FreezeGun: Let your Python tests travel through time
FreezeGun is a library that allows your Python tests to travel through time by mocking the datetime module.
Once the decorator or context manager have been invoked, all calls to datetime.datetime.now(), datetime.datetime.utcnow(), datetime.date.today(), time.time(), time.localtime(), time.gmtime(), and time.strftime() will return the time that has been frozen. time.monotonic() and time.perf_counter() will also be frozen, but as usual it makes no guarantees about their absolute value, only their changes over time.
from freezegun import freeze_time import datetime import unittest # Freeze time for a pytest style test: @freeze_time("2012-01-14") def test(): assert datetime.datetime.now() == datetime.datetime(2012, 1, 14) # Or a unittest TestCase - freezes for every test, from the start of setUpClass to the end of tearDownClass @freeze_time("1955-11-12") class MyTests(unittest.TestCase): def test_the_class(self): assert datetime.datetime.now() == datetime.datetime(1955, 11, 12) # Or any other class - freezes around each callable (may not work in every case) @freeze_time("2012-01-14") class Tester(object): def test_the_class(self): assert datetime.datetime.now() == datetime.datetime(2012, 1, 14) # Or method decorator, might also pass frozen time object as kwarg class TestUnitTestMethodDecorator(unittest.TestCase): @freeze_time('2013-04-09') def test_method_decorator_works_on_unittest(self): self.assertEqual(datetime.date(2013, 4, 9), datetime.date.today()) @freeze_time('2013-04-09', as_kwarg='frozen_time') def test_method_decorator_works_on_unittest(self, frozen_time): self.assertEqual(datetime.date(2013, 4, 9), datetime.date.today()) self.assertEqual(datetime.date(2013, 4, 9), frozen_time.time_to_freeze.today()) @freeze_time('2013-04-09', as_kwarg='hello') def test_method_decorator_works_on_unittest(self, **kwargs): self.assertEqual(datetime.date(2013, 4, 9), datetime.date.today()) self.assertEqual(datetime.date(2013, 4, 9), kwargs.get('hello').time_to_freeze.today())
from freezegun import freeze_time def test(): assert datetime.datetime.now() != datetime.datetime(2012, 1, 14) with freeze_time("2012-01-14"): assert datetime.datetime.now() == datetime.datetime(2012, 1, 14) assert datetime.datetime.now() != datetime.datetime(2012, 1, 14)
from freezegun import freeze_time freezer = freeze_time("2012-01-14 12:00:01") freezer.start() assert datetime.datetime.now() == datetime.datetime(2012, 1, 14, 12, 0, 1) freezer.stop()
from freezegun import freeze_time @freeze_time("2012-01-14 03:21:34", tz_offset=-4) def test(): assert datetime.datetime.utcnow() == datetime.datetime(2012, 1, 14, 3, 21, 34) assert datetime.datetime.now() == datetime.datetime(2012, 1, 13, 23, 21, 34) # datetime.date.today() uses local time assert datetime.date.today() == datetime.date(2012, 1, 13) @freeze_time("2012-01-14 03:21:34", tz_offset=-datetime.timedelta(hours=3, minutes=30)) def test_timedelta_offset(): assert datetime.datetime.now() == datetime.datetime(2012, 1, 13, 23, 51, 34)
FreezeGun uses dateutil behind the scenes so you can have nice-looking datetimes.
@freeze_time("Jan 14th, 2012") def test_nice_datetime(): assert datetime.datetime.now() == datetime.datetime(2012, 1, 14)
FreezeGun is able to handle function and generator objects.
def test_lambda(): with freeze_time(lambda: datetime.datetime(2012, 1, 14)): assert datetime.datetime.now() == datetime.datetime(2012, 1, 14) def test_generator(): datetimes = (datetime.datetime(year, 1, 1) for year in range(2010, 2012)) with freeze_time(datetimes): assert datetime.datetime.now() == datetime.datetime(2010, 1, 1) with freeze_time(datetimes): assert datetime.datetime.now() == datetime.datetime(2011, 1, 1) # The next call to freeze_time(datetimes) would raise a StopIteration exception.
FreezeGun has an additional
tick argument which will restart time at the given value, but then time will keep ticking. This is alternative to the default parameters which will keep time stopped.
@freeze_time("Jan 14th, 2020", tick=True) def test_nice_datetime(): assert datetime.datetime.now() > datetime.datetime(2020, 1, 14)
FreezeGun has an additional
auto_tick_seconds argument which will autoincrement the value every time by the given amount from the start value. This is alternative to the default parameters which will keep time stopped. Note that given
tick parameter will be ignored.
@freeze_time("Jan 14th, 2020", auto_tick_seconds=15) def test_nice_datetime(): first_time = datetime.datetime.now() auto_incremented_time = datetime.datetime.now() assert first_time + datetime.timedelta(seconds=15) == auto_incremented_time
FreezeGun allows for the time to be manually forwarded as well.
def test_manual_tick(): initial_datetime = datetime.datetime(year=1, month=7, day=12, hour=15, minute=6, second=3) with freeze_time(initial_datetime) as frozen_datetime: assert frozen_datetime() == initial_datetime frozen_datetime.tick() initial_datetime += datetime.timedelta(seconds=1) assert frozen_datetime() == initial_datetime frozen_datetime.tick(delta=datetime.timedelta(seconds=10)) initial_datetime += datetime.timedelta(seconds=10) assert frozen_datetime() == initial_datetime
def test_monotonic_manual_tick(): initial_datetime = datetime.datetime(year=1, month=7, day=12, hour=15, minute=6, second=3) with freeze_time(initial_datetime) as frozen_datetime: monotonic_t0 = time.monotonic() frozen_datetime.tick(1.0) monotonic_t1 = time.monotonic() assert monotonic_t1 == monotonic_t0 + 1.0
FreezeGun allows moving time to specific dates.
def test_move_to(): initial_datetime = datetime.datetime(year=1, month=7, day=12, hour=15, minute=6, second=3) other_datetime = datetime.datetime(year=2, month=8, day=13, hour=14, minute=5, second=0) with freeze_time(initial_datetime) as frozen_datetime: assert frozen_datetime() == initial_datetime frozen_datetime.move_to(other_datetime) assert frozen_datetime() == other_datetime frozen_datetime.move_to(initial_datetime) assert frozen_datetime() == initial_datetime @freeze_time("2012-01-14", as_arg=True) def test(frozen_time): assert datetime.datetime.now() == datetime.datetime(2012, 1, 14) frozen_time.move_to("2014-02-12") assert datetime.datetime.now() == datetime.datetime(2014, 2, 12)
move_to can be any valid
freeze_time date (string, date, datetime).
Note that FreezeGun will not modify default arguments. The following code will print the current date. See here for why.
from freezegun import freeze_time import datetime as dt def test(default=dt.date.today()): print(default) with freeze_time('2000-1-1'): test()
To install FreezeGun, simply:
$ pip install freezegun
On Debian systems:
$ sudo apt-get install python-freezegun
Sometimes it's desired to ignore FreezeGun behaviour for particular packages (i.e. libraries). It's possible to ignore them for a single invocation:
from freezegun import freeze_time with freeze_time('2020-10-06', ignore=['threading']): # ...
By default FreezeGun ignores following packages:
[ 'nose.plugins', 'six.moves', 'django.utils.six.moves', 'google.gax', 'threading', 'Queue', 'selenium', '_pytest.terminal.', '_pytest.runner.', 'gi', ]
It's possible to set your own default ignore list:
import freezegun freezegun.configure(default_ignore_list=['threading', 'tensorflow'])
Please note this will override default ignore list. If you want to extend existing defaults please use:
import freezegun freezegun.configure(extend_ignore_list=['tensorflow'])
Source Code: https://github.com/spulec/freezegun
License: Apache-2.0 License
In this article, learn about Machine Learning Tutorial: A Practical Guide of Unsupervised Learning Algorithms. Machine learning is a fast-growing technology that allows computers to learn from the past and predict the future. It uses numerous algorithms for building mathematical models and predicting future trends. Machine learning (ML) has widespread applications in the industry, including speech recognition, image recognition, churn prediction, email filtering, chatbot development, recommender systems, and much more.
Machine learning (ML) can be classified into three main categories; supervised, unsupervised, and reinforcement learning. In supervised learning, the model is trained on labeled data. While in unsupervised learning, unlabeled data is provided to the model to predict the outcomes. Reinforcement learning is feedback learning in which the agent collects a reward for each correct action and gets a penalty for a wrong decision. The goal of the learning agent is to get maximum reward points and deduce the error.
In unsupervised learning, the model learns from unlabeled data without proper supervision.
Unsupervised learning uses machine learning techniques to cluster unlabeled data based on similarities and differences. The unsupervised algorithms discover hidden patterns in data without human supervision. Unsupervised learning aims to arrange the raw data into new features or groups together with similar patterns of data.
For instance, to predict the churn rate, we provide unlabeled data to our model for prediction. There is no information given that customers have churned or not. The model will analyze the data and find hidden patterns to categorize into two clusters: churned and non-churned customers.
Unsupervised algorithms can be used for three tasks—clustering, dimensionality reduction, and association. Below, we will highlight some commonly used clustering and association algorithms.
Clustering, or cluster analysis, is a popular data mining technique for unsupervised learning. The clustering approach works to group non-labeled data based on similarities and differences. Unlike supervised learning, clustering algorithms discover natural groupings in data.
A good clustering method produces high-quality clusters having high intra-class similarity (similar data within a cluster) and less intra-class similarity (cluster data is dissimilar to other clusters).
It can be defined as the grouping of data points into various clusters containing similar data points. The same objects remain in the group that has fewer similarities with other groups. Here, we will discuss two popular clustering techniques: K-Means clustering and DBScan Clustering.
K-Means is the simplest unsupervised technique used to solve clustering problems. It groups the unlabeled data into various clusters. The K value defines the number of clusters you need to tell the system how many to create.
K-Means is a centroid-based algorithm in which each cluster is associated with the centroid. The goal is to minimize the sum of the distances between the data points and their corresponding clusters.
It is an iterative approach that breaks down the unlabeled data into different clusters so that each data point belongs to a group with similar characteristics.
K-means clustering performs two tasks:
An illustration of K-means clustering. Image source
“DBScan” stands for “Density-based spatial clustering of applications with noise.” There are three main words in DBscan: density, clustering, and noise. Therefore, this algorithm uses the notion of density-based clustering to form clusters and detect the noise.
Clusters are usually dense regions that are separated by lower density regions. Unlike the k-means algorithm, which works only on well-separated clusters, DBscan has a wider scope and can create clusters within the cluster. It discovers clusters of various shapes and sizes from a large set of data, which consists of noise and outliers.
There are two parameters in the DBScan algorithm:
minPts: The threshold, or the minimum number of points grouped together for a region considered as a dense region.
eps(ε): The distance measure used to locate the points in the neighborhood.
An illustration of density-based clustering. Image Source
An association rule mining is a popular data mining technique. It finds interesting correlations in large numbers of data items. This rule shows how frequently items occur in a transaction.
Market Basket Analysis is a typical example of an association rule mining that finds relationships between items in the grocery store. It enables retailers to identify and analyze the associations between items that people frequently buy.
Important terminology used in association rules:
Support: It tells us about the combination of items bought frequently or frequently bought items.
Confidence: It tells us how often the items A and B occur together, given the number of times A occurs.
Lift: The lift indicates the strength of a rule over the random occurrence of A and B. For instance, A->B, the life value is 5. It means that if you buy A, the occurrence of buying B is five times.
The Apriori algorithm is a well-known association rule based technique.
The Apriori algorithm was proposed by R. Agarwal and R. Srikant in 1994 to find the frequent items in the dataset. The algorithm’s name is based on the fact that it uses prior knowledge of frequently occurring things.
The Apriori algorithm finds frequently occurring items with minimum support.
It consists of two steps:
In this tutorial, you will learn about the implementation of various unsupervised algorithms in Python. Scikit-learn is a powerful Python library widely used for various unsupervised learning tasks. It is an open-source library that provides numerous robust algorithms, which include classification, dimensionality reduction, clustering techniques, and association rules.
Now let’s dive deep into the implementation of the K-Means algorithm in Python. We’ll break down each code snippet so that you can understand it easily.
First of all, we will import the required libraries and get access to the functions.
#Let's import the required libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns
The dataset is taken from the kaggle website. You can easily download it from the given link. To load the dataset, we use the pd.read_csv() function. head() returns the first five rows of the dataset.
my_data = pd.read_csv('Customers_Mall.csv.') my_data.head()
The dataset contains five columns: customer ID, gender, age, annual income in (K$), and spending score from 1-100.
The info() function is used to get quick information about the dataset. It shows the number of entries, columns, total non-null values, memory usage, and datatypes.
To check the missing values in the dataset, we use isnull().sum(), which returns the total number of null values.
#Check missing values my_data.isnull().sum()
The box plot or whisker plot is used to detect outliers in the dataset. It also shows a statistical five number summary, which includes the minimum, first quartile, median (2nd quartile), third quartile, and maximum.
Using Box Plot, we’ve detected an outlier in the annual income column. Now we will try to remove it before training our model.
#let's remove outlier from data med =61 my_data["Annual Income (k$)"] = np.where(my_data["Annual Income (k$)"] > 120,med,my_data['Annual Income (k$)'])
The outlier in the annual income column has been removed now to confirm we used the box plot again.
A histogram is used to illustrate the important features of the distribution of data. The hist() function is used to show the distribution of data in each numerical column.
The correlation heatmap is used to find the potential relationships between variables in the data and to display the strength of those relationships. To display the heatmap, we have used the seaborn plotting library.
plt.figure(figsize=(10,6)) sns.heatmap(my_data.corr(), annot=True, cmap='icefire').set_title('seaborn') plt.show()
The iloc() function is used to select a particular cell of the data. It enables us to select a value that belongs to a specific row or column. Here, we’ve chosen the annual income and spending score columns.
X_val = my_data.iloc[:, 3:].values X_val
# Loading Kmeans Library from sklearn.cluster import KMeans
Now we will select the best value for K using the Elbow’s method. It is used to determine the optimal number of clusters in K-means clustering.
my_val =  for i in range(1,11): kmeans = KMeans(n_clusters = i, init='k-means++', random_state = 123) kmeans.fit(X_val) my_val.append(kmeans.inertia_)
The sklearn.cluster.KMeans() is used to choose the number of clusters along with the initialization of other parameters. To display the result, just call the variable.
my_val #Visualization of clusters using elbow’s method plt.plot(range(1,11),my_val) plt.xlabel('The No of clusters') plt.ylabel('Outcome') plt.title('The Elbow Method') plt.show()
Through Elbow’s Method, when the graph looks like an arm, then the elbow on the arm is the best value of K. In this case, we’ve taken K=3, which is the optimal value for K.
kmeans = KMeans(n_clusters = 3, init='k-means++') kmeans.fit(X_val) #To show centroids of clusters kmeans.cluster_centers_ #Prediction of K-Means clustering y_kmeans = kmeans.fit_predict(X_val) y_kmeans
The scatter graph is used to plot the classification results of our dataset into three clusters.
plt.scatter(X_val[y_kmeans == 0,0], X_val[y_kmeans == 0,1], c='red',s=100) plt.scatter(X_val[y_kmeans == 1,0], X_val[y_kmeans == 1,1], c='green',s=100) plt.scatter(X_val[y_kmeans == 2,0], X_val[y_kmeans == 2,1], c='orange',s=100) plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=300, c='brown') plt.title('K-Means Unsupervised Learning') plt.show()
To implement the apriori algorithm, we will utilize “The Bread Basket” dataset. The dataset is available on Kaggle and you can download it from the link. This algorithm suggests products based on the user’s purchase history. Walmart has greatly utilized the algorithm to recommend relevant items to its users.
Let’s implement the Apriori algorithm in Python.
To implement the algorithm, we need to import some important libraries.
import pandas as pd import matplotlib.pyplot as plt import numpy as np import seaborn as sns
The dataset contains five columns and 20507 entries. The data_time is a prominent column and we can extract many vital insights from it.
my_data= pd.read_csv("bread basket.csv") my_data.head()
Convert the data_time into an appropriate format.
my_data['date_time'] = pd.to_datetime(my_data['date_time']) #Total No of unique customers my_data['Transaction'].nunique()
Now we want to extract new columns from the data_time to extract meaningful information from the data.
#Let's extract date my_data['date'] = my_data['date_time'].dt.date #Let's extract time my_data['time'] = my_data['date_time'].dt.time #Extract month and replacing it with String my_data['month'] = my_data['date_time'].dt.month my_data['month'] = my_data['month'].replace((1,2,3,4,5,6,7,8,9,10,11,12), ('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug', 'Sep','Oct','Nov','Dec'))
my_data[‘hour’] = my_data[‘date_time’].dt.hour
# Replacing hours with text
# Replacing hours with text
hr_num = (1,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23)
hr_obj = (‘1-2′,’7-8′,’8-9′,’9-10′,’10-11′,’11-12′,’12-13′,’13-14′,’14-15’,
my_data[‘hour’] = my_data[‘hour’].replace(hr_num, hr_obj)
# Extracting weekday and replacing it with String
my_data[‘weekday’] = my_data[‘date_time’].dt.weekday
my_data[‘weekday’] = my_data[‘weekday’].replace((0,1,2,3,4,5,6),
#Now drop date_time column
my_data.drop(‘date_time’, axis = 1, inplace = True)
After extracting the date, time, month, and hour columns, we dropped the data_time column.
Now to display, we simply use the head() function to see the changes in the dataset.
# cleaning the item column
my_data[‘Item’] = my_data[‘Item’].str.strip()
my_data[‘Item’] = my_data[‘Item’].str.lower()
To display the top 10 items purchased by customers, we used a barplot() of the seaborn library.
plt.figure(figsize=(10,5)) sns.barplot(x=my_data.Item.value_counts().head(10).index, y=my_data.Item.value_counts().head(10).values,palette='RdYlGn') plt.xlabel('No of Items', size = 17) plt.xticks(rotation=45) plt.ylabel('Total Items', size = 18) plt.title('Top 10 Items purchased', color = 'blue', size = 23) plt.show()
From the graph, coffee is the top item purchased by the customers, followed by bread.
Now, to display the number of orders received each month, the groupby() function is used along with barplot() to visually show the results.
mon_Tran =my_data.groupby('month')['Transaction'].count().reset_index() mon_Tran.loc[:,"mon_order"] =[4,8,12,2,1,7,6,3,5,11,10,9] mon_Tran.sort_values("mon_order",inplace=True) plt.figure(figsize=(12,5)) sns.barplot(data = mon_Tran, x = "month", y = "Transaction") plt.xlabel('Months', size = 14) plt.ylabel('Monthly Orders', size = 14) plt.title('No of orders received each month', color = 'blue', size = 18) plt.show()
To show the number of orders received each day, we applied groupby() to the weekday column.
wk_Tran = my_data.groupby('weekday')['Transaction'].count().reset_index() wk_Tran.loc[:,"wk_ord"] = [4,0,5,6,3,1,2] wk_Tran.sort_values("wk_ord",inplace=True) plt.figure(figsize=(11,4)) sns.barplot(data = wk_Tran, x = "weekday", y = "Transaction",palette='RdYlGn') plt.xlabel('Week Day', size = 14) plt.ylabel('Per day orders', size = 14) plt.title('Orders received per day', color = 'blue', size = 18) plt.show()
We import the mlxtend library to implement the association rules and count the number of items.
from mlxtend.frequent_patterns import association_rules, apriori tran_str= my_data.groupby(['Transaction', 'Item'])['Item'].count().reset_index(name ='Count') tran_str.head(8)
Now we’ll make a mxn matrix where m=transaction and n=items, and each row represents whether the item was in the transaction or not.
Mar_baskt = tran_str.pivot_table(index='Transaction', columns='Item', values='Count', aggfunc='sum').fillna(0) Mar_baskt.head()
We want to make a function that returns 0 and 1. 0 means that the item wasn’t present in the transaction, while 1 means the item exists.
def encode(val): if val<=0: return 0 if val>=1: return 1 #Let's apply the function to the dataset Basket=Mar_baskt.applymap(encode) Basket.head()
#using apriori algorithm to set min_support 0.01 means 1% freq_items = apriori(Basket, min_support = 0.01,use_colnames = True) freq_items.head()
Using the association_rules() function to generate the most frequent items from the dataset.
App_rule= association_rules(freq_items, metric = "lift", min_threshold = 1) App_rule.sort_values('confidence', ascending = False, inplace = True) App_rule.head()
From the above implementation, the most frequent items are coffee and toast, both having a lift value of 1.47 and a confidence value of 0.70.
Principal component analysis (PCA) is one of the most widely used unsupervised learning techniques. It can be used for various tasks, including dimensionality reduction, information compression, exploratory data analysis and Data de-noising.
Let’s use the PCA algorithm!
First we import the required libraries to implement this algorithm.
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline from sklearn.decomposition import PCA from sklearn.datasets import load_digits
To implement the PCA algorithm the load_digits dataset of Scikit-learn is used which can easily be loaded using the below command. The dataset contains images data which include 1797 entries and 64 columns.
#Load the dataset my_data= load_digits() #Creating features X_value = my_data.data #Creating target #Let's check the shape of X_value X_value.shape
#Each image is 8x8 pixels therefore 64px my_data.images #Let's display the image plt.gray() plt.matshow(my_data.images) plt.show()
Now let’s project data from 64 columns to 16 to show how 16 dimensions classify the data.
X_val = my_data.data y_val = my_data.target my_pca = PCA(16) X_projection = my_pca.fit_transform(X_val) print(X_val.shape) print(X_projection.shape)
Using colormap we visualize that with only ten dimensions we can classify the data points. Now we’ll select the optimal number of dimensions (principal components) by which data can be reduced into lower dimensions.
plt.scatter(X_projection[:, 0], X_projection[:, 1], c=y_val, edgecolor='white', cmap=plt.cm.get_cmap("gist_heat",12)) plt.colorbar();
pca=PCA().fit(X_val) plt.plot(np.cumsum(my_pca.explained_variance_ratio_)) plt.xlabel('Principal components') plt.ylabel('Explained variance') Based on the below graph, only 12 components are required to explain more than 80% of the variance which is still better than computing all the 64 features. Thus, we’ve reduced the large number of dimensions into 12 dimensions to avoid the dimensionality curse. pca=PCA().fit(X_val) plt.plot(np.cumsum(pca.explained_variance_ratio_)) plt.xlabel('Principal components') plt.ylabel('Explained variance') #Let's visualize how it looks like Unsupervised_pca = PCA(12) X_pro = Unsupervised_pca.fit_transform(X_val) print("New Data Shape is =>",X_pro.shape) #Let's Create a scatter plot plt.scatter(X_pro[:, 0], X_pro[:, 1], c=y_val, edgecolor='white', cmap=plt.cm.get_cmap("nipy_spectral",10)) plt.colorbar();
In this machine learning tutorial, we’ve implemented the Kmeans, Apriori, and PCA algorithms. These are some of the most widely used algorithms, having numerous industrial applications and solve many real world problems. For instance, K-means clustering is used in astronomy to study stellar and galaxy spectra, solar polarization spectra, and X-ray spectra. And, Apriori is used by retail stores to optimize their product inventory.
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The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
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Recently, after writing a microservice to manage Create-Read-Update-Delete (CRUD) operations for a MongoDB database, it logically was time to write unit test cases. As I had developed the microservice using Python and Flask, the unit test also had to be in Python. For unit testing, I used the unittest package.
Writing Unit Tests
By writing unit tests, not only was I ensuring that I could run the same tests over and over again but, assuming that the underlying functionality of the micro-service had not broken, I could expect the same results for each execution. Moreover, as the number of REST functions increased, the unit tests would help me ensure that none of the existing functionality was broken. Additionally, the unit tests would help me reduce the total amount of time I needed to spend on testing the same functionality over and over again.
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Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu" >>> LastName = "Jordan" >>> FirstName, LastName = LastName, FirstName >>> print(FirstName, LastName) ('Jordan', 'kalebu')
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Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.
In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.
Heres a solution
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
But How do we do it?
If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?
The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.
There’s a variety of hashing algorithms out there such as
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