markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Imports | import os
import re
import cv2
import time
import tensorflow
import collections
import numpy as np
import pandas as pd
from tqdm import tqdm
from glob import glob
from PIL import Image
import requests, threading
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import torch
import torchvision
im... | _____no_output_____ | MIT | image/2. Flower Classification with TPUs/kaggle/fast-pytorch-xla-for-tpu-with-multiprocessing.ipynb | nishchalnishant/Completed_Kaggle_competitions |
Dataset | DATASET_DIR = '/kaggle/input/104-flowers-garden-of-eden/jpeg-512x512'
TRAIN_DIR = DATASET_DIR + '/train'
VAL_DIR = DATASET_DIR + '/val'
TEST_DIR = DATASET_DIR + '/test'
BATCH_SIZE = 16 # per core
NUM_EPOCH = 25
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
train_transform = ... | Num training images: 16465
Num test images: 3712
| MIT | image/2. Flower Classification with TPUs/kaggle/fast-pytorch-xla-for-tpu-with-multiprocessing.ipynb | nishchalnishant/Completed_Kaggle_competitions |
Model | class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.base_model = torchvision.models.densenet201(pretrained=True)
self.base_model.classifier = nn.Identity()
self.fc = torch.nn.Sequential(
torch.nn.Linear(1920, 1024, bias = T... | Downloading: "https://download.pytorch.org/models/densenet201-c1103571.pth" to /root/.cache/torch/checkpoints/densenet201-c1103571.pth
| MIT | image/2. Flower Classification with TPUs/kaggle/fast-pytorch-xla-for-tpu-with-multiprocessing.ipynb | nishchalnishant/Completed_Kaggle_competitions |
Training | def train_model():
train = datasets.ImageFolder(TRAIN_DIR, transform=train_transform)
valid = datasets.ImageFolder(VAL_DIR, transform=train_transform)
train = torch.utils.data.ConcatDataset([train, valid])
torch.manual_seed(42)
train_sampler = torch.utils.data.distributed.DistributedSample... | Train for 128 steps per epoch
[xla:1](0) Loss=4.989 Rate=0.89 GlobalRate=0.89
[xla:3](0) Loss=4.780 Rate=0.81 GlobalRate=0.81
[xla:4](0) Loss=4.716 Rate=0.81 GlobalRate=0.81
[xla:2](0) Loss=4.723 Rate=0.69 GlobalRate=0.69
[xla:7](0) Loss=4.702 Rate=0.83 GlobalRate=0.83
[xla:0](0) Loss=4.785 Rate=0.57 GlobalRate=0.57
[x... | MIT | image/2. Flower Classification with TPUs/kaggle/fast-pytorch-xla-for-tpu-with-multiprocessing.ipynb | nishchalnishant/Completed_Kaggle_competitions |
R06725035 陳廷易* Tokenization.* Lowercasing everything.* Stemming using Porter’s algorithm.* Stopword removal.* Save the result as a txt file. | # import keras
# from keras.preprocessing.text import Tokenizer
# import gensim
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import wordpunct_tokenize
from nltk import word_tokenize
from nltk.stem.porter import *
from nltk.tokenize import RegexpTokenizer
# nltk.download('all') | [nltk_data] Downloading collection 'all'
[nltk_data] |
[nltk_data] | Downloading package abc to
[nltk_data] | /home/leoqaz12/nltk_data...
[nltk_data] | Package abc is already up-to-date!
[nltk_data] | Downloading package alpino to
[nltk_data] | /home/leoqaz12/nltk_data...
[nltk_data] | ... | MIT | preprocessing/ExtractTerms.ipynb | tychen5/IR_TextMining |
read data | file = open('data/28.txt','r')
texts = file.read()
file.close()
texts | _____no_output_____ | MIT | preprocessing/ExtractTerms.ipynb | tychen5/IR_TextMining |
main preprocessing | with open('data/stop_words.txt') as f:
stop_words_list = f.read().splitlines()
ps = PorterStemmer() # Stemming
stop_words = set(stopwords.words('english')) #Stopword
short = ['.', ',', '"', "'", '?', '!', ':', ';', '(', ')', '[', ']', '{', '}',
'\'s','\'m','\'re','\'ll','\'d','n\'t','shan\'t']
st... | _____no_output_____ | MIT | preprocessing/ExtractTerms.ipynb | tychen5/IR_TextMining |
Output | # output=""
# for token in tokens:
# output+=token+' '
# print(output)
file = open('result/output.txt','w')
file.write(token_result) #Save the result
file.close()
print(token_result)
# tokenizer = Tokenizer()
# tokenizer.fit_on_texts(texts)
# print(tokenizer.sequences_to_texts()) | _____no_output_____ | MIT | preprocessing/ExtractTerms.ipynb | tychen5/IR_TextMining |
FunctionsThink of mathematical functions...> 8 -------> 16> 10 ------> 20> 0.5 ------> 1Thus, the function of course is `f(x) = 2x`, which is a function f that doubles whatever input. What if we could write a doubling function too? | def f(x):
return 2*x
print(f(8))
print(f(10))
print(f(0.5)) | _____no_output_____ | MIT | 19T2/2_review/functions.ipynb | photomz/learn-python3 |
Programmatic functions can do a lot more. For example: | def is_even(x):
return not x % 2
print(is_even(100))
print(is_even(7))
def calculate_interest(principal=100,rate=0.05,year=5):
# principal = initial amount, rate = interest rate, year = number of years
compounded = principal
for i in range(year):
compounded = compounded * (1+rate)
year =... | _____no_output_____ | MIT | 19T2/2_review/functions.ipynb | photomz/learn-python3 |
Prepare dataset for hiveplotThis notebook currently just exports a subset of the nodes to a DOT file for import into [`jhive`](https://www.bcgsc.ca/wiki/display/jhive/Documentation). | import random
import pandas
import networkx
from networkx.drawing.nx_pydot import write_dot
node_df = pandas.read_table('../../data/nodes.tsv')
edge_df = pandas.read_table('../../data/edges.sif.gz')
node_df.head(2)
edge_df.head(2)
graph = networkx.MultiGraph()
# No colons allowed. See https://github.com/carlos-jenki... | _____no_output_____ | CC0-1.0 | viz/auto/3-hiveplot.ipynb | dhimmel/integrate |
Normalizing the data |
colsList = ["actual_area",
"poolcnt",
"latitude",
"longitude",
"unitcnt",
"lotsizesquarefeet",
"bedroomcnt",
"calculatedbathnbr",
"hashottuborspa",
"fireplacecnt",
"taxvaluedollarcnt",
"b... | -0.130731 47739
-0.199816 8968
-0.249310 7211
-0.323551 7111
-0.447286 6024
-0.364796 5956
-0.385418 5837
-0.406041 5436
-0.298804 5319
-0.220439 4743
-0.348298 4685
-0.292617 4658
-0.261684 4585
-0.302929 4520
-0.271995 4478
-0.274057 4281
... | Apache-2.0 | HW3/HW3.ipynb | jay-z007/Data-Science-Fundamentals |
Analytical Hierarchical Processing | rel_imp_matrix = pd.read_csv("rel_imp_matrix.csv", index_col=0)
# rel_imp_matrix
import fractions
for col in rel_imp_matrix.columns.values:
temp_list = rel_imp_matrix[col].tolist()
rel_imp_matrix[col] = [float(fractions.Fraction(x)) for x in temp_list]
# data = [float(fractions.Fraction(x)) for x in... | _____no_output_____ | Apache-2.0 | HW3/HW3.ipynb | jay-z007/Data-Science-Fundamentals |
SAW | sum_series = pd.Series(0, index=prop_data_ahp.index,dtype='float32')
for col in prop_data_ahp.columns:
sum_series = sum_series+ prop_data_ahp[col] * ahp_column_score[col]
prop_data_ahp["sum"] = sum_series.astype('float32')
prop_data_ahp["sum"]
# prop_data_ahp["sum"] = prop_data_ahp.sum(axis=1)
prop_data_ahp["... | _____no_output_____ | Apache-2.0 | HW3/HW3.ipynb | jay-z007/Data-Science-Fundamentals |
Distance MetricWe will be using weighted Manhattan distance as a distance metric | dist_imp_matrix = pd.read_csv("./dist_metric.csv", index_col=0)
dist_imp_matrix
import fractions
for col in dist_imp_matrix.columns.values:
temp_list = dist_imp_matrix[col].tolist()
dist_imp_matrix[col] = [float(fractions.Fraction(x)) for x in temp_list]
# dist_imp_matrix
for col in dist_imp_matrix.colu... | _____no_output_____ | Apache-2.0 | HW3/HW3.ipynb | jay-z007/Data-Science-Fundamentals |
A Crypto-Arithmetic Puzzle In this exercise we will solve the crypto-arithmetic puzzle shown in the picture below: The idea is that the letters "$\texttt{S}$", "$\texttt{E}$", "$\texttt{N}$", "$\texttt{D}$", "$\texttt{M}$", "$\texttt{O}$", "$\texttt{R}$", "$\texttt{Y}$" occurring in this puzzleare interpreted as varia... | import cspSolver | _____no_output_____ | MIT | Python/Exercises/Blatt-13.ipynb | BuserLukas/Logic |
For a set $V$ of variables, the function $\texttt{allDifferent}(V)$ generates a set of formulas that express that all the variables of $V$ are different. | def allDifferent(Variables):
return { f'{x} != {y}' for x in Variables
for y in Variables
if x < y
}
allDifferent({ 'a', 'b', 'c' }) | _____no_output_____ | MIT | Python/Exercises/Blatt-13.ipynb | BuserLukas/Logic |
Pause bis 14:23 | def createCSP():
Variables = "your code here"
Values = "your code here"
Constraints = "much more code here"
return [Variables, Values, Constraints];
puzzle = createCSP()
puzzle
%%time
solution = cspSolver.solve(puzzle)
print(f'Time needed: {round((stop-start) * 1000)} milliseconds.')... | _____no_output_____ | MIT | Python/Exercises/Blatt-13.ipynb | BuserLukas/Logic |
Solution based on Multiple Models | import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all" | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
Tokenize and Numerize - Make it ready | training_size = 20000
training_sentences = sentences[0:training_size]
testing_sentences = sentences[training_size:]
training_labels = labels[0:training_size]
testing_labels = labels[training_size:]
vocab_size = 1000
max_length = 120
embedding_dim = 16
trunc_type='post'
padding_type='post'
oov_tok = "<OOV>"
toke... | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
Plot | def plot_graphs(history, string):
plt.plot(history.history[string])
plt.plot(history.history['val_'+string])
plt.xlabel("Epochs")
plt.ylabel(string)
plt.legend([string, 'val_'+string])
plt.show()
plot_graphs(history, "accuracy")
plot_graphs(history, "loss") | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
Function to train and show | def fit_model_and_show_results (model, reviews):
model.summary()
history = model.fit(training_padded,
training_labels_final,
epochs=num_epochs,
validation_data=(validation_padded, validation_labels_final))
plot_graphs(history, "accuracy")
plot... | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
ANN Embedding | model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.GlobalAveragePooling1D(),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
model.summary()
num_epoch... | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
CNN | num_epochs = 30
model_cnn = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.Conv1D(16, 5, activation='relu'),
tf.keras.layers.GlobalMaxPooling1D(),
tf.keras.layers.Dense(1, activation='sigmoid')
])
# Default learning rate for the Adam ... | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
GRU | num_epochs = 30
model_gru = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.Bidirectional(tf.keras.layers.GRU(32)),
tf.keras.layers.Dense(1, activation='sigmoid')
])
learning_rate = 0.00003 # slower than the default learning rate
model_gru... | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
Bidirectional LSTM | num_epochs = 30
model_bidi_lstm = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim)),
tf.keras.layers.Dense(1, activation='sigmoid')
])
learning_rate = 0.00003
model_bidi_lstm.compile(loss='... | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
Multiple bidirectional LSTMs | num_epochs = 30
model_multiple_bidi_lstm = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim,
return_sequences=True)),
tf.keras.layers.B... | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
Prediction Define a function to prepare the new reviews for use with a modeland then use the model to predict the sentiment of the new reviews | def predict_review(model, reviews):
# Create the sequences
padding_type='post'
sample_sequences = tokenizer.texts_to_sequences(reviews)
reviews_padded = pad_sequences(sample_sequences,
padding=padding_type,
maxlen=max_length)
classes = ... | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
How to use examples more_reviews = [review1, review2, review3, review4, review5, review6, review7, review8, review9, review10]predict_review(model, new_reviews) | print("============================\n","Embeddings only:\n", "============================")
predict_review(model, more_reviews)
print("============================\n","With CNN\n", "============================")
predict_review(model_cnn, more_reviews)
print("===========================\n","With bidirectional GRU\n", ... | _____no_output_____ | MIT | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification |
1. Install DependenciesFirst install the libraries needed to execute recipes, this only needs to be done once, then click play. | !pip install git+https://github.com/google/starthinker
| _____no_output_____ | Apache-2.0 | colabs/dv360_data_warehouse.ipynb | arbrown/starthinker |
2. Get Cloud Project IDTo run this recipe [requires a Google Cloud Project](https://github.com/google/starthinker/blob/master/tutorials/cloud_project.md), this only needs to be done once, then click play. | CLOUD_PROJECT = 'PASTE PROJECT ID HERE'
print("Cloud Project Set To: %s" % CLOUD_PROJECT)
| _____no_output_____ | Apache-2.0 | colabs/dv360_data_warehouse.ipynb | arbrown/starthinker |
3. Get Client CredentialsTo read and write to various endpoints requires [downloading client credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_client_installed.md), this only needs to be done once, then click play. | CLIENT_CREDENTIALS = 'PASTE CLIENT CREDENTIALS HERE'
print("Client Credentials Set To: %s" % CLIENT_CREDENTIALS)
| _____no_output_____ | Apache-2.0 | colabs/dv360_data_warehouse.ipynb | arbrown/starthinker |
4. Enter DV360 Data Warehouse ParametersDeploy a BigQuery dataset mirroring DV360 account structure. Foundation for solutions on top. 1. Wait for BigQuery->->->* to be created. 1. Every table mimics the DV360 API Endpoints.Modify the values below for your use case, can be done multiple times, then click play. | FIELDS = {
'auth_bigquery': 'service', # Credentials used for writing data.
'auth_dv': 'service', # Credentials used for reading data.
'auth_cm': 'service', # Credentials used for reading data.
'recipe_slug': '', # Name of Google BigQuery dataset to create.
'partners': [], # List of account ids to pull.
... | _____no_output_____ | Apache-2.0 | colabs/dv360_data_warehouse.ipynb | arbrown/starthinker |
5. Execute DV360 Data WarehouseThis does NOT need to be modified unless you are changing the recipe, click play. | from starthinker.util.configuration import Configuration
from starthinker.util.configuration import execute
from starthinker.util.recipe import json_set_fields
USER_CREDENTIALS = '/content/user.json'
TASKS = [
{
'dataset': {
'description': 'Create a dataset for bigquery tables.',
'hour': [
4... | _____no_output_____ | Apache-2.0 | colabs/dv360_data_warehouse.ipynb | arbrown/starthinker |
Tutorial on Python for scientific computingMarcos Duarte This tutorial is a short introduction to programming and a demonstration of the basic features of Python for scientific computing. To use Python for scientific computing we need the Python program itself with its main modules and specific packages for scientific... | 1 + 2 - 30
4/5 | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
If you are using Python version 2.x instead of Python 3.x, you should have got 0 as the result of 4 divided by 5, which is wrong! The problem is that for Python versions up to 2.x, the operator '/' performs division with integers and the result will also be an integer (this behavior was changed in version 3.x). If you... | 4/5.
from __future__ import division
4/5 | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
I prefer to use the import division option (from future!); if we put this statement in the beginning of a file or IPython notebook, it will work for all subsequent commands.Another command that changed its behavior from Python 2.x to 3.x is the `print` command. In Python 2.x, the print command could be used as a stat... | print 4/5 | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
With Python 3.x, the print command bahaves as a true function and has to be called with parentheses. Let's also import this future command to Python 2.x and use it from now on: | from __future__ import print_function
print(4/5) | 0.8
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
With the `print` function, let's explore the mathematical operations available in Python: | print('1+2 = ', 1+2, '\n', '4*5 = ', 4*5, '\n', '6/7 = ', 6/7, '\n', '8**2 = ', 8**2, sep='') | 1+2 = 3
4*5 = 20
6/7 = 0.8571428571428571
8**2 = 64
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
And if we want the square-root of a number: | sqrt(9) | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
We get an error message saying that the `sqrt` function if not defined. This is because `sqrt` and other mathematical functions are available with the `math` module: | import math
math.sqrt(9)
from math import sqrt
sqrt(9) | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
The import functionWe used the command '`import`' to be able to call certain functions. In Python functions are organized in modules and packages and they have to be imported in order to be used. A module is a file containing Python definitions (e.g., functions) and statements. Packages are a way of structuring Pyth... | import sys
sys.version | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
And if you are in an IPython session: | from IPython import sys_info
print(sys_info()) | {'commit_hash': '681fd77',
'commit_source': 'installation',
'default_encoding': 'cp1252',
'ipython_path': 'C:\\Anaconda3\\lib\\site-packages\\IPython',
'ipython_version': '2.1.0',
'os_name': 'nt',
'platform': 'Windows-7-6.1.7601-SP1',
'sys_executable': 'C:\\Anaconda3\\python.exe',
'sys_platform': 'win32',
'sys... | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
The first option gives information about the Python version; the latter also includes the IPython version, operating system, etc. Object-oriented programmingPython is designed as an object-oriented programming (OOP) language. OOP is a paradigm that represents concepts as "objects" that have data fields (attributes tha... | help(math.degrees) | Help on built-in function degrees in module math:
degrees(...)
degrees(x)
Convert angle x from radians to degrees.
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Or if you are in the IPython environment, simply add '?' to the function that a window will open at the bottom of your browser with the same help content: | math.degrees? | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
And if you add a second '?' to the statement you get access to the original script file of the function (an advantage of an open source language), unless that function is a built-in function that does not have a script file, which is the case of the standard modules in Python (but you can access the Python source code ... | import scipy.fftpack
scipy.fftpack.fft?? | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
To know all the attributes of an object, for example all the functions available in `math`, we can use the function `dir`: | print(dir(math)) | ['__doc__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'hypot', 'isfinite', 'isinf', 'isnan', 'ldexp', '... | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Tab completion in IPythonIPython has tab completion: start typing the name of the command (object) and press `tab` to see the names of objects available with these initials letters. When the name of the object is typed followed by a dot (`math.`), pressing `tab` will show all available attribites, scroll down to the d... | # Import the math library to access more math stuff
import math
math.pi # this is the pi constant; a useless comment since this is obvious | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
To insert comments spanning more than one line, use a multi-line string with a pair of matching triple-quotes: `"""` or `'''` (we will see the string data type later). A typical use of a multi-line comment is as documentation strings and are meant for anyone reading the code: | """Documentation strings are typically written like that.
A docstring is a string literal that occurs as the first statement
in a module, function, class, or method definition.
""" | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
A docstring like above is useless and its output as a standalone statement looks uggly in IPython Notebook, but you will see its real importance when reading and writting codes.Commenting a programming code is an important step to make the code more readable, which Python cares a lot. There is a style guide for writt... | x = 1 | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Spaces between the statements are optional but it helps for readability.To see the value of the variable, call it again or use the print function: | x
print(x) | 1
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Of course, the last assignment is that holds: | x = 2
x = 3
x | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
In mathematics '=' is the symbol for identity, but in computer programming '=' is used for assignment, it means that the right part of the expresssion is assigned to its left part. For example, 'x=x+1' does not make sense in mathematics but it does in computer programming: | x = 1
print(x)
x = x + 1
print(x) | 1
2
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
A value can be assigned to several variables simultaneously: | x = y = 4
print(x)
print(y) | 4
4
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Several values can be assigned to several variables at once: | x, y = 5, 6
print(x)
print(y) | 5
6
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
And with that, you can do (!): | x, y = y, x
print(x)
print(y) | 6
5
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Variables must be “defined” (assigned a value) before they can be used, or an error will occur: | x = z | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Variables and typesThere are different types of built-in objects in Python (and remember that everything in Python is an object): | import types
print(dir(types)) | ['BuiltinFunctionType', 'BuiltinMethodType', 'CodeType', 'DynamicClassAttribute', 'FrameType', 'FunctionType', 'GeneratorType', 'GetSetDescriptorType', 'LambdaType', 'MappingProxyType', 'MemberDescriptorType', 'MethodType', 'ModuleType', 'SimpleNamespace', 'TracebackType', '__builtins__', '__cached__', '__doc__', '__fi... | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Let's see some of them now. Numbers: int, float, complexNumbers can an integer (int), float, and complex (with imaginary part). Let's use the function `type` to show the type of number (and later for any other object): | type(6) | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
A float is a non-integer number: | math.pi
type(math.pi) | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Python (IPython) is showing `math.pi` with only 15 decimal cases, but internally a float is represented with higher precision. Floating point numbers in Python are implemented using a double (eight bytes) word; the precison and internal representation of floating point numbers are machine specific and are available i... | sys.float_info | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Be aware that floating-point numbers can be trick in computers: | 0.1 + 0.2
0.1 + 0.2 - 0.3 | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
These results are not correct (and the problem is not due to Python). The error arises from the fact that floating-point numbers are represented in computer hardware as base 2 (binary) fractions and most decimal fractions cannot be represented exactly as binary fractions. As consequence, decimal floating-point numbers ... | 1+2j
print(type(1+2j)) | <class 'complex'>
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Each part of a complex number is represented as a floating-point number. We can see them using the attributes `.real` and `.imag`: | print((1+2j).real)
print((1+2j).imag) | 1.0
2.0
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
StringsStrings can be enclosed in single quotes or double quotes: | s = 'string (str) is a built-in type in Python'
s
type(s) | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
String enclosed with single and double quotes are equal, but it may be easier to use one instead of the other: | 'string (str) is a Python's built-in type'
"string (str) is a Python's built-in type" | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
But you could have done that using the Python escape character '\': | 'string (str) is a Python\'s built-in type' | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Strings can be concatenated (glued together) with the + operator, and repeated with *: | s = 'P' + 'y' + 't' + 'h' + 'o' + 'n'
print(s)
print(s*5) | Python
PythonPythonPythonPythonPython
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Strings can be subscripted (indexed); like in C, the first character of a string has subscript (index) 0: | print('s[0] = ', s[0], ' (s[index], start at 0)')
print('s[5] = ', s[5])
print('s[-1] = ', s[-1], ' (last element)')
print('s[:] = ', s[:], ' (all elements)')
print('s[1:] = ', s[1:], ' (from this index (inclusive) till the last (inclusive))')
print('s[2:4] = ', s[2:4], ' (from first index (inclusive) till second ... | s[0] = P (s[index], start at 0)
s[5] = n
s[-1] = n (last element)
s[:] = Python (all elements)
s[1:] = ython (from this index (inclusive) till the last (inclusive))
s[2:4] = th (from first index (inclusive) till second index (exclusive))
s[:2] = Py (till this index, exclusive)
s[:10] = Python (Pyt... | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
len()Python has a built-in functon to get the number of itens of a sequence: | help(len)
s = 'Python'
len(s) | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
The function len() helps to understand how the backward indexing works in Python. The index s[-i] should be understood as s[len(s) - i] rather than accessing directly the i-th element from back to front. This is why the last element of a string is s[-1]: | print('s = ', s)
print('len(s) = ', len(s))
print('len(s)-1 = ',len(s) - 1)
print('s[-1] = ', s[-1])
print('s[len(s) - 1] = ', s[len(s) - 1]) | s = Python
len(s) = 6
len(s)-1 = 5
s[-1] = n
s[len(s) - 1] = n
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Or, strings can be surrounded in a pair of matching triple-quotes: """ or '''. End of lines do not need to be escaped when using triple-quotes, but they will be included in the string. This is how we created a multi-line comment earlier: | """Strings can be surrounded in a pair of matching triple-quotes: \""" or '''.
End of lines do not need to be escaped when using triple-quotes,
but they will be included in the string.
""" | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
ListsValues can be grouped together using different types, one of them is list, which can be written as a list of comma-separated values between square brackets. List items need not all have the same type: | x = ['spam', 'eggs', 100, 1234]
x | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Lists can be indexed and the same indexing rules we saw for strings are applied: | x[0] | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
The function len() works for lists: | len(x) | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
TuplesA tuple consists of a number of values separated by commas, for instance: | t = ('spam', 'eggs', 100, 1234)
t | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
The type tuple is why multiple assignments in a single line works; elements separated by commas (with or without surrounding parentheses) are a tuple and in an expression with an '=', the right-side tuple is attributed to the left-side tuple: | a, b = 1, 2
print('a = ', a, '\nb = ', b) | a = 1
b = 2
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Is the same as: | (a, b) = (1, 2)
print('a = ', a, '\nb = ', b) | a = 1
b = 2
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
SetsPython also includes a data type for sets. A set is an unordered collection with no duplicate elements. | basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
fruit = set(basket) # create a set without duplicates
fruit | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
As set is an unordered collection, it can not be indexed as lists and tuples. | set(['orange', 'pear', 'apple', 'banana'])
'orange' in fruit # fast membership testing | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
DictionariesDictionary is a collection of elements organized keys and values. Unlike lists and tuples, which are indexed by a range of numbers, dictionaries are indexed by their keys: | tel = {'jack': 4098, 'sape': 4139}
tel
tel['guido'] = 4127
tel
tel['jack']
del tel['sape']
tel['irv'] = 4127
tel
tel.keys()
'guido' in tel | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
The dict() constructor builds dictionaries directly from sequences of key-value pairs: | tel = dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
tel | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Built-in Constants- **False** : false value of the bool type- **True** : true value of the bool type- **None** : sole value of types.NoneType. None is frequently used to represent the absence of a value. In computer science, the Boolean or logical data type is composed by two values, true and false, intended to repres... | True == False
not True == False
1 < 2 > 1
True != (False or True)
True != False or True | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Indentation and whitespaceIn Python, statement grouping is done by indentation (this is mandatory), which are done by inserting whitespaces, not tabs. Indentation is also recommended for alignment of function calling that span more than one line for better clarity. We will see examples of indentation in the next ses... | if True:
pass | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Which does nothing useful. Let's use the `if`...`elif`...`else` statements to categorize the [body mass index](http://en.wikipedia.org/wiki/Body_mass_index) of a person: | # body mass index
weight = 100 # kg
height = 1.70 # m
bmi = weight / height**2
if bmi < 15:
c = 'very severely underweight'
elif 15 <= bmi < 16:
c = 'severely underweight'
elif 16 <= bmi < 18.5:
c = 'underweight'
elif 18.5 <= bmi < 25:
c = 'normal'
elif 25 <= bmi < 30:
c = 'overweight'
elif 30 <= ... | For a weight of 100.0 kg and a height of 1.70 m,
the body mass index (bmi) is 34.6 kg/m2,
which is considered moderately obese.
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
forThe `for` statement iterates over a sequence to perform operations (a loop event).```for iterating_var in sequence: statements``` | for i in [3, 2, 1, 'go!']:
print(i),
for letter in 'Python':
print(letter), | P
y
t
h
o
n
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
The `range()` functionThe built-in function range() is useful if we need to create a sequence of numbers, for example, to iterate over this list. It generates lists containing arithmetic progressions: | help(range)
range(10)
range(1, 10, 2)
for i in range(10):
n2 = i**2
print(n2), | 0
1
4
9
16
25
36
49
64
81
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
whileThe `while` statement is used for repeating sections of code in a loop until a condition is met (this different than the `for` statement which executes n times):```while expression: statement```Let's generate the Fibonacci series using a `while` loop: | # Fibonacci series: the sum of two elements defines the next
a, b = 0, 1
while b < 1000:
print(b, end=' ')
a, b = b, a+b | 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Function definitionA function in a programming language is a piece of code that performs a specific task. Functions are used to reduce duplication of code making easier to reuse it and to decompose complex problems into simpler parts. The use of functions contribute to the clarity of the code.A function is created wit... | def function():
pass | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
As per construction, this function does nothing when called: | function() | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
The general syntax of a function definition is:```def function_name( parameters ): """Function docstring. The help for the function """ function body return variables```A more useful function: | def fibo(N):
"""Fibonacci series: the sum of two elements defines the next.
The series is calculated till the input parameter N and
returned as an ouput variable.
"""
a, b, c = 0, 1, []
while b < N:
c.append(b)
a, b = b, a + b
return c
fibo(100)
if 3 >... | teste
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Let's implemment the body mass index calculus and categorization as a function: | def bmi(weight, height):
"""Body mass index calculus and categorization.
Enter the weight in kg and the height in m.
See http://en.wikipedia.org/wiki/Body_mass_index
"""
bmi = weight / height**2
if bmi < 15:
c = 'very severely underweight'
elif 15 <= bmi < 16:
... | For a weight of 73.0 kg and a height of 1.70 m, the body mass index (bmi) is 25.3 kg/m2, which is considered overweight.
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Numeric data manipulation with NumpyNumpy is the fundamental package for scientific computing in Python and has a N-dimensional array package convenient to work with numerical data. With Numpy it's much easier and faster to work with numbers grouped as 1-D arrays (a vector), 2-D arrays (like a table or matrix), or hig... | import numpy as np
x1d = np.array([1, 2, 3, 4, 5, 6])
print(type(x1d))
x1d
x2d = np.array([[1, 2, 3], [4, 5, 6]])
x2d | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
len() and the Numpy functions size() and shape() give information aboout the number of elements and the structure of the Numpy array: | print('1-d array:')
print(x1d)
print('len(x1d) = ', len(x1d))
print('np.size(x1d) = ', np.size(x1d))
print('np.shape(x1d) = ', np.shape(x1d))
print('np.ndim(x1d) = ', np.ndim(x1d))
print('\n2-d array:')
print(x2d)
print('len(x2d) = ', len(x2d))
print('np.size(x2d) = ', np.size(x2d))
print('np.shape(x2d) = ', np.shape(x... | 1-d array:
[1 2 3 4 5 6]
len(x1d) = 6
np.size(x1d) = 6
np.shape(x1d) = (6,)
np.ndim(x1d) = 1
2-d array:
[[1 2 3]
[4 5 6]]
len(x2d) = 2
np.size(x2d) = 6
np.shape(x2d) = (2, 3)
np.ndim(x2d) = 2
| MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Create random data | x = np.random.randn(4,3)
x | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Joining (stacking together) arrays | x = np.random.randint(0, 5, size=(2, 3))
print(x)
y = np.random.randint(5, 10, size=(2, 3))
print(y)
np.vstack((x,y))
np.hstack((x,y)) | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Create equally spaced data | np.arange(start = 1, stop = 10, step = 2)
np.linspace(start = 0, stop = 1, num = 11) | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
InterpolationConsider the following data: | y = [5, 4, 10, 8, 1, 10, 2, 7, 1, 3] | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
Suppose we want to create data in between the given data points (interpolation); for instance, let's try to double the resolution of the data by generating twice as many data: | t = np.linspace(0, len(y), len(y)) # time vector for the original data
tn = np.linspace(0, len(y), 2 * len(y)) # new time vector for the new time-normalized data
yn = np.interp(tn, t, y) # new time-normalized data
yn | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
The key is the Numpy `interp` function, from its help: interp(x, xp, fp, left=None, right=None) One-dimensional linear interpolation. Returns the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points.A plot of the data will show what we have done: | %matplotlib inline
import matplotlib.pyplot as plt
plt.figure(figsize=(10,5))
plt.plot(t, y, 'bo-', lw=2, label='original data')
plt.plot(tn, yn, '.-', color=[1, 0, 0, .5], lw=2, label='interpolated')
plt.legend(loc='best', framealpha=.5)
plt.show() | _____no_output_____ | MIT | notebooks/PythonTutorial.ipynb | jagar2/BMC |
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