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Students can see what assignments they have submitted using `nbgrader list --inbound`: | %%bash
export HOME=/tmp/student_home && cd $HOME
nbgrader list --inbound | [ListApp | INFO] Submitted assignments:
[ListApp | INFO] example_course jhamrick ps1 2018-04-22 14:29:40.397476 UTC
| BSD-3-Clause-Clear | nbgrader/docs/source/user_guide/managing_assignment_files.ipynb | dechristo/nbgrader |
Importantly, students can run `nbgrader submit` as many times as they want, and all submitted copies of the assignment will be preserved: | %%bash
export HOME=/tmp/student_home && cd $HOME
nbgrader submit "ps1" | [SubmitApp | INFO] Source: /private/tmp/student_home/ps1
[SubmitApp | INFO] Destination: /tmp/exchange/example_course/inbound/jhamrick+ps1+2018-04-22 14:29:43.070290 UTC
[SubmitApp | INFO] Submitted as: example_course ps1 2018-04-22 14:29:43.070290 UTC
| BSD-3-Clause-Clear | nbgrader/docs/source/user_guide/managing_assignment_files.ipynb | dechristo/nbgrader |
We can see all versions that have been submitted by again running `nbgrader list --inbound`: | %%bash
export HOME=/tmp/student_home && cd $HOME
nbgrader list --inbound | [ListApp | INFO] Submitted assignments:
[ListApp | INFO] example_course jhamrick ps1 2018-04-22 14:29:40.397476 UTC
[ListApp | INFO] example_course jhamrick ps1 2018-04-22 14:29:43.070290 UTC
| BSD-3-Clause-Clear | nbgrader/docs/source/user_guide/managing_assignment_files.ipynb | dechristo/nbgrader |
Note that the `nbgrader submit` (as well as `nbgrader fetch`) command also does not rely on having access to the nbgrader database -- the database is only used by instructors. ``nbgrader`` requires that the submitted notebook names match the released notebook names for each assignment. For example if a student were to ... | %%bash
export HOME=/tmp/student_home && cd $HOME
# assume the student renamed the assignment file
mv ps1/problem1.ipynb ps1/myproblem1.ipynb
nbgrader submit "ps1" | [SubmitApp | INFO] Source: /private/tmp/student_home/ps1
[SubmitApp | INFO] Destination: /tmp/exchange/example_course/inbound/jhamrick+ps1+2018-04-22 14:29:46.167901 UTC
[SubmitApp | WARNING] Possible missing notebooks and/or extra notebooks submitted for assignment ps1:
Expected:
problem1.ipynb: MISSING
... | BSD-3-Clause-Clear | nbgrader/docs/source/user_guide/managing_assignment_files.ipynb | dechristo/nbgrader |
By default this assignment will still be submitted however only the "FOUND" notebooks (for the given assignment) can be ``autograded`` and will appear on the ``formgrade`` extension. "EXTRA" notebooks will not be ``autograded`` and will not appear on the ``formgrade`` extension. To ensure that students cannot submit an... | %%file /tmp/student_home/nbgrader_config.py
c = get_config()
c.Exchange.root = '/tmp/exchange'
c.Exchange.course_id = "example_course"
c.ExchangeSubmit.strict = True
%%bash
export HOME=/tmp/student_home && cd $HOME
nbgrader submit "ps1" | [SubmitApp | INFO] Source: /private/tmp/student_home/ps1
[SubmitApp | INFO] Destination: /tmp/exchange/example_course/inbound/jhamrick+ps1+2018-04-22 14:29:47.497419 UTC
[SubmitApp | CRITICAL] Assignment ps1 not submitted. There are missing notebooks for the submission:
Expected:
problem1.ipynb: MISSING
p... | BSD-3-Clause-Clear | nbgrader/docs/source/user_guide/managing_assignment_files.ipynb | dechristo/nbgrader |
Collecting assignments | .. seealso::
:doc:`creating_and_grading_assignments`
Details on grading assignments after they have been collected
:doc:`/command_line_tools/nbgrader-collect`
Command line options for ``nbgrader fetch``
:doc:`/command_line_tools/nbgrader-list`
Command line options for ``nbgrader l... | _____no_output_____ | BSD-3-Clause-Clear | nbgrader/docs/source/user_guide/managing_assignment_files.ipynb | dechristo/nbgrader |
First, as a reminder, here is what the instructor's `nbgrader_config.py` file looks like: | %%bash
cat nbgrader_config.py |
c = get_config()
c.Exchange.course_id = "example_course"
c.Exchange.root = "/tmp/exchange" | BSD-3-Clause-Clear | nbgrader/docs/source/user_guide/managing_assignment_files.ipynb | dechristo/nbgrader |
From the formgrader From the formgrader extension, we can collect submissions by clicking on the "collect" button:As with releasing, this will display a pop-up window when the operation is complete, telling you how many submissions were collected:Fro... | %%bash
nbgrader list --inbound | [ListApp | INFO] Submitted assignments:
[ListApp | INFO] example_course jhamrick ps1 2018-04-22 14:29:40.397476 UTC
[ListApp | INFO] example_course jhamrick ps1 2018-04-22 14:29:43.070290 UTC
[ListApp | INFO] example_course jhamrick ps1 2018-04-22 14:29:46.167901 UTC
| BSD-3-Clause-Clear | nbgrader/docs/source/user_guide/managing_assignment_files.ipynb | dechristo/nbgrader |
The instructor can then collect all submitted assignments with `nbgrader collect` and passing the name of the assignment (and as with the other nbgrader commands for instructors, this must be run from the root of the course directory): | %%bash
nbgrader collect "ps1" | [CollectApp | INFO] Processing 1 submissions of 'ps1' for course 'example_course'
[CollectApp | INFO] Collecting submission: jhamrick ps1
| BSD-3-Clause-Clear | nbgrader/docs/source/user_guide/managing_assignment_files.ipynb | dechristo/nbgrader |
This will copy the student submissions to the `submitted` folder in a way that is automatically compatible with `nbgrader autograde`: | %%bash
ls -l submitted | total 0
drwxr-xr-x 3 jhamrick staff 96 May 31 2017 bitdiddle
drwxr-xr-x 3 jhamrick staff 96 May 31 2017 hacker
drwxr-xr-x 3 jhamrick staff 96 Apr 22 15:29 jhamrick
| BSD-3-Clause-Clear | nbgrader/docs/source/user_guide/managing_assignment_files.ipynb | dechristo/nbgrader |
Common way of speeding up a machine learning algorithm is by using Principal Component Analysis (PCA). If your learning algorithm is too slow because the input dimension is too high, then using PCA to speed it up can be a reasonable choice. | X3 = pid2 # X denotes the input functions and here class defines whether the person is ill or not
print(X1)
y3 = pid['class'] #y denotes the output functions
print(y1)
from sklearn.model_selection import train_test_split #As given we are assigning 70% of data for training and 30%... | _____no_output_____ | MIT | IIT Mandi/3rd Week/IITMANDI.Assignment2(Week 3)-checkpoint.ipynb | thechiragthakur/Data-Science-Using-Python |
The Transformer Decoder: Ungraded Lab NotebookIn this notebook, you'll explore the transformer decoder and how to implement it with Trax. BackgroundIn the last lecture notebook, you saw how to translate the mathematics of attention into NumPy code. Here, you'll see how multi-head causal attention fits into a GPT-2 tr... | import sys
import os
import time
import numpy as np
import gin
import textwrap
wrapper = textwrap.TextWrapper(width=70)
import trax
from trax import layers as tl
from trax.fastmath import numpy as jnp
# to print the entire np array
np.set_printoptions(threshold=sys.maxsize) | INFO:tensorflow:tokens_length=568 inputs_length=512 targets_length=114 noise_density=0.15 mean_noise_span_length=3.0
| MIT | NLP/Attention/2/C4_W2_lecture_notebook_Transformer_Decoder.ipynb | verneh/DataSci |
Sentence gets embedded, add positional encodingEmbed the words, then create vectors representing each word's position in each sentence $\in \{ 0, 1, 2, \ldots , K\}$ = `range(max_len)`, where `max_len` = $K+1$) | def PositionalEncoder(vocab_size, d_model, dropout, max_len, mode):
"""Returns a list of layers that:
1. takes a block of text as input,
2. embeds the words in that text, and
3. adds positional encoding,
i.e. associates a number in range(max_len) with
each word in each sentence of emb... | _____no_output_____ | MIT | NLP/Attention/2/C4_W2_lecture_notebook_Transformer_Decoder.ipynb | verneh/DataSci |
Multi-head causal attentionThe layers and array dimensions involved in multi-head causal attention (which looks at previous words in the input text) are summarized in the figure below: `tl.CausalAttention()` does all of this for you! You might be wondering, though, whether you need to pass in your input text 3 times, ... | def FeedForward(d_model, d_ff, dropout, mode, ff_activation):
"""Returns a list of layers that implements a feed-forward block.
The input is an activation tensor.
Args:
d_model (int): depth of embedding.
d_ff (int): depth of feed-forward layer.
dropout (float): dropout rate (how m... | _____no_output_____ | MIT | NLP/Attention/2/C4_W2_lecture_notebook_Transformer_Decoder.ipynb | verneh/DataSci |
Decoder blockHere, we return a list containing two residual blocks. The first wraps around the causal attention layer, whose inputs are normalized and to which we apply dropout regulation. The second wraps around the feed-forward layer. You may notice that the second call to `tl.Residual()` doesn't call a normalizatio... | def DecoderBlock(d_model, d_ff, n_heads,
dropout, mode, ff_activation):
"""Returns a list of layers that implements a Transformer decoder block.
The input is an activation tensor.
Args:
d_model (int): depth of embedding.
d_ff (int): depth of feed-forward layer.
n_... | _____no_output_____ | MIT | NLP/Attention/2/C4_W2_lecture_notebook_Transformer_Decoder.ipynb | verneh/DataSci |
The transformer decoder: putting it all together A.k.a. repeat N times, dense layer and softmax for output | def TransformerLM(vocab_size=33300,
d_model=512,
d_ff=2048,
n_layers=6,
n_heads=8,
dropout=0.1,
max_len=4096,
mode='train',
ff_activation=tl.Relu):
"""Returns a Transformer... | _____no_output_____ | MIT | NLP/Attention/2/C4_W2_lecture_notebook_Transformer_Decoder.ipynb | verneh/DataSci |
Summary statistics in VCF formatmodified from the create_vcf of mrcieu/gwasvcf package to transform the mash output matrixs from the rds format into a vcf file, with a effect size = to the coef and the se = 1, named as EF:SE.Input:a collection of gene-level rds file, each file is a matrix of mash output, with colnames... | [global]
import glob
# single column file each line is the data filename
parameter: analysis_units = path
# Path to data directory
parameter: data_dir = "/"
# data file suffix
parameter: data_suffix = ""
# Path to work directory where output locates
parameter: wd = path("./output")
# An identifier for your run of analy... | _____no_output_____ | MIT | pipeline/misc/rds_to_vcf.ipynb | floutt/xqtl-pipeline |
Convolutional Dictionary Learning=================================This example demonstrates the use of [prlcnscdl.ConvBPDNDictLearn_Consensus](http://sporco.rtfd.org/en/latest/modules/sporco.dictlrn.prlcnscdl.htmlsporco.dictlrn.prlcnscdl.ConvBPDNDictLearn_Consensus) for learning a convolutional dictionary from a set of... | from __future__ import print_function
from builtins import input
import pyfftw # See https://github.com/pyFFTW/pyFFTW/issues/40
import numpy as np
from sporco.dictlrn import prlcnscdl
from sporco import util
from sporco import signal
from sporco import plot
plot.config_notebook_plotting() | _____no_output_____ | BSD-3-Clause | cdl/cbpdndl_parcns_clr.ipynb | bwohlberg/sporco-notebooks |
Load training images. | exim = util.ExampleImages(scaled=True, zoom=0.25)
S1 = exim.image('barbara.png', idxexp=np.s_[10:522, 100:612])
S2 = exim.image('kodim23.png', idxexp=np.s_[:, 60:572])
S3 = exim.image('monarch.png', idxexp=np.s_[:, 160:672])
S4 = exim.image('sail.png', idxexp=np.s_[:, 210:722])
S5 = exim.image('tulips.png', idxexp=np.s... | _____no_output_____ | BSD-3-Clause | cdl/cbpdndl_parcns_clr.ipynb | bwohlberg/sporco-notebooks |
Highpass filter training images. | npd = 16
fltlmbd = 5
sl, sh = signal.tikhonov_filter(S, fltlmbd, npd) | _____no_output_____ | BSD-3-Clause | cdl/cbpdndl_parcns_clr.ipynb | bwohlberg/sporco-notebooks |
Construct initial dictionary. | np.random.seed(12345)
D0 = np.random.randn(8, 8, 3, 64) | _____no_output_____ | BSD-3-Clause | cdl/cbpdndl_parcns_clr.ipynb | bwohlberg/sporco-notebooks |
Set regularization parameter and options for dictionary learning solver. | lmbda = 0.2
opt = prlcnscdl.ConvBPDNDictLearn_Consensus.Options({'Verbose': True,
'MaxMainIter': 200,
'CBPDN': {'rho': 50.0*lmbda + 0.5},
'CCMOD': {'rho': 1.0, 'ZeroMean': True}}) | _____no_output_____ | BSD-3-Clause | cdl/cbpdndl_parcns_clr.ipynb | bwohlberg/sporco-notebooks |
Create solver object and solve. | d = prlcnscdl.ConvBPDNDictLearn_Consensus(D0, sh, lmbda, opt)
D1 = d.solve()
print("ConvBPDNDictLearn_Consensus solve time: %.2fs" %
d.timer.elapsed('solve')) | Itn Fnc DFid Regℓ1
----------------------------------
| BSD-3-Clause | cdl/cbpdndl_parcns_clr.ipynb | bwohlberg/sporco-notebooks |
Display initial and final dictionaries. | D1 = D1.squeeze()
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0), title='D0', fig=fig)
plot.subplot(1, 2, 2)
plot.imview(util.tiledict(D1), title='D1', fig=fig)
fig.show() | _____no_output_____ | BSD-3-Clause | cdl/cbpdndl_parcns_clr.ipynb | bwohlberg/sporco-notebooks |
Get iterations statistics from solver object and plot functional value | its = d.getitstat()
plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional') | _____no_output_____ | BSD-3-Clause | cdl/cbpdndl_parcns_clr.ipynb | bwohlberg/sporco-notebooks |
A little notebook to help visualise the official numbers for personal use. Absolutely no guarantees are made.**This is not a replacement for expert advice. Please listen to your local health authorities.**The data is dynamically loaded from: https://github.com/CSSEGISandData/COVID-19 | %matplotlib inline
%config InlineBackend.figure_format ='retina'
import matplotlib.pyplot as plt
import pandas as pd
from jhu_helpers import *
jhu = aggregte_jhu_by_state(*get_jhu_data())
#jhu.confirmed.columns.tolist() # print a list of all countries in the data set
# look at recent numbers from highly affected count... | _____no_output_____ | MIT | international_cases.ipynb | debsankha/covid-19 |
Question 1: | Create a checker board generator, which takes as inputs n and 2 elements to generate
an n x n checkerboard with those two elements as alternating squares.
Examples
checker_board(2, 7, 6) [
[7, 6],
[6, 7]
]
checker_board(3, "A", "B") [
["A", "B", "A"],
["B", "A", "B"],
["A", "B", "A"]
]
checker_board(4, "c", "d... | _____no_output_____ | MIT | Python Advance Programming Assignment/Assignment_19.ipynb | kpsanjeet/Python-Programming-Basic-Assignment |
Answer : | def checker_board(n,a,b):
if a == b:
return "invalid"
board = []
for i in range(n):
temp = []
for j in range(n):
temp.append(a)
a, b = b, a
b, a = temp[0:2]
board.append(temp)
return board
for i in checker_boa... | [7, 6]
[6, 7]
['A', 'B', 'A']
['B', 'A', 'B']
['A', 'B', 'A']
['c', 'd', 'c', 'd']
['d', 'c', 'd', 'c']
['c', 'd', 'c', 'd']
['d', 'c', 'd', 'c']
| MIT | Python Advance Programming Assignment/Assignment_19.ipynb | kpsanjeet/Python-Programming-Basic-Assignment |
Question 2: | A string is an almost-palindrome if, by changing only one character, you
can make it a palindrome. Create a function that returns True if a string is an
almost-palindrome and False otherwise.
Examples
almost_palindrome("abcdcbg") True
# Transformed to "abcdcba" by changing "g" to "a".
almost_palindrome("abccia") Tr... | _____no_output_____ | MIT | Python Advance Programming Assignment/Assignment_19.ipynb | kpsanjeet/Python-Programming-Basic-Assignment |
Answer : | import string
def isPalindrome(str_):
return str_ == str_[::-1]
def almost_palindrome(str_):
check = string.ascii_lowercase + "0123456789"
for i in str_:
for j in check:
temp = str_.replace(i, j, 1)
if isPalindrome(temp):
return True
... | True
True
False
False
| MIT | Python Advance Programming Assignment/Assignment_19.ipynb | kpsanjeet/Python-Programming-Basic-Assignment |
Question 3: | Create a function that finds how many prime numbers there are, up to the given integer.
Examples
prime_numbers(10) 4
# 2, 3, 5 and 7
prime_numbers(20) 8
# 2, 3, 5, 7, 11, 13, 17 and 19
prime_numbers(30) 10
# 2, 3, 5, 7, 11, 13, 17, 19, 23 and 29 | _____no_output_____ | MIT | Python Advance Programming Assignment/Assignment_19.ipynb | kpsanjeet/Python-Programming-Basic-Assignment |
Answer : | def isPrime(n):
if n <= 1:
return False
for i in range(2, int(n**(1/2))+1):
if n % i == 0:
return False
return True
def prime_numbers(n):
if n<2:
return 0
return sum([isPrime(i) for i in range(2,n+1)])
print(prime_numbers(10))
print(prime_numbers(20))
print(... | 4
8
10
| MIT | Python Advance Programming Assignment/Assignment_19.ipynb | kpsanjeet/Python-Programming-Basic-Assignment |
Question 4: | If today was Monday, in two days, it would be Wednesday. Create a function that takes in a list of days
as input and the number of days to increment by.
Return a list of days after n number of days has passed.
Examples
after_n_days(["Thursday", "Monday"], 4) ["Monday", "Friday"]
after_n_days(["Sunday", "Sunday", "S... | _____no_output_____ | MIT | Python Advance Programming Assignment/Assignment_19.ipynb | kpsanjeet/Python-Programming-Basic-Assignment |
Answer : | def after_n_days(lst,n):
new_lst = []
if n>=7:
_, n = divmod(n,7)
for i in lst:
days = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"]
idx = days.index(i)
days = [days[idx]]+days[idx+1::]+days[0:idx]
new_lst.append(day... | ['Monday', 'Friday']
['Monday', 'Monday', 'Monday']
['Tuesday', 'Wednesday', 'Saturday']
| MIT | Python Advance Programming Assignment/Assignment_19.ipynb | kpsanjeet/Python-Programming-Basic-Assignment |
Question 5: | You are in the process of creating a chat application and want to add an
anonymous name feature. This anonymous name feature will create an alias that
consists of two capitalized words beginning with the same letter as the users first name.
Create a function that determines if the list of users is mapped to a list of
... | _____no_output_____ | MIT | Python Advance Programming Assignment/Assignment_19.ipynb | kpsanjeet/Python-Programming-Basic-Assignment |
Answer : | def is_correct_aliases(lst1,lst2):
bool_ = []
for i, j in zip(lst1,lst2):
temp = j.split()
bool_.append(i[0] == temp[0][0] and i[0] == temp[1][0])
return all(bool_)
print(is_correct_aliases(["Adrian M.", "Harriet S.", "Mandy T."],
["Amazing Artichoke", "Hopeful Hedge... | True
True
False
| MIT | Python Advance Programming Assignment/Assignment_19.ipynb | kpsanjeet/Python-Programming-Basic-Assignment |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/jupyter/enterprise/healthcare/Disambiguation.ipynb) | import json
with open('251keys.json') as f:
license_keys = json.load(f)
license_keys.keys()
# Install java
import os
! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
os.environ["PATH"] = os.environ["JAVA_HOME"] + "/bin:" + os.environ["PAT... | _____no_output_____ | Apache-2.0 | jupyter/enterprise/healthcare/Disambiguation.ipynb | richardclarus/spark-nlp-workshop |
Supply chain physics*This notebook illustrates methods to investigate the physics of a supply chain****Alessandro Tufano 2020 Import packages | import numpy as np
import matplotlib.pyplot as plt
| _____no_output_____ | MIT | examples/Supply chain physics.ipynb | aletuf93/logproj |
Generate empirical demand and production We define an yearly sample of production quantity $x$, and demand quantity $d$ | number_of_sample = 365 #days
mu_production = 105 #units per day
sigma_production = 1 # units per day
mu_demand = 100 #units per day
sigma_demand = 0.3 # units per day
x = np.random.normal(mu_production,sigma_production,number_of_sample)
#d = np.random.normal(mu_demand,sigma_demand,number_of_sample)
d = brownian(x0... | _____no_output_____ | MIT | examples/Supply chain physics.ipynb | aletuf93/logproj |
Define the inventory function $q$ The empirical inventory function $q$ is defined as the differende between production and demand, plus the residual inventory. $q_t = q_{t-1} + x_t - d_t$ | q = [mu_production] #initial inventory with production mean value
for i in range(0,len(d)):
inventory_value = q[i] + x[i] - d[i]
if inventory_value <0 :
inventory_value=0
q.append(inventory_value)
plt.plot(q)
plt.xlabel('days')
plt.ylabel('Inventory quantity $q$')
plt.title('Inventory functio... | _____no_output_____ | MIT | examples/Supply chain physics.ipynb | aletuf93/logproj |
Define pull and push forces (the momentum $p=\dot{q}$) By using continuous notation we obtain the derivative $\dot{q}=p=x-d$. The derivative of the inventory represents the *momentum* of the supply chain, i.e. the speed a which the inventory values goes up (production), and down (demand). We use the term **productivi... | p1 = [q[i]-q[i-1] for i in range(1,len(q))]
p2 = [x[i]-d[i] for i in range(1,len(d))]
plt.plot(p1)
plt.plot(p2)
plt.xlabel('days')
plt.ylabel('Value')
plt.title('Momentum function $p$')
p=np.array(p) | _____no_output_____ | MIT | examples/Supply chain physics.ipynb | aletuf93/logproj |
Define a linear potential $V(q)$ we introduce a linear potential to describe the amount of *energy* related with a given quantity of the inventory $q$. | F0 = 0.1
#eta = 1.2
#lam = mu_demand
#F0=eta*lam
print(F0)
V_q = -F0*q
V_q = V_q[0:-1] | _____no_output_____ | MIT | examples/Supply chain physics.ipynb | aletuf93/logproj |
Define the energy conservation function using the Lagrangianm and the Hamiltonian We use the Lagrangian to describe the energy conservation equation.$L(q,\dot{q}) = H = \frac{1}{2}\dot{q} - V(q)$ | H = (p**2)/2 - F0*q[0:-1]
plt.plot(H)
plt.xlabel('days')
plt.ylabel('value')
plt.title('Function $H$') | _____no_output_____ | MIT | examples/Supply chain physics.ipynb | aletuf93/logproj |
Obtain the inventory $q$, given $H$ | S_q = [H[i-1] + H[i] for i in range(1,len(H))]
plt.plot(S_q)
plt.xlabel('days')
plt.ylabel('value')
plt.title('Function $S[q]$')
#compare with q
plt.plot(q)
plt.xlabel('days')
plt.ylabel('Inventory quantity $q$')
plt.title('Inventory function $q$')
plt.legend(['Model inventory','Empirical inventory']) | _____no_output_____ | MIT | examples/Supply chain physics.ipynb | aletuf93/logproj |
Inventory control Define the Brownian process | from math import sqrt
from scipy.stats import norm
import numpy as np
def brownian(x0, n, dt, delta, out=None):
"""
Generate an instance of Brownian motion (i.e. the Wiener process):
X(t) = X(0) + N(0, delta**2 * t; 0, t)
where N(a,b; t0, t1) is a normally distributed random variable with mean a... | _____no_output_____ | MIT | examples/Supply chain physics.ipynb | aletuf93/logproj |
Define the supply chain control model | # supply chain control model
def supply_chain_control_model(p,beta,eta,F0):
#p is the productivity function defined as the defivative of q
#beta is the diffusion coefficient, i.e. the delta of the Brownian process, the std of the demand can be used
#eta represents the flexibility of the productio. It is the... | _____no_output_____ | MIT | examples/Supply chain physics.ipynb | aletuf93/logproj |
Ingest DataOriginal data was from inside airbnb, to secure the files, they were copied to google drive | import os
from google_drive_downloader import GoogleDriveDownloader as gdd | _____no_output_____ | MIT | variable_exploration/mk/1_Ingestion_Wrangling/0_data_pull.ipynb | georgetown-analytics/Airbnb-Price-Prediction |
Pull files from Google Drivelistings shared url: https://drive.google.com/file/d/1e8hVygvxFgJo3QgUrzgslsTzWD9-MQUO/view?usp=sharingcalendar shared url: https://drive.google.com/file/d/1VjlSWEr4vaJHdT9o2OF9N2Ga0X2b22v9/view?usp=sharingreviews shared url: https://drive.google.com/file/d/1_ojDocAs_LtcBLNxDHqH_TSBWjPz-Zme... | # gdd.download_file_from_google_drive(file_id='1e8hVygvxFgJo3QgUrzgslsTzWD9-MQUO',
# dest_path='../data/gdrive/listings.csv.gz'
#source_dest = {'../data/gdrive/listings.csv.gz':'1e8hVygvxFgJo3QgUrzgslsTzWD9-MQUO'}
source_dest = {'../data/gdrive/listings.csv.gz':'1e8hVygvxFgJo3QgUrzg... | _____no_output_____ | MIT | variable_exploration/mk/1_Ingestion_Wrangling/0_data_pull.ipynb | georgetown-analytics/Airbnb-Price-Prediction |
Demo for paper "First Order Motion Model for Image Animation"--- **Clone repository** | !git clone https://github.com/hamdirhibi/Deep-fake
cd Deep-fake | _____no_output_____ | MIT | deep_fake.ipynb | hamdirhibi/Deep-fake |
``` This is formatted as code```**Mount your Google drive folder on Colab** | from google.colab import drive
drive.mount('/content/gdrive') | _____no_output_____ | MIT | deep_fake.ipynb | hamdirhibi/Deep-fake |
**Add folder https://drive.google.com/drive/folders/157-wifsuylAkO1E4hBGO_QXyn22mDXET?usp=sharing to your google drive.Alternativelly you can use this mirror link https://drive.google.com/drive/folders/157-wifsuylAkO1E4hBGO_QXyn22mDXET?usp=sharing **Load driving video and source image** | import imageio
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from skimage.transform import resize
from IPython.display import HTML
import warnings
warnings.filterwarnings("ignore")
source_image = imageio.imread('/content/gdrive/My Drive/first-order-motion-model/rayen.jpg')... | _____no_output_____ | MIT | deep_fake.ipynb | hamdirhibi/Deep-fake |
**Create a model and load checkpoints** | from demo import load_checkpoints
generator, kp_detector = load_checkpoints(config_path='config/vox-256.yaml',
checkpoint_path='/content/gdrive/My Drive/first-order-motion-model/vox-cpk.pth.tar') | _____no_output_____ | MIT | deep_fake.ipynb | hamdirhibi/Deep-fake |
** **bold text**Perform image animation** | from demo import make_animation
from skimage import img_as_ubyte
predictions = make_animation(source_image, driving_video, generator, kp_detector, relative=True)
#save resulting video
imageio.mimsave('../generated.mp4', [img_as_ubyte(frame) for frame in predictions], fps=fps)
#video can be downloaded from /content fo... | _____no_output_____ | MIT | deep_fake.ipynb | hamdirhibi/Deep-fake |
**In the cell above we use relative keypoint displacement to animate the objects. We can use absolute coordinates instead, but in this way all the object proporions will be inherited from the driving video. For example Putin haircut will be extended to match Trump haircut.** | predictions = make_animation(source_image, driving_video, generator, kp_detector, relative=False, adapt_movement_scale=True)
HTML(display(source_image, driving_video, predictions).to_html5_video()) | _____no_output_____ | MIT | deep_fake.ipynb | hamdirhibi/Deep-fake |
Running on your data**First we need to crop a face from both source image and video, while simple graphic editor like paint can be used for cropping from image. Cropping from video is more complicated. You can use ffpmeg for this.** | !ffmpeg -i /content/gdrive/My\ Drive/first-order-motion-model/07.mkv -ss 00:08:57.50 -t 00:00:08 -filter:v "crop=600:600:760:50" -async 1 hinton.mp4 | _____no_output_____ | MIT | deep_fake.ipynb | hamdirhibi/Deep-fake |
**Another posibility is to use some screen recording tool, or if you need to crop many images at ones use face detector(https://github.com/1adrianb/face-alignment) , see https://github.com/AliaksandrSiarohin/video-preprocessing for preprcessing of VoxCeleb.** | source_image = imageio.imread('/content/gdrive/My Drive/first-order-motion-model/09.png')
driving_video = imageio.mimread('hinton.mp4', memtest=False)
#Resize image and video to 256x256
source_image = resize(source_image, (256, 256))[..., :3]
driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_v... | _____no_output_____ | MIT | deep_fake.ipynb | hamdirhibi/Deep-fake |
Realizando limpeza dos dados Por Adriano Santos Dentre as atividades que um cientista de dados deve realizar, o processo de limpeza e tratamento é uma das mais importantes.Nesta aula aprenderemos a:* Remover informações de um DataFrame; | # Carregando módulos
import pandas as pd
# Importando os dados para manipulação
df = pd.read_csv('../Dados/WHO.csv', delimiter=',')
print (df.head()) | Country Region Population Under15 Over60 \
0 Afghanistan Eastern Mediterranean 29825 47.42 3.82
1 Albania Europe 3162 21.33 14.93
2 Algeria Africa 38482 27.42 7.17
3 Andorra Europe ... | MIT | scripts/Introd. ciencia de dados - Parte 3.ipynb | adrianosantospb/jatic2017 |
Verificando a existência de dados missing (NaN) | # O any() possibibilitará saber, coluna a coluna, se qualquer um dos valores é inexistente.
df.isnull().any()
# Possibilitirá se existe alguma coluna em branco.
print (df.isnull().all())
print ('Número de registros:', df.shape)
# O comando dropna() remove do DataFrame qualquer linha que tenha pelo menos um NaN.
df.dro... | Número de registros: 194
| MIT | scripts/Introd. ciencia de dados - Parte 3.ipynb | adrianosantospb/jatic2017 |
Comandos para remoção de coluna | # Para remover, faça:
df.drop('CellularSubscribers', axis=1, inplace=True) # axis 1 = coluna; axis 0 = linha.
print (df.columns)
# Avaliando se existe duplicata
print(df.duplicated('Region').head()) | 0 False
1 False
2 False
3 True
4 True
dtype: bool
| MIT | scripts/Introd. ciencia de dados - Parte 3.ipynb | adrianosantospb/jatic2017 |
To determine distance between local maxima and minimaWe need to calculate both, and then concatenate the arrays, and use that to select the data | # determine the days of minimum electricity consumption
# throughout the 5 months, that is the local minima
# we use peak values but we turn the series upside down with the
# reciprocal function
valleys, _ = find_peaks(1 / elec_pday.values, height=(-np.Inf, 1/60))
valleys
# compare the number of observations in the ... | _____no_output_____ | MIT | Chapter10/R4-Calculating-distance-between-events.ipynb | paulorobertolds/Python-Feature-Engineering-Cookbook |
To determine the time elapsed between local maxima and minima, we need create a dataframe with those values executing: tmp = pd.DataFrame(elec_pday[peaksandvalleys]).reset_index(drop=False)and then, 1) add the year, 2) reconstitute the date, and 3) calculate the time between the local maxima and minima, as we have d... | import featuretools as ft
# load data set from feature tools
data_dict = ft.demo.load_mock_customer()
data = data_dict["transactions"].merge(
data_dict["sessions"]).merge(data_dict["customers"])
cols = ['customer_id',
'transaction_id',
'transaction_time',
'amount',
]
data = data[... | _____no_output_____ | MIT | Chapter10/R4-Calculating-distance-between-events.ipynb | paulorobertolds/Python-Feature-Engineering-Cookbook |
**Table of Contents**Feature engineering - quantifying access to facilities - batch modeRead the shortlisted propertiesLoop through each property and build the neighborhood facility tableFeature engineer with access to amenitiesPlot the distribution of facility accessStore to disk Feature engineering - quantifying acc... | import pandas as pd
import matplotlib.pyplot as plt
from pprint import pprint
%matplotlib inline
from arcgis.gis import GIS
from arcgis.geocoding import geocode, batch_geocode
from arcgis.features import Feature, FeatureLayer, FeatureSet, GeoAccessor, GeoSeriesAccessor
from arcgis.features import SpatialDataFrame
from... | _____no_output_____ | Apache-2.0 | talks/GeoDevPDX2018/04_feature-engineering-neighboring-facilities-batch.ipynb | nitz21/arcpy |
Connect to GIS | gis = GIS(profile='')
route_service_url = gis.properties.helperServices.route.url
route_service = RouteLayer(route_service_url, gis=gis) | _____no_output_____ | Apache-2.0 | talks/GeoDevPDX2018/04_feature-engineering-neighboring-facilities-batch.ipynb | nitz21/arcpy |
Read the shortlisted properties | prop_list_df = pd.read_csv('resources/houses_for_sale_att_filtered.csv')
prop_list_df.shape
prop_list_df = pd.DataFrame.spatial.from_xy(prop_list_df, 'LONGITUDE','LATITUDE')
type(prop_list_df) | _____no_output_____ | Apache-2.0 | talks/GeoDevPDX2018/04_feature-engineering-neighboring-facilities-batch.ipynb | nitz21/arcpy |
Loop through each property and build the neighborhood facility table | groceries_count = []
restaurants_count = []
hospitals_count = []
coffee_count = []
bars_count = []
gas_count = []
shops_service_count = []
travel_transport_count = []
parks_count = []
education_count = []
route_length = []
route_duration = []
destination_address = '309 SW 6th Ave #600, Portland, OR 97204'
count=0
for ... | 1: 18517652 : Groc : Rest : Hosp : Coffee : Bars : Gas : Shops : Travel : Parks : Edu : Route
2: 18465613 : Groc : Rest : Hosp : Coffee : Bars : Gas : Shops : Travel : Parks : Edu : Route
3: 18005102 : Groc : Rest : Hosp : Coffee : Bars : Gas : Shops : Travel : Parks : Edu : Route
4: 18216924 : Groc : Rest : Hosp : Cof... | Apache-2.0 | talks/GeoDevPDX2018/04_feature-engineering-neighboring-facilities-batch.ipynb | nitz21/arcpy |
Feature engineer with access to amenities | prop_list_df['grocery_count'] = groceries_count
prop_list_df['restaurant_count']= restaurants_count
prop_list_df['hospitals_count']= hospitals_count
prop_list_df['coffee_count']= coffee_count
prop_list_df['bars_count']=bars_count
prop_list_df['gas_count']=gas_count
prop_list_df['shops_count']=shops_service_count
prop_l... | _____no_output_____ | Apache-2.0 | talks/GeoDevPDX2018/04_feature-engineering-neighboring-facilities-batch.ipynb | nitz21/arcpy |
Plot the distribution of facility access | prop_list_df.columns
facility_list = ['grocery_count', 'restaurant_count', 'hospitals_count', 'coffee_count',
'bars_count', 'gas_count', 'shops_count', 'travel_count', 'parks_count',
'edu_count', 'commute_length', 'commute_duration']
axes = prop_list_df[facility_list].hist(bins=25, layout=(3,4), figsize=... | _____no_output_____ | Apache-2.0 | talks/GeoDevPDX2018/04_feature-engineering-neighboring-facilities-batch.ipynb | nitz21/arcpy |
From the histograms above, most houses don't have very many bars in 5 miles around them. The commute length and duration appears to be tightly clustered around the lower end of the spectrum. Most houses have at least 1 hospital or medical center near them and a large number of parks, restaurants, educational institutio... | prop_list_df.to_csv('resources/houses_facility_counts.csv')
prop_list_df.spatial.to_featureclass('resources/shp/houses_facility_counts.shp') | _____no_output_____ | Apache-2.0 | talks/GeoDevPDX2018/04_feature-engineering-neighboring-facilities-batch.ipynb | nitz21/arcpy |
**The aim of this lab is to introduce DATA and FEATURES.** Extracting features from data FMML Module 1, Lab 2 Module Coordinator : amit.pandey@research.iiit.ac.in | ! pip install wikipedia
import wikipedia
import nltk
from nltk.util import ngrams
from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import re
import unicodedata
import plotly.express as px
import pandas as pd | Collecting wikipedia
Downloading wikipedia-1.4.0.tar.gz (27 kB)
Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from wikipedia) (4.6.3)
Requirement already satisfied: requests<3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wikipedia) (2.23.0)
Requirement already... | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
**What are features?**features are individual independent variables that act like a input to your system. | import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
from mpl_toolkits.mplot3d.axes3d import get_test_data
# set up a figure twice as wide as it is tall
fig = plt.figure(figsize=plt.figaspect(0.9))
# =============
# First subplot
# =============
# set up the axes for the first plot
ax = fig... | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
**Part 2: Features of text**How do we apply machine learning on text? We can't directly use the text as input to our algorithms. We need to convert them to features. In this notebook, we will explore a simple way of converting text to features.Let us download a few documents off Wikipedia. | topic1 = 'Giraffe'
topic2 = 'Elephant'
wikipedia.set_lang('en')
eng1 = wikipedia.page(topic1).content
eng2 = wikipedia.page(topic2).content
wikipedia.set_lang('fr')
fr1 = wikipedia.page(topic1).content
fr2 = wikipedia.page(topic2).content
fr2 | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
We need to clean this up a bit. Let us remove all the special characters and keep only 26 letters and space. Note that this will remove accented characters in French also. We are also removing all the numbers and spaces. So this is not an ideal solution. | def cleanup(text):
text = text.lower() # make it lowercase
text = re.sub('[^a-z]+', '', text) # only keep characters
return text
print(eng1) | The giraffe is a tall African mammal belonging to the genus Giraffa. Specifically, It is an even-toed ungulate. It is the tallest living terrestrial animal and the largest ruminant on Earth. Traditionally, giraffes were thought to be one species, Giraffa camelopardalis, with nine subspecies. Most recently, researchers ... | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
Instead of directly using characters as the features, to understand a text better, we may consider group of tokens i.e. ngrams as features.for this example let us consider that each character is one word, and let us see how n-grams work. **nltk library provides many tools for text processing, please explore them.** No... | # convert a tuple of characters to a string
def tuple2string(tup):
st = ''
for ii in tup:
st = st + ii
return st
# convert a tuple of tuples to a list of strings
def key2string(keys):
return [tuple2string(i) for i in keys]
# plot the histogram
def plothistogram(ngram):
keys = key2string(ngram.keys())
... | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
Let us compare the histograms of English pages and French pages. Can you spot a difference? | ## we passed ngrams 'n' as 1 to get unigrams. Unigram is nothing but single token (in this case character).
unigram_eng1 = Counter(ngrams(eng1,1))
plothistogram(unigram_eng1)
plt.title('English 1')
plt.show()
unigram_eng2 = Counter(ngrams(eng2,1))
plothistogram(unigram_eng2)
plt.title('English 2')
plt.show()
unigram_f... | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
A good feature is one that helps in easy prediction and classification.for ex : if you wish to differentiate between grapes and apples, size can be one of the useful features. We can see that the unigrams for French and English are very similar. So this is not a good feature if we want to distinguish between English an... | ## Now instead of unigram, we will use bigrams as features, and see how useful bigrams are as features.
bigram_eng1 = Counter(ngrams(eng1,2)) # bigrams
plothistogram(bigram_eng1)
plt.title('English 1')
plt.show()
bigram_eng2 = Counter(ngrams(eng2,2))
plothistogram(bigram_eng2)
plt.title('English 2')
plt.show()
bigra... | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
Another way to visualize bigrams is to use a 2-dimensional graph. | ## lets have a lot at bigrams.
bigram_eng1
def plotbihistogram(ngram):
freq = np.zeros((26,26))
for ii in range(26):
for jj in range(26):
freq[ii,jj] = ngram[(chr(ord('a')+ii), chr(ord('a')+jj))]
plt.imshow(freq, cmap = 'jet')
return freq
bieng1 = plotbihistogram(bigram_eng1)
plt.show()
bieng2 = plot... | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
Let us look at the top 10 ngrams for each text. | from IPython.core.debugger import set_trace
def ind2tup(ind):
ind = int(ind)
i = int(ind/26)
j = int(ind%26)
return (chr(ord('a')+i), chr(ord('a')+j))
def ShowTopN(bifreq, n=10):
f = bifreq.flatten()
arg = np.argsort(-f)
for ii in range(n):
print(f'{ind2tup(arg[ii])} : {f[arg[ii]]}')
print('\nEngli... |
English 1:
('t', 'h') : 714.0
('h', 'e') : 705.0
('i', 'n') : 577.0
('e', 's') : 546.0
('a', 'n') : 541.0
('e', 'r') : 457.0
('r', 'e') : 445.0
('r', 'a') : 418.0
('a', 'l') : 407.0
('n', 'd') : 379.0
English 2:
('a', 'n') : 1344.0
('t', 'h') : 1271.0
('h', 'e') : 1163.0
('i', 'n') : 946.0
('e', 'r') : 744.0
('l', 'e... | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
**At times, we need to reduce the number of features. We will discuss this more in the upcoming sessions, but a small example has been discussed here. Instead of using each unique token (a word) as a feature, we reduced the number of features by using 1-gram and 2-gram of characters as features.** We observe that the ... | from keras.datasets import mnist
#loading the dataset
(train_X, train_y), (test_X, test_y) = mnist.load_data() | Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
11501568/11490434 [==============================] - 0s 0us/step
| Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
Extract a subset of the data for our experiment: | no1 = train_X[train_y==1,:,:] ## dataset corresponding to number = 1.
no0 = train_X[train_y==0,:,:] ## dataset corresponding to number = 0. | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
Let us visualize a few images here | for ii in range(5):
plt.subplot(1, 5, ii+1)
plt.imshow(no1[ii,:,:])
plt.show()
for ii in range(5):
plt.subplot(1, 5, ii+1)
plt.imshow(no0[ii,:,:])
plt.show() | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
We can even use value of each pixel as a feature. But let us see how to derive other features. Now, let us start with a simple feature: the sum of all pixels and see how good this feature is. | ## sum of pixel values.
sum1 = np.sum(no1>0, (1,2)) # threshold before adding up
sum0 = np.sum(no0>0, (1,2)) | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
Let us visualize how good this feature is: (X-axis is mean, y-axis is the digit) | plt.hist(sum1, alpha=0.7);
plt.hist(sum0, alpha=0.7); | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
We can already see that this feature separates the two classes quite well.Let us look at another, more complicated feature. We will count the number black pixels that are surrounded on four sides by non-black pixels, or "hole pixels". | def cumArray(img):
img2 = img.copy()
for ii in range(1, img2.shape[1]):
img2[ii,:] = img2[ii,:] + img2[ii-1,:] # for every row, add up all the rows above it.
#print(img2)
img2 = img2>0
#print(img2)
return img2
def getHolePixels(img):
im1 = cumArray(img)
im2 = np.rot90(cumArray(np.rot90(img)), 3) #... | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
Visualize a few: | imgs = [no1[456,:,:], no0[456,:,:]]
for img in imgs:
plt.subplot(1,2,1)
plt.imshow(getHolePixels(img))
plt.subplot(1,2,2)
plt.imshow(img)
plt.show() | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
Now let us plot the number of hole pixels and see how this feature behaves | hole1 = np.array([getHolePixels(i).sum() for i in no1])
hole0 = np.array([getHolePixels(i).sum() for i in no0])
plt.hist(hole1, alpha=0.7);
plt.hist(hole0, alpha=0.7); | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
This feature works even better to distinguish between one and zero.Now let us try the number of pixels in the 'hull' or the number with the holes filled in: Let us try one more feature, where we look at the number of boundary pixels in each image. | def minus(a, b):
return a & ~ b
def getBoundaryPixels(img):
img = img.copy()>0 # binarize the image
rshift = np.roll(img, 1, 1)
lshift = np.roll(img, -1 ,1)
ushift = np.roll(img, -1, 0)
dshift = np.roll(img, 1, 0)
boundary = minus(img, rshift) | minus(img, lshift) | minus(img, ushift) | minus(img, dshif... | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
What will happen if we plot two features together? Feel free to explore the above graph with your mouse.We have seen that we extracted four features from a 28*28 dimensional image.Some questions to explore:Which is the best combination of features?How would you test or visualize four or more features?Can you come up wi... | import pandas as pd
df = pd.read_csv('/content/sample_data/california_housing_train.csv')
df.head()
df.columns
df = df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'})
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
sns.set(style = "da... | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
**Task :** Download a CSV file from the internet, upload it to your google drive. Read the CSV file and plot graphs using different combination of features and write your analysis Ex : IRIS flower datasaet | import pandas as pd
from google.colab import drive
drive.mount('/content/drive')
iris = pd.read_csv('/content/drive/MyDrive/iris_csv.csv')
iris.head()
iris.columns
iris_trimmed = iris[['sepallength', 'sepalwidth', 'petallength', 'petalwidth', 'class']]
import seaborn as sns
import matplotlib.pyplot as plt
sns.pairplo... | _____no_output_____ | Apache-2.0 | module1_lab_2.ipynb | Ishitha2003/-fmml20211052 |
varibles and data types varibles are container is a name is given to a memory allocated with program datatypes values and datatypes are the varibles kinds like int datatype, string datatype etc. how python can identify variable and data types . so basically identify if you write a= 30 it sees no double quotes(") ... | p="harry"
a=348
b=3434.334343
print(type(p))
print(type(a))
print(type(b)) | <class 'str'>
<class 'int'>
<class 'float'>
| Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
variable = are to store a value keyword = reserverd word in python ...................................... identifier = class / function / variables Name EXAMPLE DEF, CLASS ARE THE RESERVED WORD IN PYTHON WHAT ARE DATA TYPES some data types like int = -34,-3,-1,0,3,4,6,7.. are int data types float = decimal withi... | if 89798>3:
print(True)
else:
print(False)
| True
| Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
NONE is simply denoted for represent if you want to give none values then you can use it as a=None to show in code | d=None
print(type(d))
| <class 'NoneType'>
| Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
what is type ? python has class and objects we will discuss later about that.type is function which we call and gives the outputs of which class of variable present in name or variable you created . rule for creating variable names1. variable name contains names underscore and digits.2. A variable can start with al... | a =343
b=38437498
print("sum of a+b",a+b) | _____no_output_____ | Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
assignment operator .py if you add 3 to a int variable just follow step using assignment operator | i=8
i+=3
print(i)
i=34
i-=34
print(i)
p=3
p*=4
print(p)
o=344
o/=34
print(o) | 10.117647058823529
| Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
camparision operator camparision operator campare between two entities to which is True or False like boolean. | b=4>6
print(b)
b=34>33
print(b)
b=(34>=3)
print(b)
n=(3434==34343)
print(n)
p=24!=98
print(p) | True
| Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
logical operator AND , OR and NOT are the most usable of all the time which is related to boolean algebra concept here. NOT is use only for one variable . | bool1=True
bool2=False
print("the value of bool1 and bool2",bool1 and bool2)
print("the value of bool1 or bool2",bool1 or bool2)
print("the value of not bool2", not bool2)
| the value of bool1 and bool2 False
the value of bool1 or bool2 True
the value of not bool2 True
| Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
type funcion and typecastingtype is used to find the data type of given variable in python.and typecasting is used to change one type to another datatypes like int variable to float variable. typecasting.py | a=43
a=float(a)
print(a) | 43.0
| Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
string to int literalint to string literal what is input function?input function allows to you to take input values from the user through Keyboard as a string or int value under the string datatype etc. input function.py a=input("enter your name")a=int(a)print(a) PRACTICE SET add.py | # write a program to add two number | the sum is a+b 68
| Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
a=34b=34print("the sum is a+b",a+b) | # write a program to find the remainder if a number is divisible by 2.
p=45
p/=2
print(p)
| the remainder when a is divisible by b is 0
| Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
a= 45 b= 15print("the remainder when a is divisible by b is",a%b) | # check the type of a funtion using input funtion | <class 'str'>
| Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
a=input("enter a number ")print(type(a)) | # use camparision between two variable having a=34 and b =80 and is greator or not.
a=34
b=80
print("a is greator than b is ",a>b)
| _____no_output_____ | Apache-2.0 | codeMania-python-begginer/02_Varibles-and-datatypes.ipynb | JayramMardi/codeMania |
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