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Sea level predictor
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{"cells":[{"cell_type":"markdown","metadata":{"id":"Nma_JWh-W-IF"},"source":["<div class=\"markdown-google-sans\">\n"," <h1>Welcome to Colab!</h1>\n","</div>\n","\n","If you're already familiar with Colab, check out this video to learn about interactive tables, the executed code history view, and the command palette.\n","\n","<center>\n"," <a href=\"https://www.youtube.com/watch?v=rNgswRZ2C1Y\" target=\"_blank\">\n"," <img alt='Thumbnail for a video showing 3 cool Google Colab features' 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\" height=\"188\" width=\"336\">\n"," </a>\n","</center>"]},{"cell_type":"markdown","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from scipy.stats import linregress\n","\n","def draw_plot():\n"," # Read data from file\n"," sea = pd.read_csv('epa-sea-level.csv')\n"," lreg= linregress(sea['Year'],sea['CSIRO Adjusted Sea Level'])\n"," slope,y_intercept = lreg.slope,lreg.intercept\n"," # Create scatter plot\n"," plt.plot(sea['Year'],sea['CSIRO Adjusted Sea Level'],'ro')\n"," # Create first line of best fit\n"," next = pd.Series(list(range(len))) +sea['Year'].max()+1\n"," bestfit = sea['Year'].append(next)\n"," \n"," # Create second line of best fit\n"," secfit = sea[sea['Year']>1999]['Year'].append(next)\n"," plt.plot(bestfit,slope*bestfit+y_intercept,'b+',secfit,slope*secfit+y_intercept,'p')\n","\n"," # Add labels and title\n"," plt.xlabel('Year');plt.ylabel('Sea Level (inches)')\n"," plt.grid(); plt.title('Rise of sea level')\n"," plt.legend(['Sea level','Upper Error','Lower Error','Line of the best fit over'])\n","# Save plot and return data for testing (DO NOT MODIFY)\n"," plt.savefig('sea_level_plot.png')\n"," return plt.gca()"],"metadata":{"id":"DdeEIhqo_EZT"}},{"cell_type":"markdown","metadata":{"id":"5fCEDCU_qrC0"},"source":["<div class=\"markdown-google-sans\">\n"," <h1>What is Colab?</h1>\n","</div>\n","\n","Colab, or \"Colaboratory\", allows you to write and execute Python in your browser, with \n","- Zero configuration required\n","- Access to GPUs free of charge\n","- Easy sharing\n","\n","Whether you're a **student**, a **data scientist** or an **AI researcher**, Colab can make your work easier. Watch [Introduction to Colab](https://www.youtube.com/watch?v=inN8seMm7UI) to learn more, or just get started below!"]},{"cell_type":"markdown","metadata":{"id":"GJBs_flRovLc"},"source":["<div class=\"markdown-google-sans\">\n","\n","## **Getting started**\n","</div>\n","\n","The document you are reading is not a static web page, but an interactive environment called a **Colab notebook** that lets you write and execute code.\n","\n","For example, here is a **code cell** with a short Python script that computes a value, stores it in a variable, and prints the result:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"gJr_9dXGpJ05","outputId":"9f556d03-ec67-4950-a485-cfdba9ddd14d"},"outputs":[{"data":{"text/plain":["86400"]},"execution_count":0,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["seconds_in_a_day = 24 * 60 * 60\n","seconds_in_a_day"]},{"cell_type":"markdown","metadata":{"id":"2fhs6GZ4qFMx"},"source":["To execute the code in the above cell, select it with a click and then either press the play button to the left of the code, or use the keyboard shortcut \"Command/Ctrl+Enter\". To edit the code, just click the cell and start editing.\n","\n","Variables that you define in one cell can later be used in other cells:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"-gE-Ez1qtyIA","outputId":"94cb2224-0edf-457b-90b5-0ac3488d8a97"},"outputs":[{"data":{"text/plain":["604800"]},"execution_count":0,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["seconds_in_a_week = 7 * seconds_in_a_day\n","seconds_in_a_week"]},{"cell_type":"markdown","metadata":{"id":"lSrWNr3MuFUS"},"source":["Colab notebooks allow you to combine **executable code** and **rich text** in a single document, along with **images**, **HTML**, **LaTeX** and more. When you create your own Colab notebooks, they are stored in your Google Drive account. You can easily share your Colab notebooks with co-workers or friends, allowing them to comment on your notebooks or even edit them. To learn more, see [Overview of Colab](/notebooks/basic_features_overview.ipynb). To create a new Colab notebook you can use the File menu above, or use the following link: [create a new Colab notebook](http://colab.research.google.com#create=true).\n","\n","Colab notebooks are Jupyter notebooks that are hosted by Colab. To learn more about the Jupyter project, see [jupyter.org](https://www.jupyter.org)."]},{"cell_type":"markdown","metadata":{"id":"UdRyKR44dcNI"},"source":["<div class=\"markdown-google-sans\">\n","\n","## Data science\n","</div>\n","\n","With Colab you can harness the full power of popular Python libraries to analyze and visualize data. The code cell below uses **numpy** to generate some random data, and uses **matplotlib** to visualize it. To edit the code, just click the cell and start editing."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281},"id":"C4HZx7Gndbrh","outputId":"46abc637-6abd-41b2-9bba-80a7ae992e06"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDf0QtG0PCvl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WoefPVUrZhv6Bw/NYag3mHSUMfY5RFjKN8PZ01cptbCZ6nUyO8+W7WjCv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\nwOEF7D1ZxScfPYnLd5/qymYTzkx6hvN6Y1Ma+vFKG5f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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]},"output_type":"display_data"}],"source":["import numpy as np\n","from matplotlib import pyplot as plt\n","\n","ys = 200 + np.random.randn(100)\n","x = [x for x in range(len(ys))]\n","\n","plt.plot(x, ys, '-')\n","plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)\n","\n","plt.title(\"Sample Visualization\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"4_kCnsPUqS6o"},"source":["You can import your own data into Colab notebooks from your Google Drive account, including from spreadsheets, as well as from Github and many other sources. To learn more about importing data, and how Colab can be used for data science, see the links below under [Working with Data](#working-with-data)."]},{"cell_type":"markdown","metadata":{"id":"OwuxHmxllTwN"},"source":["<div class=\"markdown-google-sans\">\n","\n","## Machine learning\n","</div>\n","\n","With Colab you can import an image dataset, train an image classifier on it, and evaluate the model, all in just [a few lines of code](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb). Colab notebooks execute code on Google's cloud servers, meaning you can leverage the power of Google hardware, including [GPUs and TPUs](#using-accelerated-hardware), regardless of the power of your machine. All you need is a browser."]},{"cell_type":"markdown","metadata":{"id":"ufxBm1yRnruN"},"source":["Colab is used extensively in the machine learning community with applications including:\n","- Getting started with TensorFlow\n","- Developing and training neural networks\n","- Experimenting with TPUs\n","- Disseminating AI research\n","- Creating tutorials\n","\n","To see sample Colab notebooks that demonstrate machine learning applications, see the [machine learning examples](#machine-learning-examples) below."]},{"cell_type":"markdown","metadata":{"id":"-Rh3-Vt9Nev9"},"source":["<div class=\"markdown-google-sans\">\n","\n","## More Resources\n","\n","### Working with Notebooks in Colab\n","\n","</div>\n","\n","- [Overview of Colaboratory](/notebooks/basic_features_overview.ipynb)\n","- [Guide to Markdown](/notebooks/markdown_guide.ipynb)\n","- [Importing libraries and installing dependencies](/notebooks/snippets/importing_libraries.ipynb)\n","- [Saving and loading notebooks in GitHub](https://colab.research.google.com/github/googlecolab/colabtools/blob/main/notebooks/colab-github-demo.ipynb)\n","- [Interactive forms](/notebooks/forms.ipynb)\n","- [Interactive widgets](/notebooks/widgets.ipynb)\n","- <img src=\"/img/new.png\" height=\"20px\" align=\"left\" hspace=\"4px\" alt=\"New\"></img>\n","\n","<div class=\"markdown-google-sans\">\n","\n","<a name=\"working-with-data\"></a>\n","### Working with Data\n","</div>\n","\n","- [Loading data: Drive, Sheets, and Google Cloud Storage](/notebooks/io.ipynb) \n","- [Charts: visualizing data](/notebooks/charts.ipynb)\n","- [Getting started with BigQuery](/notebooks/bigquery.ipynb)\n","\n","<div class=\"markdown-google-sans\">\n","\n","### Machine Learning Crash Course\n","\n","<div>\n","\n","These are a few of the notebooks from Google's online Machine Learning course. See the [full course website](https://developers.google.com/machine-learning/crash-course/) for more.\n","- [Intro to Pandas DataFrame](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/pandas_dataframe_ultraquick_tutorial.ipynb)\n","- [Linear regression with tf.keras using synthetic data](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/linear_regression_with_synthetic_data.ipynb)\n","\n","<div class=\"markdown-google-sans\">\n","\n","<a name=\"using-accelerated-hardware\"></a>\n","### Using Accelerated Hardware\n","</div>\n","\n","- [TensorFlow with GPUs](/notebooks/gpu.ipynb)\n","- [TensorFlow with TPUs](/notebooks/tpu.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"P-H6Lw1vyNNd"},"source":["<div class=\"markdown-google-sans\">\n","\n","<a name=\"machine-learning-examples\"></a>\n","\n","### Featured examples\n","\n","</div>\n","\n","- [NeMo Voice Swap](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/VoiceSwapSample.ipynb): Use Nvidia's NeMo conversational AI Toolkit to swap a voice in an audio fragment with a computer generated one.\n","\n","- [Retraining an Image Classifier](https://tensorflow.org/hub/tutorials/tf2_image_retraining): Build a Keras model on top of a pre-trained image classifier to distinguish flowers.\n","- [Text Classification](https://tensorflow.org/hub/tutorials/tf2_text_classification): Classify IMDB movie reviews as either *positive* or *negative*.\n","- [Style Transfer](https://tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization): Use deep learning to transfer style between images.\n","- [Multilingual Universal Sentence Encoder Q&A](https://tensorflow.org/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa): Use a machine learning model to answer questions from the SQuAD dataset.\n","- [Video Interpolation](https://tensorflow.org/hub/tutorials/tweening_conv3d): Predict what happened in a video between the first and the last frame.\n"]},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from scipy.stats import linregress"],"metadata":{"id":"zT9NJjrX9i1x"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sea = pd.read_csv('epa-sea-level.csv')\n","sea.describe()"],"metadata":{"id":"Vd-c4FYq9puT","executionInfo":{"status":"error","timestamp":1674998235754,"user_tz":-60,"elapsed":414,"user":{"displayName":"Randy noe Tchuisseu","userId":"14239266362476156768"}},"outputId":"d1aa9396-00af-4e26-a1d5-e07e1e88f8dd","colab":{"base_uri":"https://localhost:8080/","height":345}},"execution_count":null,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-dd328198b163>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msea\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'epa-sea-level.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msea\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1038\u001b[0m )\n\u001b[1;32m 1039\u001b[0m \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m \u001b[0;32mreturn\u001b[0m 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handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m \"\"\"\n\u001b[0;32m--> 222\u001b[0;31m self.handles = get_handle(\n\u001b[0m\u001b[1;32m 223\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'epa-sea-level.csv'"]}]},{"cell_type":"code","source":["lreg = linregress(sea['Year'],sea['CSIRO Adjusted Sea Level'])"],"metadata":{"id":"2fAWp4Gx_eZE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["slope,y_intercept = lreg.slope,lreg.intercept\n","print('slope=',slope,'y=',y_intercept)\n"],"metadata":{"id":"GvVSjq8u_u6z","executionInfo":{"status":"ok","timestamp":1672614325483,"user_tz":-60,"elapsed":219,"user":{"displayName":"Randy noe Tchuisseu","userId":"14239266362476156768"}},"outputId":"f5526273-b10f-4ca0-9430-d57039e041af","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["slope= 0.0630445840121348 y= -119.06594196773978\n"]}]},{"cell_type":"code","source":["plt.plot(sea['Year'],sea['CSIRO Adjusted Sea Level'],'ro',\n"," sea['Year'],sea['Upper Error Bound'],'g',\n"," sea['Year'],sea['Lower Error Bound'],'y')\n","next = pd.Series(list(range(len))) +sea['Year'].max()+1\n","bestfit = sea['Year'].append(next)\n","secfit = sea[sea['Year']>1999]['Year'].append(next)\n","plt.plot(bestfit,slope*bestfit+y_intercept,'b+',secfit,slope*secfit+y_intercept,'black')\n","plt.xlabel('Time in year')\n","plt.ylabel('Sea level in inches')\n","plt.grid()\n","plt.title('Rise of sea level')\n","plt.legend(['Sea level','Upper Error','Lower Error','Line of the best fit over'])\n","\n","#plt.savefig('sea_level_plot.png')"],"metadata":{"id":"hTSc5FL6n5SI","executionInfo":{"status":"ok","timestamp":1672617276247,"user_tz":-60,"elapsed":709,"user":{"displayName":"Randy noe Tchuisseu","userId":"14239266362476156768"}},"outputId":"c8bd6482-0a61-45a4-d6ac-9982795e65ee","colab":{"base_uri":"https://localhost:8080/","height":312}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f4d6b35dd00>"]},"metadata":{},"execution_count":36},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["len = 2050-sea['Year'].max()\n","print(len,sea['Year'].max())\n"],"metadata":{"id":"LOg2B_2AM9jD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1672614592134,"user_tz":-60,"elapsed":5,"user":{"displayName":"Randy noe Tchuisseu","userId":"14239266362476156768"}},"outputId":"686fab5a-932b-4bad-e90f-c676d8da124a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["37 2013\n"]},{"output_type":"execute_result","data":{"text/plain":["0 2\n","1 3\n","2 5\n","dtype: int64"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["len = 14\n","pd.Series(list( range(len) ) )\n","#pd.Series([1,2,5,3,9,488,85])+1\n","#+sea['Year'].max()+1\n","#pd.Series(list(range(4)))"],"metadata":{"id":"3N7R9u7frQv5","executionInfo":{"status":"ok","timestamp":1672747286035,"user_tz":-60,"elapsed":6,"user":{"displayName":"Randy noe Tchuisseu","userId":"14239266362476156768"}},"outputId":"2c0b7719-37fc-47df-f75a-0aa44722d30b","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0 0\n","1 1\n","2 2\n","3 3\n","4 4\n","5 5\n","6 6\n","7 7\n","8 8\n","9 9\n","10 10\n","11 11\n","12 12\n","13 13\n","dtype: int64"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["pd.Series([1,2,5]).append(pd.Series([1,2]))\n"],"metadata":{"id":"k0u5T2kpznNq","executionInfo":{"status":"ok","timestamp":1672616389988,"user_tz":-60,"elapsed":258,"user":{"displayName":"Randy noe Tchuisseu","userId":"14239266362476156768"}},"outputId":"dd6204b9-f7b2-41b5-e4a6-9bc0aa75e0a0","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0 1\n","1 2\n","2 5\n","0 1\n","1 2\n","dtype: int64"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","source":["pd.Series(list(range(len)))+sea['Year'].max()+1"],"metadata":{"id":"nY-m9LkTAaEf","colab":{"base_uri":"https://localhost:8080/","height":165},"executionInfo":{"status":"error","timestamp":1672746715022,"user_tz":-60,"elapsed":16,"user":{"displayName":"Randy noe Tchuisseu","userId":"14239266362476156768"}},"outputId":"ce85087a-8ed9-4e54-dbae-eef5d6802b47"},"execution_count":null,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-7-cd9d99a61708>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0msea\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mTypeError\u001b[0m: 'builtin_function_or_method' object cannot be interpreted as an integer"]}]},{"cell_type":"code","source":["\n","\n","def draw_plot():\n"," # Read data from file\n"," sea = pd.read_csv('epa-sea-level.csv')\n"," lreg= linregress(sea['Year'],sea['CSIRO Adjusted Sea Level'])\n"," slope,y_intercept = lreg.slope,lreg.intercept\n"," # Create scatter plot\n"," plt.plot(sea['Year'],sea['CSIRO Adjusted Sea Level'],'ro')\n"," # Create first line of best fit\n"," len = 2050 - sea['Year'].max()\n"," next = pd.Series(list(range(len)))+sea['Year'].max()+1\n"," bestfit = sea['Year'].append(next)\n"," \n"," # Create second line of best fit\n"," secfit = sea[sea['Year']>1999]['Year'].append(next)\n"," plt.plot(bestfit,slope*bestfit+y_intercept,'b+',secfit,slope*secfit+y_intercept,'p')\n","\n"," # Add labels and title\n"," plt.xlabel('Year');plt.ylabel('Sea Level (inches)')\n"," plt.grid(); plt.title('Rise of sea level')\n"," plt.legend(['Sea level','Upper Error','Lower Error','Line of the best fit over'])\n","# Save plot and return data for testing (DO NOT MODIFY)\n"," plt.savefig('sea_level_plot.png')\n"," return plt.gca()"],"metadata":{"id":"69vm0mUeAeqX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["D = draw_plot()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"WJb2l7YvcodC","executionInfo":{"status":"ok","timestamp":1672747417141,"user_tz":-60,"elapsed":363,"user":{"displayName":"Randy noe Tchuisseu","userId":"14239266362476156768"}},"outputId":"4aa400cb-c2c5-4e32-ee79-9d001a8cfe36"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["if D.get_title()!='Rise of sea level':\n"," print(\"Expected line plot title to be 'Rise in Sea Level'\")"],"metadata":{"id":"AJeR0qo1cxpL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"qcnDV65Jhgi4"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"/v2/external/notebooks/intro.ipynb","timestamp":1672359607693}]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"nbformat":4,"nbformat_minor":0}