Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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11,300 | <ASSISTANT_TASK:>
Python Code:
def straight_line(x):
return 5 * x + 8
straight_line(25)
straight_line(1.254)
np.random.seed(5)
samples = 150
x_vals = pd.Series(np.random.rand(samples) * 20)
y_vals = x_vals.map(straight_line)
# Add random noise
y_noisy_vals = y_vals + np.random.randn(samples) * 3
df = pd.DataFrame({... | <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: <h4>Training and Evaluation Set</h4>
Step2: <h4>Test Set</h4>
Step3: <h4>Read the target predicted by AWS ML</h4>
Step4: <h4>AWS ML Estimated... |
11,301 | <ASSISTANT_TASK:>
Python Code:
import requests
url="https://api.forecast.io/forecast/64f4867f7d4c86182f3d1c6ed881dbfc/17.3850,78.4867"
response=requests.get(url)
data=response.json()
data.keys()
data['currently'].keys()
print("The current wind speed is",data['currently']['windSpeed'],"miles per hour.")
apparentTempera... | <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: 2) What's the current wind speed? How much warmer does it feel than it actually is?
Step2: 3) The first daily forecast is the forecast for toda... |
11,302 | <ASSISTANT_TASK:>
Python Code:
import numpy
from rmtk.vulnerability.common import utils
from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF import MSA_utils
from rmtk.vulnerability.derivation_fragility.NLTHA_on_SDOF.read_pinching_parameters import read_parameters
from rmtk.vulnerability.derivation_fragility.NLTH... | <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 capacity curves
Step2: Load ground motion records
Step3: Load damage state thresholds
Step4: Calculate fragility function
Step5: Fit lo... |
11,303 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
from IPython.display import HTML
import ephem
import matplotlib
%pylab inline
pylab.rcParams['figure.figsize'] = (15, 10)
import matplotl... | <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 section specific modules
Step2: 3.2 Hour Angle (HA) and Local Sidereal Time (LST)
|
11,304 | <ASSISTANT_TASK:>
Python Code:
fileName='book.txt'
import re
def removePunctuation(text):
return re.sub('[^a-z| |0-9]', '', text.strip().lower())
shakespeareRDD = (sc
.textFile(fileName, 8)
.map(removePunctuation))
shakespeareRDD.take(4)
print '\n'.join(shakespeareRDD
... | <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: Ahora vamos a eliminar todo aquello que no se consideren cadenas de texto válidas. Para ello definiremos una función que elimine aquello que no ... |
11,305 | <ASSISTANT_TASK:>
Python Code:
import NotebookImport
from Imports import *
import seaborn as sns
sns.set_context('paper',font_scale=1.5)
sns.set_style('white')
matched_rna = pd.read_hdf('/data_ssd/RNASeq_2014_07_15.h5', 'matched_tn')
rna_microarray = pd.read_hdf('/data_ssd/GEO_microarray_dx.h5', 'data')
matched_rna = ... | <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: Read in matched Gene expression data.
Step2: Run a simple screen for DX probes
Step3: Pathway and Gene Annotation Analysis
Step4: Overexpress... |
11,306 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import numpy as np
from sklearn.linear_model import Ridge
from flexible_linear import FlexibleLinearRegression
import matplotlib
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
%matplotlib inline
np.random.seed(1)
N = 500
A = 50
B = 3
... | <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 some data
Step2: Let's try adding Gaussian (normal) noise...
Step3: ... or some Cauchy (heavy-tailed) noise
Step4: Trying to recover... |
11,307 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
import random
X = [random.random() * 16 for i in range(0,1000)]
Y = [ int(x**0.5) % 2 for x in X]
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(X, Y, '.')
nuage = [(x,y) for x,y in zip(X,Y)]
nuage.sort()
nuag... | <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: Q1 - échantillon aléatoire
Step2: Q1 - dessiner le nuage de points - donnée
Step3: Q2 - tri
Step4: Q3 - moyenne
Step5: Q4 - distance
Step6: ... |
11,308 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'sandbox-3', 'seaice')
# 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: 2... |
11,309 | <ASSISTANT_TASK:>
Python Code:
from ftplib import FTP
import os
import numpy as np
ftp = FTP('ftp.sltac.cls.fr')
ftp.login('pprandi','PierreCMEMS2017')
ftp.retrlines('LIST')
ftp.cwd('Core/SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047/dataset-duacs-rep-global-merged-allsat-phy-l4-v3/2016/')
ftp.retrlines('LIST')
fil... | <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: init the connection to the ftp server
Step2: What is in the current directory ?
Step3: change to the directory of level 4 delayed-time global ... |
11,310 | <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: Passage Ranking using TFR-BERT
Step2: Import TensorFlow Ranking and useful libraries through the notebook.
Step3: Data preparation
Step4: Ove... |
11,311 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import oandapy
import configparser
config = configparser.ConfigParser()
config.read('../config/config_v1.ini')
account_id = config['oanda']['account_id']
api_key = config['oanda']['api_key']
oanda = oandapy.API(environment="practice",
access_token... | <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 oandapy wraps the Oanda API in a format that make our codes cleaner and the task of extracting information from the API easier.
Step2: We i... |
11,312 | <ASSISTANT_TASK:>
Python Code:
import wicked as w
import time
from IPython.display import display, Math, Latex
def latex(expr):
Function to render any object that has a member latex() function
display(Math(expr.latex()))
w.reset_space()
w.add_space('o','fermion','occupied',['i','j','k','l','m','n'])
w.add_spac... | <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: Automatic generation of coupled cluster equations
Step2: Define the orbital spaces
Step3: Define the Hamiltonian operator
Step4: Define a fun... |
11,313 | <ASSISTANT_TASK:>
Python Code:
def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].uppe... | <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: Note
Step2: Lesson
Step3: Project 1
Step4: We'll create three Counter objects, one for words from postive reviews, one for words from negativ... |
11,314 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-1', 'toplevel')
# 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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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
11,315 | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -v -u -d -p scipy,scikit-learn,numpy,matplotlib
from scipy.spatial.distance import pdist, squareform
from scipy import exp
from scipy.linalg import eigh
import numpy as np
def stepwise_kpca(X, gamma, n_components):
Implementation of a RBF kernel PC... | <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: <font size="1.5em">More information about the watermark magic command extension.</font>
Step3: <br>
Step4: <br>
Step5: As we can see, the res... |
11,316 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import tensorflow as tf
import urllib.request
# Define a constant indicating the number of layers in our loaded model. We're loading a
# resnet-50 model.
RESNET_SIZE = 50
# Model and serving directories
MODEL_DIR="resnet_model_checkpoints"
SERVING_DIR="esti... | <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: Download model checkpoint
Step2: Import the Model Architecture
Step3: Exercise
Step4: Build the Servable from the Estimator API
Step6: Helpe... |
11,317 | <ASSISTANT_TASK:>
Python Code:
import ee
ee.Initialize()
from geetools import bitreader, cloud_mask
options = {
'0-1': {0:'clear', 1:'cloud', 2:'mix'}, # cloud state
'2-2': {0: 'no_shadow', 1:'shadow'}, # cloud shadow (bit 0 is not needed)
'6-7': {0:'climatology', 1:'low', 2:'average', 3:'high'} # land/water flag... | <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: Internally it computes a dict with
Step2: DECODE ONE VALUE
Step3: MATCH ONE VALUE
Step4: ENCODE A VALUE (EXCLUSIVELLY)
Step5: ENCODE A VALUE... |
11,318 | <ASSISTANT_TASK:>
Python Code:
from zipline.pipeline.data import USEquityPricing as USEP
from zipline.pipeline.factors import SimpleMovingAverage
# sma30 and sma90 are Factors.
# Factors represent computations producing numerical-valued outputs.
sma30 = SimpleMovingAverage(inputs=[USEP.close], window_length=30)
sma90 =... | <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: Example
Step3: Example
|
11,319 | <ASSISTANT_TASK:>
Python Code:
# this is a hidden cell
print(
<div class="output_area rendered_html docutils container">
{table}
</div>
.format(table = table.replace('\n', "")))
import pandas as pd
from siuba import _, mutate
my_data = pd.DataFrame({
'g': ['a', 'a', 'b'],
'x': [1,2,3],
})
# pandas
my_data.as... | <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: Key features
Step2: Built on pandas
Step3: Note how you can debug both pieces of code by running and inspecting df.a.mean().
Step4: Notice ho... |
11,320 | <ASSISTANT_TASK:>
Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
import phoebe
from phoebe import u # units
import numpy as np
b = phoebe.default_binary()
b = phoebe.default_binary()
b.add_dataset('lc', times=phoebe.linspace(0,5,1001))
b.run_compute()
times = b.get_value('times@model')
fluxes = b.get_value('fluxes@... | <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 get started with some basic imports.
Step2: And then we'll build a synthetic "dataset" and initialize a new bundle with those data
Step3:... |
11,321 | <ASSISTANT_TASK:>
Python Code:
# Uncomment and run this one time only
# !pip install http://download.pytorch.org/whl/cu75/torch-0.1.12.post2-cp27-none-linux_x86_64.whl
# !pip install torchvision==0.1.8
# !pip install tabulate
# !pip install --upgrade scikit-learn
# !pip install --upgrade numpy
# !pip install h5py
# !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:
Step1: <br>
Step2: <br>
Step7: <br>
Step8: Select a simulation file to test
Step9: Load the parameters for the models
Step10: <br>
Step11: <br>
S... |
11,322 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
train = pd.read_csv('titanic_train.csv')
train.head(25)
sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')
sns.set_style('whitegrid')
sns.countplot(x='Su... | <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: The Data
Step2: Exploratory Data Analysis
Step3: Roughly 20 percent of the Age data is missing. The proportion of Age missing is likely small ... |
11,323 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy import linalg
import mne
from mne.datasets import sample
from mne.viz import plot_sparse_source_estimates
data_path = sample.data_path()
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
ave_fname = data_path + '/MEG/sample/sample_audv... | <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: Auxiliary function to run the solver
Step4: Define your solver
Step5: Apply your custom solver
Step6: View in 2D and 3D ("glass" brain like 3... |
11,324 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import json
import graphviz
import matplotlib.pyplot as plt
from sklearn import tree
from sklearn.model_selection import train_test_split
pd.set_option("display.max_rows",6)
%matplotlib inline
df_data = pd.read_csv(r'varsom_ml_preproc_3y.csv', index_... | <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: RANDOM FORESTS
Step2: The first avalanche problem dictates the danger level - that was expected
Step3: Looks like there is little gain when u... |
11,325 | <ASSISTANT_TASK:>
Python Code:
# Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
%%sql
-- Create a table of criminals
CREATE TABLE criminals (pid, name, age, sex, city, minor);
INSERT INTO criminals VALUES (412, 'James Smith', 15, 'M', 'Santa Rosa', 1);
INSERT INTO criminals VALUES (234, 'Bill Ja... | <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: Create Data
Step2: Alias Criminals Table A C, Then Select All Names From C
|
11,326 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (0, 10)
DON'T MODIFY AN... | <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: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
11,327 | <ASSISTANT_TASK:>
Python Code:
#TODO add to cointainer
!pip install umap-learn
import numpy as np
from numpy.random import seed
import scipy.stats as stats
from scipy.stats import uniform, truncnorm, randint
import random
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.preprocessing import Standa... | <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: <a id='1_data_cleaning'></a>
Step2: Construction "./" means we use the current folder of the script. "../" would mean - one level higher relati... |
11,328 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'hadgem3-gc31-hm', 'aerosol')
# 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... |
11,329 | <ASSISTANT_TASK:>
Python Code:
# This block of code checks to make sure that a particular directory is present.
if "divvy_2013" not in os.listdir('datasets/'):
print('Unzip the divvy_2013.zip file in the datasets folder.')
stations = pd.read_csv('datasets/divvy_2013/Divvy_Stations_2013.csv', parse_dates=['online da... | <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: At this point, we have our stations and trips data loaded into memory.
Step2: Then, let's iterate over the stations DataFrame, and add in the ... |
11,330 | <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
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<USER_TASK:>
Description:
Step2: <h2> Explore data </h2>
Step4: Let's write a query to find the unique values for a given column and see how the number of babies and their aver... |
11,331 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
# From https://stackoverflow.com/a/36218558 .
def sparkImport(module_name, module_directory):
Convenience function.
Tells the SparkContext sc (must already exist) to load
module module_name on every computational node before
executing an RDD... | <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: Tweet Count Analysis
Step2: Count 'Em
Step3: Less than $1\%$ of our tweets are duplicates, so we have approximately the quantity of tweets tha... |
11,332 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from scipy.io import loadmat, savemat
from numpy import random
from os import path
mat = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))
Xall = mat['data']
Yall = np.array(ma... | <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: Let us plot the first five examples of the train data (first row) and test data (second row).
Step2: Then we import shogun components and conve... |
11,333 | <ASSISTANT_TASK:>
Python Code:
import espressomd
espressomd.assert_features(["ELECTROSTATICS", "ROTATION", "ROTATIONAL_INERTIA", "EXTERNAL_FORCES",
"MASS", "VIRTUAL_SITES_RELATIVE", "CUDA", "LENNARD_JONES"])
from espressomd import interactions
from espressomd import electrostatics
from espre... | <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 parameter <tt>box_l</tt> sets the size of the simulation box. In general, one should check for finite
Step2: The skin is used for construct... |
11,334 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-mh', 'ocnbgchem')
# 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... |
11,335 | <ASSISTANT_TASK:>
Python Code:
import os
import warnings
import tqdm
import numpy as np
import pandas as pd
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)
%load_ext autoreload
%autoreload 2
import socceraction.spadl as spadl
import socceraction.vaep.features as fs
import socceraction.vaep... | <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: Select data
Step2: Train models
|
11,336 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.5
from google.cloud import bigquery
import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
CSV_COLUMNS = ['fa... | <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: <h2> 1. Refactor the input </h2>
Step2: <h2> 2. Refactor the way features are created. </h2>
Step3: <h2> Create and train the model </h2>
Step... |
11,337 | <ASSISTANT_TASK:>
Python Code:
import os
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
REGION = 'us-central1' # Choose an available region for Cloud MLE from https://cloud.google.com/ml-engine/docs/regions.
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME. Use a regional bucket in th... | <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: Allow the Cloud ML Engine service account to read/write to the bucket containing training data.
Step2: <h2> Packaging up the code </h2>
Step3: ... |
11,338 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
l = [('A', 'a', '1'), ('A', 'b', '2'), ('B','a', '1'), ('A', 'b', '1'), ('B','b', '1'), ('A', 'a', '2')]
np.random.seed(1)
df = pd.DataFrame(np.random.randn(5, 6), columns=l)
def g(df):
df=df[sorted(df.columns.to_list())]
df.columns = pd.Mu... | <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:
|
11,339 | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver impo... | <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: Fully-Connected Neural Nets
Step4: Affine layer
Step5: Affine layer
Step6: ReLU layer
Step7: ReLU layer
Step8: "Sandwich" layers
Step9: Lo... |
11,340 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1 || pip install tensorflow==2.1
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
print(tf.__version__)... | <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: Operations on Tensors
Step2: Point-wise operations
Step3: NumPy Interoperability
Step4: You can convert a native TF tensor to a NumPy array u... |
11,341 | <ASSISTANT_TASK:>
Python Code:
lst_2d = [
[2, 4, 'unicorn'],
[False, 39],
[None],
]
lst_3d = [
[[1, 1, 2], [3, 5], [8, 13]],
[[21, 34], [55]]
]
matrix = [
[0, 0, 1, 5],
[1, 0, 2, 0],
[0, 3, 1, 0],
]
matrix = [
[0, 0, 1, 5],
[1, 0, 2, 0],
[0, 3, 1, 0],
]
print(matrix[1][2])... | <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: Похожим образом можно сделать трёхмерный список.
Step2: Чаще всего используются двумерные списки с равным количеством элементов в каждой строке... |
11,342 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import h5py
import matplotlib.pyplot as plt
from testCases_v2 import *
from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] ... | <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: 2 - Outline of the Assignment
Step4: Expected output
Step6: Expected output
Step8: Expected output
Step10: Expected output
Step12: <table s... |
11,343 | <ASSISTANT_TASK:>
Python Code:
#type your code here
runningTotal = 0
listOfNumbers = [4,7,9,1,8,6]
#type your code here
print(listOfNumbers)
print("The average of these numbers is {0:.2f}".format(average))
word = "Python"
print(len(word))
#type your code here
numbers = (1, 2, 3, 4, 5, 6, 7, 8, 9) # Declaring the tup... | <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: 1.1
Step2: 1.1
Step3: 1.2
Step4: 1.3
Step5: 1.4
Step6: 1.5 (Multi-Part)
Step7: b. Use the print() function to print your list.
Step8: c. ... |
11,344 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# Richard Höchenberger <richard.hoechenberger@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
impo... | <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: Set parameters
Step2: Frequency analysis
Step3: Now let's take a look at the spatial distributions of the PSD.
Step4: Alternatively, you can ... |
11,345 | <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: Use XLA with tf.function
Step2: Then define some necessary constants and prepare the MNIST dataset.
Step3: Finally, define the model and the o... |
11,346 | <ASSISTANT_TASK:>
Python Code:
# Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inl... | <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's show the symbols data, to see how good the recommender has to be.
Step2: Let's run the trained agent, with the test set
Step3: And now a... |
11,347 | <ASSISTANT_TASK:>
Python Code:
import scipy.integrate
import math
import numpy as np
def NDfx(x):
return((1/math.sqrt((2*math.pi)))*(math.e**((-.5)*(x**2))))
x = 2.5
u = 1
o2 = 3
norm = (x-u)/o2
prob = scipy.integrate.quad(NDfx, -np.inf, norm)[0]
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Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
|
11,348 | <ASSISTANT_TASK:>
Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
... | <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: Run solver
Step2: Plot dipole activations
Step3: Show the evoked response and the residual for gradiometers
Step4: Generate stc from dipoles
... |
11,349 | <ASSISTANT_TASK:>
Python Code:
#%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegressi... | <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: Luego se cargan los datos de la competencia
Step2: Visualización de datos y estudio inicial
Step3: Como puede observarse hay un total de 12 co... |
11,350 | <ASSISTANT_TASK:>
Python Code:
def quad_roots(a=1.0, b=2.0, c=0.0):
Returns the roots of a quadratic equation: ax^2 + bx + c = 0.
INPUTS
=======
a: float, optional, default value is 1
Coefficient of quadratic term
b: float, optional, default value is 2
Coefficient of linear term
... | <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: Lecture 7
Step3: Documenting Invariants
Step4: Accessing Documentation (1)
Step5: Accessing Documentation (2)
Step6: Testing
Step7: Princip... |
11,351 | <ASSISTANT_TASK:>
Python Code:
# to make sure things are working, run this
import pandas as pd
print('Pandas version: ', pd.__version__)
import pandas as pd
import matplotlib.pyplot as plt
import datetime as dt
%matplotlib inline
url = 'http://pages.stern.nyu.edu/~dbackus/Data/beer_production_1947-2004.xlsx'
beer ... | <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: If you get something like "Pandas version
Step2: Remind yourself
Step3: Question. Can you see consolidation here?
Step4: Answer these questio... |
11,352 | <ASSISTANT_TASK:>
Python Code:
%%bash
pip install apache-beam[gcp]
import os
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
MODEL_TYPE = 'tpu'
# do not change th... | <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: After doing a pip install, click on Reset Session so that the Python environment picks up the new package
Step2: Preprocess JPEG images to TF R... |
11,353 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import astropy.io.fits as fits
## make matplotlib appear in the notebook rather than in a new window
%matplotlib inline
datadir = ''
objname = '2016HO3'
def plotfits(imno):
img = fits.open(datadir+objname+'_{0:02d}.fits'.format(num... | <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: 0.1 Directory Set up
Step2: 0.2 Display images
Step3: 1. Photometry set up
Step4: Define starting values. Fill in values here
Step5: Apertur... |
11,354 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'awi-cm-1-0-hr', 'land')
# 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
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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
11,355 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#Reading the dataset in a dataframe using Pandas
df = pd.read_csv('../data/master.csv')
#Print first observations
df.head() ... | <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: 1. Import dataset
Step2: Throughout the Machine Learning part of this project we will be using scikit-learn, an open source machine learning li... |
11,356 | <ASSISTANT_TASK:>
Python Code:
# Author: Annalisa Pascarella <a.pascarella@iac.cnr.it>
#
# License: BSD (3-clause)
import os.path as op
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne import setup_volume_source_space
from mne import make_forward_solution
from mne.minimum_norm import ... | <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: Set up our source space.
Step2: Export source positions to nift file
|
11,357 | <ASSISTANT_TASK:>
Python Code:
plt.figure(figsize=(10,6));
plt.scatter(Peaks,Energy);
plt.xlim(0,240)
plt.ylim(0,1000)
plt.xlabel('x (mm)');
plt.ylabel('y (mm)');
plt.plot(xlots,yfit);
plt.legend(['data','Fit'],loc='lower right');
plt.text(5,900,'a = %.3f +/- %.3f keV' % (plsq[0], np.sqrt(pcov[0,0])),size=17)
plt.text(... | <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
|
11,358 | <ASSISTANT_TASK:>
Python Code:
from learning import *
from notebook import *
train_img, train_lbl, test_img, test_lbl = load_MNIST()
print("Training images size:", train_img.shape)
print("Training labels size:", train_lbl.shape)
print("Testing images size:", test_img.shape)
print("Testing labels size:", test_lbl.shap... | <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: CONTENTS
Step2: Check the shape of these NumPy arrays to make sure we have loaded the database correctly.
Step3: Visualizing Data
Step4: Let'... |
11,359 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pickle
%matplotlib inline
def read_weather():
with open('data/weather.pkl', 'rb') as f:
return pickle.load(f)
# The file weather.pkl contains a list of dictionaries
Data = read_weather()
Data[0]
# Implement Q1 part 1... | <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 purpose of the exersise is to manipulate and plot the current weather of a number of European cities. The data has been downloaded from Open... |
11,360 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import cProfile
from pstatsviewer import StatsViewer
from qgrid import nbinstall
nbinstall()
# Construct two 5000 x 8 frames with random floats.
df1 = pd.DataFrame(
np.random.randn(5000, 8),
columns=[chr(ord('A') + i) for i... | <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: Generating stats files with cProfile
Step2: Table/Grid View
Step3: Chart View
Step5: Comparing Alternative Implementations
Step6: Comparison... |
11,361 | <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: Making new Layers and Models via subclassing
Step2: The Layer class
Step3: You would use a layer by calling it on some tensor input(s), much l... |
11,362 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
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), proj=True)
print(evoked)
evoked_l_aud ... | <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: In this tutorial we focus on plotting functions of
Step2: Notice that evoked is a list of
Step3: Let's start with a simple one. We plot even... |
11,363 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# Intialize random number generator
np.random.seed(123)
# True parameter values
alpha, sigma = 1, 1
beta = [1, 2.5]
# Size of dataset
size = 100
# Predictor variable
X1 = np.linspace(0, 1, size)
X2 = np.linspace(0,.2, size)
# Simulate outcome variable
Y = alpha + beta[0... | <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: Here is what the simulated data look like. We use the pylab module from the plotting library matplotlib.
Step2: Model Specification
Step3: Now... |
11,364 | <ASSISTANT_TASK:>
Python Code:
## import ipyrad and give it a shorter name
import ipyrad as ip
## create a test assembly
data = ip.Assembly("data")
data.set_params('project_dir', 'test')
data.set_params('raw_fastq_path', 'ipsimdata/rad_example_R1_.fastq.gz')
data.set_params('barcodes_path', 'ipsimdata/rad_example_barc... | <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: Either create a new ipyrad assembly or load an existing one
Step2: Or load a finished assembly from its JSON file
Step3: Look at the stats sum... |
11,365 | <ASSISTANT_TASK:>
Python Code:
from pandas import Series, DataFrame
import pandas as pd
f = r'/home/hase/Documents/ZHAW/InfoEng/Lectures/Scripting/data/titanic3_test.csv'
fo = r'/home/hase/Documents/ZHAW/InfoEng/Lectures/Scripting/data/submit/titanic3_test_gender.csv'
df = pd.read_csv(f, sep=';', index_col='id', usec... | <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: Define path to working csv files, input and output
Step2: Create a dataframe from the csv file
Step3: Add a new column named "survived"
Step4:... |
11,366 | <ASSISTANT_TASK:>
Python Code:
!pip install hanlp_restful -U
from hanlp_restful import HanLPClient
HanLP = HanLPClient('https://www.hanlp.com/api', auth=None, language='zh') # auth不填则匿名，zh中文，mul多语种
doc = HanLP('2021年HanLPv2.1为生产环境带来次世代最先进的多语种NLP技术。', tasks='srl')
print(doc)
doc.pretty_print()
for i, pas in enumera... | <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: 创建客户端
Step2: 申请秘钥
Step3: 返回值为一个Document
Step4: doc['srl']字段为语义角色标注结果，每个四元组的格式为[论元或谓词, 语义角色标签, 起始下标, 终止下标]。其中，谓词的语义角色标签为PRED，起止下标对应以tok开头的第一个单... |
11,367 | <ASSISTANT_TASK:>
Python Code:
#### Libraries
# Third Party Libraries
import numpy as np
from sklearn.model_selection import train_test_split
import theano
import theano.tensor as T
from theano.tensor.nnet import conv2d
from theano.tensor.signal import pool
class ConvLayer(object):
def __init__(self, input, filter... | <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: Convolution Layer
Step2: Pooling layer
Step3: Fully Connected Layer
Step5: Building The Model
Step6: Convolutional Neural Network
Step7: Co... |
11,368 | <ASSISTANT_TASK:>
Python Code:
from pydna.all import *
frags = parse('''
>1|random sequence|A: 0.25|C: 0.25|G: 0.25|T: 0.25|length: 50 bp
ccagaatacagtgccttagatctacggatcgtatctgcgatttggccgat
>2|random sequence|A: 0.25|C: 0.25|G: 0.25|T: 0.25|length: 50 bp
gccctgcttggtagatcaggcgagccaataacattctatagtgtagcctt
>3|random sequ... | <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: The sequences below were generated here.
Step2: We make a list of amplicons (sequences with pairs of primers from the Dseqrecords)
Step3: We n... |
11,369 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# All the imports
from __future__ import print_function, division
from math import *
import random
import sys
import matplotlib.pyplot as plt
# TODO 1: Enter your unity ID here
__author__ = "pwang13"
class O:
Basic Class which
- Helps dynamic updates
... | <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: Optimizing Real World Problems
Step12: The Generic Problem Class
Step14: Great. Now that the class and its basic methods is defined, lets exte... |
11,370 | <ASSISTANT_TASK:>
Python Code:
from numpy.random import choice
from scipy.stats import beta
class DirichletProcessSample():
def __init__(self, base_measure, alpha):
self.base_measure = base_measure
self.alpha = alpha
self.cache = []
self.weights = []
self.total_stic... | <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's illustrate again with a standard normal base measure. We can construct a function base_measure that generates samples from it.
Step2: Bec... |
11,371 | <ASSISTANT_TASK:>
Python Code:
def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].uppe... | <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: Lesson
Step2: Project 1
Step5: Transforming Text into Numbers
|
11,372 | <ASSISTANT_TASK:>
Python Code:
#@title License
# 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... | <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: Spectral Representations of Natural Images
Step2: Image Upload
Step3: We rescale images to a reasonable resolution, otherwise this would take ... |
11,373 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display
%matplotlib inline
train_data = pd.read_csv("train.csv")
train_data = train_data.drop('Id', axis=1)
test_data = pd.read_csv("test.csv")
test_data... | <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: Now for a bit of exploratory data analysis so we can get to know our data
Step2: Plot the data
Step3: I'm sure there are more creative and inf... |
11,374 | <ASSISTANT_TASK:>
Python Code:
# install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/A... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step4: Here the code from previous chapters we'll reuse.
Step5: In the previous chapter we defined metrics that quantify the performance of bike shari... |
11,375 | <ASSISTANT_TASK:>
Python Code:
wadiz_df = pd.DataFrame(columns=["project_id", "title", "area", "category", "target", "result", "duration", "comment_all", "comment_user",
"comment_provider", "money_supporter", "sign_supporter"])
project_money_all = pd.DataFrame()
for page in range(1, ... | <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. wadiz_df Data 정리
Step2: 3. Project_money Data 처리
Step3: 4. Project_money_all, Wadiz_df 합치기
Step4: 5. Data 추가
Step5: 5. Comment Crawling
S... |
11,376 | <ASSISTANT_TASK:>
Python Code:
import json
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
loans = pd.read_csv("lending-club-data_assign_2.csv")
# safe_loans = 1 => safe
# safe_loans = -1 => risky
loans... | <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: Load LendingClub Loans dataset
Step2: The target column (label column) of the dataset that we are interested in is called bad_loans. In this co... |
11,377 | <ASSISTANT_TASK:>
Python Code:
# Load library
import cv2
import numpy as np
from matplotlib import pyplot as plt
# Load image as grayscale
image = cv2.imread('images/plane.jpg', cv2.IMREAD_GRAYSCALE)
# Show image
plt.imshow(image, cmap='gray'), plt.axis("off")
plt.show()
# Save image
cv2.imwrite('images/plane_new.jpg... | <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: Load Image As Greyscale
Step2: Save Image
|
11,378 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-3', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <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... |
11,379 | <ASSISTANT_TASK:>
Python Code:
# print("\x1b[30;1m\"red\"\x1b[0m")
# print("\x1b[31;1m\"red\"\x1b[0m")
# print("\x1b[32;1m\"red\"\x1b[0m")
# print("\x1b[33;1m\"red\"\x1b[0m")
# print("\x1b[34;1m\"red\"\x1b[0m")
# print("\x1b[35;1m\"red\"\x1b[0m")
# print("\x1b[36;1m\"red\"\x1b[0m")
# print("\x1b[37;1m\"red\"\x1b[0m")
#... | <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: Insults package
Step2: Identifying quotation marks
Step3: We need to clean up the open and closed inverted commas with straight ones.
Step4: ... |
11,380 | <ASSISTANT_TASK:>
Python Code:
# Author: Denis A. Engemann <denis.engemann@gmail.com>
# Victoria Peterson <victoriapeterson09@gmail.com>
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import Epochs
from mne.datasets.fieldtrip_cmc import data_path
from mne.decoding import SSD
fname ... | <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: Define parameters
Step2: Let's investigate spatial filter with max power ratio.
Step3: Let's also look at the power spectrum of that source an... |
11,381 | <ASSISTANT_TASK:>
Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 3
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.datasets import sample
from mne.beamformer import make_lcmv, apply_lcmv
print(__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: Get epochs
Step2: Run beamformers and look at maximum outputs
Step3: We can also look at the spatial distribution
|
11,382 | <ASSISTANT_TASK:>
Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.preprocessing import ICA, create_ecg_epochs
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.... | <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: Fit ICA model using the FastICA algorithm, detect and inspect components
|
11,383 | <ASSISTANT_TASK:>
Python Code:
import h2o
# Start an H2O Cluster on your local machine
h2o.init()
# This will not actually do anything since it's a fake IP address
# h2o.init(ip="123.45.67.89", port=54321)
#csv_url = "http://www.stat.berkeley.edu/~ledell/data/eeg_eyestate_splits.csv"
csv_url = "https://h2o-public-tes... | <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: If you already have an H2O cluster running that you'd like to connect to (for example, in a multi-node Hadoop environment), then you can specify... |
11,384 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import openmc
import openmc.mgxs as mgxs
from openmc.source import Source
from openmc.stats import Box
import openmoc
from openmoc.compatible import get_openmoc_geometry
import pyne.ace
%matplotlib inline
# Instanti... | <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 need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the mat... |
11,385 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style('darkgrid')
sns.set_context('talk')
def errorbarjitter(df, groupByCol, statsCol, fig=None, xlab='group', ylab='units', rotate = 0):
grouped = df.groupby([grou... | <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: errorbarjitter function
Step2: Example 1
Step3: Iris dataset example
Step4: References
Step5: Learning Index
|
11,386 | <ASSISTANT_TASK:>
Python Code:
from beakerx import *
f = EasyForm("Form and Run")
f.addTextField("first")
f.addTextField("last")
f['first'] = "First"
f['last'] = "Last"
f.addButton("Go!", tag="run")
f
"Good morning " + f["first"] + " " + f["last"]
f['last'][::-1] + '...' + f['first']
f['first'] = 'Beaker'
f['last'] = '... | <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: Default Values and placeholder
Step2: JupyterJSWidgets work with EasyForm
|
11,387 | <ASSISTANT_TASK:>
Python Code:
import requests
import string
import random
lorem = requests.get('http://loripsum.net/api/plaintext').text
WORDS = [word.lower()
for word in filter(lambda c: c not in string.punctuation, lorem).split()]
def random_words(n=2):
return '_'.join(random.choice(WORDS) for i in ran... | <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: Using Provenance in Adama
Step2: Connect the adama Python object to your API server of choice. The official one is https
Step3: Create a rand... |
11,388 | <ASSISTANT_TASK:>
Python Code:
!pip install -q tf-models-official
import tensorflow as tf
import tensorflow_hub as hub
tfhub_handle_encoder = 'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3'
bert_saved_model_path = 'bert_base'
bert_model = hub.load(tfhub_handle_encoder)
tf.saved_model.save(bert_model, b... | <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. Inference
Step2: 2.1 Helper functions
Step3: 2.2 Convert the model with TF-TRT
Step4: 2.3 Run inference with converted model
Step5: Compa... |
11,389 | <ASSISTANT_TASK:>
Python Code:
cisla = [(1, 1), (2, 4), (3, 9), (4, 16), (5, 25), (6,36), (7, 49), (8, 64), (9, 81), (10, 100)]
mocniny = dict(cisla)
print(mocniny)
import random
while True:
odpoved = input('Na kolik odpovědí chceš hrát? ')
try:
odpoved = int(odpoved)
break
except ValueErro... | <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: Skautská hra
Step2: Řešení bez slovníků, ale hlavně takové, kde by nebylo úplně snadné přidat další otázky.
Step3: Řešení, kde přidání, změna ... |
11,390 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
def calc_diff_err_p1(n):
h = 2.*np.pi/n
x = tf.range(1., n+1.)[:, None]*h - np.pi
u = tf.exp(tf.sin(x))
u_prime = tf.cos(x) * u
e = tf.cast(tf.ones(tf.c... | <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: Program 1
Step2: Program 2
|
11,391 | <ASSISTANT_TASK:>
Python Code:
writer = pytablewriter.LatexMatrixWriter()
writer.table_name = "B"
writer.value_matrix = [
["a_{11}", "a_{12}", "\\ldots", "a_{1n}"],
["a_{21}", "a_{22}", "\\ldots", "a_{2n}"],
[r"\vdots", "\\vdots", "\\ddots", "\\vdots"],
["a_{n1}", "a_{n2}", "\\ldots", "a_{nn}"],
]
write... | <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: \begin{equation}
Step4: \begin{array}{r | r | l | l | l | l} \hline
|
11,392 | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import, division, print_function
import os
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
# Import from monitor
from desc.monitor import RefLightCurves
import desc.monitor as monitor... | <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: Observations
Step2: Obtaining the parameters from the database
Step3: The RefLightCurves Class
Step4: Find the number of objects in the table... |
11,393 | <ASSISTANT_TASK:>
Python Code:
from bigbang.archive import Archive
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
url = "ipython-user"
arx = Archive(url)
fernandos = Archive(arx.data[arx.data.From.map(lambda x: 'Fernando' in x)])
fernandos.data[:3]
[x for x in fernandos.get_activity()]
not_f... | <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's get all the available date from the IPython community. For now, this is just the mailing lists. One day, BigBang will also get its issue t... |
11,394 | <ASSISTANT_TASK:>
Python Code:
# The usual imports
%pylab inline
from ipywidgets import *
# Some extra imports for 3D
from mpl_toolkits.mplot3d import *
# These are only needed to make things pretty..
# they are mostly refered to in the formatting part of the figures
# and enshure us to have the figures also present ... | <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 control freak sequence
Step2: Below we write a generic function that takes the functions $u(t)$,$v(t)$ and $w(t)$ as an argument and then v... |
11,395 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
def model_inputs(real_dim, z_dim):
inputs_real = tf.placeholde... | <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: Model Inputs
Step2: Generator network
Step3: Discriminator
Step4: Hyperparameters
Step5: Build network
Step6: Discriminator and Generator L... |
11,396 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
import os
os.stat("c:/temp/fr.openfoodfacts.org.products.csv").st_size / 2**30, 'Go'
import pyensae
%load_ext pyensae
%head -n 2 c:/temp/fr.openfoodfacts.org.products.csv
import pandas
df = pandas.read_csv("c:/temp/fr.openf... | <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: A quoi ça ressemble
Step2: C'est gros.
Step3: Idée de la compétation
Step4: Le code qui suit est construit après plusieurs essais en fonction... |
11,397 | <ASSISTANT_TASK:>
Python Code:
import random
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import csv
import scipy.misc
import time
import collections
import os
import utils as ut
import importlib
import copy
importlib.reload(ut)
# This is a bit of magic to make matplotlib figures appear in... | <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: Load the data from *.csv file
Step2: Explore the correct data
Step3: Prepare the Data for CNN
Step4: Prepare the data for CNN
Step5: Model 1... |
11,398 | <ASSISTANT_TASK:>
Python Code:
# Show matplotlib plots inline (nicely formatted in the notebook)
%matplotlib inline
# Import libraries necessary for this project
import numpy as np
import pandas as pd
import renders as rs
import seaborn as sns
from matplotlib import pylab as plt
from IPython.display import display # ... | <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: Data Exploration
Step2: Implementation
Step3: Question 1
Step4: Answer
Step5: The heatmap above shows the percentile ranks of samples' categ... |
11,399 | <ASSISTANT_TASK:>
Python Code:
from collections import OrderedDict
INPUT_PATH = "gauss/"
(MIN_PREFIX, MAX_PREFIX) = ("C015", "C055") # for instance filenames
OUTPUT_PATH = "plots/"
WINDOW = 150 # size of the subsets of instances used as a moving window
SOLVER_NAMES = OrderedDict([
("GeneticGroup", "Grouping GA"),
... | <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: Tools
Step2: Reading the instances
Step3: Statistical difficulty and average multiplicity
Step4: The dispersion of the pagination sizes could... |
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