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600 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Intro to Autoencoders
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Load the dataset
To s... | Python Code:
#@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
# dist... |
601 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Lasso
Modified from the github repo
Step1: Hitters dataset
Let's load the dataset from the previous lab. | Python Code:
# %load ../standard_import.txt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import scale
from sklearn.model_selection import LeaveOneOut
from sklearn.linear_model import LinearRegression, lars_path, Lasso, LassoCV
%matplotlib inline
n=100
p=1000
X = np.... |
602 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Keras 中的权重聚类示例
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step2: 在不使用聚类的情况下为 MNIST 训练 tf.keras 模型
... | Python Code:
#@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
# dist... |
603 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Groupby operations
Some imports
Step1: Recap
Step2: Using the filtering and reductions operations we have seen in the previous notebooks, we could do something like
Step3: Pandas does not... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
try:
import seaborn
except ImportError:
pass
pd.options.display.max_rows = 10
Explanation: Groupby operations
Some imports:
End of explanation
df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],
... |
604 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div style="width
Step1: 3. Download settings
3.1 Choose download location
The original data can either be downloaded from the original data sources as specified below or from the opsd-Serv... | Python Code:
# Import all functions from external file
from download_and_process_DE_functions import *
# Jupyter functions
%matplotlib inline
Explanation: <div style="width:100%; background-color: #D9EDF7; border: 1px solid #CFCFCF; text-align: left; padding: 10px;">
<b>Conventional Power Plants: Power Plants in ... |
605 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
USDA Food Data - Preliminary Analysis
USDA Food Data is obtained from a consolidated dataset published by the Open Food Facts organization (https
Step1: Preliminary look at the USDA data
St... | Python Code:
# load pre-requisite imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import re
from gensim import corpora, models, similarities
# load world food data into a pandas dataframe
world_food_facts =pd.read_csv("../w209finalproject_data/data/en.openfoodfacts.org.products.tsv", sep=... |
606 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tinkering with Keras
The goal of this notebook is to store useful insights made through my learning process of Keras. Since I will be closely working with a colleague who uses Theano, the id... | Python Code:
%reset -f
import keras.backend as K
x = K.variable(42.)
# Solution 1:
sess = K.get_session()
print sess.run(x)
# Solution 2 (seamless):
print K.eval(x)
Explanation: Tinkering with Keras
The goal of this notebook is to store useful insights made through my learning process of Keras. Since I will be closely ... |
607 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
We're going to start by grabbing the geometry for the Austin community area.
Step1: Now let's get the shootings data.
Step2: Now let's iterate through the shootings, generate shapely point... | Python Code:
import requests
from shapely.geometry import shape, Point
r = requests.get('https://data.cityofchicago.org/api/geospatial/cauq-8yn6?method=export&format=GeoJSON')
for feature in r.json()['features']:
if feature['properties']['community'] == 'AUSTIN':
austin = feature
poly = shape(austin['geomet... |
608 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bagging元估计器
Bagging是Bootstrap Aggregating的简称,意思就是再取样(Bootstrap)然后在每个样本上训练出来的模型进行集成.
通常如果目标是分类,则集成的方式是投票;如果目标是回归,则集成方式是取平均.
在集成算法中,bagging方法会在原始训练集的随机子集上构建一类黑盒估计器的多个实例,然后把这些估计器的预测结果结合起来形成最终的预... | Python Code:
import requests
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder,StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import classification_report
from sklearn.ensemble import BaggingClassifier
Explanatio... |
609 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Uncovering Configuration and Behavior Drift
When debugging network issues, it is important to understand how the network is different today compared to yesterday or to the desired golden sta... | Python Code:
# Use recursive diff, followed by some pretty printing hacks
!diff -ur networks/drift/reference networks/drift/snapshot | sed -e 's;diff.*snapshot/\(configs.*cfg\);^-----------\1---------;g' | tr '^' '\n' | grep -v networks/drift
Explanation: Uncovering Configuration and Behavior Drift
When debugging netwo... |
610 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pymagicc Usage Examples
Step1: Scenarios
The four RCP scenarios are already preloaded in Pymagicc. They are loaded as MAGICCData objects with metadata attributes. metadata contains metadata... | Python Code:
# NBVAL_IGNORE_OUTPUT
from pprint import pprint
import pymagicc
from pymagicc import MAGICC6
from pymagicc.io import MAGICCData
from pymagicc.scenarios import rcp26, rcp45, rcps
%matplotlib inline
from matplotlib import pyplot as plt
plt.style.use("ggplot")
plt.rcParams["figure.figsize"] = 16, 9
Explanatio... |
611 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interpolating Parameters
If parameters are changing significantly on dynamical timescales (e.g. mass transfer at pericenter on very eccentric orbits) you need a specialized numerical scheme ... | Python Code:
import numpy as np
data = np.loadtxt('m.txt') # return (N, 2) array
mtimes = data[:, 0] # return only 1st col
masses = data[:, 1] # return only 2nd col
data = np.loadtxt('r.txt')
rtimes = data[:, 0]
Rsuns = data[:, 1] # data in Rsun units
# convert Rsun to AU
radii = np.zeros(Rsuns.si... |
612 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="http
Step1: Create the grid
We are going to build a uniform rectilinear grid with a node spacing of 10 km in the y-direction and 20 km in the x-direction on which we will solve the... | Python Code:
%matplotlib inline
import numpy as np
Explanation: <a href="http://landlab.github.io"><img style="float: left" src="../../landlab_header.png"></a>
Using the Landlab flexure component
<hr>
<small>For more Landlab tutorials, click here: <a href="https://landlab.readthedocs.io/en/latest/user_guide/tutorials.h... |
613 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examples and Exercises from Think Stats, 2nd Edition
http
Step1: Time series analysis
Load the data from "Price of Weed".
Step3: The following function takes a DataFrame of transactions an... | Python Code:
from __future__ import print_function, division
%matplotlib inline
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
import numpy as np
import pandas as pd
import random
import thinkstats2
import thinkplot
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thin... |
614 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Subselect / Unterabfragen)
Zur Durchführung einer Abfrage werden Informationen benötigt, die erst durch eine eigene Abfrage geholt werden müssen.
Sie können stehen
als Vertreter für einen We... | Python Code:
%load_ext sql
%sql mysql://steinam:steinam@localhost/versicherung_complete
Explanation: Subselect / Unterabfragen)
Zur Durchführung einer Abfrage werden Informationen benötigt, die erst durch eine eigene Abfrage geholt werden müssen.
Sie können stehen
als Vertreter für einen Wert
als Vertreter für eine Lis... |
615 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to TensorFlow, fitting point by point
In this notebook, we introduce TensorFlow by fitting a line of the form y=m*x+b point by point. This is a derivation of Jared Ostmeyer's Na... | Python Code:
import numpy as np
np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
tf.set_random_seed(42)
Explanation: Introduction to TensorFlow, fitting point by point
In this notebook, we introduce TensorFlow by fitting a line of the form y=m*x+b point b... |
616 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmos
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'sandbox-3', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, T... |
617 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src="../Pierian-Data-Logo.PNG">
<br>
<strong><center>Copyright 2019. Created by Jose Marcial Portilla.</center></strong>
RNN for Text Generation
Generating Text (encoded variables)
We s... | Python Code:
import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: <img src="../Pierian-Data-Logo.PNG">
<br>
<strong><center>Copyright 2019. Created by Jose Marcial Portilla.</center></strong>
RNN for Text Generation
Generati... |
618 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Manipulating the Pandas DataFrame
The iPython notebook for this demo can be found in
Step1: First I'm going to pull out a small subset to work with
Step2: I happen to like the way that's o... | Python Code:
import os
import pyNastran
pkg_path = pyNastran.__path__[0]
from pyNastran.op2.op2 import read_op2
import pandas as pd
pd.set_option('precision', 2)
op2_filename = os.path.join(pkg_path, '..', 'models', 'iSat', 'iSat_launch_100Hz.op2')
from pyNastran.op2.op2 import read_op2
isat = read_op2(op2_filename, bu... |
619 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<br />
<br />
Gilles Pirio @ Ripple Research & Data Team
August 20, 2015
Visualizing order books on Ripple
Ripple is a distributed ledger that is not limited to one currency. The Ripple prot... | Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
from pyripple.feed import syncfeed
feed = syncfeed.SyncFeed()
Explanation: <br />
<br />
Gilles Pirio @ Ripple Research & Data Team
August 20, 2015
Visualizing order books on Ripple
Ripple is a distributed ledger that is not limited to one currency. The Ri... |
620 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Importar GraphLab
Step1: Cargar el dataset
Step2: Los datos contienen articulos de wikipedia sobre diferentes personas.
Step3: Buscaremos al expresidente Barack Obama
Step4: Contar las p... | Python Code:
import graphlab
Explanation: Importar GraphLab
End of explanation
people = graphlab.SFrame('people_wiki.gl/')
Explanation: Cargar el dataset
End of explanation
people.head()
len(people)
Explanation: Los datos contienen articulos de wikipedia sobre diferentes personas.
End of explanation
obama = people[peop... |
621 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Poster popularity by state
This notebook loads data of poster viewership at the SfN 2016 annual meeting, organized by the states that were affiliated with each poster.
We find that the poste... | Python Code:
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
import pandas as pd
# Load data
df = pd.DataFrame.from_csv('./posterviewers_by_state.csv')
key_N = 'Number of people'
Explanat... |
622 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
EAS Testing - Recents Fling on Android
The goal of this experiment is to collect frame statistics while swiping up and down tabs of recently opened applications on a Nexus N5X running Androi... | Python Code:
import logging
reload(logging)
log_fmt = '%(asctime)-9s %(levelname)-8s: %(message)s'
logging.basicConfig(format=log_fmt)
# Change to info once the notebook runs ok
logging.getLogger().setLevel(logging.INFO)
%pylab inline
import os
from time import sleep
# Support to access the remote target
import devlib
... |
623 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Problem Set 2 Exercise 5
Part 2.5.d.iv
Step1: First we need to load the training and testing data sets and shape the data to run the analysis.
Step2: Then we compute the boosting data usin... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import load_data as ld
import random_booster as rb
import stump_booster as sb
import errors_over_time as eot
Explanation: Problem Set 2 Exercise 5
Part 2.5.d.iv
End of explanation
training_data, testing_data = ld.load_dataset('boosting-train.csv', 'boostin... |
624 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
Step1: Import raw data
The user needs to specify the directories containing the data of interest. Each sample type should have a key which corresponds to the directory path. Ad... | Python Code:
import deltascope as ds
import deltascope.alignment as ut
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
from scipy.optimize import minimize
import os
import tqdm
import json
import time
Explanation: Introduction: Landmarks
End of explanat... |
625 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Use the astropy interface to get the location of Jupiter as the time that you want to use.
Step1: Conclusion | Python Code:
dt = 0.
# Using JPL Horizons web interface at 2017-05-19T01:34:40
horizon_ephem = SkyCoord(*[193.1535, -4.01689]*u.deg)
for orbit in orbits:
tstart = orbit[0]
tend = orbit[1]
print()
# print('Orbit duration: ', tstart.isoformat(), tend.isoformat())
on_time = (tend - tstart).total_seconds... |
626 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocean
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-1', 'ocean')
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: TEST-INSTITUTE-2
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Ti... |
627 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Twitter data
Copyright and Licensing
You are free to use or adapt this notebook for any purpose you'd like. However, please respect the Simplified BSD License that governs its use.
Twitter A... | Python Code:
import pickle
import os
if not os.path.exists('secret_twitter_credentials.pkl'):
Twitter={}
Twitter['Consumer Key'] = ''
Twitter['Consumer Secret'] = ''
Twitter['Access Token'] = ''
Twitter['Access Token Secret'] = ''
with open('secret_twitter_credentials.pkl','wb') as f:
pi... |
628 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mapping Names to Sequence Elements
Problem
You have code that accesses list or tuple elements by position, but this makes the code somewhat difficult to read at times. You’d also like to be ... | Python Code:
from collections import namedtuple
Subscriber = namedtuple('Subscriber', ['addr', 'joined'])
sub = Subscriber('jonesy@example.com', '2012-10-19')
sub
print(sub.addr)
print(sub.joined)
Explanation: Mapping Names to Sequence Elements
Problem
You have code that accesses list or tuple elements by position, but... |
629 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Autoregressive Moving Average (ARMA)
Step1: Sunpots Data
Step2: Does our model obey the theory?
Step3: This indicates a lack of fit.
In-sample dynamic prediction. How good does our model ... | Python Code:
%matplotlib inline
from __future__ import print_function
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.graphics.api import qqplot
Explanation: Autoregressive Moving Average (ARMA): Sunspots data
This notebook rep... |
630 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MetPy Declarative Syntax Tutorial
The declarative syntax that is a part of the MetPy packaged is designed to aid in simple
data exploration and analysis needs by simplifying the plotting con... | Python Code:
from datetime import datetime, timedelta
import xarray as xr
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.io import metar
from metpy.plots.declarative import (BarbPlot, ContourPlot, FilledContourPlot, MapPanel,
PanelContainer, PlotObs)
fr... |
631 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Centralities
In this section, I'm going to learn how Centrality works and try to interpret the data based on small real dataset. I'm using Facebook DataSet from SNAP https
Step1: Now let's ... | Python Code:
%matplotlib inline
import networkx as nx
import matplotlib.pyplot as plt
import operator
import timeit
g_fb = nx.read_edgelist('facebook_combined.txt', create_using = nx.Graph(), nodetype = int)
print nx.info(g_fb)
print nx.is_directed(g_fb)
Explanation: Centralities
In this section, I'm going to learn how... |
632 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python's iterators and generators
TODO
* https
Step1: Iterator
TODO...
Step2: Generator
Goal
Step3: Generator iterator
https
Step4: Generator expression
https
Step5: Strange test | Python Code:
# import python packages here...
Explanation: Python's iterators and generators
TODO
* https://stackoverflow.com/questions/2776829/difference-between-pythons-generators-and-iterators
* https://docs.python.org/3/glossary.html#term-generator
* https://docs.python.org/3/glossary.html#term-generator-iterator
*... |
633 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NumPy
Step1: 1. Создание векторов
Самый простой способ создать вектор в NumPy — задать его явно с помощью numpy.array(list, dtype=None, ...).
Параметр list задает итерируемый объект, из кот... | Python Code:
import numpy as np
Explanation: NumPy: векторы и операции над ними
В этом ноутбуке нам понадобятся библиотека NumPy. Для удобства импортируем ее под более коротким именем:
End of explanation
a = np.array([1, 2, 3, 4])
print 'Вектор:\n', a
b = np.array([1, 2, 3, 4, 5], dtype=float)
print 'Вещественный векто... |
634 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Загрузим файл о именах,поле и числе детей с сайта
https
Step1: Посчитаем количество родившихся в зависимости от пола
Step2: Теперь создадим первую сводную таблицу
Step3: Построим график... | Python Code:
import pandas as pd
import matplotlib.pyplot as plt
names1880 = pd.read_csv('/Users/kirill/Downloads/names/yob1880.txt',
names= ['name', 'sex', 'births'])
names1880
Explanation: Загрузим файл о именах,поле и числе детей с сайта
https://www.ssa.gov/oact/babynames/limits.html
Собер... |
635 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Use decision optimization to determine Cloud balancing.
This tutorial includes everything you need to set up decision optimization engines, build mathematical programming models, and a solve... | Python Code:
import sys
try:
import docplex.mp
except:
raise Exception('Please install docplex. See https://pypi.org/project/docplex/')
Explanation: Use decision optimization to determine Cloud balancing.
This tutorial includes everything you need to set up decision optimization engines, build mathematical prog... |
636 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: MNIST Test
WNixalo 2018/5/19-20;25-26
Making sure I have a working baseline for the MNIST dataset. PyTorch version
Step2: 1. Data
1.1 PyTorch method
Step4: 1.1.1 Aside
Step5: 1.3 F... | Python Code:
%matplotlib inline
%reload_ext autoreload
%autoreload 2
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from pathlib import Path
import os
import struct # for IDX conversion
import gzip # for IDX conversion
from urllib.request import urlretriev... |
637 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Препроцессинг фич
Step1: Обучение моделей
Step2: Submission | Python Code:
# train_raw = pd.read_csv("data/train.csv")
train_raw = pd.read_csv("data/train_without_noise.csv")
macro = pd.read_csv("data/macro.csv")
train_raw.head()
def preprocess_anomaly(df):
df["full_sq"] = map(lambda x: x if x > 10 else float("NaN"), df["full_sq"])
df["life_sq"] = map(lambda x: x if x > 5... |
638 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Neural Nets for Digit Classification
by Khaled Nasr as a part of a <a href="https
Step1: Creating the network
To create a neural network in shogun, we'll first create an instance of NeuralN... | Python Code:
%pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from scipy.io import loadmat
from shogun import features, MulticlassLabels, Math
# load the dataset
dataset = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))
Xall = dataset['data']
# the u... |
639 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generic fit for approximating with a polynomial
Step1: Define a quality-of-fit measure, $\chi^2$
Step2: We want to minimized $\chi^2$. Take derivative wrt the coefficients ($a_k$) and set ... | Python Code:
f = Symbol('f') # Function to approximate
f_approx = Symbol('fbar') # Approximating function
w = Symbol('w') # weighting function
chi2 = Symbol('chi^2')
f, f_approx, w, chi2
M = Symbol('M', integer=True)
k = Symbol('k', integer=True,positive=True)
a = IndexedBase('a',(M,)) # coefficient
h = IndexedBase... |
640 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab
Step1: Some helper functions
Step2: Plotting the data
Step3: 2. Build a Linear Regression Model
Create a regression model and assign the weights to the array bmi_life_model.
Fit the m... | Python Code:
import numpy as np
import pandas as pd
# TODO: Load the data in Pandas
bmi_life_data = None
# Print the data
bmi_life_data
Explanation: Lab: Predicting Life Expectancy from BMI in Countries using Linear Regression
In this lab, you'll be working with data on the average life expectancy at birth and the aver... |
641 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Software Engineering for Data Scientists
Sophisticated Data Manipulation
DATA 515 A
1. Python's Data Science Ecosystem
With this simple Python computation experience under our belt, we can n... | Python Code:
import numpy as np
Explanation: Software Engineering for Data Scientists
Sophisticated Data Manipulation
DATA 515 A
1. Python's Data Science Ecosystem
With this simple Python computation experience under our belt, we can now move to doing some more interesting analysis.
Python's Data Science Ecosystem
In a... |
642 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Speci... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-1', 'ocnbgchem')
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: MOHC
Source ID: SANDBOX-1
Topic: Ocnbgchem
Sub-Topics: Tracers.
Prop... |
643 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Accessing Databases via Web APIs
Step1: 1. Constructing API GET Request
In the first place, we know that every call will require us to provide
Step2: You often want to send some sort of da... | Python Code:
# Import required libraries
import requests
import json
from __future__ import division
import math
import csv
import matplotlib.pyplot as plt
Explanation: Accessing Databases via Web APIs
End of explanation
# set key
key="be8992a420bfd16cf65e8757f77a5403:8:44644296"
# set base url
base_url="http://api.nyt... |
644 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Grove Temperature Sensor 1.2
This example shows how to use the Grove Temperature Sensor v1.2. You will also see how to plot a graph using matplotlib. The Grove Temperature sensor produces an... | Python Code:
from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
Explanation: Grove Temperature Sensor 1.2
This example shows how to use the Grove Temperature Sensor v1.2. You will also see how to plot a graph using matplotlib. The Grove Temperature sensor produces an analog signal, and requires a... |
645 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: repeat(1,10000)重复一次，每次10000遍
Step2: 这个就没有重复多少次，就一次，一次10000遍
注意这里timeit.timeit的函数声明，第三个参数是timer，为了避过该参数，到了number这里，显式的声明该参数 | Python Code:
setup_sum='sum=0'
run_sum=
for i in range(1,1000):
if i % 3 ==0:
sum = sum + i
print(timeit.Timer(run_sum, setup="sum=0").repeat(1,10000))
Explanation: repeat(1,10000)重复一次，每次10000遍
End of explanation
t=timeit.timeit(run_sum,setup_sum,number=10000)
print("Time for built-in sum(): {}".format(t))
... |
646 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Contexts
The Vcsn platform relies on a central concept
Step1: If instead of a simple accepter that returns "yes" or "no", you want to compute an integer, work in $\mathbb{Z}$
Step2: To use... | Python Code:
import vcsn
vcsn.context('lal<char(abc)>, b')
Explanation: Contexts
The Vcsn platform relies on a central concept: "contexts". They denote typing information about automata, rational expressions, etc. This information is alike a function type: an input type (the label), and an output type (the weight).
C... |
647 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
plot line chats between two arrays k and l
| Python Code::
import matplotlib.pyplot as plt
plt.plot(k,l)
|
648 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Properties of Rectangular Waveguide
Introduction
This example demonstrates how to use scikit-rf to calculate some properties of rectangular waveguide. For more information regarding the theo... | Python Code:
%matplotlib inline
import skrf as rf
rf.stylely()
# imports
from scipy.constants import mil,c
from skrf.media import RectangularWaveguide, Freespace
from skrf.frequency import Frequency
import matplotlib as mpl
# plot formating
mpl.rcParams['lines.linewidth'] = 2
# create frequency objects for standard b... |
649 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Module 2
Step1: As the tide moves through the Strait, it creates a change in the elevation of the water surface. Below we'll cycle through a tidal cycle and look at how the tide moves throu... | Python Code:
import tydal.module2_utils as tide
import tydal.quiz2
stationmap = tide.add_station_maps()
stationmap
Explanation: Module 2: Tides in the Puget Sound
Learning Objectives
I. Tidal Movement
II. Tidal Cycle and Connection to Sea Surface Elevation
Let's take a closer look at the movement of tides through the ... |
650 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have a table of measured values for a quantity that depends on two parameters. So say I have a function fuelConsumption(speed, temperature), for which data on a mesh are known. | Problem:
import numpy as np
import scipy.interpolate
s = np.linspace(-1, 1, 50)
t = np.linspace(-2, 0, 50)
x, y = np.ogrid[-1:1:10j,-2:0:10j]
z = (x + y)*np.exp(-6.0 * (x * x + y * y))
spl = scipy.interpolate.RectBivariateSpline(x, y, z)
result = spl(s, t, grid=False) |
651 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data were munged here.
Step1: <h5>First
Step2: <h3>When did River Grove open, when did the last owners take over, and how many companies have owned the facility?</h3>
Step3: <h3>How many ... | Python Code:
import pandas as pd
import numpy as np
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
df = pd.read_csv('../../data/processed/complaints-3-29-scrape.csv')
owners = pd.read_csv('../../data/raw/APD_HistOwner.csv')
Explanation: Data were mun... |
652 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Network Tour of Data Science
Pierre Vandergheynst, Full Professor, and Michaël Defferrard, PhD student, EPFL LTS2.
Exercise 5
Step1: 1 Graph
Goal
Step2: Step 2
Step3: Step 3
Step4: Ste... | Python Code:
import numpy as np
import scipy.spatial
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: A Network Tour of Data Science
Pierre Vandergheynst, Full Professor, and Michaël Defferrard, PhD student, EPFL LTS2.
Exercise 5: Graph Signals and Fourier Transform
The goal of this exercise is to experi... |
653 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1><span style="color
Step1: Species tree model
Step2: Coalescent simulations
The SNPs output is saved to an HDF5 database file.
Step3: [optional] Build an IMAP dictionary
A dictionary m... | Python Code:
# conda install ipyrad -c conda-forge -c bioconda
# conda install ipcoal -c conda-forge
import ipyrad.analysis as ipa
import ipcoal
import toyplot
import toytree
Explanation: <h1><span style="color:gray">ipyrad-analysis toolkit:</span> distance</h1>
Genetic distance matrices are used in many contexts to st... |
654 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Remote Sensing Systems (RSS, http
Step2: Weight functions
Step3: Netcdf data
Step4: We need to calculate the element area (on a unit sphere) as follows
Step5: Let's create averaging weig... | Python Code:
#!wget http://www.remss.com/data/msu/data/netcdf/uat4_tb_v03r03_avrg_chTLT_197812_201308.nc3.nc
#!mv uat4_tb_v03r03_avrg_chTLT_197812_201308.nc3.nc data/
#!wget http://www.remss.com/data/msu/data/netcdf/uat4_tb_v03r03_anom_chTLT_197812_201308.nc3.nc
#!mv uat4_tb_v03r03_anom_chTLT_197812_201308.nc3.nc data/... |
655 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Recuperando Tweets
Para utilizar qualquer API do Twitter temos que importar os módulos e definir as chaves e tokens de acesso.
Step1: Com as chaves e tokens de acesso, iremos criar a autent... | Python Code:
import tweepy
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''
Explanation: Recuperando Tweets
Para utilizar qualquer API do Twitter temos que importar os módulos e definir as chaves e tokens de acesso.
End of explanation
autorizar = tweepy.OAuthHandler(consumer_key, consum... |
656 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
User comparison tests
Table of Contents
Preparation
User data vectors
User lists
Sessions' checkpoints
Assembly
Time
Preparation
<a id=preparation />
Step1: Data vectors of users
<a id=user... | Python Code:
%run "../Functions/1. Google form analysis.ipynb"
%run "../Functions/4. User comparison.ipynb"
Explanation: User comparison tests
Table of Contents
Preparation
User data vectors
User lists
Sessions' checkpoints
Assembly
Time
Preparation
<a id=preparation />
End of explanation
#getAllResponders()
setAnswerT... |
657 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Exploration of a publicly available dataset.
<img align="right" src="http
Step1: Two columns that are a mistaken copy of each other?...
We also suspect that the 'inactive' column and t... | Python Code:
# This exercise is mostly for us to understand what kind of data we have and then
# run some simple stats on the fields/values in the data. Pandas will be great for that
import pandas as pd
pd.__version__
# Set default figure sizes
pylab.rcParams['figure.figsize'] = (14.0, 5.0)
# This data url can be a web... |
658 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmos
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc-es2l', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC-ES2L
Topic: Atmos
Sub-Topics: Dynamical Core, Radiat... |
659 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model Evaluation, Scoring Metrics, and Dealing with Imbalanced Classes
In the previous notebook, we already went into some detail on how to evaluate a model and how to pick the best model. S... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
np.set_printoptions(precision=2)
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
digits = load_digits()
X, y = digits.data, digits.target
X_train, X_test, y_... |
660 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Summary" data-toc-modified-id="Summary-1"><span class="toc-item-num">1 </span>Summary</a></div><div class="lev1 toc-item"... | Python Code:
%run ../../code/version_check.py
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Summary" data-toc-modified-id="Summary-1"><span class="toc-item-num">1 </span>Summary</a></div><div class="lev1 toc-item"><a href="#Version-Control" data-toc-modified-id="Version-Control-2"><s... |
661 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Note
Step1: How old are the programmers that answered this survey?
Step2: What industries are these individuals working in?
Step3: What text editor do these individuals prefer?
Step4: Wh... | Python Code:
# workon dataanalysis - my virtual environment
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# df = pd.read_table('34933-0001-Data.tsv')
odf = pd.read_csv('accreditation_2016_03.csv')
odf.head()
odf.columns
odf['Campus_City'].value_counts().head(10)
top_cities = odf['Campus_City'].... |
662 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NOTAS
CALCULAR EN BASE AL MODELO DE CURVAS LAS DERIVADAS Y DONDE HACE EL PICO EN EDAD
DOWNLOAD DATA
Step1: NEW VARIABLES FOR MODEL
Graficos exploratorios
Step2: PLOTS FOR LnINCOME ~ EDUC A... | Python Code:
#get data
getEPHdbf('t310')
data1 = pd.read_csv('data/cleanDatat310.csv')
data2 = categorize.categorize(data1)
data3 = schoolYears.schoolYears(data2)
data = make_dummy.make_dummy(data3)
dataModel = functionsForModels.prepareDataForModel(data)
dataModel.head()
Explanation: NOTAS
CALCULAR EN BASE AL MODELO D... |
663 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tensor Transformations
Step1: NOTE on notation
* _x, _y, _z, ...
Step2: Q2. Let X be a tensor [[1, 2], [3, 4]] of int32. Convert the data type of X to float64.
Step3: Q3. Let X be a tenso... | Python Code:
from __future__ import print_function
import tensorflow as tf
import numpy as np
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"
tf.__version__
np.__version__
sess = tf.InteractiveSession()
Explanation: Tensor Transformations
End of explanation
_... |
664 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Assignment 2 - Building CNNs
ASSIGNMENT DEADLINE
Step4: Convolution
Step5: FOR SUBMISSION
Step7: Aside
Step8: Convolution
Step9: ReLU layer
Step10: FOR SUBMISSION
Step11: Max p... | Python Code:
# A bit of setup
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from code_base.classifiers.cnn import *
from code_base.data_utils import get_CIFAR2_data
from code_base.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from code_base.layer... |
665 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 5
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
Step2: Interact with SVG display
SVG is a simple way of drawing vec... | Python Code:
# YOUR CODE HERE
import matplotlib as plt
import numpy as np
import IPython as ipy
from IPython.display import SVG
from IPython.html.widgets import interactive, fixed
from IPython.html import widgets
from IPython.display import display
Explanation: Interact Exercise 5
Imports
Put the standard imports for M... |
666 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Programmatic Access to Genome Nexus
This notebook gives some examples in Python for programmatic access to http
Step1: Connect with cBioPortal API
cBioPortal also uses Swagger for their API... | Python Code:
from bravado.client import SwaggerClient
client = SwaggerClient.from_url('https://www.genomenexus.org/v2/api-docs',
config={"validate_requests":False,"validate_responses":False})
print(client)
dir(client)
for a in dir(client):
client.__setattr__(a[:-len('-controller')], ... |
667 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Santandar Customer Satisfaction
Step 1
Step1: Exercise 1 Find column types for train and test.
Exercise 2 Find unique column types for train and test
Exercise 3 Find number of rows and col... | Python Code:
import numpy as np
import pandas as pd
#Read train, test and sample submission datasets
train = pd.read_csv("../data/train.csv")
test = pd.read_csv("../data/test.csv")
samplesub = pd.read_csv("../data/sample_submission.csv")
Explanation: Santandar Customer Satisfaction
Step 1: Frame
From frontline support ... |
668 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
9 - Advanced topics - 1 axis torque tube Shading for 1 day (Research Documentation)
Recreating JPV 2019 / PVSC 2018 Fig. 13
Calculating and plotting shading from torque tube on 1-axis tracki... | Python Code:
import os
from pathlib import Path
testfolder = str(Path().resolve().parent.parent / 'bifacial_radiance' / 'TEMP' / 'Tutorial_09')
if not os.path.exists(testfolder):
os.makedirs(testfolder)
print ("Your simulation will be stored in %s" % testfolder)
# VARIABLES of the simulation:
lat = 35.1 # ABQ
lon ... |
669 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Collocational Analysis
"You shall know a word by the company it keeps!" These are the oft-quoted words of the linguist J.R. Firth in describing the meaning and spirit of collocational analys... | Python Code:
# This is where the modules are imported
import csv
import sys
import codecs
import nltk
import nltk.collocations
import collections
import statistics
from nltk.metrics.spearman import *
from nltk.collocations import *
from nltk.stem import WordNetLemmatizer
from os import listdir
from os.path import split... |
670 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
How do I apply sort to a pandas groupby operation? The command below returns an error saying that 'bool' object is not callable | Problem:
import pandas as pd
df = pd.DataFrame({'cokey':[11168155,11168155,11168155,11168156,11168156],
'A':[18,0,56,96,0],
'B':[56,18,96,152,96]})
def g(df):
return df.groupby('cokey').apply(pd.DataFrame.sort_values, 'A')
result = g(df.copy()) |
671 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Implementing the Gradient Descent Algorithm
In this lab, we'll implement the basic functions of the Gradient Descent algorithm to find the boundary in a small dataset. First, we'll start wit... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
#Some helper functions for plotting and drawing lines
def plot_points(X, y):
admitted = X[np.argwhere(y==1)]
rejected = X[np.argwhere(y==0)]
plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color... |
672 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2017 Google LLC.
Step1: # Creación y manipulación de tensores
Objetivos de aprendizaje
Step2: ## Suma de vectores
Puedes realizar muchas operaciones matemáticas en los tensores (... | Python Code:
# 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
# distribute... |
673 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
This tutorial introduces the basic features for simulating titratable systems via the constant pH method.
The constant pH method is one of the methods implemented for simulating... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
import scipy.constants # physical constants
import espressomd
import pint # module for working with units and dimensions
from espressomd import electrostatics, polymer, reaction_ensemble
from espressomd.interactions import HarmonicBond
ureg = pint.UnitRe... |
674 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Pipeline
In this notebook, we show how to run the flowers classification workflow as a pipeline
Set up
Step1: Build the container
Step2: Convert JPEG files to TF Records
S... | Python Code:
%pip install --upgrade --user kfp
# CHANGE AS needed
REGION = 'us-central1' # Change as needed to a region where you have quota
KFPHOST = 'https://40e09ee3a33a422-dot-us-central1.pipelines.googleusercontent.com' # Note name of launched Kubeflow Pipelines cluster
PROJECT = !gcloud config get-value project... |
675 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using Machine Learning to Predict Breast Cancer
Matt Massie, UC Berkeley Computer Sciences
Machine learning (ML) is data driven. Machine learning algorithms are constructed to learn from and... | Python Code:
import numpy as np
import pandas as pd
def load_data(filename):
import csv
with open(filename, 'rb') as csvfile:
csvreader = csv.reader(csvfile, delimiter=',')
df = pd.DataFrame([[-1 if el == '?' else int(el) for el in r] for r in csvreader])
df.columns=["patient_id", "radiu... |
676 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Stiffness matrix for a perfectly square bilinear element.
Summary
This notebook describes the computational steps required in the computation of the displacement based finite element stiffne... | Python Code:
%matplotlib notebook
from __future__ import division
import numpy as np
import sympy as sym
import matplotlib.pyplot as plt
from IPython.display import Image
Explanation: Stiffness matrix for a perfectly square bilinear element.
Summary
This notebook describes the computational steps required in the comput... |
677 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
AutoGraph
Step1: Fibonacci numbers
https
Step2: Generated code
Step3: Fizz Buzz
https
Step4: Generated code
Step5: Conway's Game of Life
https
Step6: Game of Life for AutoGraph
Note
St... | Python Code:
!pip install -U -q tf-nightly-2.0-preview
import tensorflow as tf
tf = tf.compat.v2
tf.enable_v2_behavior()
Explanation: AutoGraph: examples of simple algorithms
This notebook shows how you can use AutoGraph to compile simple algorithms and run them in TensorFlow.
It requires the nightly build of TensorFlo... |
678 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: scikit-image advanced panorama tutorial
Enhanced from the original demo as featured in the scikit-image paper.
Multiple overlapping images of the same scene, combined into a single im... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
def compare(*images, **kwargs):
Utility function to display images side by side.
Parameters
----------
image0, image1, image2, ... : ndarrray
Images to display.
labels : list
Labels for the different images.
... |
679 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial
Step1: In this profile, diffusivity drops to 0 at $y=0.5$ and at $y=0$ and $y=1$. In the absence of advection, particles starting out in one half of the domain should remain confin... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
from datetime import timedelta
from parcels import ParcelsRandom
from parcels import (FieldSet, Field, ParticleSet, JITParticle, AdvectionRK4, ErrorCode,
DiffusionUniformKh, AdvectionDiffusionM1, ... |
680 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Authors.
Step1: Human Pose Classification with MoveNet and TensorFlow Lite
This notebook teaches you how to train a pose classification model using MoveNet and... | Python Code:
#@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
# dist... |
681 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Wing modelling example
In this example, we demonstrate, how to build up a wing surface by starting with a list of curves. These curves are then interpolated using a B-spline suface interpola... | Python Code:
import tigl3.curve_factories
import tigl3.surface_factories
from OCC.gp import gp_Pnt
from OCC.Display.SimpleGui import init_display
import numpy as np
Explanation: Wing modelling example
In this example, we demonstrate, how to build up a wing surface by starting with a list of curves. These curves are the... |
682 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
Step1: Import raw data
The user needs to specify the directories containing the data of interest. Each sample type should have a key which corresponds to the directory path. Ad... | Python Code:
import deltascope as ds
import deltascope.alignment as ut
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
from scipy.optimize import minimize
import os
import tqdm
import json
import time
Explanation: Introduction: Landmarks
End of explanat... |
683 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Assignment - part 2
Now that we have a better understanding of how to set up a basic neural network in Tensorflow, let's see if we can convert our dataset to a classificiation problem, and t... | Python Code:
%matplotlib inline
import math
import random
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
'''Since this is a classification problem, we will need to
represent our targets as one-hot encoding vectors (see previous lab).
To do this we wil... |
684 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The first step in any data analysis is acquiring and munging the data
Our starting data set can be found here
Step1: Problems
Step2: Problems
Step3: Problems
Step4: If we want to look at... | Python Code:
running_id = 0
output = [[0]]
with open("E:/output.txt") as file_open:
for row in file_open.read().split("\n"):
cols = row.split(",")
if cols[0] == output[-1][0]:
output[-1].append(cols[1])
output[-1].append(True)
else:
output.append(cols)
... |
685 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Speci... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'sandbox-2', 'ocnbgchem')
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Proper... |
686 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Convolutional VAE
Step1: Import TensorFlow and enable Eager execution
Step2: Load the... | Python Code:
# to generate gifs
!pip install imageio
Explanation: Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Convolutional VAE: An example with tf.keras and eager
<table class="tfo-notebook-buttons" align="left"><td>
<a target="_blank" href="https://colab.res... |
687 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Name
Step1: Part One
Step2: plot xkcd style
Step3: Part Two
Step4: Calculating the letter frequency for male names.
Step5: Calculating the letter frequency for female names.
Step6: Ca... | Python Code:
#import required libraries
import pandas as pd
import numpy as np
#for counter operations
from collections import Counter
#for plotting graphs
import matplotlib.pyplot as plt
# Make the graphs a bit prettier, and bigger
pd.set_option('display.mpl_style', 'default')
pd.set_option('display.width', 5000)
pd.s... |
688 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Figure 4 csv data generation
Figure data consolidation for Figure 4, which shows patterns entropy for taxa and across the phylogeny
Figure 4a
Step1: Figure 4b
Step2: Figure 4c
Step3: Figu... | Python Code:
# read in exported table for genus
fig4a_genus = pd.read_csv('../../../data/07-entropy-and-covariation/genus-level-distribution.csv', header=0)
# read in exported table for otu
fig4a_otu = pd.read_csv('../../../data/07-entropy-and-covariation/otu-level-distribution-400.csv', header=0)
Explanation: Figure 4... |
689 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Graded = 9/10
Homework 11
Step1: 4. "Date first observed" is a pretty weird column, but it seems like it has a date hiding inside. Using a function with .apply, transform the string (e.g. "... | Python Code:
import pandas as pd
dates=['Issue Date', 'Vehicle Expiration Date'] #Importing dates as datetime
col_types={'Plate ID': 'str','Date First Observed':'str'} #Importing Plate ID and the Date First Observed as a string, because it has to be made into a time by a function.
df=pd.read_csv("small-violations.csv"... |
690 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
INTRODUCTION TO COMPUTING WITH TENSOR FLOW
Tensor flow can be visualised as computations in a graph with nodes that has definite operations. In simple terms, tensor flow carries out computat... | Python Code:
#```python
# Objective of the program is to do a simple multiplication on an input tensor of
# constant values
# To use tensor flow; import it
import tensorflow as tf
# tf.constant creates constant values. The below command creates a tensor;shape (2,2)
# with constant values. Be sure that each element ar... |
691 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logistic Regression
Resources
Step1: The logistic regression equation has a very simiar representation like linear regression. The difference is that the output value being modelled is bina... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn
%matplotlib inline
x = np.linspace(-6, 6, num = 1000)
plt.figure(figsize = (12,8))
plt.plot(x, 1 / (1 + np.exp(-x))); # Sigmoid Function
plt.title("Sigmoid Function");
Explanation: Logistic Regression
Resources:
Logistic Regression Tutorial ... |
692 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generating human faces with Adversarial Networks (5 points)
<img src="https
Step1: Generative adversarial nets 101
<img src="https
Step2: Discriminator
Discriminator is your usual convolut... | Python Code:
from torchvision import utils
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import torch, torch.nn as nn
import torch.nn.functional as F
from itertools import count
from IPython import display
import warnings
import time
plt.rcParams.update({'axes.titlesize': 'small'})
from sklearn.... |
693 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: Retraining an Image Classifier
<table class="tfo-notebook-buttons" align="l... | Python Code:
# Copyright 2021 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... |
694 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2A.ml - Machine Learning et données cryptées - correction
Comment faire du machine learning avec des données cryptées ? Ce notebook propose d'en montrer un principe exposés CryptoNets
Step1:... | Python Code:
%matplotlib inline
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: 2A.ml - Machine Learning et données cryptées - correction
Comment faire du machine learning avec des données cryptées ? Ce notebook propose d'en montrer un principe exposés CryptoNets: Applying Neural Networks t... |
695 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial
Step1: Note
Step2: Definition of the layers
So let us define the layers for the convolutional net. In general, layers are assembled in a list. Each element of the list is a tuple ... | Python Code:
import os
import matplotlib.pyplot as plt
%pylab inline
import numpy as np
from lasagne.layers import DenseLayer
from lasagne.layers import InputLayer
from lasagne.layers import DropoutLayer
from lasagne.layers import Conv2DLayer
from lasagne.layers import MaxPool2DLayer
from lasagne.nonlinearities import ... |
696 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mastr
Mastr es la nueva herramienta de valoración y agregación diseñada encima de PALM, que permite describir carteras y escenarios de riesgo de manera separada a la implementación de los pr... | Python Code:
from mastr.idn.sandbox import Sandbox
sandbox = Sandbox()
Explanation: Mastr
Mastr es la nueva herramienta de valoración y agregación diseñada encima de PALM, que permite describir carteras y escenarios de riesgo de manera separada a la implementación de los pricers y los objetos. PALM se utiliza para pode... |
697 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Matplotlib Exercise 1
Imports
Step1: Line plot of sunspot data
Download the .txt data for the "Yearly mean total sunspot number [1700 - now]" from the SILSO website. Upload the file to the ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Explanation: Matplotlib Exercise 1
Imports
End of explanation
import os
assert os.path.isfile('yearssn.dat')
Explanation: Line plot of sunspot data
Download the .txt data for the "Yearly mean total sunspot number [1700 - now]" from the S... |
698 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural network that performs sentiment analysis. Using an RNN rather than a feedfoward network is more accurate ... | Python Code:
import numpy as np
import tensorflow as tf
with open('reviews.txt', 'r') as f:
reviews = f.read()
with open('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 network that performs sentiment ana... |
699 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Titanic 沉没
这是一个分类任务，特征包含离散特征和连续特征，数据如下：Kaggle地址。目标是根据数据特征预测一个人是否能在泰坦尼克的沉没事故中存活下来。接下来解释下数据的格式：
survival 目标列，是否存活，1代表存活 (0 = No; 1 = Yes)
pclass 乘坐的舱位级别 (1 = 1st; 2 = 2nd; 3 ... | Python Code:
# -*- coding: UTF-8 -*-
%matplotlib inline
import pandas as pd
import string
import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
def substrings_in_string(big_string, substrings):
for substring in substrings... |
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