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Calculate the weighted returns for the portfolio assuming an equal number of shares for each stock | # Set weights
weights = [1/3, 1/3, 1/3]
# Calculate portfolio return
# Display sample data
| _____no_output_____ | ADSL | .ipynb_checkpoints/whale.py-checkpoint.ipynb | charbelnehme/pandas-homework |
Join your portfolio returns to the DataFrame that contains all of the portfolio returns | # Join your returns DataFrame to the original returns DataFrame
# Only compare dates where return data exists for all the stocks (drop NaNs)
| _____no_output_____ | ADSL | .ipynb_checkpoints/whale.py-checkpoint.ipynb | charbelnehme/pandas-homework |
Re-run the risk analysis with your portfolio to see how it compares to the others Calculate the Annualized Standard Deviation | # Calculate the annualized `std`
| _____no_output_____ | ADSL | .ipynb_checkpoints/whale.py-checkpoint.ipynb | charbelnehme/pandas-homework |
Calculate and plot rolling `std` with 21-day window | # Calculate rolling standard deviation
# Plot rolling standard deviation
| _____no_output_____ | ADSL | .ipynb_checkpoints/whale.py-checkpoint.ipynb | charbelnehme/pandas-homework |
Calculate and plot the correlation | # Calculate and plot the correlation
| _____no_output_____ | ADSL | .ipynb_checkpoints/whale.py-checkpoint.ipynb | charbelnehme/pandas-homework |
Calculate and Plot the 60-day Rolling Beta for Your Portfolio compared to the S&P 60 TSX | # Calculate and plot Beta
| _____no_output_____ | ADSL | .ipynb_checkpoints/whale.py-checkpoint.ipynb | charbelnehme/pandas-homework |
Using the daily returns, calculate and visualize the Sharpe ratios using a bar plot | # Calculate Annualized Sharpe Ratios
# Visualize the sharpe ratios as a bar plot
| _____no_output_____ | ADSL | .ipynb_checkpoints/whale.py-checkpoint.ipynb | charbelnehme/pandas-homework |
Saying the same thing multiple ways What happens when someone comes across a file in our file format? How do they know what it means? If we can make the tag names in our model globally unique, then the meaning of the file can be made understandablenot just to us, but to people and computers all over the world.Two file formats which give the same information, in different ways, are *syntactically* distinct,but so long as they are **semantically** compatible, I can convert from one to the other. This is the goal of the technologies introduced this lecture. The URI The key concept that underpins these tools is the URI: uniform resource **indicator**. These look like URLs: `www.turing.ac.uk/rsd-engineering/schema/reaction/element`But, if I load that as a web address, there's nothing there!That's fine.A UR**N** indicates a **name** for an entity, and, by using organisational web addresses as a prefix,is likely to be unambiguously unique.A URI might be a URL or a URN, or both. XML Namespaces It's cumbersome to use a full URI every time we want to put a tag in our XML file.XML defines *namespaces* to resolve this: | %%writefile system.xml
<?xml version="1.0" encoding="UTF-8"?>
<system xmlns="http://www.turing.ac.uk/rsd-engineering/schema/reaction">
<reaction>
<reactants>
<molecule stoichiometry="2">
<atom symbol="H" number="2"/>
</molecule>
<molecule stoichiometry="1">
<atom symbol="O" number="2"/>
</molecule>
</reactants>
<products>
<molecule stoichiometry="2">
<atom symbol="H" number="2"/>
<atom symbol="O" number="1"/>
</molecule>
</products>
</reaction>
</system>
from lxml import etree
with open("system.xml") as xmlfile:
tree = etree.parse(xmlfile)
print(etree.tostring(tree, pretty_print=True, encoding=str)) | <system xmlns="http://www.turing.ac.uk/rsd-engineering/schema/reaction">
<reaction>
<reactants>
<molecule stoichiometry="2">
<atom symbol="H" number="2"/>
</molecule>
<molecule stoichiometry="1">
<atom symbol="O" number="2"/>
</molecule>
</reactants>
<products>
<molecule stoichiometry="2">
<atom symbol="H" number="2"/>
<atom symbol="O" number="1"/>
</molecule>
</products>
</reaction>
</system>
| CC-BY-3.0 | ch09fileformats/11ControlledVocabularies.ipynb | jack89roberts/rsd-engineeringcourse |
Note that our previous XPath query no longer finds anything. | tree.xpath("//molecule/atom[@number='1']/@symbol")
namespaces = {"r": "http://www.turing.ac.uk/rsd-engineering/schema/reaction"}
tree.xpath("//r:molecule/r:atom[@number='1']/@symbol", namespaces=namespaces) | _____no_output_____ | CC-BY-3.0 | ch09fileformats/11ControlledVocabularies.ipynb | jack89roberts/rsd-engineeringcourse |
Note the prefix `r` used to bind the namespace in the query: any string will do - it's just a dummy variable. The above file specified our namespace as a default namespace: this is like doing `from numpy import *` in python. It's often better to bind the namespace to a prefix: | %%writefile system.xml
<?xml version="1.0" encoding="UTF-8"?>
<r:system xmlns:r="http://www.turing.ac.uk/rsd-engineering/schema/reaction">
<r:reaction>
<r:reactants>
<r:molecule stoichiometry="2">
<r:atom symbol="H" number="2"/>
</r:molecule>
<r:molecule stoichiometry="1">
<r:atom symbol="O" number="2"/>
</r:molecule>
</r:reactants>
<r:products>
<r:molecule stoichiometry="2">
<r:atom symbol="H" number="2"/>
<r:atom symbol="O" number="1"/>
</r:molecule>
</r:products>
</r:reaction>
</r:system> | Overwriting system.xml
| CC-BY-3.0 | ch09fileformats/11ControlledVocabularies.ipynb | jack89roberts/rsd-engineeringcourse |
Namespaces and Schema It's a good idea to serve the schema itself from the URI of the namespace treated as a URL, but it's *not a requirement*: it's a URN not necessarily a URL! | %%writefile reactions.xsd
<xs:schema xmlns:xs="http://www.w3.org/2001/XMLSchema"
targetNamespace="http://www.turing.ac.uk/rsd-engineering/schema/reaction"
xmlns:r="http://www.turing.ac.uk/rsd-engineering/schema/reaction">
<xs:element name="atom">
<xs:complexType>
<xs:attribute name="symbol" type="xs:string"/>
<xs:attribute name="number" type="xs:integer"/>
</xs:complexType>
</xs:element>
<xs:element name="molecule">
<xs:complexType>
<xs:sequence>
<xs:element ref="r:atom" maxOccurs="unbounded"/>
</xs:sequence>
<xs:attribute name="stoichiometry" type="xs:integer"/>
</xs:complexType>
</xs:element>
<xs:element name="reactants">
<xs:complexType>
<xs:sequence>
<xs:element ref="r:molecule" maxOccurs="unbounded"/>
</xs:sequence>
</xs:complexType>
</xs:element>
<xs:element name="products">
<xs:complexType>
<xs:sequence>
<xs:element ref="r:molecule" maxOccurs="unbounded"/>
</xs:sequence>
</xs:complexType>
</xs:element>
<xs:element name="reaction">
<xs:complexType>
<xs:sequence>
<xs:element ref="r:reactants"/>
<xs:element ref="r:products"/>
</xs:sequence>
</xs:complexType>
</xs:element>
<xs:element name="system">
<xs:complexType>
<xs:sequence>
<xs:element ref="r:reaction" maxOccurs="unbounded"/>
</xs:sequence>
</xs:complexType>
</xs:element>
</xs:schema> | Overwriting reactions.xsd
| CC-BY-3.0 | ch09fileformats/11ControlledVocabularies.ipynb | jack89roberts/rsd-engineeringcourse |
Note we're now defining the target namespace for our schema. | with open("reactions.xsd") as xsdfile:
schema_xsd = xsdfile.read()
schema = etree.XMLSchema(etree.XML(schema_xsd))
parser = etree.XMLParser(schema=schema)
with open("system.xml") as xmlfile:
tree = etree.parse(xmlfile, parser)
print(tree) | <lxml.etree._ElementTree object at 0x106978960>
| CC-BY-3.0 | ch09fileformats/11ControlledVocabularies.ipynb | jack89roberts/rsd-engineeringcourse |
Note the power of binding namespaces when using XML files addressing more than one namespace.Here, we can clearly see which variables are part of the schema defining XML schema itself (bound to `xs`)and the schema for our file format (bound to `r`) Using standard vocabularies The work we've done so far will enable someone who comes across our file format to track down something about its significance, by following the URI in the namespace. But it's still somewhat ambiguous. The word "element" means (at least) two things: an element tag in an XML document, and a chemical element. (It also means a heating element in a toaster, and lots of other things.) To make it easier to not make mistakes as to the meaning of **found data**, it is helpful to usestandardised namespaces that already exist for the concepts our file format refers to.So that when somebody else picks up one of our data files, the meaning of the stuff it describes is obvious. In this example, it would be hard to get it wrong, of course, but in general, defining file formats so that they are meaningful as found data should be desirable. For example, the concepts in our file format are already part of the "DBPedia ontology",among others. So, we could redesign our file format to exploit this, by referencing for example [https://dbpedia.org/ontology/ChemicalCompound](https://dbpedia.org/ontology/ChemicalCompound): | %%writefile chemistry_template3.mko
<?xml version="1.0" encoding="UTF-8"?>
<system xmlns="https://www.turing.ac.uk/rsd-engineering/schema/reaction"
xmlns:dbo="https://dbpedia.org/ontology/">
%for reaction in reactions:
<reaction>
<reactants>
%for molecule in reaction.reactants.molecules:
<dbo:ChemicalCompound stoichiometry="${reaction.reactants.molecules[molecule]}">
%for element in molecule.elements:
<dbo:ChemicalElement symbol="${element.symbol}"
number="${molecule.elements[element]}"/>
%endfor
</dbo:ChemicalCompound>
%endfor
</reactants>
<products>
%for molecule in reaction.products.molecules:
<dbo:ChemicalCompound stoichiometry="${reaction.products.molecules[molecule]}">
%for element in molecule.elements:
<dbo:ChemicalElement symbol="${element.symbol}"
number="${molecule.elements[element]}"/>
%endfor
</dbo:ChemicalCompound>
%endfor
</products>
</reaction>
%endfor
</system> | Overwriting chemistry_template3.mko
| CC-BY-3.0 | ch09fileformats/11ControlledVocabularies.ipynb | jack89roberts/rsd-engineeringcourse |
Explorer | modeller = msm.convert('alanine_dipeptide.pdb', to_form='openmm.Modeller')
topology = modeller.topology
positions = modeller.positions
forcefield = app.ForceField('amber10.xml', 'amber10_obc.xml')
system = forcefield.createSystem(topology, constraints=app.HBonds, nonbondedMethod=app.NoCutoff)
explorer = oe.Explorer(topology, system, platform='CUDA')
explorer.set_coordinates(positions)
explorer.get_potential_energy()
explorer.get_potential_energy_gradient()
explorer.get_potential_energy_hessian()
coordinates = explorer.get_coordinates()
explorer_2 = explorer.replicate()
explorer_2 | _____no_output_____ | MIT | docs/contents/.ipynb_checkpoints/Explorer-checkpoint.ipynb | uibcdf/OpenMembrane |
Quenching | explorer.set_coordinates(positions)
explorer.quench.l_bfgs()
explorer.get_potential_energy()
explorer.set_coordinates(positions)
explorer.quench.fire()
explorer.get_potential_energy()
explorer.set_coordinates(positions)
explorer.quench.gradient_descent()
explorer.get_potential_energy() | _____no_output_____ | MIT | docs/contents/.ipynb_checkpoints/Explorer-checkpoint.ipynb | uibcdf/OpenMembrane |
Moves | explorer.set_coordinates(positions)
explorer.move.random_atoms_shifts()
explorer.get_potential_energy()
explorer.set_coordinates(positions)
explorer.move.random_atoms_max_shifts()
explorer.get_potential_energy()
explorer.set_coordinates(positions)
explorer.move.random_atoms_rsmd()
explorer.get_potential_energy()
explorer.set_coordinates(positions)
explorer.move.random_atoms_max_rsmd()
explorer.get_potential_energy()
explorer.set_coordinates(positions)
explorer.move.random_dihedral_shifts()
explorer.get_potential_energy()
explorer.set_coordinates(positions)
explorer.move.random_dihedral_max_shifts()
explorer.get_potential_energy()
explorer.set_coordinates(positions)
explorer.move.random_dihedral_rmsd()
explorer.get_potential_energy()
explorer.set_coordinates(positions)
explorer.move.random_dihedral_max_rmsd()
explorer.get_potential_energy() | _____no_output_____ | MIT | docs/contents/.ipynb_checkpoints/Explorer-checkpoint.ipynb | uibcdf/OpenMembrane |
Dynamics | explorer.set_coordinates(positions)
explorer.md.langevin(500)
explorer.get_potential_energy() | _____no_output_____ | MIT | docs/contents/.ipynb_checkpoints/Explorer-checkpoint.ipynb | uibcdf/OpenMembrane |
Distance | explorer.set_coordinates(coordinates)
explorer.md.langevin(500)
explorer.distance.rmsd(positions)
explorer.distance.least_rmsd(positions)
explorer.set_coordinates(positions)
explorer_2 = explorer.replicate()
explorer.md.langevin(500)
explorer.distance.rmsd(explorer_2)
explorer.distance.least_rmsd(explorer_2) | _____no_output_____ | MIT | docs/contents/.ipynb_checkpoints/Explorer-checkpoint.ipynb | uibcdf/OpenMembrane |
Basic PythonIntroduction to some basic python data types. | x = 1
y = 2.0
s = "hello"
l = [1, 2, 3, "a"]
d = {"a": 1, "b": 2, "c": 3} | _____no_output_____ | BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Operations behave as per what you would expect. | z = x * y
print(z)
# Getting item at index 3 - note that Python uses zero-based indexing.
print(l[3])
# Getting the index of an element
print(l.index(2))
# Concatenating lists is just using the '+' operator.
print(l + l) | a
1
[1, 2, 3, 'a', 1, 2, 3, 'a']
| BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Dictionaries are essentially key-value pairs | print(d["c"]) # Getting the value associated with "c" | 3
| BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Numpy and scipy By convention, numpy is import as np and scipy is imported as sp. | import numpy as np
import scipy as sp | _____no_output_____ | BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
An array is essentially a tensor. It can be an arbitrary number of dimensions. For simplicity, we will stick to basic 1D vectors and 2D matrices for now. | x = np.array([[1, 2, 3],
[4, 7, 6],
[9, 4, 2]])
y = np.array([1.5, 0.5, 3])
print(x)
print(y) | [[1 2 3]
[4 7 6]
[9 4 2]]
[1.5 0.5 3. ]
| BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
By default, operations are element-wise. | print(x + x)
print(x * x)
print(y * y)
print(np.dot(x, x))
print(np.dot(x, y)) | [11.5 27.5 21.5]
| BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Or you can use the @ operator that is available in Python 3.7 onwards. | print(x @ x)
print(x @ y) | [[36 28 21]
[86 81 66]
[43 54 55]]
[11.5 27.5 21.5]
| BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Numpy also comes with standard linear algebra operations, such as getting the inverse. | print(np.linalg.inv(x)) | [[ 0.16949153 -0.13559322 0.15254237]
[-0.77966102 0.42372881 -0.10169492]
[ 0.79661017 -0.23728814 0.01694915]]
| BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Eigen values and vectors | print(np.linalg.eig(x)) | (array([12.50205135, -3.75787445, 1.2558231 ]), array([[-0.27909662, -0.40149786, 0.3019769 ],
[-0.79317124, -0.32770088, -0.78112084],
[-0.5412804 , 0.85522605, 0.54649811]]))
| BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Use of numpy vectorization is key to efficient coding. Here we use the Jupyter %time magic function to demonstrate the relative speeds to two methods of calculation the L2 norm of a very long vector. | r = np.random.rand(10000, 1)
%time sum([i**2 for i in r])**0.5
%time np.sqrt(np.sum(r**2))
%time np.linalg.norm(r) | CPU times: user 17.7 ms, sys: 1.14 ms, total: 18.8 ms
Wall time: 18.6 ms
CPU times: user 86 µs, sys: 30 µs, total: 116 µs
Wall time: 93.9 µs
CPU times: user 1.33 ms, sys: 347 µs, total: 1.67 ms
Wall time: 723 µs
| BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Scipy has all the linear algebra functions as numpy and more. Moreover, scipy is always compiled with fast BLAS and LAPACK. | import scipy.linalg as linalg
linalg.inv(x)
import scipy.constants as const
print(const.e)
print(const.h)
import scipy.stats as stats
dist = stats.norm(0, 1) # Gaussian distribution
dist.cdf(1.96) | _____no_output_____ | BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Pandaspandas is one of the most useful packages that you will be using extensively during this course. You should become very familiar with the Series and DataFrame objects in pandas. Here, we will read in a csv (comma-separated value) file downloaded from figshare. While you can certainly manually download the csv and just called pd.read_csv(filename), we will just use the request method to directly grab the file and read it in using a StringIO stream. | import pandas as pd
from io import StringIO
import requests
from IPython.display import display
# Get the raw text of the data directly from the figshare url.
url = "https://ndownloader.figshare.com/files/13007075"
raw = requests.get(url).text
# Then reads in the data as a pandas DataFrame.
data = pd.read_csv(StringIO(raw))
display(data) | _____no_output_____ | BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Here, we will get one column from the DataFrame - this is a Pandas Series object. | print(data["Enorm (eV)"])
df = data[data["Enorm (eV)"] >= 0]
df.describe() | _____no_output_____ | BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Pandas dataframes come with some conveience functions for quick visualization. | df.plot(x="Enorm (eV)", y="E_raw (eV)", kind="scatter"); | _____no_output_____ | BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
SeabornHere we demonstrate some basic statistical data visualization using the seaborn package. A helpful resource is the [seaborn gallery](https://seaborn.pydata.org/examples/index.html) which has many useful examples with source code. | import seaborn as sns
%matplotlib inline
sns.distplot(df["Enorm (eV)"], norm_hist=False);
sns.scatterplot(x="Enorm (eV)", y="E_raw (eV)", data=df); | _____no_output_____ | BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Materials API using pymatgen The MPRester.query method allows you to perform direct queries to the Materials Project to obtain data. What is returned is a list of dict of properties. | from pymatgen.ext.matproj import MPRester
mpr = MPRester()
data = mpr.query(criteria="*-O", properties=["pretty_formula", "final_energy", "band_gap", "elasticity.K_VRH"])
# What is returned is a list of dict. Let's just see what the first item in the list looks out.
import pprint
pprint.pprint(data[0]) | _____no_output_____ | BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
The above is not very friendly for manipulation and visualization. Thankfully, we can easily convert this to a pandas DataFrame since the DataFrame constructor takes in lists of dicts as well. | df = pd.DataFrame(data)
display(df) | _____no_output_____ | BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Oftentimes, you only want the subset of data with valid values. In the above data, it is clear that some of the entries do not have elasticity.K_VRH data. So we will use the dropna method of the pandas DataFrame to get a new DataFrame with just valid data. Note that a lot of Pandas methods returns a new DataFrame. This ensures that you always have the original object to compare to. If you want to perform the operation in place, you can usually supply `inplace=True` to the method. | valid_data = df.dropna()
print(valid_data) | pretty_formula final_energy band_gap elasticity.K_VRH
1 BaO2 -16.991508 2.1206 28.0
2 BaO -23.550004 2.3711 67.0
5 Bi2O3 -28.415230 1.1772 117.0
6 CeO2 -49.897720 0.6980 148.0
7 CeO2 -51.753294 1.9556 132.0
... ... ... ... ...
2234 ZnO -105.067224 0.5298 167.0
2251 WO3 -151.123549 0.0000 89.0
2253 WO3 -120.135441 1.8967 37.0
2261 WO3 -120.093040 1.6755 50.0
2267 WO2 -87.834801 0.0000 116.0
[387 rows x 4 columns]
| BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Seaborn works very well with Pandas DataFrames... | sns.scatterplot(x="band_gap", y="elasticity.K_VRH", data=valid_data); | _____no_output_____ | BSD-3-Clause | lectures/notebooks/Lecture 01 - Python for Data Science.ipynb | materialsvirtuallab/nano281 |
Implement Canny edge detection | # Try Canny using "wide" and "tight" thresholds
wide = cv2.Canny(gray, 30, 100)
tight = cv2.Canny(gray, 200, 240)
# Display the images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title('wide')
ax1.imshow(wide, cmap='gray')
ax2.set_title('tight')
ax2.imshow(tight, cmap='gray') | _____no_output_____ | MIT | 1_2_Convolutional_Filters_Edge_Detection/5. Canny Edge Detection.ipynb | Abdulrahman-Adel/CVND-Exercises |
TODO: Try to find the edges of this flowerSet a small enough threshold to isolate the boundary of the flower. | # Read in the image
image = cv2.imread('images/sunflower.jpg')
# Change color to RGB (from BGR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image)
# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
## TODO: Define lower and upper thresholds for hysteresis
# right now the threshold is so small and low that it will pick up a lot of noise
lower = 70
upper = 210
edges = cv2.Canny(gray, lower, upper)
plt.figure(figsize=(20,10))
plt.imshow(edges, cmap='gray') | _____no_output_____ | MIT | 1_2_Convolutional_Filters_Edge_Detection/5. Canny Edge Detection.ipynb | Abdulrahman-Adel/CVND-Exercises |
Load some point-clouds and make two sets (sample_pcs, ref_pcs) from them. The ref_pcs is considered as the __ground-truth__ data while the sample_pcs corresponds to a set that is matched against it, e.g. comes from a generative model. | # top_in_dir = '../data/shape_net_core_uniform_samples_2048/' # Top-dir of where point-clouds are stored.
# top_in_dir = '../data/ShapeNetV1PCOutput/' # Top-dir of where point-clouds are stored.
top_in_dir = '../data/ShapeNetCore.v2.PC15k/'
class_name = 'chair'
syn_id = snc_category_to_synth_id()[class_name]
class_dir = osp.join(top_in_dir , syn_id, 'val')
# all_pc_data = load_all_point_clouds_under_folder(class_dir, n_threads=8, file_ending='.ply', verbose=True)
all_pc_data = load_all_point_clouds_under_folder(
class_dir, n_threads=8, file_ending='.npy', verbose=True, normalize=True, rotation_axis=1)
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
def plot_3d(pcl, axis=[0,1,2]):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(pcl[:,axis[0]], pcl[:,axis[1]], pcl[:,axis[2]], s=1)
ax.set_xlabel('axis-0')
ax.set_ylabel('axis-1')
ax.set_zlabel('axis-2')
plt.show()
from random import choice
pcls, _, _ = all_pc_data.next_batch(100)
plot_3d(pcls[choice(range(pcls.shape[0]))], axis=[0,2,1])
top_in_dir = '../data/ModelNet40.PC15k/'
# class_name = raw_input('Give me the class name (e.g. "chair"): ').lower()
class_name = "chair"
class_dir = osp.join(top_in_dir , class_name, 'test')
# all_pc_data = load_all_point_clouds_under_folder(class_dir, n_threads=8, file_ending='.ply', verbose=True)
all_pc_data = load_all_point_clouds_under_folder(
class_dir, n_threads=8, file_ending='.npy', verbose=True, normalize=True, rotation_axis=1)
from random import choice
pcls, _, _ = all_pc_data.next_batch(100)
plot_3d(pcls[choice(range(pcls.shape[0]))], axis=[0,2,1]) | _____no_output_____ | MIT | notebooks/VisualizeRotation.ipynb | stevenygd/latent_3d_points |
Python programming for [email protected] Agenda1. Background, why Python, [installation](installation), IDE, setup2. Variables, Boolean, None, numbers (integers, floating point), check type3. List, Set, Dictionary, Tuple4. Text and regular expressions5. Conditions, loops6. Objects and Functions7. Special functions (range, enumerate, zip), Iterators8. I/O working with files, working directory, projects9. Packages, pip, selected packages (xmltodict, biopython, xlwings, pyautogui, sqlalchemy, cx_Oracle, pandas)10. Errors and debugging (try, except)11. virtual environments What is a Programming language? Why Python Advantages:* Opensource/ free - explanation* Easy to learn* Old* Popular* All purpose* Simple syntaxis* High level* Scripting* Dynamically typed Disadvantages:* Old* Dynamically typed* Inconsistent development Installation[Python](http://python.org/)[Anaconda](https://www.anaconda.com/products/individual) Integrated Development Environment (IDE)* IDLE – comes with Python* [Jupiter notebook](https://jupyter.org/install)* [google colab](https://colab.research.google.com/notebooks/basic_features_overview.ipynbscrollTo=KR921S_OQSHG)* Spyder – comes with Anaconda* [Visual Studio Code](https://code.visualstudio.com/)* [PyCharm community](https://www.jetbrains.com/toolbox-app/) Python filesPython files are text files with .py extension CommentsComments are pieces of code that are not going to be executed. In python everything after hashtag () on the same line is comment.Comments are used to describe code: what is this particular piece of code doing and why you have created it. | # this is a comment it will be ignored when running the python file | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
VariablesAssigning value to a variable | my_variable = 3
print(my_variable) | 3
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Naming variablesVariable names cannot start with the number, cannot contain special characters or space except _Should not be name of python function.* variable1 -> this is OK* 1variable -> this is not OK* Important-variable! -> this is not OK* myVariable -> this is OK* my_variable -> this is OK Data types Numbers 1. integers (whole numbers) | var2 = 2
my_variable + var2
print(my_variable + 4)
my_variable = 6
print(my_variable +4) | 7
10
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
we can assign the result to another variable | result = my_variable + var2
print(result) | 8
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
2. Doubles (floating point number) | double = 2.05
print(double) | 2.05
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Mathematical operations- Additon and substraction | 2 + 3
5 - 2 | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
- Multiplication and division | 2 * 3
6 / 2 | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Note: the result of division is float not int! - Exponential | 2 ** 4
# 2**4 is equal to 2*2*2*2 | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
- Floor division | 7 // 3 | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
7/3 is 2.3333 the floor division is giving the whole number 2 (how many times you can fit 3 in 7) - Modulo | 7.0 % 2 | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
7//3 is 2, modulo is giving the remainder of the operation (what is left when you fit 2 times 3 in 7 ; 7 =2*3 + 1)Note: Floor division and modulo results are inegers if integers are used as arguments and float if one of the arguments is float Special variables 1. NoneNone means variable without data type, nothing | var = None
print(var) | None
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
2. BoleanNote: Bolean is type of integer that can take only values of 0 or 1 | var = True # or 1
var2 = False # or 0
print(var)
print(var+1) | True
2
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Check variable type 1. type() function | print(type(True))
print(type(1))
print(type(my_variable)) | <class 'bool'>
<class 'int'>
<class 'int'>
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
2. isinstance() function | print(isinstance(True, bool))
print(isinstance(False, int))
print(isinstance(1, int)) | True
True
True
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Comparing variables | print(1 == 1)
print(1 == 2)
print(1 != 2)
print(1 < 2)
print(1 > 2)
my_variable = None
print(my_variable == None)
print(my_variable is None)
my_variable = 1.5
print(my_variable == 1.5)
print(my_variable is 1.5)
print(my_variable is not None) | True
True
True
False
True
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Note as a general rule of thumb use "is" "is not" when checking if variable is **None**, **True** or **False** in all other cases use "==" Converting Int to Float and vs versa 1. float() function | float(3) | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
2. int() functionNote the int() conversion is taking in to account only the whole number int(2.9) = 2! | int(2.9) | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Tupletuple is a collection which is ordered and unchangeable. | my_tuple = (3, 8, 5, 7, 5) | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
access tuple items by index Note Python is 0 indensing language = it starts to count from 0!  | print(my_tuple[0])
print(my_tuple[2:4])
print(my_tuple[2:])
print(my_tuple[:2])
print(my_tuple[-1])
print(my_tuple[::3])
print(my_tuple[1::2])
print(my_tuple[::-1])
print(my_tuple[-2::]) | 3
(5, 7)
(5, 7, 5)
(3, 8)
5
(3, 7)
(8, 7)
(5, 7, 5, 8, 3)
(7, 5)
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Tuple methodsMethods are functions inside an object (every variable in Python is an object) 1.count() method - Counts number of occurrences of item in a tuple | my_tuple.count(6) | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
2.index() method - Returns the index of first occurence of an item in a tuple | my_tuple.index(5) | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Other operations with tuplesAdding tuples | my_tuple + (7, 2, 1) | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Nested tuples = tuples containing tuples | tuple_of_tuples = ((1,2,3),(3,4,5))
print(tuple_of_tuples)
print(tuple_of_tuples[0])
print(tuple_of_tuples[1][2]) | ((1, 2, 3), (3, 4, 5))
(1, 2, 3)
5
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
ListList is a collection which is ordered and changeable. | my_list = [3, 8, 5, 7, 5] | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Accesing list members is exactly the same as accesing tuple members, .count() and .index() methods work the same way with lists.The difference is that list members can be changed | my_list[1] = 9
print(my_list)
my_tuple[1] = 9 | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Lists are having more methods than tuples 1.count() method same as with tuple 2.index() methodsame as with tuple 3.reverse() methodinverting the list same as my_list[::-1] | my_list.reverse()
print(my_list)
my_list = my_list[::-1]
print(my_list) | [5, 7, 5, 9, 3]
[3, 9, 5, 7, 5]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
4.sort() methodsorting the list from smallest to largest or alphabetically in case of text | my_list.sort()
print(my_list)
my_list.sort(reverse=True)
print(my_list) | [3, 5, 5, 7, 9]
[9, 7, 5, 5, 3]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
5.clear() methodremoving everything from a list, equal to my_list = [] | my_list.clear()
print(my_list) | []
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
6.remove() methodRemoves the first item with the specified value | my_list = [3, 8, 5, 7, 5]
my_list.remove(7)
print(my_list) | [3, 8, 5, 5]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
7.pop() methodRemoves the element at the specified position | my_list.pop(0)
print(my_list) | [8, 5, 5]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
8.copy() methodReturns a copy of the list | my_list_copy = my_list.copy()
print(my_list_copy) | [3, 8, 5, 7, 5]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
what is the problem with my_other_list = my_list? | my_other_list = my_list
print(my_other_list)
my_list.pop(0)
print(my_list)
print(my_list_copy)
print(my_other_list)
my_other_list.pop(0)
print(my_list)
print(my_list_copy)
print(my_other_list) | [5, 7, 5]
[3, 8, 5, 7, 5]
[5, 7, 5]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
9.insert() methodAdds an element at the specified position, displacing the following members with 1 position | my_list = [3, 8, 5, 7, 5]
my_list.insert(3, 1)
print(my_list) | [3, 8, 5, 1, 7, 5]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
10.append() methodAdds an element at the end of the list | my_list.append(6)
print(my_list)
my_list.append([6,7])
print(my_list) | [3, 8, 5, 1, 7, 5, 6]
[3, 8, 5, 1, 7, 5, 6, [6, 7]]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
11.extend() methoddd the elements of a list (or any iterable), to the end of the current list | another_list = [2, 6, 8]
my_list.extend(another_list)
print(my_list)
another_tuple = (2, 6, 8)
my_list.extend(another_tuple)
print(my_list) | [3, 8, 5, 1, 7, 5, 6, 2, 6, 8, 2, 6, 8]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
End of first session SetSet is an unordered collection of unique objects. | my_set = {3, 8, 5, 7, 5}
print(my_set)
print(my_set) | {8, 3, 5, 7}
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Set methods.add() - Adds an element to the set.clear() - Removes all the elements from the set.copy() - Returns a copy of the set.difference() - Returns a set containing the difference between two or more sets.difference_update() - Removes the items in this set that are also included in another, specified set.discard() - Remove the specified item.intersection() - Returns a set, that is the intersection of two other sets.intersection_update() - Removes the items in this set that are not present in other, specified set(s).isdisjoint() - Returns whether two sets have a intersection or not.issubset() - Returns whether another set contains this set or not.issuperset() - Returns whether this set contains another set or not.pop() - Removes an element from the set.remove() - Removes the specified element.symmetric_difference() - Returns a set with the symmetric differences of two sets.symmetric_difference_update() - Inserts the symmetric differences from this set and another.union() - Return a set containing the union of sets.update() - Update the set with the union of this set and others | set_a = {1,2,3,4,5}
set_b = {4,5,6,7,8}
print(set_a.union(set_b))
print(set_a.intersection(set_b)) | {1, 2, 3, 4, 5, 6, 7, 8}
{4, 5}
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Converting tuple to list to set we can convert any tuple or set to list with **list()** functionwe can convert any list or set to tuple with **tuple()** functionwe can convert any tuple or list to set with **set()** function | my_list = [3, 8, 5, 7, 5]
print(my_list)
my_tuple = tuple(my_list)
print(my_tuple)
my_set =set(my_list)
print(my_set)
my_list2 = list(my_set)
print(my_list2) | [3, 8, 5, 7, 5]
(3, 8, 5, 7, 5)
{8, 3, 5, 7}
[8, 3, 5, 7]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
this functions can be nested | my_unique_list = list(set(my_list))
print(my_unique_list) | [8, 3, 5, 7]
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Checking if something is in a list, set, tuple | print(3 in my_set)
print(9 in my_set)
print(3 in my_list)
print(9 in my_tuple) | True
False
True
False
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Dictionarydictionary is a collection which is unordered, changeable and indexed as a key-value pair | my_dict = {1: 2.3,
2: 8.6}
print(my_dict[2])
print(my_dict[3])
print(my_dict.keys())
print(my_dict.values())
print(1 in my_dict.keys())
print(2.3 in my_dict.values())
print(my_dict.items()) | dict_keys([1, 2])
dict_values([2.3, 8.6])
True
True
dict_items([(1, 2.3), (2, 8.6)])
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Stringsstrings are ordered sequence of characters, strings are unchangable | print(my_dict.get(2))
my_string = 'this is string'
other_string = "this is string as well"
multilane_string = '''this is
a multi lane
string'''
print(my_string)
print(other_string)
print(multilane_string)
my_string = 'this "word" is in quotes'
my_other_string = "This is Maria's book"
print(my_string)
print(my_other_string)
my_string = "this \"word\" is in quotes"
my_other_string = 'This is Maria\'s book'
print(my_string)
print(my_other_string)
my_number = 9
my_string = '9'
print(my_number+1)
print(my_string+1)
print(my_string+'1')
print(int(my_string)+1)
print(my_number+int('1')) | 91
10
10
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Accesing list members is exactly the same as with lists and tuples | print(other_string)
print(other_string[0])
print(other_string[::-1]) | this is string as well
t
llew sa gnirts si siht
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
String methods.capitalize() - Converts the first character to upper case.casefold() - Converts string into lower case.center() - Returns a centered string.count() - Returns the number of times a specified value occurs in a string.encode() - Returns an encoded version of the string.endswith() - Returns true if the string ends with the specified value.expandtabs() - Sets the tab size of the string.find() - Searches the string for a specified value and returns the position of where it was found.format() - Formats specified values in a string.format_map() - Formats specified values in a string.index() - Searches the string for a specified value and returns the position of where it was found.isalnum() - Returns True if all characters in the string are alphanumeric.isalpha() - Returns True if all characters in the string are in the alphabet.isdecimal() - Returns True if all characters in the string are decimals.isdigit() - Returns True if all characters in the string are digits.isidentifier() - Returns True if the string is an identifier.islower() - Returns True if all characters in the string are lower case.isnumeric() - Returns True if all characters in the string are numeric.isprintable() - Returns True if all characters in the string are printable.isspace() - Returns True if all characters in the string are whitespaces.istitle() - Returns True if the string follows the rules of a title.isupper() - Returns True if all characters in the string are upper case.join() - Joins the elements of an iterable to the end of the string.ljust() - Returns a left justified version of the string.lower() - Converts a string into lower case.lstrip() - Returns a left trim version of the string.maketrans() - Returns a translation table to be used in translations.partition() - Returns a tuple where the string is parted into three parts.replace() - Returns a string where a specified value is replaced with a specified value.rfind() - Searches the string for a specified value and returns the last position of where it was found.rindex() - Searches the string for a specified value and returns the last position of where it was found.rjust() - Returns a right justified version of the string.rpartition() - Returns a tuple where the string is parted into three parts.rsplit() - Splits the string at the specified separator, and returns a list.rstrip() - Returns a right trim version of the string.split() - Splits the string at the specified separator, and returns a list.splitlines() - Splits the string at line breaks and returns a list.startswith() - Returns true if the string starts with the specified value.strip() - Returns a trimmed version of the string.swapcase() - Swaps cases, lower case becomes upper case and vice versa.title() - Converts the first character of each word to upper case.translate() - Returns a translated string.upper() - Converts a string into upper case.zfill() - Fills the string with a specified number of 0 values at the beginning | my_string = ' string with spaces '
print(my_string)
my_stripped_string = my_string.strip()
print(my_stripped_string)
print('ABC' == 'ABC')
print('ABC' == ' ABC ')
list_of_words = my_string.split()
print(list_of_words)
text = 'id1, id2, id3, id4'
ids_list = text.split(', ')
print(ids_list)
new_text = ' / '.join(ids_list)
print(new_text)
xml_text = 'this is <body>text</body> with xml tags'
xml_text.find('<body>')
xml_body = xml_text[xml_text.find('<body>')+len('<body>'):xml_text.find('</body>')]
print(xml_body) | text
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Other operations with stringscombinig (adding) strings | text = 'text1'+'text2'
print(text)
text = 'text1'*4
print(text) | text1text1text1text1
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
row and formated string | file_location = 'C:\Users\U6047694\Documents\job\Python_Projects\file.txt'
file_location = r'C:\Users\U6047694\Documents\job\Python_Projects\file.txt'
print(file_location)
var1 = 5
var2 = 6
print(f'Var1 is: {var1}, var2 is: {var2} and the sum is: {var1+var2}')
# this is the same as
print('Var1 is: '+str(var1)+', var2 is: '+str(var2)+' and the sum is: '+str(var1+var2)) | Var1 is: 5, var2 is: 6 and the sum is: 11
Var1 is: 5, var2 is: 6 and the sum is: 11
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Regular expressions in PythonThe regular expressions in python are stored in separate package **re** this package should be imported in order to access its functionality (methods). Methods in re package* re.search() - Check if given pattern is present anywhere in input string. Output is a re.Match object, usable in conditional expressions* re.fullmatch() - ensures pattern matches the entire input string* re.compile() - Compile a pattern for reuse, outputs re.Pattern object* re.sub() - search and replace* re.escape() - automatically escape all metacharacters* re.split() - split a string based on RE text matched by the groups will be part of the output* re.findall() - returns all the matches as a list* re.finditer() - iterator with re.Match object for each match* re.subn() - gives tuple of modified string and number of substitutions re characters'.' - Match any character except newline'^' - Match the start of the string'$' - Match the end of the string'*' - Match 0 or more repetitions'+' - Match 1 or more repetitions'?' - Match 0 or 1 repetitions re set of characters'[]' - Match a set of characters'[a-z]' - Match any lowercase ASCII letter'[lower-upper]' - Match a set of characters from lower to upper'[^]' - Match characters NOT in a setCheet Sheetre reference | text = 'this is a sample text for re testing'
t_words = re.findall('t[a-z]* ', text)
print(t_words)
new_text = re.sub('t[a-z]* ', 'replace ', text)
print(new_text) | ['this ', 'text ']
replace is a sample replace for re testing
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Conditions IF, ELIF, ELSE conditionsif condition sintacsis: | a = 3
if a == 2:
print('a is 2')
if a == 3:
print('a is 3')
else:
print('a is not 2')
if a == 2:
pring('a is 2')
elif a == 3:
print('a is 3')
else:
print('a is not 2 or 3')
if a == 2:
pring('a is 2')
if a == 3:
print('a is 3')
else:
print('a is not 2 or 3')
if a > 2:
print('a is bigger than 2')
if a < 4:
print('a is smaller than 4')
else:
print('a is something else')
if a > 2:
print('a is bigger than 2')
elif a < 4:
print('a is smaller than 4')
else:
print('a is something else') | a is bigger than 2
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
OR / AND in conditional statement | b = 4
if a > 2 or b < 2:
print(f'a is: {a} b is: {b}.') | a is: 3 b is: 4.
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Nested conditional statements | a = 2
if a == 2:
if b > a:
print('b is bigger than a')
else:
print('b is not bigger than a')
else:
print(f'a is {a}') | b is bigger than a
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Loops FOR loop | my_list = [1, 3, 5]
for item in my_list:
print(item) | 1
3
5
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
WHILE loop | a = 0
while a < 5:
a = a + 1 # or alternatively a += 1
print(a) | 1
2
3
4
5
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
You can put else statement in the while loop as well | a = 3
while a < 5:
a = a + 1 # or alternatively a += 1
print(a)
else:
print('This is the end!') | 4
5
This is the end!
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Loops can be nested as well | columns = ['A', 'B', 'C']
rows = [1, 2, 3]
for column in columns:
print(column)
for row in rows:
print(row) | A
1
2
3
B
1
2
3
C
1
2
3
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
Break and Continue. Break is stopping the loop, continue is skipping to the next item in the loop | for column in columns:
print(column)
if column == 'B':
break | A
B
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
If we have nested loops break will stop only the loop in which is used | columns = ['A', 'B', 'C']
rows = [1, 2, 3]
for column in columns:
print(column)
for row in rows:
print(row)
if row == 2:
break
i = 0
while i < 6:
i += 1
if i == 3:
continue
print(i) | 1
2
4
5
6
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
ObjectsEverything in Python is Object | class Player:
def __init__(self, name):
self.name = name
print(f'{self.name} is a Player')
def run(self):
return f'{self.name} is running'
player1 = Player('Messi')
player1.run() | Messi is a Player
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
InheritanceInheritance allows us to define a class that inherits all the methods and properties from another class.Parent class is the class being inherited from, also called base class.Child class is the class that inherits from another class, also called derived class. | class Futbol_player(Player):
def kick_ball(self):
return f'{self.name} is kicking the ball'
class Basketball_player(Player):
def catch_ball(self):
return f'{self.name} is catching the ball'
player2 = Futbol_player('Leo Messi')
player2.kick_ball()
player2.run()
player3 = Basketball_player('Pau Gasol')
player3.catch_ball()
player3.kick_ball()
class a_list(list):
def get_3_element(self):
return self[3]
my_list = ['a', 'b', 'c', 'd']
my_a_list = a_list(['a', 'b', 'c', 'd'])
my_a_list.get_3_element()
my_list.get_3_element()
my_a_list.count('a') | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
FunctionsA function is a block of code which only runs when it is called.You can pass data, known as arguments or parameters, into a function.A function can return data as a result or not. | def my_func(n):
'''this is power function'''
result = n*n
return result | _____no_output_____ | MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
You can assign the result of a function to another variable | power5 = my_func(5)
print(power5) | 25
| MIT | Python for beginners.ipynb | avkch/Python-for-beginners |
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