repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/05. Variables & Assignment.ipynb | mit | the_number_four = 4
print(the_number_four)
"""
Explanation: Variables & Assignment
What is assignment? Well, the short (and simple) answer is to say assignment is the process whereby we give a value a unique name. Once something has a name, we can use it later on.
Please note that this is a bit of a oversimplification... |
Hexiang-Hu/mmds | final/.ipynb_checkpoints/Final-basic-checkpoint.ipynb | mit | ## Q2 Solution.
def hash(x):
return math.fmod(3 * x + 2, 11)
for i in xrange(1,12):
print hash(i)
"""
Explanation: Q1. Solution
3-shingles for "hello world":
hel, ell, llo, lo_, o_w ,_wo, wor, orl, rld => 9 in total
Q2. Solution
End of explanation
"""
## Q3 Solution.
prob = 1.0 / 10
a = (1 - prob)**4
pr... |
khalido/algorithims | breadth first search.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from geopy.distance import great_circle
from collections import deque
"""
Explanation: First, we need a graph. A graph is just a bunch of objects, typically called nodes which are connected to each other. The connections are call... |
michaelgat/Udacity_DL | intro-to-tflearn/TFLearn_Digit_Recognition-MG.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
erdc-cm/air-water-vv | 3d/bathyduck/Read Lidar.ipynb | mit | %ls lidar
"""
Explanation: lidar subdir has the files
- line.data.mat have "raw" data but includes some filtering and water level extraction
- line.science.mat have processed data
- Some jpg are also provide
End of explanation
"""
import tables
lineData = tables.openFile(r"lidar/20150927-0000-01.FRFNProp.line.data.... |
ShivakumarSwamy/MovieAnalysis | plotDataset2.ipynb | apache-2.0 | import random
import pandas as pd
from plotly.graph_objs import *
"""
Explanation: <h1> <center> Season Movie Analysis </center> </h1>
End of explanation
"""
from plotly.offline import init_notebook_mode, iplot, plot
init_notebook_mode(connected=True)
"""
Explanation: Using plotly offline mode
End of explanation
""... |
nhuntwalker/gatspy | examples/FastLombScargle.ipynb | bsd-2-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# use seaborn's default plotting styles for matplotlib
import seaborn; seaborn.set()
"""
Explanation: Fast Lomb-Scargle Periodograms in Python
The Lomb-Scargle Periodogram is a well-known method of finding periodicity in irregularly-sampled time-se... |
tensorflow/docs-l10n | site/en-snapshot/tutorials/images/transfer_learning.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
gsorianob/fiuba-python | .ipynb_checkpoints/Clase 04 - Excepciones, funciones lambda, búsquedas y ordenamientos-checkpoint.ipynb | apache-2.0 | lista_de_numeros = [1, 6, 3, 9, 5, 2]
lista_ordenada = sorted(lista_de_numeros)
print lista_ordenada
"""
Explanation: 27/10
Ordenamientos y búsquedas
Funciones anónimas.
Excepciones.
Ordenamiento de listas
Las listas se pueden ordenar fácilmente usando la función sorted:
End of explanation
"""
lista_de_numeros = [1... |
cdt15/lingam | examples/MultiGroupDirectLiNGAM.ipynb | mit | import numpy as np
import pandas as pd
import graphviz
import lingam
from lingam.utils import print_causal_directions, print_dagc, make_dot
print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])
np.set_printoptions(precision=3, suppress=True)
np.random.seed(0)
"""
Explanation: MultiGroupDi... |
GoogleCloudPlatform/training-data-analyst | self-paced-labs/vertex-ai/vertex-distributed-tensorflow/Qwiklab_Running_Distributed_TensorFlow_using_Vertex_AI.ipynb | apache-2.0 | import os
! pip3 install --user --upgrade google-cloud-aiplatform
"""
Explanation: Running Distributed TensorFlow on Vertex AI
Overview
This tutorial demonstrates
how to
Train a model using distribution strategies on Vertex AI using the SDK for Python
Deploy a custom image classification model for online predictio... |
tylere/jupyterlab-ee | ipynb/ee-test.ipynb | apache-2.0 | import ipywidgets
ipywidgets.IntSlider()
"""
Explanation: Test ipywidgets
End of explanation
"""
import ipyleaflet
ipyleaflet.Map(zoom=2)
"""
Explanation: Test ipyleaflet
End of explanation
"""
import ee
from IPython.display import Image
ee.Initialize()
url = ee.Image("CGIAR/SRTM90_V4").getThumbUrl({'min':0, 'm... |
tensorflow/agents | docs/tutorials/9_c51_tutorial.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
sailuh/perceive | Notebooks/Full_Disclosure/full_disclosure_corpus_statistics.ipynb | gpl-2.0 | #Input year for which the word count histogram is being plotted
year='2012'
#Directory with the files
path = 'data/input/bodymessage_corpus/'+year
file_name_list=[]
file_wc_list=[]
file_uq_wc_list=[]
file_wc_df = pd.DataFrame(columns = ["file_name","word_count","unique_word_count"])
#function that returns the word co... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_run_ica.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.preprocessing import ICA, create_ecg_epochs
from mne.datasets import sample
print(__doc__)
"""
Explanation: Compute ICA components on epochs
ICA is fit to MEG raw data.
We assume that the non-stationary EOG artifacts... |
fweik/espresso | doc/tutorials/lennard_jones/lennard_jones.ipynb | gpl-3.0 | import espressomd
required_features = ["LENNARD_JONES"]
espressomd.assert_features(required_features)
from espressomd import observables, accumulators, analyze
# Importing other relevant python modules
import numpy as np
import matplotlib.pyplot as plt
from scipy import optimize
np.random.seed(42)
plt.rcParams.update(... |
sfomel/ipython | Swan.ipynb | gpl-2.0 | %%file data.scons
Flow('trace',None,'spike n1=2001 d1=0.001 k1=1001 | ricker1 frequency=30')
Flow('gather','trace','spray axis=2 n=49 d=25 o=0 label=Offset unit=m | nmostretch inv=y half=n v0=2000')
Result('gather','window f1=888 n1=392 | grey title=Gather')
from m8r import view
view('gather')
"""
Explanation: Mak... |
apozas/BIST-Python-Bootcamp | 3_Types_Functions_FlowControl.ipynb | gpl-3.0 | x_int = 3
x_float = 3.
x_string = 'three'
x_list = [3, 'three']
type(x_float)
type(x_string)
type(x_list)
"""
Explanation: 3 Types, Functions and Flow Control
Data types
End of explanation
"""
abs(-1)
import math
math.floor(4.5)
math.exp(1)
math.log(1)
math.log10(10)
math.sqrt(9)
round(4.54,1)
"""
Expl... |
merryjman/astronomy | Word_frequency.ipynb | gpl-3.0 | import re
import pandas as pd
import urllib.request
frequency = {}
document_text = urllib.request.urlopen \
('http://www.textfiles.com/etext/FICTION/bronte-jane-178.txt') \
.read().decode('utf-8')
text_string = document_text.lower()
match_pattern = re.findall(r'\b[a-z]{3,15}\b', text_string)
for word in ma... |
crowd-course/datascience | 5-classification/5.2 - Logistic Regression in Classifying Breast Cancer .ipynb | mit | import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import cm # this is for colormaps for 2D surfaces
from mpl_toolkits.mplot3d import Axes3D # this is for 3D plots
p... |
nre-aachen/GeMpy | Tutorial/ch1.ipynb | mit | # These two lines are necessary only if gempy is not installed
import sys, os
sys.path.append("../")
# Importing gempy
import gempy as gp
# Embedding matplotlib figures into the notebooks
%matplotlib inline
# Aux imports
import numpy as np
"""
Explanation: Chapter 1: GemPy Basic
In this first example, we will show ... |
gaufung/PythonStandardLibrary | DataStructures/array.ipynb | mit | import array
import binascii
s= b'this is a array'
a = array.array('b', s)
print('As byte string', s)
print('As array ', a)
print('As hex', binascii.hexlify(a))
"""
Explanation: Type code for array
code | type | minimum size
:---: | :---: | :---:
b | int | 1
B | int | 1
h | signed int | 2
H | unsigned int | 2
i | sig... |
UWashington-Astro300/Astro300-W17 | 09_Python_LaTeX.ipynb | mit | %matplotlib inline
import sympy as sp
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Python, SymPy, and $\LaTeX$
End of explanation
"""
sp.init_printing() # Turns on pretty printing
np.sqrt(8)
sp.sqrt(8)
"""
Explanation: # Symbolic Mathematics (SymPy)
End of explanation
"""
x, y, z = ... |
tuanavu/python-cookbook-3rd | notebooks/ch01/12_determine_the_top_n_items_occurring_in_a_list.ipynb | mit | words = [
'look', 'into', 'my', 'eyes', 'look', 'into', 'my', 'eyes',
'the', 'eyes', 'the', 'eyes', 'the', 'eyes', 'not', 'around', 'the',
'eyes', "don't", 'look', 'around', 'the', 'eyes', 'look', 'into',
'my', 'eyes', "you're", 'under'
]
from collections import Counter
word_counts = Counter(words)
top_thr... |
ledeprogram/algorithms | class6/donow/Lee_Dongjin_6_Donow.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)
import statsmodels.formula.api as smf # package we'll be using for linear regression
import numpy as np
import scipy as sp
"""
Explanation: 1. Import the necessary packages to read in the... |
jtwhite79/pyemu | verification/henry/verify_unc_results.ipynb | bsd-3-clause | %matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pyemu
"""
Explanation: verify pyEMU results with the henry problem
End of explanation
"""
la = pyemu.Schur("pest.jco",verbose=False,forecasts=[])
la.drop_prior_information()
jco_ord = la.jco.get(la.pst.obs_name... |
dmnfarrell/gordon-group | mirnaseq/miRNA_features_analysis.ipynb | apache-2.0 | import glob,os
import pandas as pd
import numpy as np
import mirnaseq.mirdeep2 as mdp
from mirnaseq import base, analysis
pd.set_option('display.width', 130)
%matplotlib inline
import pylab as plt
from mpl_toolkits.mplot3d import Axes3D
base.seabornsetup()
plt.rcParams['savefig.dpi']=80
plt.rcParams['font.size']=16
hom... |
hasadna/knesset-data-pipelines | jupyter-notebooks/Render site pages for development and debugging.ipynb | mit | !{'cd /pipelines; KNESSET_LOAD_FROM_URL=1 dpp run --concurrency 4 '\
'./committees/kns_committee,'\
'./people/committee-meeting-attendees,'\
'./members/mk_individual'}
"""
Explanation: Render site pages
dpp runs the knesset data pipelines periodically on our server.
This notebook shows how to run pipelines that ... |
tacticsiege/TacticToolkit | examples/2017-09-11_TacticToolkit_Intro.ipynb | mit | # until we can install, add parent dir to path so ttk is found
import sys
sys.path.insert(0, '..')
# basic imports
import pandas as pd
import numpy as np
import re
import matplotlib
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
import matplotlib.pyplot as plt
"""
Explanation: TacticToolkit ... |
luofan18/deep-learning | sentiment-rnn/Sentiment_RNN_Solution.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
FRESNA/atlite | examples/logfiles_and_messages.ipynb | gpl-3.0 | import logging
logging.basicConfig(level=logging.INFO)
"""
Explanation: Logfiles and messages
Atlite uses the logging library for displaying
messages with different purposes.
Minimum information
We recommend that you always use logging when using atlite with information messages enabled.
The simplest way is to
End of ... |
karlnapf/shogun | doc/ipython-notebooks/classification/Classification.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from shogun import *
import shogun as sg
#Needed lists for the final plot
classifiers_linear = []*10
classifiers_non_linear = []*10
classifiers_names = []*10
fadings = []*10
""... |
atulsingh0/MachineLearning | python_DC/Pandas_#1.ipynb | gpl-3.0 | # # Create array of DataFrame values: np_vals
# np_vals = df.values
# # Create new array of base 10 logarithm values: np_vals_log10
# np_vals_log10 = np.log10(np_vals)
# # Create array of new DataFrame by passing df to np.log10(): df_log10
# df_log10 = np.log10(df)
# # Print original and new data containers
# print(... |
girving/tensorflow | tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb | apache-2.0 | !pip install unidecode
"""
Explanation: Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Text Generation using a RNN
<table class="tfo-notebook-buttons" align="left"><td>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/tensorflow/blob/... |
WormLabCaltech/mprsq | src/stats_tutorials/Model Selection.ipynb | mit | # important stuff:
import os
import pandas as pd
import numpy as np
import statsmodels.tools.numdiff as smnd
import scipy
# Graphics
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import rc
rc('text', usetex=True)
rc('text.latex', preamble=r'\usepackage{cmbright}')
rc('f... |
jrmontag/STLDecompose | STL-usage-example.ipynb | mit | def get_statsmodels_df():
"""Return packaged data in a pandas.DataFrame"""
# some hijinks to get around outdated statsmodels code
dataset = sm.datasets.co2.load()
start = dataset.data['date'][0].decode('utf-8')
index = pd.date_range(start=start, periods=len(dataset.data), freq='W-SAT')
obs = pd.... |
jbn/fast_proportional_selection | index.ipynb | mit | import random
from bisect import bisect_left
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
"""
Explanation: Fast Proportional Selection
[RETWEET]
Proportional selection -- or, roulette wheel selection -- comes up frequently when developing agent-based ... |
istinspring/products-matching | notebooks/0.2-group-sizes.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pandas as pd
from scipy.stats import entropy
from tabulate import tabulate
from pymongo import MongoClient
import matplotlib.pyplot as plt
plt.style.use('seaborn')
plt.rcParams["figure.figsize"] = (20,8)
db = MongoClient()['stores']
TOTAL_NUMBER_OF_PRODUCTS = db.data.coun... |
interactiveaudiolab/nussl | docs/recipes/wham/ideal_ratio_mask.ipynb | mit | from nussl import datasets, separation, evaluation
import os
import multiprocessing
from concurrent.futures import ThreadPoolExecutor
import logging
import json
import tqdm
import glob
import numpy as np
import termtables
# set up logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
"""
Explanation: Eva... |
jmschrei/pomegranate | tutorials/B_Model_Tutorial_5_Bayes_Classifiers.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn; seaborn.set_style('whitegrid')
import numpy
from pomegranate import *
numpy.random.seed(0)
numpy.set_printoptions(suppress=True)
%load_ext watermark
%watermark -m -n -p numpy,scipy,pomegranate
"""
Explanation: Naive Bayes and Bayes Classifiers
autho... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/06_structured/labs/6_deploy.ipynb | apache-2.0 | # change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
os.environ['TFVERSION'] = '1.13'
%%bash
if ! gsutil ls | grep -q gs://${BUCKET}/; t... |
jo-tez/aima-python | knowledge_FOIL.ipynb | mit | from knowledge import *
from notebook import pseudocode, psource
"""
Explanation: KNOWLEDGE
The knowledge module covers Chapter 19: Knowledge in Learning from Stuart Russel's and Peter Norvig's book Artificial Intelligence: A Modern Approach.
Execute the cell below to get started.
End of explanation
"""
psource(FOI... |
mne-tools/mne-tools.github.io | 0.17/_downloads/99b2d0c9aaf0ce2af85d098f7ac4577c/plot_head_positions.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from os import path as op
import mne
print(__doc__)
data_path = op.join(mne.datasets.testing.data_path(verbose=True), 'SSS')
pos = mne.chpi.read_head_pos(op.join(data_path, 'test_move_anon_raw.pos'))
"""
Explanation: Visualize subject he... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/text_classification/labs/fine_tune_bert.ipynb | apache-2.0 | !pip install -q -U "tensorflow-text==2.8.*"
!pip install -q tf-models-official==2.4.0
"""
Explanation: Fine-Tuning a BERT Model
Learning objectives
Get the dataset from TensorFlow Datasets.
Preprocess the data.
Build the model.
Train the model.
Re-encoding a large dataset.
Introduction
In this notebook, you will wo... |
PMEAL/OpenPNM | examples/simulations/transient/transient_fickian_diffusion.ipynb | mit | import numpy as np
import openpnm as op
%config InlineBackend.figure_formats = ['svg']
np.random.seed(10)
%matplotlib inline
np.set_printoptions(precision=5)
"""
Explanation: Transient Fickian Diffusion
The package OpenPNM allows for the simulation of many transport phenomena in porous media such as Stokes flow, Ficki... |
elastic/examples | Machine Learning/Query Optimization/notebooks/1 - Query tuning.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
import importlib
import os
import sys
from elasticsearch import Elasticsearch
from skopt.plots import plot_objective
# project library
sys.path.insert(0, os.path.abspath('..'))
import qopt
importlib.reload(qopt)
from qopt.notebooks import evaluate_mrr100_dev, optimize_query_mrr10... |
richjimenez/mysql-data-raspberry-pi | deliver/lesson-2-jupyter-notebook-for-data-analysis.ipynb | mit | %load_ext sql
"""
Explanation: Lesson 2: Setup Jupyter Notebook for Data Analysis
Learning Objectives:
<ol>
<li>Create Python tools for data analysis using Jupyter Notebooks</li>
<li>Learn how to access data from MySQL databases for data analysis</li>
</ol>
Exercise 1: Install Anaconda
Access https://conda.i... |
kirichoi/tellurium | examples/notebooks/core/tellurium_stochastic.ipynb | apache-2.0 | from __future__ import print_function
import tellurium as te
te.setDefaultPlottingEngine('matplotlib')
%matplotlib inline
import numpy as np
r = te.loada('S1 -> S2; k1*S1; k1 = 0.1; S1 = 40')
r.integrator = 'gillespie'
r.integrator.seed = 1234
results = []
for k in range(1, 50):
r.reset()
s = r.simulate(0, 40... |
adelle207/pyladies.cz | original/v1/s010-data/data.ipynb | mit | import numpy
pole = numpy.array([0, 1, 2, 3, 4, 5, 6])
pole
pole[0]
pole[1:-2]
pole[0] = 9
pole
"""
Explanation: IPython
IPython je nástroj, který zjednodušuje interaktivní práci v Pythonu, zvlášť výpočty a experimenty. Dá se spustit z příkazové řádky jako ipython – pak se chová podobně jako python, jen s barvičkam... |
ethen8181/machine-learning | model_deployment/onnxruntime/text_classification_onnxruntime.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(css_style='custom2.css', plot_style=False)
os.chdir(path)
# 1. magic for inline plot... |
roebius/deeplearning_keras2 | nbs2/translate-pytorch.ipynb | apache-2.0 | %matplotlib inline
import re, pickle, collections, bcolz, numpy as np, keras, sklearn, math, operator
from gensim.models import word2vec, KeyedVectors # - added KeyedVectors.load_word2vec_format
import torch, torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
... |
ernestyalumni/servetheloop | packetDef/podCommands.ipynb | mit | # find out where we are on the file directory
import os, sys
print( os.getcwd())
print( os.listdir(os.getcwd()))
"""
Explanation: server/udp/podCommands.js - from node.js /JavaScript to Python (object)
End of explanation
"""
wherepodCommandsis = os.getcwd()+'/reactGS/server/udp/'
print(wherepodCommandsis)
"""
Exp... |
ES-DOC/esdoc-jupyterhub | notebooks/dwd/cmip6/models/mpi-esm-1-2-hr/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'mpi-esm-1-2-hr', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: DWD
Source ID: MPI-ESM-1-2-HR
Topic: Aerosol
Sub-Topics: Transport, Emission... |
tensorflow/docs | site/en/tutorials/estimator/keras_model_to_estimator.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
graphistry/pygraphistry | demos/demos_databases_apis/gpu_rapids/part_iii_gpu_blazingsql.ipynb | bsd-3-clause | !wget -Pq data/ https://blazingsql-colab.s3.amazonaws.com/netflow_parquet/1_0_0.parquet
!wget -Pq data/ https://blazingsql-colab.s3.amazonaws.com/netflow_parquet/1_1_0.parquet
!wget -Pq data/ https://blazingsql-colab.s3.amazonaws.com/netflow_parquet/1_2_0.parquet
!wget -Pq data/ https://blazingsql-colab.s3.amazonaws.c... |
elliotk/twitter_eda | develop/20171010_realdonaldtrump_tweet_counts.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('fivethirtyeight')
import tweepy
import numpy as np
import pandas as pd
from collections import Counter
from datetime import datetime
# Turn on retina mode for high-quality inline plot resolution
from IPython.display import set_mat... |
mroberge/hydrofunctions | docs/notebooks/Writing_Valid_Requests_for_NWIS.ipynb | mit | # First, import hydrofunctions.
import hydrofunctions as hf
"""
Explanation: Writing Valid Requests for NWIS
The USGS National Water Information System (NWIS) is capable of handling a wide range of requests. A few features in Hydrofunctions are set up to help you write a successful request.
End of explanation
"""
mi... |
const-yield/const-yield.github.io | notebooks/2017-10-13/Demonstration_of_Linear_Discriminant_Anysis_on_Sythetic_Data.ipynb | mit | import numpy as np
import numpy.random as rand
import matplotlib.pyplot as plt
matplotlib inline
mu1, mu2, mu3 = [15,20], [24,25], [38,40]
cov = [[10, 0], [0, 10]]
n_samples = 5000
data1 = rand.multivariate_normal(mu1, cov, n_samples)
data2 = rand.multivariate_normal(mu2, cov, n_samples)
data3 = rand.multivariate_... |
wayfair/gists | data-science/ViolinPlot_BlogPost/ViolinPlots_BlogPost.ipynb | mit | %matplotlib inline
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from fuzzywuzzy import fuzz
import numpy as np
# some settings to be used throughout the notebook
pd.set_option('max_colwidth', 70)
wf_colors = ["#C7DEB1","#9763A4"]
# make some fake data for a demo split-violin plot
data1 = ... |
trangel/Data-Science | deep_learning_ai/Convolution+model+-+Application+-+v1.ipynb | gpl-3.0 | import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from cnn_utils import *
%matplotlib inline
np.random.seed(1)
"""
Explanation: Convolutional Neural Networks: Appli... |
arongdari/python-topic-model | notebook/HMM_LDA_example.ipynb | apache-2.0 | import logging
from ptm.nltk_corpus import get_reuters_token_list_by_sentence
from ptm import HMM_LDA
from ptm.utils import get_top_words
logger = logging.getLogger('HMM_LDA')
logger.propagate=False
"""
Explanation: Example of HMM-LDA
End of explanation
"""
n_docs = 1000
voca, corpus = get_reuters_token_list_by_sen... |
donK23/pyData-Projects | HolmesClustering/holmes_clustering/notebook/2_Modeling.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Modeling
ML Tasks
End of explanation
"""
from sklearn.datasets import load_files
corpus = load_files("../data/")
doc_count = len(corpus.data)
print("Doc count:", doc_count)
assert doc_count is 56, "Wrong num... |
karst87/ml | 01_openlibs/tensorflow/00_resource/tf_examples/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb | mit | # Import MNIST
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# Load data
X_train = mnist.train.images
Y_train = mnist.train.labels
X_test = mnist.test.images
Y_test = mnist.test.labels
"""
Explanation: MNIST Dataset Introduction
Most examples ... |
Bio204-class/bio204-notebooks | 2016-03-30-ANOVA-simulations.ipynb | cc0-1.0 | ## simulate one way ANOVA under the null hypothesis of no
## difference in group means
groupmeans = [0, 0, 0, 0]
k = len(groupmeans) # number of groups
groupstds = [1] * k # standard deviations equal across groups
n = 25 # sample size
# generate samples
samples = [stats.norm.rvs(loc=i, scale=j, size = n) for (i,j) ... |
glasslion/data-science-notebooks | thinkstats/chapter1.ipynb | mit | import matplotlib
import pandas as pd
%matplotlib inline
"""
Explanation: Chapter 1 Exploratory data analysis
Anecdotal evidence usually fails, because:
- Small number of observations
- Selection bias
- Confirmation bias
- Inaccuracy
To address the limitations of anecdotes, we will use the tools of statistics, whic... |
jo-tez/aima-python | games.ipynb | mit | from games import *
from notebook import psource, pseudocode
"""
Explanation: GAMES OR ADVERSARIAL SEARCH
This notebook serves as supporting material for topics covered in Chapter 5 - Adversarial Search in the book Artificial Intelligence: A Modern Approach. This notebook uses implementations from games.py module. Let... |
tclaudioe/Scientific-Computing | SC1v2/Bonus - 11 - Pendulum, double pendulum and chaos.ipynb | bsd-3-clause | from ipywidgets import interact, fixed, IntSlider, FloatSlider, Checkbox
import sympy as sym
sym.init_printing()
import numpy as np
import ipywidgets as widgets
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
matplotlib.rc('xtick', labelsize=20)
matplo... |
quantopian/research_public | notebooks/data/quandl.fred_gdpdef/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.quandl import fred_gdpdef
# Since this data is public domain and provided by Quandl for free, there is no _free version of this
# data set, as found in the premium sets. This import gets you the entirety of this data set.
# import data operations
from odo import od... |
sdpython/ensae_teaching_cs | _doc/notebooks/data/deal_flow_espace_vert.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: Deal flow espaces verts 2018 - 2019
Ce jeu de données est proposé pour la réalisation d'un projet de module python pour partager une fonction de graphe. Un exemple de ce projet est proposé : td2a_plotting.
End of expla... |
StingraySoftware/notebooks | Lightcurve/Analyze light curves chunk by chunk - an example.ipynb | mit | from stingray.simulator.simulator import Simulator
from scipy.ndimage.filters import gaussian_filter1d
from stingray.utils import baseline_als
from scipy.interpolate import interp1d
np.random.seed(1034232)
# Simulate a light curve with increasing variability and flux
length = 10000
dt = 0.1
times = np.arange(0, lengt... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch1-Problem_1-18.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 1
Problem 1-18
End of explanation
"""
V = 208.0 * exp(-1j*30/180*pi) # [V]
I = 2.0 * exp( 1j*20/180*pi) # [A]
"""
Explanation: Description
Assume that the voltage applied to a load is $\vec{V} = 208\,V\angle -30^\circ$ and the c... |
harishkrao/DSE200x | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | mit | import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
"""
Explanation: <p style="font-family: Arial; font-size:2.75em;color:purple; font-style:bold">
Classification of Weather Data <br><br>
using scikit-learn... |
feststelltaste/software-analytics | courses/big_data_meetup/Production Coverage Demo Notebook.ipynb | gpl-3.0 | import pandas as pd
coverage = pd.read_csv("datasets/jacoco.csv")
coverage = coverage[['PACKAGE', 'CLASS', 'LINE_COVERED' ,'LINE_MISSED']]
coverage['LINES'] = coverage.LINE_COVERED + coverage.LINE_MISSED
coverage.head(1)
"""
Explanation: Context
John Doe remarked in #AP1432 that there may be too much code in our appli... |
vascotenner/holoviews | doc/Tutorials/Dynamic_Map.ipynb | bsd-3-clause | import holoviews as hv
import numpy as np
hv.notebook_extension()
"""
Explanation: The Containers Tutorial introduced the HoloMap, a core HoloViews data structure that allows easy exploration of parameter spaces. The essence of a HoloMap is that it contains a collection of Elements (e.g. Images and Curves) that you ca... |
StudyExchange/Udacity | MachineLearning(Advanced)/p2_finding_donors/.ipynb_checkpoints/finding_donors-checkpoint.ipynb | mit | # 为这个项目导入需要的库
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # 允许为DataFrame使用display()
# 导入附加的可视化代码visuals.py
import visuals as vs
# 为notebook提供更加漂亮的可视化
%matplotlib inline
# 导入人口普查数据
data = pd.read_csv("census.csv")
# 成功 - 显示第一条记录
display(data.head())
"""
Explanati... |
arasdar/DL | impl-dl/etc/misc/nn_smartwatch.ipynb | unlicense | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Introduction: Neural Network for learning/processing time-series/historical data/signal
In this project, we'll build a neural network (NN) to learn and process hist... |
dolittle007/dolittle007.github.io | notebooks/GLM-rolling-regression.ipynb | gpl-3.0 | %matplotlib inline
import pandas as pd
import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt
"""
Explanation: Rolling Regression
Author: Thomas Wiecki
Pairs trading is a famous technique in algorithmic trading that plays two stocks against each other.
For this to work, stocks must be correlated (coint... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/sandbox-3/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'sandbox-3', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-3
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balance... |
hzzyyy/pymcef | Quickstart tutorial.ipynb | bsd-3-clause | import pandas as pd
returns = pd.read_json('data/Russel3k_return.json')
"""
Explanation: PyMCEF Quickstart tutorial
<br>
<br>
Prerequisites
Install
Please install package PyMCEF through either conda or pip:
<pre>
$ conda install -c hzzyyy pymcef
$ pip install pymcef
</pre>
conda packages are available on anaconda c... |
arogozhnikov/einops | docs/2-einops-for-deep-learning.ipynb | mit | from einops import rearrange, reduce
import numpy as np
x = np.random.RandomState(42).normal(size=[10, 32, 100, 200])
# utility to hide answers
from utils import guess
"""
Explanation: Einops tutorial, part 2: deep learning
Previous part of tutorial provides visual examples with numpy.
What's in this tutorial?
work... |
ML4DS/ML4all | C4.Classification_SVM/.ipynb_checkpoints/SupportVectorMachines-checkpoint.ipynb | mit | # To visualize plots in the notebook
%matplotlib inline
# Imported libraries
#import csv
#import random
#import matplotlib
#import matplotlib.pyplot as plt
#import pylab
#import numpy as np
#from mpl_toolkits.mplot3d import Axes3D
#from sklearn.preprocessing import PolynomialFeatures
#from sklearn import linear_model... |
Kaggle/learntools | notebooks/feature_engineering_new/raw/tut5.ipynb | apache-2.0 | #$HIDE_INPUT$
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from IPython.display import display
from sklearn.feature_selection import mutual_info_regression
plt.style.use("seaborn-whitegrid")
plt.rc("figure", autolayout=True)
plt.rc(
"axes",
labelweight="bold",
... |
sdpython/ensae_teaching_cs | _doc/notebooks/1a/nbheap.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: Heap
La structure heap ou tas en français est utilisée pour trier. Elle peut également servir à obtenir les k premiers éléments d'une liste.
End of explanation
"""
%matplotlib inline
"""
Explanation: Un tas est peut être considéré comm... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/ukesm1-0-mmh/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'ukesm1-0-mmh', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: MOHC
Source ID: UKESM1-0-MMH
Topic: Ocnbgchem
Sub-Topics: Tracers.
Prope... |
dssg/diogenes | doc/notebooks/read.ipynb | mit | import diogenes
sample_csv_text = 'id,name,age\n0,Anne,57\n1,Bill,76\n2,Cecil,26\n'
with open('sample.csv', 'w') as csv_in:
csv_in.write(sample_csv_text)
sample_table = diogenes.read.open_csv('sample.csv')
"""
Explanation: The Read Module
The :mod:diogenes.read module provides tools for reading data from externa... |
theideasmith/theideasmith.github.io | _notebooks/AsymptoticConvergence/Asymptotic Convergence of Gradient Descent for Linear Regression Least Squares Optimization.ipynb | mit | from pylab import *
from numpy import random as random
random.seed(1)
N=1000.
w = array([14., 30.]);
x = zeros((2, int(N))).astype(float32)
x[0,:] = arange(N).astype(float32)
x[1,:] = 1
y = w.dot(x) + random.normal(size=int(N), scale=100.)
"""
Explanation: Supplementary Materials
This code accompanies the paper Asymp... |
jmlon/PythonTutorials | scipy/scipyOptimize.ipynb | gpl-3.0 | from scipy.optimize import minimize
import numpy as np
"""
Explanation: scipy : Optimización
Inicialmente se importan los módulos de optimización y numpy
End of explanation
"""
# Algunas constantes
a=1
b=2
# La función a optimizar
def parabola(x):
return (x[0]-a)**2+(x[1]-b)**2
# Un punto inicial para arrancar... |
ajul/zerosum | python/examples/super_street_fighter_2_turbo.ipynb | bsd-3-clause | import _initpath
import numpy
import dataset.matchup
import dataset.csv
import zerosum.balance
from pandas import DataFrame
# Balances a Super Street Fighter 2 Turbo matchup chart using a logistic handicap.
# Produces a .csv file for the initial game and the resulting game.
init = dataset.matchup.ssf2t.sorted_by_sum... |
saturn77/CythonBootstrap | .ipynb_checkpoints/memoize-checkpoint.ipynb | gpl-2.0 | from __future__ import print_function
"""
Explanation: This is based on the previous dojo-20150131-memoization notebook.
End of explanation
"""
def fib(n):
if n == 0:
return 0
elif n == 1:
return 1
else:
return fib(n-1) + fib(n-2)
"""
Explanation: We start with a function that ca... |
metpy/MetPy | v0.5/_downloads/Point_Interpolation.ipynb | bsd-3-clause | import cartopy
import cartopy.crs as ccrs
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import numpy as np
from metpy.cbook import get_test_data
from metpy.gridding.gridding_functions import (interpolate, remove_nan_observations,
remove_repeat... |
bjshaw/phys202-2015-work | assignments/assignment09/IntegrationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
"""
Explanation: Integration Exercise 1
Imports
End of explanation
"""
def trapz(f, a, b, N):
"""Integrate the function f(x) over the range [a,b] with N points."""
h = (b-a)/N
k = np.arange(1,N)
I = h * ... |
recrm/Udebs | tutorial.ipynb | mit | import udebs
game_config = """
<udebs>
<config>
<logging>True</logging>
</config>
<entities>
<xplayer />
<oplayer />
</entities>
</udebs>
"""
game = udebs.battleStart(game_config)
"""
Explanation: Udebs -- A discrete game analysis engine for python
Udebs is a game engine that... |
EvanBianco/striplog | tutorial/Well_object.ipynb | apache-2.0 | from striplog import Well
print(Well.__doc__)
fname = 'P-129_out.LAS'
well = Well(fname)
well.data['GR']
well.well.DATE.data
"""
Explanation: Make a well
End of explanation
"""
from striplog import Striplog, Legend
legend = Legend.default()
f = 'P-129_280_1935.png'
name, start, stop = f.strip('.png').split('_')
... |
duncanwp/python_for_climate_scientists | course_content/notebooks/numpy_intro.ipynb | gpl-3.0 | import numpy as np
"""
Explanation: An introduction to NumPy
NumPy provides an efficient representation of multidimensional datasets like vectors and matricies, and tools for linear algebra and general matrix manipulations - essential building blocks of virtually all technical computing
Typically NumPy is imported as ... |
ES-DOC/esdoc-jupyterhub | notebooks/pcmdi/cmip6/models/sandbox-3/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-3', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: PCMDI
Source ID: SANDBOX-3
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Bal... |
dipanjank/ml | text_classification_and_clustering/problem_statement.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import pandas as pd
import numpy as np
import seaborn as sns
raw_input = pd.read_pickle('input.pkl')
raw_input.head()
"""
Explanation: <h1 align="center">EF Machine Learning Homework</h1>
The Machine Learning Tasks
The task(s) is(are) to bu... |
weikang9009/pysal | notebooks/viz/splot/libpysal_non_planar_joins_viz.ipynb | bsd-3-clause | from pysal.lib.weights.contiguity import Queen
import pysal.lib
from pysal.lib import examples
import matplotlib.pyplot as plt
import geopandas as gpd
%matplotlib inline
from pysal.viz.splot.pysal.lib import plot_spatial_weights
"""
Explanation: splot.pysal.lib: assessing neigbors & spatial weights
In spatial analysi... |
astroNN/astroNN | notebooks/2_fullyconnected.ipynb | mit | import h5py
import numpy as np
import tensorflow as tf
# this package
from astronn.data import fetch_notMNIST
"""
Explanation: Deep Learning
Assignment 2
Previously in 1_notmnist.ipynb, we
1. Downloaded a test dataset with training, development, and testing subsets (based on the notMNIST dataset).
2. We visualize... |
googledatalab/notebooks | samples/contrib/mlworkbench/text_classification_20newsgroup/Text Classification --- 20NewsGroup (small data).ipynb | apache-2.0 | import numpy as np
import pandas as pd
import os
import re
import csv
from sklearn.datasets import fetch_20newsgroups
# data will be downloaded. Note that an error message saying something like "No handlers could be found for
# logger sklearn.datasets.twenty_newsgroups" might be printed, but this is not an error.
new... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/300_Segmentation_Overview.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
from ipywidgets import interact, FloatSlider
import SimpleITK as sitk
# Download data to work on
%run update_path_to_download_script
from downloaddata import fetch_data as fdata
from myshow import myshow, myshow3d
img_T1 = sitk.ReadImage(fdata("nac-hncma-atlas2013-S... |
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