Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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0 | <ASSISTANT_TASK:>
Python Code:
short_freq = (hdulist[0].header['STEPSIZE'] / (60. * 60. * 24.))
med_freq = (10. / (60. * 24.))
long_freq = 1. / 24.
max_freq = 10. / 24.
from scipy.signal import lombscargle
short_periods = np.linspace(short_freq, med_freq, len(times))
med_periods = np.linspace(med_freq, long_freq, len(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Lomb-Scargle Periodogram via scipy
Step2: Lomb-Scargle Periodogram via astroML
Step3: Try with more data!
Step4: Try with noisy data
Step5: ... |
1 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
def checkerboard(size):
Return a 2d checkboard of 0.0 and 1.0 as a NumPy array
check = np.zeros((size,size),float)
check.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Checkerboard
Step3: Use vizarray to visualize a checkerboard of size=20 with a block size of 10px.
Step4: Use vizarray to visualize a checkerb... |
2 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import sys
from corner import corner
sys.path.append('.')
from src.mocklc import MockLC, SimulationSetup
from src.blendlpf import MockLPF
import src.plotting as pl
lc = MockLC(SimulationSetup('M', 0.1, 0.0, 0.0, 'short_transit', cteff=5500, know_orbit=True))
lc.create(wnsi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Create a mock light curve
Step2: Initialize the log posterior function
Step3: Optimize
Step4: Estimate the posterior
Step5: Analysis
Step6: ... |
3 | <ASSISTANT_TASK:>
Python Code:
df1 = pd.read_csv('listings/30042015/30042015.csv', sep = ";")
df2 = pd.read_csv('listings/17072015/17072015.csv', sep = ";")
df3 = pd.read_csv('listings/02102015/02102015.csv', sep = ";")
df4 = pd.read_csv('listings/03012016/03012016.csv', sep = ";")
df5 = pd.read_csv('listings/08122016/... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: feim un DataFrame on cada columna conté els host_id de cada scrap i de nom li posam la data de l'scrap
Step2: Feim un dataframe amb l'índex del... |
4 | <ASSISTANT_TASK:>
Python Code:
!conda install -y netcdf4
from netCDF4 import Dataset, num2date, date2num
from numpy import *
import matplotlib.pyplot as plt
%matplotlib inline
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
x = linspace(0, 1, 100) # generates a hundred values between... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Add to the function to allow amplitude to be varied and aadd in an additional slider to vary both f and a
Step2: Climate data
Step3: Plotting ... |
5 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import numexpr as ne
ne.set_num_threads(10);
rho = np.empty((512,512,512), dtype=np.float32)
rho[:] = np.random.random(rho.shape)
rho_mean = rho.mean(dtype=np.float64).astype(np.float32) # Use double precision for intermediate accumulations
%%timeit
delta = np.exp((rh... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The NumPy way
Step2: The Numexpr way
Step3: We were using 10 cores. Did our speedup come from multi-threading or loop-blocking/vectorization?... |
6 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
import tqdm
import numpy as np
import espressomd.observables
import espressomd.accumulators
espressomd.assert_features(
["ENGINE", "ROTATION", "MASS", "ROTATIONAL_INERTIA", "CUDA"])
ED_PARAMS = {... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Exercise
Step2: No more setup needed! We can run the simulation and plot our observables.
Step4: The Mean Square Displacement of an active par... |
7 | <ASSISTANT_TASK:>
Python Code:
import xarray as xr
import numpy as np
import os, sys
import matplotlib.pyplot as plt
import cartopy
import cartopy.crs as ccrs
%matplotlib inline
def read_data(file_name):
Read netcdf file and return variables:
rlat, rlon, var, px and py.
# read the dataset
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Allow to display the output of plotting commands in notebook
Step3: Function read_data
Step5: Function main
Step6: Run main
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8 | <ASSISTANT_TASK:>
Python Code:
df
df=pd.read_csv(csv_path)
df[(df[u'year'] <= 2016)]
print pd.Timestamp.min
print pd.Timestamp.max
year2=[]
for i in df['year']:
try: year2.append(int(i[6:10]))
except: year2.append(np.nan)
df['year']=year2
df[(df[u'year'] <= 2016)]
df = df[(df[u'reclat'] != 0.0) & (df[u'rec... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: lassuk lepesekben
Step2: ugy nez ki ez a kifejezes a hibas a 2016-al. ez a zert van, mert ez az oszlop nem valos datumkent van ertelmezve. ket ... |
9 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import make_regression
from sklearn.cross_validation import train_test_split
X, y, true_coefficient = make_regression(n_samples=80, n_features=30, n_informative=10, noise=100, coef=True, random_state=5)
X_train, X_test, y_train, y_test = train_test_split(X, y, random... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Linear Regression
Step2: Ridge Regression (L2 penalty)
Step3: Lasso (L1 penalty)
Step4: Linear models for classification
Step5: Multi-Class ... |
10 | <ASSISTANT_TASK:>
Python Code:
# conda install ipyrad -c bioconda
# conda install toytree -c eaton-lab
import pandas as pd
import toytree
# load the tree table from CSV
tree_table = pd.read_csv(
"./analysis-treeslider/test.tree_table.csv",
index_col=0,
)
# examine top of table
tree_table.head()
outfile = open... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Short Tutorial
Step2: Write the trees column to a file
Step3: Get Astral
Step4: Run Astral
Step5: Plot astral species tree
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11 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
from sg2lib import *
gamma = 1
Sx = 2
Fs = array([[1, gamma], [0, 1]])
Fp = array([[Sx, 0], [0, 1/Sx]])
n = 10
Fsi = array([[1, gamma/n], [0, 1]])
print('Incremental deformation gradient:')
print(Fsi)
array_equal(matrix_power(Fsi, n), Fs)
Fpi = array([[Sx**(1/n), 0], [0, ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Naive concept of simultaneous deformation
Step2: To divide simple shear deformation with $\gamma$=1 to n incremental steps
Step3: To check tha... |
12 | <ASSISTANT_TASK:>
Python Code:
import os
import mne
from mne.preprocessing import (ICA, create_eog_epochs, create_ecg_epochs,
corrmap)
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <div class="alert alert-info"><h4>Note</h4><p>Before applying ICA (or any artifact repair strategy), be sure to observe
Step2: We can get a sum... |
13 | <ASSISTANT_TASK:>
Python Code:
# import variable setting dictionaries from dkrz data ingest tool chain
# and remove __doc__ strings from dictionary (would clutter PROV graph visualizations)
from provtemplates import workflow_steps
from collections import MutableMapping
from contextlib import suppress
def delete_keys_fr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Template representation variant 1
Step2: Template representation variant 2
Step3: Template representation variant 3
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14 | <ASSISTANT_TASK:>
Python Code:
training = sqlContext.read.parquet("s3://zoltanctoth-flights/training.parquet")
test = sqlContext.read.parquet("s3://zoltanctoth-flights/training.parquet")
test.printSchema()
test.first()
training.cache()
test.cache()
from pyspark.sql.types import DoubleType
from pyspark.sql.functions im... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Generate label column for the training data
Step2: Create and fit Spark ML model
Step3: Predict whether the aircraft will be late
Step4: Chec... |
15 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp1d, InterpolatedUnivariateSpline
from scipy.optimize import bisect
import json
from functools import partial
clas... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: And some more specialized dependencies
Step2: Configuration for this figure.
Step3: Open a chest located on a remote globus endpoint and load ... |
16 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
def find_peaks(a):
Find the indices of the local maxima in a sequence.
# YOUR CODE HERE
#raise NotImplementedError()
ind=[]
#next two if checks end points
if a[0]> a[1... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Peak finding
Step3: Here is a string with the first 10000 digits of $\pi$ (after the decimal). Write code to perform the following
|
17 | <ASSISTANT_TASK:>
Python Code:
def g(x, alpha, beta):
assert alpha >= 0 and beta >= 0
return (alpha*x)/(1 + (beta * x))
def plot_cobg(x, alpha, beta):
y = np.linspace(x[0],x[1],300)
g_y = g(y, alpha, beta)
cobweb(lambda x: g(x, alpha, beta), y, g_y)
# configura gráfica interactiva
interact(plot_co... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Búsqueda algebráica de puntos fijos
Step2: Punto fijo oscilatorio
Step3: ¿Qué pasará con infinitas iteraciones?
|
18 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
root_folder = os.path.dirname(os.getcwd())
sys.path.append(root_folder)
import ResoFit
from ResoFit.calibration import Calibration
from ResoFit.fitresonance import FitResonance
from ResoFit.experiment import Experiment
from ResoFit._utilities import Layer
import numpy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Global paramters
Step2: File locations
Step3: Preview data using Experiment()
Step4: Data
Step5: Spectra
Step6: Remove unwanted data points... |
19 | <ASSISTANT_TASK:>
Python Code:
#Like before, we're going to select the relevant columns from the database:
connection = psycopg2.connect('dbname= threeoneone user=threeoneoneadmin password=threeoneoneadmin')
cursor = connection.cursor()
cursor.execute('''SELECT createddate, closeddate, borough FROM service;''')
data = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Let's extract years and months again
Step2: And now, we're going to filter out bad cases again. However, this time, we're going to be a bit mor... |
20 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set() # for plot styling
import numpy as np
import threading
import time
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans
import sys
sys.path.append("../")
from IoTPy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Part 1
Step2: Sklearn function to generate random points
Step3: Function to compute kmeans and plot clusters.
Step4: Function to change the p... |
21 | <ASSISTANT_TASK:>
Python Code:
# Importando Bibliotecas
import csv
import matplotlib.pyplot as plt
from math import sqrt
from random import randrange
# Definição da função que transforma um conjunto de dados inteiro em float
def str_column_to_float(data):
newData = []
for lines in data:
aux = [float(x) ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Utilização da Regressão Linear e Avaliação do Algoritmo
Step2: Visualização da Regressão Linear
|
22 | <ASSISTANT_TASK:>
Python Code:
!rm -rf hello setup.py && mkdir hello
%%file hello/hello.py
#pythran export hello()
def hello():
Wave hello.
print("Hello from Pythran o/")
%%file hello/__init__.py
Hello package, featuring a Pythran kernel.
from hello import hello
%%file setup.py
from distutils.core ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Project layout
Step4: And so is the __init__.py file.
Step5: The setup.py file contains the classical metadata, plus a special header. this he... |
23 | <ASSISTANT_TASK:>
Python Code:
from pylab import *
t = arange(0.0, 2.0,0.01)
y = sin(2*pi*t)
plot(t, y)
xlabel('Time (s)')
ylabel('Voltage (mV)')
title('The simplest one, buddies')
grid(True)
show()
from pylab import *
t = arange(0.0, 2.0,0.01)
y = sin(2*pi*t)
plot(t, y, color='red')
xlabel('Time (s)')
ylabe... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Custom plot line
Step2: A custom 2D plot, based on our first example.
|
24 | <ASSISTANT_TASK:>
Python Code:
#Example conditional statements
x = 1
y = 2
x<y #x is less than y
#x is greater than y
x>y
#x is less-than or equal to y
x<=y
#x is greater-than or equal to y
x>=y
#Example of and operator
(1<2)and(2<3)
#Example of or operator
(1<2)or(2>3)
#Example of not operator
not(1>2)
x = 1
y = 2
i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: If you let a and b be conditional statements (like the above statements, e.g. a = x < y), then you can combine the two together using logical op... |
25 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.array([[ 0, 1, 2, 3, 4, 5],
[ 5, 6, 7, 8, 9, 10],
[10, 11, 12, 13, 14, 15],
[15, 16, 17, 18, 19, 20],
[20, 21, 22, 23, 24, 25]])
result = np.diag(np.fliplr(a))
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
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26 | <ASSISTANT_TASK:>
Python Code:
# загрузка из файла
reviews_test = pd.read_csv('data/reviews_test.csv', header=0, encoding='utf-8')
reviews_train = pd.read_csv('data/reviews_train.csv', header=0, encoding='utf-8')
reviews_internet = pd.read_csv('data/internet_reviews.csv', header=0, encoding='utf-8')
# обучающая выборка... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Загрузка обработчика комментариев
Step2: Обработка данных
Step3: Обучение модели
Step4: Результаты
Step5: Классификатор 5 / не 5
Step6: Cни... |
27 | <ASSISTANT_TASK:>
Python Code:
# Python
// JavaScript
# No output
# Plain text output
"Hello world"
True
False
42
import math
math.pi
dict(a=1,b=2)
list(range(10))
dict(a='string', b=1, c=3.14, d=[1, 2, 3], e=dict(f=1))
# Stream output
print("Just a string")
# Matplotlib
import matplotlib.pyplot as plt
import numpy ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The following code cells illustrate how different types of cell outputs are decoded.
Step2: Primitive outputs
Step3: Image outputs
Step4: HTM... |
28 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
import tensorflow as tf
tf.logging.set_verbosity(t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: How does Tensorflow Low Level API look like?
Step2: Interactive usage of Low Level API
Step3: Calling a TensorFlow Model deployed on Google Cl... |
29 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot
# We have this here to trigger matplotlib's font cache stuff.
# This cell is hidden from the output
import pandas as pd
import numpy as np
np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Here's a boring example of rendering a DataFrame, without any (visible) styles
Step2: Note
Step4: The row0_col2 is the identifier for that par... |
30 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Set parameters
Step2: Show event related fields images
|
31 | <ASSISTANT_TASK:>
Python Code:
#export
from exp.nb_01 import *
def get_data():
path = datasets.download_data(MNIST_URL, ext='.gz')
with gzip.open(path, 'rb') as f:
((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding='latin-1')
return map(tensor, (x_train,y_train,x_valid,y_valid))
d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Foundations version
Step2: Tinker practice
Step3: From pytorch docs
Step4: Loss function
Step5: We need squeeze() to get rid of that trailin... |
32 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
# sphinx_gallery_thumbnail_number = 9
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
evoked = mne.read_evokeds(fname, baseline=(None, 0), p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First we read the evoked object from a file. Check out
Step2: Notice that evoked is a list of
Step3: Let's start with a simple one. We plot e... |
33 | <ASSISTANT_TASK:>
Python Code:
# run this cell first!
fruits = {"apple":"red", "banana":"yellow", "grape":"purple"}
print fruits["banana"]
query = "apple"
print fruits[query]
print fruits[0]
print fruits.keys()
print fruits.values()
for key in fruits:
print fruits[key]
del fruits["banana"]
print fruits
print fruit... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: There's no concept of "first element" in a dictionary, since it's unordered. (Of course, if you happened to have a key in your dictionary that w... |
34 | <ASSISTANT_TASK:>
Python Code:
# download sample files
!wget -P data -nc ftp://ftp.nersc.no/nansat/test_data/obpg_l2/A2015121113500.L2_LAC.NorthNorwegianSeas.hdf
!wget -P data -nc ftp://ftp.nersc.no/nansat/test_data/obpg_l2/A2015122122000.L2_LAC.NorthNorwegianSeas.hdf
import numpy as np
import matplotlib.pyplot as plt
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Open MODIS/Aqua files with chlorophyll in the North Sea and fetch data
Step2: Plot chlorophyll-a maps in swath projection
Step3: Colocate data... |
35 | <ASSISTANT_TASK:>
Python Code:
import itertools
# heads = True
# tails = False
# Initialize coins to all heads
coins = [True]*100
for factor in range(100):
# This will generate N zeros, then a 1. This repeats forever
flip_generator = itertools.cycle([0]*factor+[1])
# This will take the first 100 items... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Classic Riddler
Step2: If I would not have seen this particular tweet (https
|
36 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df= pd.read_excel("NHL 2014-15.xls")
!pip install xlrd
df.columns.value_counts()
df.head()
df.columns
df['Ctry'].value_counts().head(10)
df['Nat'].value_counts().head(10)
df['Birth City'].value_counts().head(1... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Here's all of our data
Step2: Here are each of the columns in the data set
Step3: Let's count how many players are from each country
Step4: L... |
37 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
x = np.linspace(0, 10, 50)
dy = 0.8
y = np.sin(x) + dy * np.random.randn(50)
# yerr表示y的误差
plt.errorbar(x, y, yerr=dy, fmt='.k');
plt.errorbar(x, y, yerr=dy, fmt='o', color='black',
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 这里的fmt是控制线和点外观的格式代码，并且具有与plt.plot中使用的简写相同的语法，在Simple Line Plots和Simple Scatter Plots中进行了概述。
Step2: 除了这些选项之外，还可以指定水平误差线（xerr），单面误差线和许多其他变体。有关可用选... |
38 | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import pandas as pd
import numpy as np
import pkg_resources
import matplotlib.pyplot as plt
import seaborn as sns
import time
import scipy.stats as stats
from sklearn imp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load and pre-process data sets
Step2: Let's examine some rows in these datasets. Note that columns like toxicity and male are percent scores.
... |
39 | <ASSISTANT_TASK:>
Python Code:
# read raw data
raw_data = pd.read_csv('/home/phoenix/Documents/session_1_data_train.csv')
test_data = pd.read_csv('/home/phoenix/Documents/session_1_data_test.csv')
test_data.columns = raw_data.columns
raw_data.head()
raw_data.label.value_counts().keys()
test_data.label.value_counts().ke... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Outcomes of sprint 1
Step2: Train test split
Step3: Evaluation function
Step4: Objective of Sprint 3
Step5: Logistic Regression
Step6: Sup... |
40 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import division
import numpy as np
from numpy import linalg as LA
k_a=0.2
k_b=0.2
k_p = 0.5
P = np.matrix([[1-k_a-k_b, k_a ,k_b, 0, 0, 0],
[k_a, 1-k_a-k_b, 0, k_b, 0, 0],
[k_b, 0, 1-k_a-k_b, k_a, 0, 0],
[0, k_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The markov chain seems to be irreducible
Step2: EDIT
Step3: Stationary state is given by $\pi = (0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.166... |
41 | <ASSISTANT_TASK:>
Python Code:
from reprophylo import *
pj = unpickle_pj('outputs/my_project.pkpj', git=False)
genera_with_porocalices = ['Cinachyrella',
'Cinachyra',
'Amphitethya',
'Fangophilina',
'Acanthotet... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 3.10.1 Updating the metadata after the tree has been built
Step2: while others do not
Step3: The following command will add the value 'present... |
42 | <ASSISTANT_TASK:>
Python Code:
password = input("Please enter the password:")
if password == "Simsim":
print("\t> Welcome to the cave")
x = "Mayank"
y = "TEST"
if y == "TEST":
print(x)
if y:
print("Hello World")
z = None
if z:
print("TEST")
x = 11
if x > 10:
print("Hello")
if x > 10.999999999999... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: if ... else statement
Step2: if ...elif ... else statement
Step3: Imagine that in the above program, 23 is the temperature which was read by ... |
43 | <ASSISTANT_TASK:>
Python Code:
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
Y = iris.target
# print(iris.DESCR)
from sklearn.neural_network import MLPClassifier
clf = MLPClassifier(random_state=1960)
clf.fit(X, Y)
#clf.__dict__
def test_ws_sql_gen(pickle_data):
WS_URL="https://sklearn2s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Generate SQL Code from the Model
Step2: Execute the SQL Code
Step3: Scikit-learn Prediction
Step4: Comparing the SQL and Scikit-learn Predict... |
44 | <ASSISTANT_TASK:>
Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
with open('anna.txt', 'r') as f:
text=f.read()
vocab = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
chars = np.array([vocab_to_int[c] for c ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First we'll load the text file and convert it into integers for our network to use.
Step3: Now I need to split up the data into batches, and in... |
45 | <ASSISTANT_TASK:>
Python Code:
import re
import pubchempy as pcp
import logging
logging.getLogger('pubchempy').setLevel(logging.DEBUG)
def get_substructure_cas(smiles):
cas_rns = []
results = pcp.get_synonyms(smiles, 'smiles', searchtype='substructure')
for result in results:
for syn in result.get... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Enable debug logging to make it easier to see what is going on
Step2: A function to get the CAS registry numbers for compounds with a particula... |
46 | <ASSISTANT_TASK:>
Python Code:
import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
! pip3 install -U google-cloud-storage tensorflow $USER_FLAG
import os
if not os.g... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Install the latest GA version of google-cloud-storage and tensorflow libraries as well.
Step2: Restart the kernel
Step3: Before you begin
Step... |
47 | <ASSISTANT_TASK:>
Python Code:
df['Age'].describe()
df.groupby('Gender')['Income'].describe()
df['Income'].describe()
df['SchoolMajor'].value_counts()
df['SchoolDegree'].value_counts()
df.sort_values(by='StudentDebtOwe', ascending=False).head()
df[(df['BootcampFullJobAfter']==1) & (df['BootcampLoanYesNo']==1)].he... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2. What are the maximum income for female programmers?
Step2: 3. how much does a programmer make on average per year?
Step3: 4. what is the mo... |
48 | <ASSISTANT_TASK:>
Python Code:
import torch
import torch.nn as nn
import torch.nn.functional as F # adds some efficiency
from torch.utils.data import DataLoader # lets us load data in batches
from torchvision import datasets, transforms
import numpy as np
import pandas as pd
from sklearn.metrics import confus... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load the MNIST dataset
Step2: Load the training set
Step3: Load the test set
Step4: Examine a training record
Step5: Calling the first recor... |
49 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function
%matplotlib inline
#format the book
import book_format
book_format.set_style()
import numpy as np
from numpy.random import randn
import matplotlib.pyplot as plt
N = 5000
a = np.pi/2. + (randn(N) * 0.35)
r = 50.0 + (randn(N) * 0.4)
xs = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Introduction
Step2: We can see that out intuition failed us because the nonlinearity of the problem forced all of the errors to be biased in on... |
50 | <ASSISTANT_TASK:>
Python Code:
#$HIDE_INPUT$
from google.cloud import bigquery
# Create a "Client" object
client = bigquery.Client()
# Construct a reference to the "nhtsa_traffic_fatalities" dataset
dataset_ref = client.dataset("nhtsa_traffic_fatalities", project="bigquery-public-data")
# API request - fetch the datase... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step2: Let's use the table to determine how the number of accidents varies with the day of the week. Since
Step3: As usual, we run it as follows
|
51 | <ASSISTANT_TASK:>
Python Code:
import twothirds
import random
N = 2000
guesses = [int(round(random.triangular(0, 100, 44), 0)) for k in range(N)]
g = twothirds.TwoThirdsGame(guesses)
g.two_thirds_of_the_average()
g.find_winner()
import string
def randomword(length):
A function to generate a random name: http:... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Let as assume we have the following list of random guesses
Step2: Now we create a single game instance
Step3: Let's find the two thirds of the... |
52 | <ASSISTANT_TASK:>
Python Code:
shopping_list = [ 'Milk', 'Eggs', 'Bread', 'Beer']
item_count = len(shopping_list)
print("List: %s has %d items" % (shopping_list, item_count))
for item in shopping_list:
print("I need to buy some %s " % (item))
# or with f-strings
for item in shopping_list:
print(f"I need to buy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Enumerating the Items in a List
Step2: 1.1 You Code
Step3: Indexing Lists
Step4: For Loop with Index
Step5: 1.2 You Code
Step6: Lists are M... |
53 | <ASSISTANT_TASK:>
Python Code:
PROJECT = "cloud-training-demos" # Replace with your PROJECT
BUCKET = "cloud-training-bucket" # Replace with your BUCKET
REGION = "us-central1" # Choose an available region for Cloud MLE
TFVERSION = "1.14" # TF version for CMLE to use
import os
os.environ["BUCK... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Deploy trained model
Step2: We'll now deploy our model. This will take a few minutes. Once the cell below completes, you should be able to see ... |
54 | <ASSISTANT_TASK:>
Python Code:
from keras.datasets import imdb
idx = imdb.get_word_index()
idx_arr = sorted(idx, key=idx.get)
idx_arr[:10]
idx2word = {v: k for k, v in idx.iteritems()}
path = get_file('imdb_full.pkl',
origin='https://s3.amazonaws.com/text-datasets/imdb_full.pkl',
md5_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: This is the word list
Step2: ...and this is the mapping from id to word
Step3: We download the reviews using code copied from keras.datasets
S... |
55 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
! pip3 install -U google-cloud-storage $USER_FLAG
if not os.getenv("IS_TESTING... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Step3: Before you begin
Step4: Region
Step5:... |
56 | <ASSISTANT_TASK:>
Python Code:
##Some code to run at the beginning of the file, to be able to show images in the notebook
##Don't worry about this cell
#Print the plots in this screen
%matplotlib inline
#Be able to plot images saved in the hard drive
from IPython.display import Image
#Make the notebook wider
from IPy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1. Clustering
Step2: 1a. Clustering with K-means
Step3: 1b. Clustering with DBSCAN
Step4: 1c. Hierarchical clustering
Step5: 2. Imputation o... |
57 | <ASSISTANT_TASK:>
Python Code:
print("Exemplo 4.1")
import numpy as np
#Para vs = 12V
#6i1 + 2i1 + 4(i1 - i2) = -12
#12i1 - 4i2 = -12
#3i1 - i2 = -3
#-3vx -12 + 4(i2 - i1) + 8i2 + 4i2 = 0
#vx = 2i1
#-6i1 + 16i2 - 4i1 = 12
#-10i1 + 16i2 = 12
#-5i1 + 8i2 = 6
#i0 = i2
coef = np.matrix('3 -1;-5 8')
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Problema Prático 4.1
Step2: Superposição
Step3: Problema Prático 4.3
Step4: Exemplo 4.4
Step5: Problema Prático 4.4
Step6: Exemplo 4.5
Step... |
58 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
pylab.rc("savefig", dpi=120) # set resolution of inline figures
import echidna.core.spectra as spectra
import echidna
config = spectra.SpectraConfig.load_from_file(echidna.__echidna_base__ +
"/echidna/config/example.yml")
prin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Spectra creation
Step2: Now we need a config file to create the spectrum from. There is an example config file in echidna/config. If we look at... |
59 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-1', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
60 | <ASSISTANT_TASK:>
Python Code:
import os
from gensim import utils
from gensim.models import translation_matrix
from gensim.models import KeyedVectors
train_file = "OPUS_en_it_europarl_train_5K.txt"
with utils.smart_open(train_file, "r") as f:
word_pair = [tuple(utils.to_unicode(line).strip().split()) for line in f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: For this tutorial, we'll train our model using the English -> Italian word pairs from the OPUS collection. This corpus contains 5000 word pairs.... |
61 | <ASSISTANT_TASK:>
Python Code:
import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
events_f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Annotating bad spans of data
Step2: You can see that you need to add a description first to start with
Step3: Now we can confirm that the anno... |
62 | <ASSISTANT_TASK:>
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 writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Migrate from TPUEstimator to TPUStrategy
Step2: TensorFlow 1
Step3: With those functions defined, create a tf.distribute.cluster_resolver.TPUC... |
63 | <ASSISTANT_TASK:>
Python Code:
import os
IS_COLAB_BACKEND = 'COLAB_GPU' in os.environ # this is always set on Colab, the value is 0 or 1 depending on GPU presence
if IS_COLAB_BACKEND:
from google.colab import auth
# Authenticates the Colab machine and also the TPU using your
# credentials so that they can access... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Updating tensorboard_plugin_profile
Step2: Enabling and testing the TPU
Step3: Input data
Step4: Let's take a peek at the training dataset we... |
64 | <ASSISTANT_TASK:>
Python Code:
# RUN THIS CELL to perform standard imports:
import spacy
nlp = spacy.load('en_core_web_sm')
# Enter your code here:
with open('../TextFiles/owlcreek.txt') as f:
doc = nlp(f.read())
# Run this cell to verify it worked:
doc[:36]
len(doc)
sents = [sent for sent in doc.sents]
len(sent... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1. Create a Doc object from the file owlcreek.txt<br>
Step2: 2. How many tokens are contained in the file?
Step3: 3. How many sentences are co... |
65 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
import inspect
import numpy as np
import datetime as dt
import time
import pytz
import pandas as pd
import pdb
import tmpo
#import charts
from opengrid import config
from opengrid.library import houseprint
c=config.Config()
DEV = c.get('env', 'type') == 'dev' # DEV is... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Script settings
Step2: We create one big dataframe, the columns are the sensors
|
66 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import time
import meshcat
import meshcat.geometry as g
import meshcat.transformations as tf
# Create a new visualizer
vis = meshcat.Visualizer()
vis.open()
vis.url()
vis.set_object(g.Box([0.2, 0.2, 0.2]))
for theta in np.linspace(0, 2 * np.pi, 200):
v... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: By default, creating the Visualizer will start up a meshcat server for you in the background. The easiest way to open the visualizer is with its... |
67 | <ASSISTANT_TASK:>
Python Code:
# Let's find out the number of neighbors that individual #7 has.
G.neighbors(9)
# Possible Answers:
sorted([n for n in G.nodes()], key=lambda x:len(G.neighbors(x)), reverse=True)
sorted([(n, G.neighbors(n)) for n in G.nodes()], key=lambda x: len(x[1]), reverse=True)
nx.degree_centrality... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Exercise
Step2: Approach 2
Step3: If you inspect the dictionary closely, you will find that node 19 is the one that has the highest degree cen... |
68 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as ss
import sympy as sp
sns.set_context('notebook')
%matplotlib inline
x = np.linspace(.01, .99, num=1e3)
doppler = lambda x : np.sqrt(x * (1 - x)) * np.sin(1.2 * np.pi / (x + .05))
plt.plot(x, d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Doppler function
Step2: Derivative of Doppler function
Step3: Left and right truncated exponentials
Step4: Draw the densitites
Step5: Kernel... |
69 | <ASSISTANT_TASK:>
Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
with open('anna.txt', 'r') as f:
text=f.read()
vocab = sorted(set(text))
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
encoded = np.array([vocab_to_int... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: First we'll load the text file and convert it into integers for our network to use. Here I'm creating a couple dictionaries to convert the chara... |
70 | <ASSISTANT_TASK:>
Python Code:
#$HIDE_INPUT$
from google.cloud import bigquery
# Create a "Client" object
client = bigquery.Client()
# Construct a reference to the "hacker_news" dataset
dataset_ref = client.dataset("hacker_news", project="bigquery-public-data")
# API request - fetch the dataset
dataset = client.get_dat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Let's use the table to see which comments generated the most replies. Since
Step3: Now that our query is ready, let's run it and store the res... |
71 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.insert(0, './code')
# Go into the subdirectory
from thinkbayes import Pmf
# Grab the thinkbayes script
help(Pmf)
# What is this object?
pmf = Pmf()
# intialize the object
for x in [1,2,3,4,5,6]:
# for x in array
pmf.Set(x, 1/6.0)
# Set the frequen... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The following code builds a Pmf to represent the distribution of
Step2: This is a Probability Mass Function object, which includes some pre-def... |
72 | <ASSISTANT_TASK:>
Python Code:
# Import pyoptools to load all contents
from pyoptools.all import *
from math import pi
#Example 2.1 : Plane surfaces
P1=Plane(shape=Circular(radius=(20)),reflectivity=1)
P2=Plane(shape=Rectangular(size=(40,25)))
P3=Plane(shape=Triangular(coord=((-15,15),(5,-20),(18,12))))
Plot3D(P1,cent... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Table of contents
Step2: 2.2 Spherical surfaces <a class="anchor" id="2.2"></a>
Step3: 2.3 Cylinders and cylidrical surfaces <a class="anchor"... |
73 | <ASSISTANT_TASK:>
Python Code:
# Authors: Robert Luke <mail@robertluke.net>
#
# License: BSD (3-clause)
import os
import mne
from mne.preprocessing.nirs import (optical_density,
temporal_derivative_distribution_repair)
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_c... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Import data
Step2: We can see some small artifacts in the above data from movement around 40,
Step3: Apply temporal derivative distribution re... |
74 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-2', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
75 | <ASSISTANT_TASK:>
Python Code:
from thermostate import State, Q_, units, set_default_units
p_1 = Q_(101325, 'Pa')
p_1 = Q_(1.01325, 'bar')
p_1 = Q_(14.7, 'psi')
p_1 = Q_(1.0, 'atm')
T_1 = 460*units.degR
T_1 = 25*units.degC
T_1 = 75*units.degF
T_1 = 400*units.K
Q_(101325, 'Pa') == 1.0*units.atm
substance = 'water'
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Pint and Units
Step2: We can use whatever units we'd like, Pint supports a wide variety of units.
Step3: Another way to specify the units is t... |
76 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'sandbox-2', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
77 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
c, v = np.loadtxt('data.csv', delimiter=',', usecols=(6,7), unpack=True)
c
v
#选择第4列，开盘价
opening_price = np.loadtxt('data.csv', delimiter=',', usecols=(3,), unpack=True)
print opening_price
vwap = np.average(c, weights=v)
print "VWAP =", vwap
t = np.arange(len(c))
pri... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: data.csv文件是苹果公司的历史股价数据。第一列为股票代码，第二列为dd-mm-yyyy格式的日期，第三列为空，随后各列依次是开盘价(4)、最高价(5)、最低价(6)和收盘价(7)，最后一列为当日的成交量(8)。
Step2: 2. 计算平均值
Step3: TWAP是Time0... |
78 | <ASSISTANT_TASK:>
Python Code:
# Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../../style/custom.css'
HTML(open(css_file, "r").read())
# Import Libraries
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# Define parameters
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Computation of Green's functions and seismograms for the acoustic wave equation
Step2: 2D Green's function
Step3: 3D Green's function
Step4: ... |
79 | <ASSISTANT_TASK:>
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 writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Keras 예제의 가중치 클러스터링
Step2: 클러스터링을 사용하지 않고 MNIST용 tf.keras 모델 훈련하기
Step3: 기준 모델을 평가하고 나중에 사용할 수 있도록 저장하기
Step4: 클러스터링을 사용하여 사전 훈련된 모델 미세 조정하기
... |
80 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
! pip3 install -U google-cloud-storage $USER_FLAG
if not os.getenv("IS_TESTING... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Step3: Before you begin
Step4: Region
Step5:... |
81 | <ASSISTANT_TASK:>
Python Code:
__author__ = 'ATSC-301 UBC'
import glob
import numpy as np
import matplotlib.pyplot as plt
from __future__ import division
from __future__ import print_function
% matplotlib inline
import h5py
import scipy.io
from mpl_toolkits.basemap import Basemap
hdf5_L1B=glob.glob('_data/MODIS_L1... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Content
Step2: We import h5py to read HDF5 files
Step3: scipy.io for saving data in *.mat format
Step4: For the map view of data, we need mpl... |
82 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
# This is needed to display the images.
%... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Env setup
Step2: Object detection imports
Step3: Model preparation
Step4: Download Model
Step5: Load a (frozen) Tensorflow model into memory... |
83 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
from ipywidgets import widgets
from ipywidgets import interact, interactive, fixed
from IPython.display import display,HTML,clear_output
import os
HTML('''<script>code_show=true;function code_toggle() {if (code_show... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Definition and proxy for usefull functions
Step2: Analyse
|
84 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import matplotlib
#matplotlib.rc('xtick', labelsize=20)
#matplotlib.rc('ytick', labelsize=20)
from scipy.spatial import distance
x = np.loadtxt("data.txt", comments='//')
x.shape
print(x.shape)
# Plot 2 measurements
#for i in x:
# plt.plot(i[0],i[1], 'ko');
plt.scatte... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Consider the following data set consisting of the scores of two variables on each of 17 experiments
Step2: This data set is to be grouped into ... |
85 | <ASSISTANT_TASK:>
Python Code:
sc.addPyFile("https://github.com/ibm-watson-data-lab/simple-data-pipe-connector-flightstats/raw/master/flightPredict/training.py")
sc.addPyFile("https://github.com/ibm-watson-data-lab/simple-data-pipe-connector-flightstats/raw/master/flightPredict/run.py")
import training
import run
%matp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: load data from training data set and print the schema
Step2: Visualize classes in scatter plot based on 2 features
Step3: Load the training da... |
86 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
# comment out this line if you don't have seaborn installed
import seaborn as sns
sns.set_palette("colorblind")
import numpy as np
# execute this line:
from astroquery.sdss import SDSS
TSquery = SELECT TOP 10000
p.psfMag_r,... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: First, we're going to need some data. We'll work with the star-galaxy data from the first session. This uses the astroquery package and then que... |
87 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
df = pd.read_excel('https://github.com/chris1610/pbpython/blob/master/data/sample-salesv3.xlsx?raw=true')
df.dtypes
df['date'] = pd.to_datetime(df['date'])
df.head()
df.dtypes
df[df["account number"]==307599].head()
df[df["quantity"] > 22].head(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load in the Excel data that represents a year's worth of sales.
Step2: Take a quick look at the data types to make sure everything came through... |
88 | <ASSISTANT_TASK:>
Python Code:
import pandas_datareader as pdr
import pandas as pd
import statsmodels.api as sm
from statsmodels.regression.rolling import RollingOLS
import matplotlib.pyplot as plt
import seaborn
seaborn.set_style('darkgrid')
pd.plotting.register_matplotlib_converters()
%matplotlib inline
factors = pd... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: pandas-datareader is used to download data from
Step2: The first model estimated is a rolling version of the CAPM that regresses
Step3: We nex... |
89 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# Let's grab some libraries to help us manipulate symbolic equations
from __future__ import print_function
from __future__ import division
import numpy as np
import sympy
from sympy import symbols, sin, cos, pi, simplify
def makeT(a, alpha, d, theta):
# create a mod... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Puma Example
Step2: Puma
|
90 | <ASSISTANT_TASK:>
Python Code:
import os
import requests
from bs4 import BeautifulSoup
import re
import json
import time
import praw
import dominate
from dominate.tags import *
from time import gmtime, strftime
#import nose
#import unittest
import numpy as np
import pandas as pd
from pandas import *
from PIL import I... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Something is wrong with the script and it's no longer creating these dir in the correct folder. How did this break?
Step2: if i save the data t... |
91 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
from IPython.core.display import HTML
from __future__ import print_function, division
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
Image(url= "https://cdn-images-1.medium.com/max/1600/1*UkI9za9zTR-HL8uM15Wmzw.png")
#hyperpar... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The figure below shows the input data-matrix, and the current batch batchX_placeholder
Step2: As you can see in the picture below that is done... |
92 | <ASSISTANT_TASK:>
Python Code:
workDir = '/home/nick/notebook/SIPSim/dev/fullCyc_trim/'
emp_data = 'SIP-core_unk_trm'
emp_data_preFrac = 'bulk-core_trm'
import os
import sys
%load_ext rpy2.ipython
%load_ext pushnote
if not os.path.isdir(workDir):
os.makedirs(workDir)
%cd $workDir
!/home/nick/notebook/SIPSim/... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Init
Step2: Making a table of shannon index for each fraction community
Step3: Making a table of variance in BD spans
Step4: Making a communi... |
93 | <ASSISTANT_TASK:>
Python Code:
print "Hello world"
s="Hello world"
print s
print s.upper()
print s.replace("o","O")
2
-7897
3.4
-7213.6241
2.66e-23
'Ovo je niz znakova.'
"Ovo je isto niz znakova."
"Ovo je 'niz znakova' u kojem se nalazi 'kombinacija' navodnika."
''
""
'3.14'
3.14
'Ovo je niz.'[0]
niz='Ovo je ni... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Ime varijable je s, a vrijednost varijable je Hello world. Navedeno ime varijable s navedenom vrijednosti te varijable je instanca klase.
Step2:... |
94 | <ASSISTANT_TASK:>
Python Code:
import requests #to handle http requests to the API
from psycopg2 import connect
stationid = 3
#We'll find out the full range of possible stations further down.
lineid = 1
#[1,2,4]
# The url for the request
base_url = "http://www.ttc.ca/Subway/loadNtas.action"
# Our query parameters for... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: So now we've just received our first request from the API and the response is stored in the requests object r. From previous examination of the ... |
95 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = [('reduce_dim', PCA()), ('poly', PolynomialFeatures()), ('svm', SVC())]
clf = Pi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
96 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncar', 'sandbox-3', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
97 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
from IPython.html import widgets
from IPython.display import Image
assert True # leave this to grade the import stat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Basic rich display
Step2: Use the HTML object to display HTML in the notebook that reproduces the table of Quarks on this page. This will requi... |
98 | <ASSISTANT_TASK:>
Python Code:
s3_client = boto3.client('s3')
resource = boto3.resource('s3')
# Disable signing for anonymous requests to public bucket
resource.meta.client.meta.events.register('choose-signer.s3.*', disable_signing)
def file_list(client, bucket, prefix=''):
paginator = client.get_paginator('list_ob... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: NEXRAD at O'Hare Zip 60666
Step2: Wunderground
|
99 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
from pylab import *
from __future__ import print_function
import sys, os
from ipywidgets import interact,interact_manual, Image
from ptha_paths import data_dir, events_dir
# Read in topography data:
fixed_grid_file = os.path.join(data_dir, 'MapsTopo', 'fixedgrid_xyB_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Read in the topography data and define a function to make a contour plot
Step2: Read in image of Crescent City as background for plots
Step3: ... |
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