<jupyter_start><jupyter_text>[View in Colaboratory](https://colab.research.google.com/github/rainu1729/data-analysis/blob/master/Hello,_Colaboratory.ipynb)  Welcome to Colaboratory!Colaboratory is a Google research project created to help disseminate machine learning education and research. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud.Colaboratory notebooks are stored in [Google Drive](https://drive.google.com) and can be shared just as you would with Google Docs or Sheets. Colaboratory is free to use.For more information, see our [FAQ](https://research.google.com/colaboratory/faq.html).  Local runtime supportColab also supports connecting to a Jupyter runtime on your local machine. For more information, see our [documentation](https://research.google.com/colaboratory/local-runtimes.html).  Python 3Colaboratory supports both Python2 and Python3 for code execution. * When creating a new notebook, you'll have the choice between Python 2 and Python 3.* You can also change the language associated with a notebook; this information will be written into the `.ipynb` file itself, and thus will be preserved for future sessions.<jupyter_code>import sys
print('Hello, Colaboratory from Python {}!'.format(sys.version_info[0]))<jupyter_output>Hello, Colaboratory from Python 3!<jupyter_text>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
import numpy as np

with tf.Session():
  input1 = tf.constant(1.0, shape=[2, 3])
  input2 = tf.constant(np.reshape(np.arange(1.0, 7.0, dtype=np.float32), (2, 3)))
  output = tf.add(input1, input2)
  result = output.eval()

result<jupyter_output><empty_output><jupyter_text>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. For recipes to import commonly used libraries, refer to the [importing libraries example notebook](/notebooks/snippets/importing_libraries.ipynb)<jupyter_code># Only needs to be run once at the top of the notebook.
!pip install -q matplotlib-venn

# Now the newly-installed library can be used anywhere else in the notebook.
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': 0.11721386928505677, 'fraction_numerical': 0.023013194231359314, 'mean_word_length': 5.082089552238806, 'pattern_counts': {'":': 0, '<': 20, '<?xml version=': 0, '>': 20, 'https://': 5, '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': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '137446', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 1099, 'n_tokens_neox': 1043, 'n_words': 351, 'path': 'Hello,_Colaboratory.ipynb', 'repo_name': 'rainu1729/data-analysis', 'size': '3259'}