<jupyter_start><jupyter_text>[View in Colaboratory](https://colab.research.google.com/github/sochachai/decart-data-visualization/blob/master/Hello,_Colaboratory.ipynb) Welcome to Colaboratory!Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. See our [FAQ](https://research.google.com/colaboratory/faq.html) for more info.  Getting Started- [Overview of Colaboratory](/notebooks/basic_features_overview.ipynb)- [Loading and saving data: Local files, Drive, Sheets, Google Cloud Storage](/notebooks/io.ipynb)- [Importing libraries and installing dependencies](/notebooks/snippets/importing_libraries.ipynb)- [Using Google Cloud BigQuery](/notebooks/bigquery.ipynb)- [Forms](/notebooks/forms.ipynb), [Charts](/notebooks/charts.ipynb), [Markdown](/notebooks/markdown_guide.ipynb), & [Widgets](/notebooks/widgets.ipynb)- [TensorFlow with GPU](/notebooks/gpu.ipynb)- [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/): [Intro to Pandas](/notebooks/mlcc/intro_to_pandas.ipynb) & [First Steps with TensorFlow](/notebooks/mlcc/first_steps_with_tensor_flow.ipynb)  Highlighted Features SeedbankLooking for Colab notebooks to learn from? Check out [Seedbank](https://tools.google.com/seedbank/), a place to discover interactive machine learning examples.  TensorFlow execution Colaboratory allows you to execute TensorFlow code in your browser with a single click. The example below adds two matrices.$\begin{bmatrix}  1. & 1. & 1. \\  1. & 1. & 1. \\\end{bmatrix} +\begin{bmatrix}  1. & 2. & 3. \\  4. & 5. & 6. \\\end{bmatrix} =\begin{bmatrix}  2. & 3. & 4. \\  5. & 6. & 7. \\\end{bmatrix}$<jupyter_code>import tensorflow as tf

input1 = tf.ones((2, 3))
input2 = tf.reshape(tf.range(1, 7, dtype=tf.float32), (2, 3))
output = input1 + input2

with tf.Session():
  result = output.eval()
result  
import tensorflow as tf
from tensorflow import keras
from tensorflow.python.client import device_lib

def get_available_gpus():
   local_device_protos = device_lib.list_local_devices()
   return [x.name for x in local_device_protos]

print("Devices available:", get_available_gpus())
print("Tensorflow version:", tf.__version__)
print("Keras version:", keras.__version__)<jupyter_output>Devices available: ['/device:CPU:0', '/device:GPU:0']
Tensorflow version: 1.10.0
Keras version: 2.1.6-tf<jupyter_text>GitHubYou can save a copy of your Colab notebook to Github by using File > Save a copy to GitHub…You can load any .ipynb on GitHub by just adding the path to colab.research.google.com/github/ . For example, [colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/_index.ipynb](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/_index.ipynb) will load [this .ipynb](https://github.com/tensorflow/models/blob/master/samples/core/get_started/_index.ipynb) on GitHub.  Visualization Colaboratory includes widely used libraries like [matplotlib](https://matplotlib.org/), simplifying visualization.<jupyter_code>import matplotlib.pyplot as plt
import numpy as np

x = np.arange(20)
y = [x_i + np.random.randn(1) for x_i in x]
a, b = np.polyfit(x, y, 1)
_ = plt.plot(x, y, 'o', np.arange(20), a*np.arange(20)+b, '-')<jupyter_output><empty_output><jupyter_text>Want to use a new library?  `pip install` it at the top of the notebook. Then that library can be used anywhere else in the notebook. For recipes to import commonly used libraries, refer to the [importing libraries example notebook](/notebooks/snippets/importing_libraries.ipynb).<jupyter_code>!pip install -q matplotlib-venn

from matplotlib_venn import venn2
_ = venn2(subsets = (3, 2, 1))<jupyter_output><empty_output><jupyter_text>FormsForms can be used to parameterize code. See the [forms example notebook](/notebooks/forms.ipynb) for more details.<jupyter_code>#@title Examples

text = 'value' #@param 
date_input = '2018-03-22' #@param {type:"date"}
number_slider = 0 #@param {type:"slider", min:-1, max:1, step:0.1}
dropdown = '1st option' #@param ["1st option", "2nd option", "3rd option"]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1311988444872412, 'fraction_numerical': 0.01661049590755898, 'mean_word_length': 5.402157164869029, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 17, 'https://': 7, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '103973', 'license': 'MIT', 'n_tokens_mistral': 1458, 'n_tokens_neox': 1389, 'n_words': 362, 'path': 'Hello,_Colaboratory.ipynb', 'repo_name': 'sochachai/decart-data-visualization', 'size': '4154'}