEhg22+zwkkgAleOkc9LpvSePOTa6S71Qgo0eP6+eWRzjVqAn7IawtBd3Naw+4BfWuei/J5LKtyYzbITUJGPruxsc8dRh234WTSOY+YkbmQt3Ow7gABJZOj+ter1WZ5sOYt02SMFyWEQcTZH8R2hhIYwkNYDsOfAFZxMPkpiurm0mdb5RaIi+Vt8/os1U8sRM33QPojt2KeV1NZy1pk9lmOxt66+Ih0cTRDbnkgrtBP1MLSGAD3c+Z3Oiweey1eabW2QxsNiheZHHGG2T6ZjcU6ThjdXh4Sx7fXa53MFxc4+qCdukaZ6JMXQvZCzDBC2vkaTKcdRkZaK8T2OZca1/GeJs3qEjYbFgUdd0P5N8DMRLnpJNPRSNLaBqxttOgjeJGVX2gOJ0YIHt25DkNgB3jHwKqpmZ15b5TGURnEW0n5d7N+0w5mZ93w8G16YY8LVqG+cVRv5PLPjgxsclWOSW5bmYGwF+44nRsYGucT+6wDcEhb3od0PHgscyA8Lrdk9ffma1reOd45taGgBsbPsgAbcvvX63RHHm2ZWzM2aHH02VsPS6vZtJzhtZmJJIklfwsAdy2AA/dBUzXgxMb8OMOmb9Z8o8Pr4OFVf8AjyxPv9dhEReZyEREBERAREQEREBERAREQEREGp1LqGtjhVNlzmjI36+Pr8DHP3s2iWwtdw/ZaS07uPILbLmX7REj4qeHsiC1YZj9UYi3ZZRqTW5m14JJHSyCGBrnuAHuHtHvXPukbNPv5avdnOroMRPg+LT8eEq5GrJ9NMt2I7Lb9eJgkjtcDavVi0BCWF5PIu3CxyKpkmX1DTw74Z49Qy29Q6Ew9PFOhivyvjzkb7kVrr5R/k+2BPA90kpYS1m+5LdlM7eCzz5NaX60+W+k6ULINPVpLM7afHNgaHpE1Su89TNOZxIGu5tbIw7bEu3CwC0urtTUcTDFYvTNgjtXalKJx/fs3JmwQt/Ddxc4/utY8nkFxDoPeRqKm2hJq2TF/wAm7vpp1IMiKwyptY4lsfp4B9K4BJxBv1fM9WecqzenLS2T1Vl3YyCnXfi8Bj5DM7KuuVq1nJ5avLDHNVfFC70h9OsS4PaS1slpwPrR8g7Hk9S1q+Qx+MkL/SsxFemqBrN4yzHtruscb9/UO1mLb38/ctyq3WbGorjtOyipYbnsLgtbYuexJWkFc5mvVx0FKyJpYxE6G0+Bs0bzsx4c7bk07RTourZ11bNtfltQMjl0zMLpdiNQOtVso9zQ2xX+krL3yZJg60OZSLWOadweJrCAt4iqXjbmT4NKTcGonej5CeuMc1+omx3YzlomfSTbs31kLBCHOFTJBzeoL2hw3DjkZd+SbftGaXV30+dZVeKOsL/0H/J8Ziua7mdUPRPQfQ+DfY9bx8fH6vW7haxaXNamq1LuOoTGQT5t9mOnwsLmF1WD0iUSOB9T1N9veVwPTuKz8E+Nv158y6/ksvrGnPDfsXJaEVaJmZkwrZa0+8VeD0mKm9khA3EgAJaQFpejirM/L6UfwarlvVfpN2opM3FkH1K2SlxsjD1UlpvVxyOkDw0wERuaGb+twgBbBY1q/BFJBFLNFHLce6OrFJKxkliRkb5nxwMcd5XiOOR5DdyGsce4FVVyeAz9fSen5xdznFlJYJdVS2n5i5bgayrIytC6tRlZegptkDWvbAQ7cRufxetvsMDpLI5GPRUmSsZqx6Nn8qxloDJ46eDGijcfUfZD5nTMJliYxk0zhI6KZrHc3OCCwNnW+KZYx9X02J82bsXK2P6jjsRzWMeHG5CZ4GuiikjLHtIe5p4mOb3ghSJVq6JNNXcZNpuONuWbDLqzVrsgyzJdljbA2LJxUpJxLuI45A2B4c7k97w8budubKoCIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIuVSZW/quSWPD3pMZp6tLLWsZ2mGnIZaxE50U8OGlkBZVoxPDmOu8LnPexwiAa3rHavBaen0/q3F0qGQydvHagxOVmyNLJ5GfICtNjX0upvQvsuc+J8jrgjcAdjv3chsHaUREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBRvpThfJgs1HFI6KSXDZJkcrDs6N7qczWSNI7nNJBB+5SRcs/abztmHDHFY4B+W1bKMNjmcXCGC2Ort2nuHOOKKBz/rB9l0kSDSdGXSvho8Hh8fgoLWau18NQYMViIesfWd6Oxm2SuSFtXHnrGvDnTyAkh2wceRn2hNO22T2MtlnwvyuQjZD1NYudVxlGNznxY+q94DpfXcZJZyGmV5HJrGRsbzn9h7TTsXpmaCaIR3PpzJx3wCHEWKc/oLmFw5EN9G2G3LvPtK7sgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIi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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "YouTubeVideo(\"UpwEsguMtY4\",width=800, height=450)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> _Exercise 4: Authors overall activity_. We will start by studying the distribution of comments per author. \n", + "> 1. Compute the total number of comments per author using the _comments dataset_. Then, make a histogram of the number of comments per author, using the function [``numpy.histogram``](https://numpy.org/doc/stable/reference/generated/numpy.histogram.html), using logarithmic binning. Here are some important points on histograms (they should be already quite clear if you have watched the video above):\n", + "> * __Binning__: By default numpy makes 10 equally spaced bins, but you always have to customize the binning. The number and size of bins you choose for your histograms can completely change the visualization. If you use too few bins, the histogram doesn't portray well the data. If you have too many, you get a broken comb look. Unfortunately is no \"best\" number of bins, because different bin sizes can reveal different features of the data. Play a bit with the binning to find a suitable number of bins. Define a vector $\\nu$ including the desired bins and then feed it as a parameter of numpy.histogram, by specifying _bins=$\\nu$_ as an argument of the function. You always have at least two options:\n", + "> * _Linear binning_: Use linear binning, when the data is not heavy tailed, by using ``np.linspace`` to define bins.\n", + "> * _Logarithmic binning_: Use logarithmic binning, when the data is [heavy tailed](https://en.wikipedia.org/wiki/Fat-tailed_distribution), by using ``np.logspace`` to define your bins.\n", + "> * __Normalization__: To plot [probability densities](https://en.wikipedia.org/wiki/Probability_density_function), you can set the argument _density=True_ of the ``numpy.histogram`` function.\n", + ">\n", + "> 3. Compute the mean and the median value of the number of comments per author and plot them as vertical lines on top of your histogram. What do you observe? Which value do you think is more meaningful?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> _Exercise 5: Authors lifespan_. We will now move on to study authors lifespan, using a two-dimensional histogram.\n", + ">\n", + "> 1. For each author, find the time of publication of their first comment, _minTime_, and the time of publication of their last comment, _maxTime_, in [unix timestamp](https://www.unixtimestamp.com/). \n", + "> 2. Compute the \"lifespan\" of authors as the difference between _maxTime_ and _minTime_. Note that timestamps are measured in seconds, but it is appropriate here to compute the lifespan in days. Make a histogram showing the distribution of lifespans, choosing appropriate binning. What do you observe?\n", + "> 3. Now, we will look at how many authors joined and abandoned the discussion on GME over time. First, use the numpy function [numpy.histogram2d](https://numpy.org/doc/stable/reference/generated/numpy.histogram2d.html) to create a 2-dimensional histogram for the two variables _minTime_ and _maxTime_. A 2D histogram, is nothing but a histogram where bins have two dimensions, as we look simultaneously at two variables. You need to specify two arrays of bins, one for the values along the x-axis (_minTime_) and the other for the values along the y-axis (_maxTime_). Choose bins with length 1 week.\n", + "> 4. Now, use the matplotlib function [``plt.imshow``](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.imshow.html) to visualize the 2d histogram. You can follow [this example](https://stackoverflow.com/questions/2369492/generate-a-heatmap-in-matplotlib-using-a-scatter-data-set) on StackOverflow. To show dates instead of unix timestamps in the x and y axes, use [``mdates.date2num``](https://matplotlib.org/api/dates_api.html#matplotlib.dates.date2num). More details in this [StackOverflow example](https://stackoverflow.com/questions/23139595/dates-in-the-xaxis-for-a-matplotlib-plot-with-imshow), see accepted answer.\n", + "> 5. Make sure that the colormap allows to well interpret the data, by passing ``norm=mpl.colors.LogNorm()`` as an argument to imshow. This will ensure that your colormap is log-scaled. Then, add a [colorbar](https://matplotlib.org/3.1.0/gallery/color/colorbar_basics.html) on the side of the figure, with the appropriate [colorbar label](https://matplotlib.org/3.1.1/api/colorbar_api.html#matplotlib.colorbar.ColorbarBase.set_label).\n", + "> 6. As usual :) Look at the figure, and write down three key observations.\n", + "> 7. Based on the data visualizations in Exercises 4 and 5, what can you conclude on the question: *Do all redditors played the same role in the GME saga?*\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/personal_albert/week2/Week2.ipynb b/personal_albert/week2/Week2.ipynb index 8fa9434..228e676 100644 --- a/personal_albert/week2/Week2.ipynb +++ b/personal_albert/week2/Week2.ipynb @@ -64,12 +64,11 @@ " src=\"https://www.youtube.com/embed/oLSdlg3PUO0\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", - " \n", " >\n", " " ], "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -126,7 +125,510 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 y1 x2 y2 x3 y3 x4 y4\n", + "0 10 8.04 10 9.14 10 7.46 8 6.58\n", + "1 8 6.95 8 8.14 8 6.77 8 5.76\n", + "2 13 7.58 13 8.74 13 12.74 8 7.71\n", + "3 9 8.81 9 8.77 9 7.11 8 8.84\n", + "4 11 8.33 11 9.26 11 7.81 8 8.47\n", + "5 14 9.96 14 8.10 14 8.84 8 7.04\n", + "6 6 7.24 6 6.13 6 6.08 8 5.25\n", + "7 4 4.26 4 3.10 4 5.39 19 12.50\n", + "8 12 10.84 12 9.13 12 8.15 8 5.56\n", + "9 7 4.82 7 7.26 7 6.42 8 7.91\n", + "10 5 5.68 5 4.74 5 5.73 8 6.89" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# exercise 1\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from tqdm.notebook import tqdm\n", + "\n", + "# specify url for retrieval of data\n", + "data = ['data1', 'data2', 'data3', 'data4']\n", + "url_path = 'https://raw.githubusercontent.com/suneman/socialdataanalysis2020/master/files/'\n", + "\n", + "# retrieved data using pandas + url\n", + "df = pd.DataFrame()\n", + "for i, data_name in enumerate(data):\n", + " data_i = pd.read_csv(url_path+data_name+'.tsv', sep='\\t', header=None, names=[f'x{i+1}', f'y{i+1}'])\n", + " df[[f'x{i+1}',f'y{i+1}']] = data_i\n", + "\n", + "# show dataframe\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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x1y1x2y2x3y3x4y4
count11.00011.00011.00011.00011.00011.0011.00011.000
mean9.0007.5019.0007.5019.0007.509.0007.501
std3.3172.0323.3172.0323.3172.033.3172.031
min4.0004.2604.0003.1004.0005.398.0005.250
25%6.5006.3156.5006.6956.5006.258.0006.170
50%9.0007.5809.0008.1409.0007.118.0007.040
75%11.5008.57011.5008.95011.5007.988.0008.190
max14.00010.84014.0009.26014.00012.7419.00012.500
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" + ], + "text/plain": [ + " x1 y1 x2 y2 x3 y3 x4 y4\n", + "count 11.000 11.000 11.000 11.000 11.000 11.00 11.000 11.000\n", + "mean 9.000 7.501 9.000 7.501 9.000 7.50 9.000 7.501\n", + "std 3.317 2.032 3.317 2.032 3.317 2.03 3.317 2.031\n", + "min 4.000 4.260 4.000 3.100 4.000 5.39 8.000 5.250\n", + "25% 6.500 6.315 6.500 6.695 6.500 6.25 8.000 6.170\n", + "50% 9.000 7.580 9.000 8.140 9.000 7.11 8.000 7.040\n", + "75% 11.500 8.570 11.500 8.950 11.500 7.98 8.000 8.190\n", + "max 14.000 10.840 14.000 9.260 14.000 12.74 19.000 12.500" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# describing the dataframe with 3 digits\n", + "np.round(df.describe(), 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean value of x1 = 9.00\n", + "Mean value of y1 = 7.50\n", + "\n", + "Mean value of x2 = 9.00\n", + "Mean value of y2 = 7.50\n", + "\n", + "Mean value of x3 = 9.00\n", + "Mean value of y3 = 7.50\n", + "\n", + "Mean value of x4 = 9.00\n", + "Mean value of y4 = 7.50\n" + ] + } + ], + "source": [ + "# loop through columns in dataframe\n", + "for i, column in enumerate(df):\n", + " if np.logical_and(i > 0, i % 2 == 0): # simply for better overview\n", + " print(\"\")\n", + " \n", + " # compute and print mean value with 2 decimal places \n", + " print('Mean value of {:s} = {:.2f}'.format(column, df[column].mean())) " + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean value of x1 = 11.000\n", + "Mean value of y1 = 4.127\n", + "\n", + "Mean value of x2 = 11.000\n", + "Mean value of y2 = 4.128\n", + "\n", + "Mean value of x3 = 11.000\n", + "Mean value of y3 = 4.123\n", + "\n", + "Mean value of x4 = 11.000\n", + "Mean value of y4 = 4.123\n" + ] + } + ], + "source": [ + "# loop through columns in dataframe\n", + "for i, column in enumerate(df):\n", + " if np.logical_and(i > 0, i % 2 == 0): # simply for better overview\n", + " print(\"\")\n", + " \n", + " # compute and print variance value with 3 decimal places \n", + " print('Mean value of {:s} = {:.3f}'.format(column, df[column].var())) " + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import pearsonr\n", + "from scipy import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation between x0 and y0 = 0.816 (p-value = 0.002)\n", + "Characteristics from linear regression: a = 0.500, b= 3.000\n", + "\n", + "Correlation between x1 and y1 = 0.816 (p-value = 0.002)\n", + "Characteristics from linear regression: a = 0.500, b= 3.001\n", + "\n", + "Correlation between x2 and y2 = 0.816 (p-value = 0.002)\n", + "Characteristics from linear regression: a = 0.500, b= 3.002\n", + "\n", + "Correlation between x3 and y3 = 0.817 (p-value = 0.002)\n", + "Characteristics from linear regression: a = 0.500, b= 3.002\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# setup figure\n", + "fig, axs = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True, figsize=((10,8)))\n", + "\n", + "# loop through x/y-pairs\n", + "for i in range(4):\n", + " # compute correlation coef.\n", + " x, y = df.iloc[:, 2*i], df.iloc[:, 2*i+1]\n", + " corr, p = pearsonr(x, y)\n", + " print('Correlation between x{:d} and y{:d} = {:.3f} (p-value = {:.3f})'.format(i, i, corr, p)) \n", + " \n", + " # fit linear regression\n", + " res = stats.linregress(x,y)\n", + " slope, intercept, r_value, p_value, std_err = res\n", + " \n", + " print('Characteristics from linear regression: a = {:.3f}, b= {:.3f}\\n'.format(slope, intercept)) \n", + "\n", + " \n", + " # add subplot to figure\n", + " plot_x = np.linspace(x.min()-3, x.max()+2, x.__len__()) # for a longer regression line\n", + " axs[i//2, i%2].scatter(x,y)\n", + " axs[i//2, i%2].plot(plot_x, intercept + slope*plot_x, 'k', label='fitted line')\n", + " axs[i//2, i%2].set_title(f'(x{i+1}, y{i+1})')\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Comment:**\n", + "\n", + "The idea of the exercise is to show that visualizations *do* in fact have the ability to tell a different story about the data than statistics. This is because all four data sets have identical statistical properties (like mean, variance and correlation) yet look completely different when visualizing the data. This is known as Anscombe's Quartet." + ] + }, + { + "cell_type": "code", + "execution_count": 142, "metadata": { "scrolled": true }, @@ -142,15 +644,14 @@ " src=\"https://www.youtube.com/embed/DbJyPELmhJc\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", - " \n", " >\n", " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -185,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -199,15 +700,14 @@ " src=\"https://www.youtube.com/embed/sdszHGaP_ag\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", - " \n", " >\n", " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -392,7 +892,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -406,7 +906,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.7.4" } }, "nbformat": 4,