Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
13,200 | <ASSISTANT_TASK:>
Python Code:
try:
import OpenMRS as om
except:
# At this point, you probably haven't installed OpenMRS. You can install it by:
# sudo pip install git+https://github.com/BerryAI/Acai
# Now we are going to import OpenMRS from the source.
# Note: This assumes you are currently 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: Load example data
Step2: Start and train a recomemndation engine
Step3: Now the recommendation engine knows about users and their ratings. We ... |
13,201 | <ASSISTANT_TASK:>
Python Code:
warnings, url = gf.get_warnings_as_json([3022, 3023, 3024], "2017-12-01", "2018-05-31", lang_key=1, simple=False, recursive_count=5)
print(url)
warnings_flattened = (flatten(w) for w in warnings)
df = pd.DataFrame(warnings_flattened)
df.head(5)
df.columns
df_a = df
# define individual col... | <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: See full list at http
Step2: List relevant parameters for the regression analysis
Step3: Clean and save dataframe
Step4: Plotting
Step5: Dec... |
13,202 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame({'user':[1,1,2,2,3], 'time':[20,10,11,18, 15], 'amount':[10.99, 4.99, 2.99, 1.99, 10.99]})
### Output your answer into variable 'result'
def g(df):
return df.groupby('user')[['time', 'amount']].apply(lambda x: x.values.tolist()[::-1]).to_frame(nam... | <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:
|
13,203 | <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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<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... |
13,204 | <ASSISTANT_TASK:>
Python Code:
# Standard imports
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
# x-axis range
x = np.linspace(0, 10, 100)
# Create first of two panels
plt.subplot(2,1,1) # (row, col, panel no.)
plt.plot(x, np.sin(x))
# Create second of two pa... | <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: Matplotlib's Two Interfaces
Step2: In order to change the figure and axes you could use the plt.gcf() (get current figure) and plt.gca() (get c... |
13,205 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from PIL import Image
img = Image.open("img/colonies.jpg")
plt.imshow(img)
arr = np.array(img)
print("x,y,RGB ->",arr.shape)
arr = np.array(img)
plt.imshow(arr)
plt.show()
arr[:,:,1] = 255
plt.imshow(arr)
x = ... | <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 Python Image Library (PIL)
Step2: You can load images as Image instances
Step3: Image instances can be interconverted with numpy arrays
St... |
13,206 | <ASSISTANT_TASK:>
Python Code:
oxp = Symbol("Omega_x'")
b = Symbol("b")
n = Symbol("n")
theta = Symbol("theta")
w = Symbol("w")
s = Symbol("s")
a = Symbol("a")
subsampledOmega = (binomial(s, b) * binomial(n - s, a - b)) / binomial(n, a)
subsampledFpF = Sum(subsampledOmega, (b, theta, s))
subsampledOmegaSlow = (binomial... | <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: where n refers to the size of the population of cells, a is the number of active cells at any instance in time, s is the number of actual synaps... |
13,207 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from landlab import RasterModelGrid
grid = RasterModelGrid((200, 400), xy_spacing=(10e3, 20e3))
grid.dy, grid.dx
from landlab.components.flexure import Flexure
Flexure.input_var_names
Flexure.var_units("lithosphere__overlying_pressure_increment")... | <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: Create the grid
Step2: Create a rectilinear grid with a spacing of 10 km between rows and 20 km between columns. The numbers of rows and columm... |
13,208 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.read_csv('data/airline_delay_causes_2015.csv')
df.head()
df.columns = df.columns.str.strip()
df['month'] = df['month'].map(lambda x: '0' + str(x) if len(str(x)) < 2 else x)
df.month.unique()
agg_month_sum = df.groupby('month',as_index=False).sum()
not_ontime_... | <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: Since month in dimple only except 2 digit format, change this
Step2: We want to have total number of operations and total minutes delay. So we'... |
13,209 | <ASSISTANT_TASK:>
Python Code:
# Import modules
import numpy as np
import scipy
import sympy as sym
from scipy import sparse
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from IPython.display import Math
from IPython.display import display
sym.init_printing(use_latex=True)
def heatfd(xl,... | <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: 8.1 Parabolic Equations
Step2: Backward Difference Method
Step3: Example
Step4: Example
Step5: Crank-Nicolson Method
Step6: Example
Step7: ... |
13,210 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
data = pd.read_csv("train.csv")
data.head()
print data.iloc[1][0]
plt.imshow(data.iloc[1][1:].reshape(28,28),cmap='Greys')
plt.show()
print data.iloc[28][0]
plt.imshow(data.iloc[28][1:].reshape(28,2... | <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 that's what the dataset looks like, each pixel is a column, each row is an image in the dataset. Actually I have a corresponding 'test' datas... |
13,211 | <ASSISTANT_TASK:>
Python Code:
import vpython as vp
#Code
def charge_color(charge):
if charge>0:
charge_color = vp.color.red
elif charge <0:
charge_color = vp.color.blue
else:
charge_color = vp.color.white
return charge_color
#
def getfield(position):
r = position
field =... | <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: Se configura la escena y el sistema coordenado
Step2: Se configura la fuente del campo eléctrico
Step3: Se representa la fuente del Campo Eléc... |
13,212 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# a vector: the argument to the array function is a Python list
v = np.array([1,2,3,4])
v
# a matrix: the argument to the array function is a nested Python list
M = np.array([[1, 2], [3, 4]])
M
type(v), type(M)
v.shape
M.shape
M.size
np.shape(M)
np.size(M)
M.dtype... | <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: In the numpy package the terminology used for vectors, matrices and higher-dimensional data sets is array.
Step2: The v and M objects are both... |
13,213 | <ASSISTANT_TASK:>
Python Code:
cred=db.login.find_one({})
#Tweepy Login credensials are stored in mongodb databse
auth = tweepy.OAuthHandler(cred["consumerKey"], cred["consumerSecret"])
auth.set_access_token(cred["oauthTocken"], cred["oauthTokenSecret"])
api = tweepy.API(auth)
api
cities=["Delhi","Kolkata","Bangalore"... | <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: <h2>Where On Earth ID (WOEID)</h2>
Step2: Performing Latest Trend Query & Strore them
Step3: Clean OUr Data drop unneccesary content
Step4: I... |
13,214 | <ASSISTANT_TASK:>
Python Code:
from collections import defaultdict
from itertools import chain
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
from ipywidgets import interact
from chempy import Substance, Reaction, ReactionSystem
from chempy.kinetics.rates import Arrhenius, MassAction
from chempy.... | <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: We will use a generic model representing a decay-chain with two decays
Step2: "Arrhenius" behaviour means that the rate of reaction depends exp... |
13,215 | <ASSISTANT_TASK:>
Python Code:
def ptrans(f,t):
import numpy as np
g = np.empty_like(f)
if f.ndim == 1:
W = f.shape[0]
col = np.arange(W)
g = f[(col-t)%W]
elif f.ndim == 2:
H,W = f.shape
rr,cc = t
row,col = np.indices(f.shape)
g = f[(row-rr)%H, (col-cc)%W]
... | <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: Examples
Step2: Example 1
Step3: Example 2
Step4: Equation
|
13,216 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from micromeritics import util
from micromeritics import isotherm_examples as ex
import matplotlib.pyplot as plt
carb = ex.carbon_black() # example isotherm of Carbon Black with N2 at 77K
sial = ex.silica_alumina() # example isotherm of Silica Alumina with N2 at 77K
m... | <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: It is also useful to show the isotherm with the Pressure axis scaled as logarithmic.
Step2: While it is more common to show isotherm data using... |
13,217 | <ASSISTANT_TASK:>
Python Code::
import matplotlib.pyplot as plt
plt.plot(k,l)
<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:
|
13,218 | <ASSISTANT_TASK:>
Python Code:
# Limit processing of protocol parts for development
PROCESS_PARTS_LIMIT = 500
# Enable caching of protocol parts data (not efficient, should only be used for local development with sensible PROCESS_PARTS_LIMIT)
PROCESS_PARTS_CACHE = True
# Filter the meetings to be processed, these kwarg... | <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: Load source data
Step2: Inspect the datapackages which will be loaded
Step3: Run the flow
Step4: Aggregate and print stats
|
13,219 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
import mne
from mne import io
from mne.datasets import sample
from mne.cov import compute_covariance
print(__doc__)
data_path = sample.data_path()... | <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: Compute covariance using automated regularization
Step3: Show the evoked data
Step4: We can then show whitening for our... |
13,220 | <ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy 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:
Step3: First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The lab... |
13,221 | <ASSISTANT_TASK:>
Python Code:
import geopandas as gpd
import matplotlib.pyplot as plt
import seaborn as sns
from cartoframes.auth import set_default_credentials
from cartoframes.data.observatory import *
from cartoframes.data.services import Isolines
from cartoframes.viz import *
sns.set_style('whitegrid')
%matplotlib... | <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 CARTO default credentials
Step3: Note about credentials
Step4: <a id='section2'></a>
Step5: Visualize isochrones
Step6: <a id='section3'... |
13,222 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import datetime as dt
from QuantLib import *
plt.style.use('fivethirtyeight')
bid_rates = [0.044696, 0.0441, 0.0441, 0.0439, 0.0439, 0.04405, 0.0441, 0.043954, 0.044863]
mid_rates = [0.045128, 0.044413, 0.044288, 0.044, 0.044125, 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: 市场数据
Step2: 构造收益率曲线
Step3: 获取收益率曲线数据
|
13,223 | <ASSISTANT_TASK:>
Python Code:
2+2
from gensim.models import Word2Vec
import gensim
model = gensim.models.KeyedVectors.load_word2vec_format(
'./private_data/GoogleNews-vectors-negative300.bin', binary=True)
# What does a word vector look like?
model.word_vec("cheeseburger")
cheeseburger = model.word_vec("cheesebu... | <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 notebook understand that I was asking for the execution of a command. OK, with that concept of what you are looking at out of the way, let's... |
13,224 | <ASSISTANT_TASK:>
Python Code:
problem = lambda m, i: 0 < m < i
no_problem = lambda m, i: not problem(m, i) and not problem(3 - m, 3 - i)
def next_states(state):
m, i, b = state
if b == 1:
return { (m-mb, i-ib, 0) for mb in range(m+1)
for ib in range(i+1)
... | <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: $\texttt{no_problem}(m, i)$ is true if there is no problem on either side.
Step2: A state is represented as a triple. The triple $(m, i, b)$ s... |
13,225 | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('Iris.csv')
df.head()
x_data = df.iloc[:, :-1].values.astype(np.float32)
y_datalabel = df.iloc[:, -1]
y_data = LabelEncoder().fit_transform(df.iloc[:, -1])
onehot = np.zeros((y_data.shape[0], np.unique(y_data).shape[0]))
for i in range(y_data.shape[0]):
onehot[i, y_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: ```text
Step2: text
|
13,226 | <ASSISTANT_TASK:>
Python Code:
from pliers.extractors import FaceRecognitionFaceLocationsExtractor
# A picture of Barack Obama
image = join(get_test_data_path(), 'image', 'obama.jpg')
# Initialize Extractor
ext = FaceRecognitionFaceLocationsExtractor()
# Apply Extractor to image
result = ext.transform(image)
result.to_... | <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: Face detection with multiple inputs
Step2: Note how the merged pandas DataFrame contains 5 rows, even though there were only 3 input images. Th... |
13,227 | <ASSISTANT_TASK:>
Python Code:
import os
import tensorflow as tf
import numpy as np
from google.cloud import bigquery
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-centra... | <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: We will use this helper funciton to write lists containing article ids, categories, and authors for each article in our database to local file.
... |
13,228 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import sys
sys.path.append("../../")
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from... | <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: Load and check data
Step2: ## Analysis
Step3: What are optimal levels of hebbian and weight pruning
|
13,229 | <ASSISTANT_TASK:>
Python Code:
from robots.robots import Robot
from numpy import pi
params = [[ "l1", 0, 0, "q1"]]
robot1 = Robot("Pendulo simple", "R", [0.4], [0], params, "cinematico")
robot1.inicializar_puertos()
%matplotlib widget
robot1.visualizador()
<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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<USER_TASK:>
Description:
Step1: Se definen los parametros DH del manipulador a visualizar, se le da un nombre, se define el tipo de articulaciones que tiene el manipulador, las... |
13,230 | <ASSISTANT_TASK:>
Python Code:
#Initialization of iPython, some helper functions.
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('ggplot')
from sympy import *
from IPython.display import display, Math, Latex
init_printing(use_latex="mathjax")
_Omega=u'\u03A9'
def... | <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: Basic assumptions
Step2: Rated tip power (40 W) is defined for typical supply voltage (12 V). For lower supply voltage maximum power is lower t... |
13,231 | <ASSISTANT_TASK:>
Python Code:
# Author: Mathurin Massias <mathurin.massias@gmail.com>
# Yousra Bekhti <yousra.bekhti@gmail.com>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import os.path as op
import mne
fr... | <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: Load somatosensory MEG data
Step2: Run iterative reweighted multidict TF-MxNE solver
Step3: Generate stc from dipoles
Step4: Show the evoked ... |
13,232 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import igraph
import timeit
import itertools
def enumerate_matrix(gmat, i):
def enumerate_adj_list(adj_list, i):
def enumerate_edge_list(edge_list, i):
def do_sim(n):
retlist = []
nrep = 10
nsubrep = 10
# this is (sort of) a Python way of d... | <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: Now, define a function that returns the index numbers of the neighbors of a vertex i, when the
Step2: Define a function that enumerates the ne... |
13,233 | <ASSISTANT_TASK:>
Python Code:
# Import libraries
import numpy as np
import pandas as pd
from time import time
from sklearn.metrics import f1_score
# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
# TODO: Calculate number of students
n_students = len(student_da... | <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: Implementation
Step2: Preparing the Data
Step3: Preprocess Feature Columns
Step4: Implementation
Step5: Training and Evaluating Models
Step6... |
13,234 | <ASSISTANT_TASK:>
Python Code:
import os
try:
os.mkdir('me')
except OSError:
pass
os.chdir('me')
%%bash
echo 'How does our directory look like?'
ls -al
git init
echo 'How does our directory look like now?'
ls -al
git status
# Let us create files for tracking.
echo 'My Project' > README
echo 'pei... | <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: Setting Up
Step2: Recording Changes to the Repository
Step3: Removing and Moving File
Step4: Viewing the Commit History
Step5: Adding a Remo... |
13,235 | <ASSISTANT_TASK:>
Python Code:
#importar los paquetes que se van a usar
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import datetime
from datetime import datetime
import scipy.stats as stats
import scipy as sp
import scipy.optimize as scopt
import matplotlib.pyplot as plt
import seaborn 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:
Step1: 2. Uso de Pandas para descargar datos de precios de cierre
Step2: Una vez cargados los paquetes, es necesario definir los tickers de las accion... |
13,236 | <ASSISTANT_TASK:>
Python Code:
import keras
from os.path import join
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout,Activation, Lambda,Input
from keras.layers import Embedding
from keras.layers import Convolution1D
from keras.datasets import imdb
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: POS当作一个通道。
Step2: 情感极性当作一个通道。
Step3: 构建情感极性强度通道
Step4: 否定词。
Step5: Glove训练好的词向量
Step6: 获取训练好的word embedding 数组，用来初始化 Embedding
Step7: 将一个b... |
13,237 | <ASSISTANT_TASK:>
Python Code:
def myFun(x):
return (x**x)**x
myFun(9)
timeit(myFun(12))
%timeit 10*1000000
# this syntax allows comments ... note that if you leave off the numeric argument, %timeit seems to do nothing
myFun(12)
%timeit 10*1000000
# this syntax allows comments ... note that if you leave off the ... | <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: For this example, timeit() needs to be the only function in the cell, and then your code is called in as a valid function call as in this demo
S... |
13,238 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
class CubicSpline():
''' cubic spline of a function
- equally-spaced knots
- derivatives specified at endpoints
'''
def __init__(self, fn, xmin, xmax, n, df_left, df_right):
... | <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: Try it out
Step2: Try it out over a range
|
13,239 | <ASSISTANT_TASK:>
Python Code:
class TestIterator:
def __init__(self, max_value):
self._current_value = 0
self._max_value = max_value
def __next__(self):
self._current_value += 1
if self._current_value > self._max_value:
raise StopIteration()
return ... | <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: When you perform the iteration manually you should use the builtin next function to call the magic __next__ method.
Step2: Of course you can al... |
13,240 | <ASSISTANT_TASK:>
Python Code:
# Imports for pandas, and numpy
import numpy as np
import pandas as pd
# imports for seaborn to and matplotlib to allow graphing
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="whitegrid")
%matplotlib inline
# import Titanic CSV - NOTE: adjust file path as neccessar... | <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: Distribution of Passengers
Step2: Distribution of Genders in pClass populations
Step3: Age - Analysis | Graph
Step4: Distrbution of Age in pa... |
13,241 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
print("NumPy version:",np.__version__)
print("Pandas version:",pd.__version__)
%ls
steel_df = pd.read_excel("steel1045.xls")
al_df = pd.read_excel("aluminum6061.xls")
steel_df.head()
al_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: Ensure the two .xls data files are in the same folder as the Jupyter notebook
Step2: We can see our Jupyter notebook stress_strain_curve_with_p... |
13,242 | <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
import matplotlib.image as mpimg
from IPython.display import Image
from astropy.io import fits
import aplpy
#Disable astropy/aplpy loggin... | <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: Import section specific modules
Step5: 6.4 Residuals and Image Quality<a id='deconv
Step6: Figure
Step7: Left
Step8: Method 1 will always re... |
13,243 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
from halomod import HaloModel
from scipy.interpolate import InterpolatedUnivariateSpline as spline
hm = HaloModel(profile_model="Einasto")
_ = hm.profile.rho(hm.r,hm.m)
hm.update(profile_model="Einasto")
plot(hm.r, hm.profile.rho(hm.r,1e12),label="m=12",color="b")
plot(hm.... | <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: Density Profile
Step2: Now plot versus $r$
Step3: Now plot versus $m$
Step4: Fourier Transform
Step5: Now plot against $m$
Step6: We may ha... |
13,244 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units
col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', ... | <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: Upper air data can be obtained using the siphon package, but for this example we will use
Step2: We will pull the data out of the example datas... |
13,245 | <ASSISTANT_TASK:>
Python Code:
# initialize hod model
model = PrebuiltHodModelFactory('zheng07', threshold=-21)
halocat = CachedHaloCatalog(simname='multidark', redshift=0, halo_finder='rockstar')
model.populate_mock(halocat, enforce_PBC=False)
N_sat = len(np.where(model.mock.galaxy_table['gal_type'] == 'satellites')[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: Note that changing the PBC condition enforce_PBC option does not change the $f_{sat}$ value.
|
13,246 | <ASSISTANT_TASK:>
Python Code:
# Importamos librerías
import numpy as np
import pandas as pd
import pandas_datareader as data
import matplotlib.pyplot as plt
%matplotlib inline
# Creamos la función
def load_adj_close(ticker, data_source, start_date, end_date):
panel_data = data.DataReader(ticker, data_source, start... | <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. Proyección de rendimientos diarios
Step2: Habiendo caracterizado los rendimientos diarios como una variable aleatoria normal con la media y ... |
13,247 | <ASSISTANT_TASK:>
Python Code:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as m... | <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: MNIST
Step2: Binary classifier
Step3: Note
Step4: Note
Step5: ROC curves
Step6: Note
Step8: Multiclass classification
Step9: Multilabel c... |
13,248 | <ASSISTANT_TASK:>
Python Code:
# check Python version
!python -V
import pandas as pd # download library to read data into dataframe
pd.set_option('display.max_columns', None)
recipes = pd.read_csv("https://ibm.box.com/shared/static/5wah9atr5o1akuuavl2z9tkjzdinr1lv.csv")
print("Data read into dataframe!") # takes about... | <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 the data from the IBM server into a pandas dataframe.
Step2: Show the first few rows.
Step3: Get the dimensions of the dataframe.
|
13,249 | <ASSISTANT_TASK:>
Python Code:
utc = 0
sma = 1
ecc = 2
inc = 3
raan = 4
aop = 5
ma = 6
ta = 7
#fig1 = plt.figure(figsize = [15,8], facecolor='w')
fig_peri = plt.figure(figsize = [15,8], facecolor='w')
fig_apo = plt.figure(figsize = [15,8], facecolor='w')
fig3 = plt.figure(figsize = [15,8], facecolor='w')
fig4 = plt.fi... | <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: Plot the orbital parameters which are vary significantly between different tracking files.
|
13,250 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import bokeh.plotting as bkp
from mpl_toolkits.axes_grid1 import make_axes_locatable
# read in readmissions data provided
hospital_read_df = pd.read_csv('data/cms_hospital_readmissions.csv')
hospital... | <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: Preliminary Analysis
Step2: Preliminary Report
|
13,251 | <ASSISTANT_TASK:>
Python Code:
import sqlite3 as sql
import os
from pprint import pprint
class DB:
backend = 'sqlite3' # default
target_path = os.getcwd() # current directory
db_name = ":file:" # lets work directly with a file
db_name = os.path.join(target_path, 'shapes_lib.db')
@cla... | <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 DB class contains information for connecting to a SQLite database, which may be accessed directly, as a text file, no need for a special ser... |
13,252 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import nibabel
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
import mne
from mne.transforms import apply_trans
from mne.io.constants import FIFF
data_path = mne.datasets.sample.data_path()
subjects_dir = os.path.join(data_path,... | <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: MRI coordinate frames
Step2: Notice that the axes in the
Step3: These data are voxel intensity values. Here they are unsigned integers in the
... |
13,253 | <ASSISTANT_TASK:>
Python Code:
def fibonacci(n):
a, b = 0, 1
while n:
a, b = b, a + b
n -= 1
return a
for n in range(10):
print(fibonacci(n))
[fibonacci(n) for n in range(10)]
def gen_fibo(n):
a, b = 0, 1
while n:
yield a
a, b = b, a + b
n -= 1
g10 = gen_... | <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 generator
Step2: Fibonacci numbers and the golden ratio
|
13,254 | <ASSISTANT_TASK:>
Python Code:
!head -n12 $LISA_HOME/logging.conf
!head -n30 $LISA_HOME/logging.conf | tail -n5
import logging
from conf import LisaLogging
LisaLogging.setup(level=logging.INFO)
from env import TestEnv
te = TestEnv({
'platform' : 'linux',
'board' : 'juno',
'host' ... | <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: Each module has a unique name which can be used to assign a priority level for messages generated by that module.
Step2: The default logging le... |
13,255 | <ASSISTANT_TASK:>
Python Code:
# Create a SystemML MLContext object
from systemml import MLContext, dml
ml = MLContext(sc)
%%sh
mkdir -p data/mnist/
cd data/mnist/
curl -O http://pjreddie.com/media/files/mnist_train.csv
curl -O http://pjreddie.com/media/files/mnist_test.csv
script_string =
source("mnist_lenet.dml") ... | <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: Download Data - MNIST
Step3: SystemML "LeNet" Neural Network
Step5: 2. Compute Test Accuracy
Step6: 3. Extract Model Into Spark DataFrames Fo... |
13,256 | <ASSISTANT_TASK:>
Python Code:
import pandas
import numpy
from folding_group import FoldingGroupClassifier
from rep.data import LabeledDataStorage
from rep.report import ClassificationReport
from rep.report.metrics import RocAuc
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_curve, ... | <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: Reading initial data
Step2: Remove rows with NAN from data
Step3: Add diff_pt and cos(diff_phi)
Step4: Add max, sum among PIDs
Step5: define... |
13,257 | <ASSISTANT_TASK:>
Python Code:
prime_ministers = ['David Ben-Gurion', 'Moshe Sharett', 'David Ben-Gurion', 'Levi Eshkol', 'Yigal Alon', 'Golda Meir']
print(prime_ministers)
type(prime_ministers)
numbers = [1, 2, 3, 4, 5, 6, 7]
wtf = ['The cake is a', False, 42]
empty_list = []
# Index 0 1 ... | <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: <p style="text-align
Step2: <p style="text-align
Step3: <p style="text-align
Step4: <p style="text-align
Step5: <p style="text-align
Step6: ... |
13,258 | <ASSISTANT_TASK:>
Python Code::
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
<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:
|
13,259 | <ASSISTANT_TASK:>
Python Code:
###ignore this block of code - it is required only to show the map in iPython - you won't need it!
from IPython.core.display import display, HTML
display(HTML('<iframe width="800" height="600" frameborder="1" scrolling ="no" src="./qgis2threejs/ACT_elevs_test_1.html"></iframe>'))
display... | <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: and there - your first 3D interactive map, made with no coding and using web data services!
Step2: Now we have an elevation map coloured by gre... |
13,260 | <ASSISTANT_TASK:>
Python Code:
import kfp
import kfp.gcp as gcp
import kfp.dsl as dsl
import kfp.compiler as compiler
import kfp.components as comp
import datetime
import kubernetes as k8s
# Required Parameters
PROJECT_ID='<ADD GCP PROJECT HERE>'
GCS_BUCKET='gs://<ADD STORAGE LOCATION HERE>'
# Optional Parameters, but... | <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: Create client
Step2: Build reusable components
Step3: Create a Docker container
Step4: Build docker image
Step5: If you want to use docker t... |
13,261 | <ASSISTANT_TASK:>
Python Code:
stations = pd.read_csv('datasets/divvy_2013/Divvy_Stations_2013.csv', parse_dates=['online date'], index_col='id')
stations
trips = pd.read_csv('datasets/divvy_2013/Divvy_Trips_2013.csv', parse_dates=['starttime', 'stoptime'], index_col=['trip_id'])
trips = trips.sort()
trips
G = nx.DiGr... | <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: 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 ... |
13,262 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from astropy.table import Table as tbl
import urllib.request
import urllib.parse
import subprocess
import matplotlib.pyplot as plt
from cesium import featurize
%matplotlib inline
import sqlite3
url = "http://irsa.ipac.caltech.edu/cgi-bin/Gator/nph-query?"
values = {'ca... | <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: Query for the given objects
Step2: Im not sure why subprocess.call doesnt seem to work for this specific case. However, the urllib work below d... |
13,263 | <ASSISTANT_TASK:>
Python Code:
def mysum(a, b):
return a + b
abs(-3.2)
help("abs")
def mysum(a, b):
내가 정의한 덧셈이다.
인자 a와 b에 각각 두 숫자를 입력받아 합을 되돌려준다.
return a + b
help(mysum)
x = 2
y = 3
z = mysum(x,y)
print(z)
no_return = print(3)
print(no_return)
type(no_return)
def print42():
print(... | <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: 문서화 문자열(docstring) 활용
Step3: 보이는 내용을 설명하면 다음과 같다.
Step4: mysum 함수에 대해 알아보자.
Step5: mysum 함수를 정의할 때 추가한 문서화 문자열이 그대로 출력됨을 확인할 수 있다.
Step6: 주의... |
13,264 | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv((
"https://raw.githubusercontent.com/Thinkful-Ed/data-201-resources/"
"master/ESS_practice_data/ESSdata_Thinkful.csv")).dropna()
# Define outcome and predictors.
# Set our outcome to 0 and 1.
y = df['partner'] - 1
X = df.loc[:, ~df.columns.isin(['partner', 'cntry'... | <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: Since we're now working with a binary outcome, we've switched to a classifier. Now our loss function can't be the residuals. Our options are "... |
13,265 | <ASSISTANT_TASK:>
Python Code:
x, sr = librosa.load('audio/c_strum.wav')
ipd.Audio(x, rate=sr)
plt.figure(figsize=(14, 5))
librosa.display.waveplot(x, sr)
# Because the autocorrelation produces a symmetric signal, we only care about the "right half".
r = numpy.correlate(x, x, mode='full')[len(x)-1:]
print(x.shape, r.s... | <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: numpy.correlate
Step2: Plot the autocorrelation
Step3: librosa.autocorrelate
Step4: librosa.autocorrelate conveniently only keeps one half of... |
13,266 | <ASSISTANT_TASK:>
Python Code:
# General imports
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
# Figure config
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
LEGEND_SIZE = 15
TITLE_SIZE = 25
AXIS_SIZE = 15
FIGURE_SIZE = (12, 8)
# for reproducibility
np.random.see... | <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 SEIR epidemic model
Step2: Load SEIR model
Step3: <a id='section-task1'></a>
Step4: Get initial observations
Step5: Create Emukit surrog... |
13,267 | <ASSISTANT_TASK:>
Python Code:
import geopandas as gpd
denue = gpd.read_file("datos/DENUE_INEGI_09_.shp")
denue.head()
denue[["codigo_act", "nom_estab"]].head()
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(3, 3))
df
def square(x):
return x**2
df[[0]].apply(square)
df['squared'] = df[... | <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: Como pueden ver, el archivo tiene 42 columnas, las que nos interesan en este momento son las que describen la actividad de cada unidad
Step2: E... |
13,268 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# plots graphs within the notebook
%config InlineBackend.figure_format='svg' # not sure what this does, may be default images to svg format
from IPython.display import Image
from IPython.core.display import HTML
def header(text):
raw_html = '<h4>' + str(text) + '</... | <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: Lecture 4
Step2: <p class='alert alert-success'>
Step3: <h2>Compact Finite Difference Schemes</h2>
Step4: This is the Matrix approach
Step5: ... |
13,269 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-lm', 'ocean')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "emai... | <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... |
13,270 | <ASSISTANT_TASK:>
Python Code:
%%capture
# install histogrammar (if not installed yet)
import sys
!"{sys.executable}" -m pip install histogrammar
import histogrammar as hg
import pandas as pd
import numpy as np
import matplotlib
# open a pandas dataframe for use below
from histogrammar import resources
df = pd.read_cs... | <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: Dataset
Step2: Comparing histogram types
Step3: Q
Step4: Q
Step5: Q
Step6: Multi-dimensional histograms
Step7: Q
|
13,271 | <ASSISTANT_TASK:>
Python Code:
retorno = api.update_with_media(filename='fia.jpg',status='Test. Upload media via python')
print(retorno.text)
print(retorno.id)
print(retorno.created_at)
print(retorno.lang)
print(retorno.text)
print(retorno.user.screen_name)
print(retorno.user.friends_count)
print(retorno.user.time_zon... | <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: Exercício 2 - Salve o retorno do tweet do exercício anterior e imprima as seguintes informações
Step2: Exercício 3 - Utilizando o método home_t... |
13,272 | <ASSISTANT_TASK:>
Python Code:
from scipy import stats
import random
import numpy as np
def poisson_simul(rate, T):
time = random.expovariate(rate)
times = [0]
while (times[-1] < T):
times.append(time+times[-1])
time = random.expovariate(rate)
return times[1:]
rate = 1.0
T = 100.0
times ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
13,273 | <ASSISTANT_TASK:>
Python Code:
import math
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def sine(x):
return np.sin(2 * math.pi * x)
x = np.linspace(0., 1., num=256, endpoint=False)
plt.plot(x, sine(x))
import magma as m
m.set_mantle_target("ice40")
import mantle
from loam.boards.icestick 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: To implement our sine wave generator, we'll use a counter to index into a ROM that is programmed to output the value of discrete points in the s... |
13,274 | <ASSISTANT_TASK:>
Python Code:
import time
print('Last updated: %s' %time.strftime('%d/%m/%Y'))
import platform
import multiprocessing
def print_sysinfo():
print('\nPython version :', platform.python_version())
print('compiler :', platform.python_compiler())
print('\nsystem :', platfo... | <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: Sorting Algorithms
Step2: Bubble sort
Step4: Bubble sort implemented in (C)Python
Step6: <br>
Step7: Verifying that all implementations work... |
13,275 | <ASSISTANT_TASK:>
Python Code:
col.find_one({'name': 'Alessandro'}) #find first value equality
list(col.find({'name': 'Alessandro'})) #find all value equality
cursor = col.find({'name': 'Alessandro'}) #can also use it as a generator
cursor.next()
col.find_one({'name': 'Alessandro'}, {'phone': True}) #this is a projecti... | <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: Updating the document replaces the whole original document
Step2: Here's how to update all doc
|
13,276 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import sklearn
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression... | <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: Preprocessing
Step2: Training the regressor
Step3: Results
Step4: Classification
Step5: Training the classifier
Step6: Results
Step7: Hype... |
13,277 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
import random
import numpy as np
import pandas as pd
from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import sklearn.ensemble as ske
import tensorflow as tf
from tensorflow.contrib import learn as skflow
r... | <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's look at the data
Step2: Let's look at what percentage of the drivers are using the map?
Step3: 47% of the drivers are following the map.... |
13,278 | <ASSISTANT_TASK:>
Python Code:
bool('ok')
bool(8)
bool('')
num=input('Enter a number:')
if num>0:
print 'positive'
elif num<0:
print 'negative'
else:
print 'zero'
x=1
while x<=3:
print x
x+=1
nums=[1,2,3]
for n in nums:
print n
range(0,10)
range(10)
range(10,0,-2) #-2表示步长
d ={'x':1,'y':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: 2.2 条件执行和if语句、else子句、elif子句
Step2: 2.3 更复杂的条件
Step3: 3.2 for循环
Step4: 因为迭代（循环的另外一种说法）某范围的数字是很常见的，所以有个内建的范围函数供使用：
Step5: range函数的工作方式类似于分片。它包... |
13,279 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import numpy.matlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
%matplotlib inline
import math
import random
import time
import os
import pickle
import tensorflow as tf #built with TensorFlow version 0.9
# in the real project class, we use argparse (http... | <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 hyperparameters
Step2: Model overview
Step3: Initialize LSTMs and build LSTM 1
Step4: In the cell above we use the TensorFlow seq2seq ... |
13,280 | <ASSISTANT_TASK:>
Python Code:
import yahoo_finance
import requests
import datetime
def print_unix_timestamp_date(timestamp):
print(
datetime.datetime.fromtimestamp(
int(timestamp)
).strftime('%Y-%m-%d %H:%M:%S')
)
print_unix_timestamp_date("1420077600")
print_unix_timestamp_date("14... | <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: Getting the data
Step2: So, Google has a limit of 15 years of data on each query
Step3: Keep dictionary or use multiindex?
|
13,281 | <ASSISTANT_TASK:>
Python Code::
import cv2
import numpy as np
img = cv2.imread('gradient.jpg',0)
_,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
_,th2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV) #check every pixel with 127
cv2.imshow("img",img)
cv2.imshow("th1",th1)
cv2.imshow("th2",th2)
<END_TASK>
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Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
13,282 | <ASSISTANT_TASK:>
Python Code:
! pip uninstall -y tensorflow
! pip install -q tensorflow-model-optimization
! pip install --upgrade tensorflow==2.6
import tempfile
import os
import tensorflow as tf
from tensorflow import keras
# Show the currently installed version of TensorFlow
print("TensorFlow version: ",tf.version... | <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 notebook uses TF2.x.
Step2: Train a model for MNIST without quantization aware training
Step3: Clone and fine-tune pre-trained model with... |
13,283 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import pymc3 as pm
from pymc3.distributions.timeseries import GaussianRandomWalk
import seaborn as sns
from statsmodels import datasets
from theano import tensor as T
df = datasets.get_rdataset('mastectomy', 'HSAU... | <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: Fortunately, statsmodels.datasets makes it quite easy to load a number of data sets from R.
Step2: Each row represents observations from a woma... |
13,284 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import lxmls.readers.sentiment_reader as srs
from lxmls.deep_learning.utils import AmazonData
corpus = srs.SentimentCorpus("books")
data = AmazonData(corpus=corpus)
from lxmls.deep_learning.utils import Model, glorot_weight_init
import numpy as np
impor... | <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:
Step4: Train Log Linear in Pytorch
Step5: Once you understand the model you can instantiate it and run it using the standard training loop we have use... |
13,285 | <ASSISTANT_TASK:>
Python Code:
# Useful additional packages
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from math import pi
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit import available_backends, execute, register, get_backend
from qiskit.tools.visualizati... | <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: Single Qubit Quantum states
Step2: u gates
Step3: The $u2(\phi, \lambda) =u3(\pi/2, \phi, \lambda)$ has the matrix form
Step4: The $u1(\lambd... |
13,286 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
# Read data, sort by year & month
dateparse = lambda x: pd.datetime.strptime(x, '%Y%m%d')
noaa_monthly = pd.read_csv('mpls-noaa.csv', index_col=2,
parse_dates=True, date_parser=dateparse, na_values=-9999)
noaa_monthly = noaa_monthly.groupby([... | <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: Definition of variables
Step2: The Badness Index of each winter
|
13,287 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
import os
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original', data_home='datasets/')
# Convert sklearn 'datasets bunch' object to Pandas DataFrames
y = pd.Series(mnist.target).astype('int').astype('category')... | <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 Shape, Summary Stats
Step2: Below we see min, max, mean and most-common pixel-intensity values for our rows/images. As suggested by the fi... |
13,288 | <ASSISTANT_TASK:>
Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
# Chris Holdgraf <choldgraf@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from mayavi import mlab
import mne
from mne.viz import plot_alignment, snapshot_brai... | <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 load some ECoG electrode locations and names, and turn them into
Step2: Now that we have our electrode positions in MRI coordinates, we c... |
13,289 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# Import neurom module
import neurom as nm
# Import neurom visualization module
from neurom import viewer
# Load a single morphology
neuron = nm.load_neuron('../test_data/valid_set/Neuron.swc')
# Load a population of morphologies from a set of files
pop = nm.load_neu... | <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. Loading a morphology or a population
Step2: 2. Morphology visualization
Step3: 3. Morphology analysis
Step4: 3.2 Analyze different types o... |
13,290 | <ASSISTANT_TASK:>
Python Code:
def tokenize(message):
message = message.lower()
all_words = re.findall('[a-z0-9]+', message)
return set(all_words)
from collections import defaultdict
def count_words(training_set):
training set consists of pairs (message, is_spam)
counts = defaultdict(lambda: [0, 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:
Step2: Spam Filter Implementation
Step3: Download the following files and extract them into a folder which we will reference below
|
13,291 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from astropy.table import Table, join
import matplotlib.pyplot as plt
%matplotlib inline
idp = "idata/main_pdf_v0.8-b"
idp_old = "idata/main_pdf_v0.8"
lnew = Table.read(idp+"/lofar_m5.fits")
lold = Table.read(idp_old+"/lofar_m5.fits")
merged = join(lnew, lold, keys=["S... | <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
Step2: Check the shape of the data
Step3: Compute the difference between the LR
Step4: Explore the differences
Step5: Plot of ... |
13,292 | <ASSISTANT_TASK:>
Python Code:
import requests
import json
#import ibmseti
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
import pickle
import time
#!sudo pip install sklearn
import os
from sklearn.metrics import confusion_matrix
from sklearn import metrics
### SET YOUR T... | <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 your team folder
Step2: Import dataset reader
Step3: Download data
Step4: Load data SETI
Step5: Network Parameters
Step6: Inputs
Step7:... |
13,293 | <ASSISTANT_TASK:>
Python Code:
import time
import math
import sys
import pickle
import copy
import os
import re
import numpy as np
from chainer import cuda, Variable, FunctionSet, optimizers
import chainer.functions as F
#-------------Explain7 in the Qiita-------------
n_epochs = 30
n_units = 625
batchsize = ... | <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: `導入するライブラリの代表例は下記です。
Step2: 3.データ入力
Step7: 4.リカレントニューラル言語モデル設定(ハンズオン）
Step8: RNNLM(リカレントニューラル言語モデルの設定を行っています）
Step9: 5.学習を始める前の設定
Step10: 6... |
13,294 | <ASSISTANT_TASK:>
Python Code:
import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG... | <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: Before you begin
Step2: Otherwise, set your project ID here.
Step3: Timestamp
Step4: Authenticate your Google Cloud account
Step5: Create a ... |
13,295 | <ASSISTANT_TASK:>
Python Code:
import imaginet.defn.visual as visual
import imaginet.task
model = imaginet.task.load(path="vis/model.10.zip")
reload(visual)
emb = visual.embeddings(model)
print(emb.shape)
symb = visual.symbols(model)
print " ".join(symb.values())
%pylab inline
from sklearn.decomposition import PCA
... | <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 model
Step2: Symbol embeddings
Step3: The table of IPA symbols corresponding to the 49 dimensions
Step4: Let's display the embeddin... |
13,296 | <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 the SavedModel workflow
Step2: TensorFlow 1
Step3: 2. Build a SavedModel for serving
Step4: 3. Export the Estimator inference graph a... |
13,297 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import numpy.linalg as la
from scipy.stats import rankdata
%matplotlib inline
## Construct a regression model
def lm_model( X, Y, intercept = True ) :
T = np.array( Y, dtype = float )
M = np.array( X, dtype = float )
if interc... | <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 continusous random variable $X$ is distributed accorind to the power law (also known as Pareto distibution) if it's probability density functi... |
13,298 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('/home/jbourbeau/cr-composition')
print('Added to PYTHONPATH')
import argparse
from collections import defaultdict
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn.apionly as sns
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: Data preprocessing
Step2: Feature selection
Step3: Feature transform
Step4: Produce 10-fold CV learning curve
|
13,299 | <ASSISTANT_TASK:>
Python Code:
def encode_shift(s: str): return "".join([chr(((ord(ch) + 5 - ord("a")) % 26) + ord("a")) for ch in s])def decode_shift(s: str): takes as input string encoded with encode_shift function. Returns decoded string. return "".join([chr(((ord(ch) - 5 - ord("a")) % 26) + ord... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
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