The article contains some of the most commonly used advanced statistical concepts along with their Python implementation.In my previous articles Beginners Guide to Statistics in Data Science and The Inferential Statistics Data Scientists Should Know we have talked about almost all the basics(Descriptive and Inferential) of statistics which are commonly used in understanding and working with any data science case study. In this article, lets go a little beyond and talk about some advance concepts which are not part of the buzz.Concept #1 - Q-Q(quantile-quantile) PlotsBefore understanding QQ plots first understand what is a Quantile?A quantile defines a particular part of a data set, i.e. a quantile determines how many values in a distribution are above or below a certain limit. Special quantiles are the quartile (quarter), the quintile (fifth), and percentiles (hundredth).An example:If we divide a distribution into four equal portions, we will speak of four quartiles. The first quartile includes all values that are smaller than a quarter of all values. In a graphical representation, it corresponds to 25% of the total area of distribution. The two lower quartiles comprise 50% of all distribution values. The interquartile range between the first and third quartile equals the range in which 50% of all values lie that are distributed around the mean. In Statistics, A Q-Q(quantile-quantile) plot is a scatterplot created by plotting two sets of quantiles against one another. If both sets of quantiles came from the same distribution, we should see the points forming a line that’s roughly straight(y=x).Q-Q plotFor example, the median is a quantile where 50% of the data fall below that point and 50% lie above it. The purpose of Q Q plots is to find out if two sets of data come from the same distribution. A 45-degree angle is plotted on the Q Q plot; if the two data sets come from a common distribution, the points will fall on that reference line.It’s very important for you to know whether the distribution is normal or not so as to apply various statistical measures on the data and interpret it in much more human-understandable visualization and their Q-Q plot comes into the picture. The most fundamental question answered by the Q-Q plot is if the curve is Normally Distributed or not.Normally distributed, but why?The Q-Q plots are used to find the type of distribution for a random variable whether it is a Gaussian Distribution, Uniform Distribution, Exponential Distribution, or even Pareto Distribution, etc. You can tell the type of distribution using the power of the Q-Q plot just by looking at the plot. In general, we are talking about Normal distributions only because we have a very beautiful concept of the 68–95–99.7 rule which perfectly fits into the normal distribution So we know how much of the data lies in the range of the first standard deviation, second standard deviation and third standard deviation from the mean. So knowing if a distribution is Normal opens up new doors for us to experiment with Types of Q-Q plots. Source Skewed Q-Q plotsQ-Q plots can find skewness(measure of asymmetry) of the distribution. If the bottom end of the Q-Q plot deviates from the straight line but the upper end is not, then the distribution is Left skewed(Negatively skewed).Now if upper end of the Q-Q plot deviates from the staright line and the lower is not, then the distribution is Right skewed(Positively skewed).Tailed Q-Q plotsQ-Q plots can find Kurtosis(measure of tailedness) of the distribution.The distribution with the fat tail will have both the ends of the Q-Q plot to deviate from the straight line and its centre follows the line, where as a thin tailed distribution will term Q-Q plot with very less or negligible deviation at the ends thus making it a perfect fit for normal distribution.Q-Q plots in Python(Source)Suppose we have the following dataset of 100 values:import numpy as np #create dataset with 100 values that follow a normal distribution np.random.seed(0) data = np.random.normal(0,1, 1000) #view first 10 values data[:10] array([ 1.76405235, 0.40015721, 0.97873798, 2.2408932 , 1.86755799, -0.97727788, 0.95008842, -0.15135721, -0.10321885, 0.4105985 ])To create a Q-Q plot for this dataset, we can use the qqplot() function from the statsmodels library:import statsmodels.api as sm import matplotlib.pyplot as plt #create Q-Q plot with 45-degree line added to plot fig = sm.qqplot(data, line='45') plt.show()In a Q-Q plot, the x-axis displays the theoretical quantiles. This means it doesn’t show your actual data, but instead, it represents where your data would be if it were normally distributed.The y-axis displays your actual data. This means that if the data values fall along a roughly straight line at a 45-degree angle, then the data is normally distributed.We can see in our Q-Q plot above that the data values tend to closely follow the 45-degree, which means the data is likely normally distributed. This shouldn’t be surprising since we generated the 100 data values by using the numpy.random.normal() function.Consider instead if we generated a dataset of 100 uniformly distributed values and created a Q-Q plot for that dataset:#create dataset of 100 uniformally distributed values data = np.random.uniform(0,1, 1000) #generate Q-Q plot for the dataset fig = sm.qqplot(data, line='45') plt.show()The data values clearly do not follow the red 45-degree line, which is an indication that they do not follow a normal distribution.Concept #2- Chebyshev's InequalityIn probability, Chebyshev’s Inequality, also known as “Bienayme-Chebyshev” Inequality guarantees that, for a wide class of probability distributions, only a definite fraction of values will be found within a specific distance from the mean of a distribution.Source: https://www.thoughtco.com/chebyshevs-inequality-3126547 Chebyshev’s inequality is similar to The Empirical rule(68-95-99.7); however, the latter rule only applies to normal distributions. Chebyshev’s inequality is broader; it can be applied to any distribution so long as the distribution includes a defined variance and mean.So Chebyshev’s inequality says that at least (1-1/k^2) of data from a sample must fall within K standard deviations from the mean (or equivalently, no more than 1/k^2 of the distribution’s values can be more than k standard deviations away from the mean).Where K --> Positive real numberIf the data is not normally distributed then different amounts of data could be in one standard deviation. Chebyshev’s inequality provides a way to know what fraction of data falls within K standard deviations from the mean for any data distribution.Also read: 22 Statistics Questions to Prepare for Data Science InterviewsCredits: https://calcworkshop.com/joint-probability-distribution/chebyshev-inequality/ Chebyshev’s inequality is of great value because it can be applied to any probability distribution in which the mean and variance are provided.Let us consider an example, Assume 1,000 contestants show up for a job interview, but there are only 70 positions available. In order to select the finest 70 contestants amongst the total contestants, the proprietor gives tests to judge their potential. The mean score on the test is 60, with a standard deviation of 6. If an applicant scores an 84, can they presume that they are getting the job?The results show that about 63 people scored above a 60, so with 70 positions available, a contestant who scores an 84 can be assured they got the job.Chebyshev's Inequality in Python(Source) Create a population of 1,000,000 values, I use a gamma distribution(also works with other distributions) with shape = 2 and scale = 2.import numpy as np import random import matplotlib.pyplot as plt #create a population with a gamma distribution shape, scale = 2., 2. #mean=4, std=2*sqrt(2) mu = shape*scale #mean and standard deviation sigma = scale*np.sqrt(shape) s = np.random.gamma(shape, scale, 1000000)Now sample 10,000 values from the population.#sample 10000 values rs = random.choices(s, k=10000)Count the sample that has a distance from the expected value larger than k standard deviation and use the count to calculate the probabilities. I want to depict a trend of probabilities when k is increasing, so I use a range of k from 0.1 to 3.#set k ks = [0.1,0.5,1.0,1.5,2.0,2.5,3.0] #probability list probs = [] #for each k for k in ks: #start count c = 0 for i in rs: # count if far from mean in k standard deviation if abs(i - mu) > k * sigma : c += 1 probs.append(c/10000)Plot the results:plot = plt.figure(figsize=(20,10)) #plot each probability plt.xlabel('K') plt.ylabel('probability') plt.plot(ks,probs, marker='o') plot.show() #print each probability print("Probability of a sample far from mean more than k standard deviation:") for i, prob in enumerate(probs): print("k:" + str(ks[i]) + ", probability: " \ + str(prob)[0:5] + \ " | in theory, probability should less than: " \ + str(1/ks[i]**2)[0:5])From the above plot and result, we can see that as the k increases, the probability is decreasing, and the probability of each k follows the inequality. Moreover, only the case that k is larger than 1 is useful. If k is less than 1, the right side of the inequality is larger than 1 which is not useful because the probability cannot be larger than 1.Concept #3- Log-Normal DistributionIn probability theory, a Log-normal distribution also known as Galton's distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed.Thus, if the random variable X is log-normally distributed, then Y = ln(X) has a normal distribution. Equivalently, if Y has a normal distribution, then the exponential function of Y i.e, X = exp(Y), has a log-normal distribution. Skewed distributions with low mean and high variance and all positive values fit under this type of distribution. A random variable that is log-normally distributed takes only positive real values. The general formula for the probability density function of the lognormal distribution is:The location and scale parameters are equivalent to the mean and standard deviation of the logarithm of the random variable.The shape of Lognormal distribution is defined by 3 parameters:σis the shape parameter, (and is the standard deviation of the log of the distribution)θ or μ is the location parameter (and is the mean of the distribution)m is the scale parameter (and is also the median of the distribution)The location and scale parameters are equivalent to the mean and standard deviation of the logarithm of the random variable as explained above.If x = θ, then f(x) = 0. The case where θ = 0 and m = 1 is called the standard lognormal distribution. The case where θ equals zero is called the 2-parameter lognormal distribution.The following graph illustrates the effect of the location(μ) and scale(σ) parameter on the probability density function of the lognormal distribution: Source: https://www.sciencedirect.com/topics/mathematics/lognormal-distribution Log-Normal Distribution in Python(Source)Let us consider an example to generate random numbers from a log-normal distribution with μ=1 and σ=0.5 using scipy.stats.lognorm function.import numpy as np import matplotlib.pyplot as plt from scipy.stats import lognorm np.random.seed(42) data = lognorm.rvs(s=0.5, loc=1, scale=1000, size=1000) plt.figure(figsize=(10,6)) ax = plt.subplot(111) plt.title('Generate wrandom numbers from a Log-normal distribution') ax.hist(data, bins=np.logspace(0,5,200), density=True) ax.set_xscale("log") shape,loc,scale = lognorm.fit(data) x = np.logspace(0, 5, 200) pdf = lognorm.pdf(x, shape, loc, scale) ax.plot(x, pdf, 'y') plt.show()Concept #4- Power Law distributionIn statistics, a Power Law is a functional relationship between two quantities, where a relative change in one quantity results in a proportional relative change in the other quantity, independent of the initial size of those quantities: one quantity varies as a power of another. For instance, considering the area of a square in terms of the length of its side, if the length is doubled, the area is multiplied by a factor of four.A power law distribution has the form Y = k Xα, where:X and Y are variables of interest,α is the law’s exponent,k is a constant.Source: https://en.wikipedia.org/wiki/Power_law Power-law distribution is just one of many probability distributions, but it is considered a valuable tool to assess uncertainty issues that normal distribution cannot handle when they occur at a certain probability.Many processes have been found to follow power laws over substantial ranges of values. From the distribution in incomes, size of meteoroids, earthquake magnitudes, the spectral density of weight matrices in deep neural networks, word usage, number of neighbors in various networks, etc. (Note: The power law here is a continuous distribution. The last two examples are discrete, but on a large scale can be modeled as if continuous).Also read: Statistical Measures of Central TendencyPower-law distribution in Python(Source) Let us plot the Pareto distribution which is one form of a power-law probability distribution. Pareto distribution is sometimes known as the Pareto Principle or ‘80–20’ rule, as the rule states that 80% of society’s wealth is held by 20% of its population. Pareto distribution is not a law of nature, but an observation. It is useful in many real-world problems. It is a skewed heavily tailed distribution.import numpy as np import matplotlib.pyplot as plt from scipy.stats import pareto x_m = 1 #scale alpha = [1, 2, 3] #list of values of shape parameters plt.figure(figsize=(10,6)) samples = np.linspace(start=0, stop=5, num=1000) for a in alpha: output = np.array([pareto.pdf(x=samples, b=a, loc=0, scale=x_m)]) plt.plot(samples, output.T, label='alpha {0}' .format(a)) plt.xlabel('samples', fontsize=15) plt.ylabel('PDF', fontsize=15) plt.title('Probability Density function', fontsize=15) plt.legend(loc='best') plt.show()Concept #5- Box cox transformationThe Box-Cox transformation transforms our data so that it closely resembles a normal distribution.The one-parameter Box-Cox transformations are defined as In many statistical techniques, we assume that the errors are normally distributed. This assumption allows us to construct confidence intervals and conduct hypothesis tests. By transforming your target variable, we can (hopefully) normalize our errors (if they are not already normal).Additionally, transforming our variables can improve the predictive power of our models because transformations can cut away white noise.Original distribution(Left) and near-normal distribution after applying Box cox transformation. Source At the core of the Box-Cox transformation is an exponent, lambda (λ), which varies from -5 to 5. All values of λ are considered and the optimal value for your data is selected; The “optimal value” is the one that results in the best approximation of a normal distribution curve. The one-parameter Box-Cox transformations are defined as:and the two-parameter Box-Cox transformations as:Moreover, the one-parameter Box-Cox transformation holds for y > 0, i.e. only for positive values and two-parameter Box-Cox transformation for y > -λ, i.e. negative values. The parameter λ is estimated using the profile likelihood function and using goodness-of-fit tests.If we talk about some drawbacks of Box-cox transformation, then if interpretation is what you want to do, then Box-cox is not recommended. Because if λ is some non-zero number, then the transformed target variable may be more difficult to interpret than if we simply applied a log transform.A second stumbling block is that the Box-Cox transformation usually gives the median of the forecast distribution when we revert the transformed data to its original scale. Occasionally, we want the mean and not the median.Box-Cox transformation in Python(Source)SciPy’s stats package provides a function called boxcox for performing box-cox power transformation that takes in original non-normal data as input and returns fitted data along with the lambda value that was used to fit the non-normal distribution to normal distribution.#load necessary packages import numpy as np from scipy.stats import boxcox import seaborn as sns #make this example reproducible np.random.seed(0) #generate dataset data = np.random.exponential(size=1000) fig, ax = plt.subplots(1, 2) #plot the distribution of data values sns.distplot(data, hist=False, kde=True, kde_kws = {'shade': True, 'linewidth': 2}, label = "Non-Normal", color ="red", ax = ax[0]) #perform Box-Cox transformation on original data transformed_data, best_lambda = boxcox(data) sns.distplot(transformed_data, hist = False, kde = True, kde_kws = {'shade': True, 'linewidth': 2}, label = "Normal", color ="red", ax = ax[1]) #adding legends to the subplots plt.legend(loc = "upper right") #rescaling the subplots fig.set_figheight(5) fig.set_figwidth(10) #display optimal lambda value print(f"Lambda value used for Transformation: {best_lambda}") Concept #6- Poisson distributionIn probability theory and statistics, the Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event.In very simple terms, A Poisson distribution can be used to estimate how likely it is that something will happen "X" number of times. Some examples of Poisson processes are customers calling a help center, radioactive decay in atoms, visitors to a website, photons arriving at a space telescope, and movements in a stock price. Poisson processes are usually associated with time, but they do not have to be. The Formula for the Poisson Distribution Is:Where:e is Euler's number (e = 2.71828...)k is the number of occurrencesk! is the factorial of kλ is equal to the expected value of kwhen that is also equal to its varianceLambda(λ) can be thought of as the expected number of events in the interval. As we change the rate parameter, λ, we change the probability of seeing different numbers of events in one interval. The below graph is the probability mass function of the Poisson distribution showing the probability of a number of events occurring in an interval with different rate parameters. Probability Mass function for Poisson Distribution with varying rate parameters.Source The Poisson distribution is also commonly used to model financial count data where the tally is small and is often zero. For one example, in finance, it can be used to model the number of trades that a typical investor will make in a given day, which can be 0 (often), or 1, or 2, etc.As another example, this model can be used to predict the number of "shocks" to the market that will occur in a given time period, say over a decade.Poisson distribution in Pythonfrom numpy import random import matplotlib.pyplot as plt import seaborn as sns lam_list = [1, 4, 9] #list of Lambda values plt.figure(figsize=(10,6)) samples = np.linspace(start=0, stop=5, num=1000) for lam in lam_list: sns.distplot(random.poisson(lam=lam, size=10), hist=False, label='lambda {0}'.format(lam)) plt.xlabel('Poisson Distribution', fontsize=15) plt.ylabel('Frequency', fontsize=15) plt.legend(loc='best') plt.show()As λ becomes bigger, the graph looks more like a normal distribution.I hope you have enjoyed reading this article, If you have any questions or suggestions, please leave a comment. Also read: False Positives vs. False NegativesFeel free to connect me on LinkedIn for any query.Thanks for reading!!!Referenceshttps://calcworkshop.com/joint-probability-distribution/chebyshev-inequality/ https://corporatefinanceinstitute.com/resources/knowledge/data-analysis/chebyshevs-inequality/ https://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm https://www.statology.org/q-q-plot-python/ https://gist.github.com/chaipi-chaya/9eb72978dbbfd7fa4057b493cf6a32e7 https://stackoverflow.com/a/41968334/7175247
Feb 23, 2021
A deep dive into the benefits of each toolTable of ContentsIntroductionPandasSQLSummaryReferencesIntroductionBoth of these tools are important to not only data scientists, but also to those in similar positions like data analytics and business intelligence. With that being said, when should data scientists specifically use pandas over SQL and vice versa? In some situations, you can get away with just using SQL, and some other times, pandas is much easier to use, especially for data scientists who focus on research in a Jupyter Notebook setting. Below, I will discuss when you should use SQL and when you should use pandas. Keep in mind that both of these tools have specific use cases, but there are many times where their functionality overlap, and that is what I will be comparing below as well.PandasPhoto by Kalen Kemp on Unsplash [2].Pandas [3] is an open-source data analysis tool in the Python programing language. The benefit of pandas starts when you already have your main dataset, usually from a SQL query. This main difference can mean that the two tools are separate, however, you can also perform several of the same functions in each respective tool, for example, you can create new features from existing columns in pandas, perhaps easier and faster than in SQL.It is important to note that I am not comparing what Pandas does that SQL cannot do and vice versa. I will be picking the tool that can do the function more efficiently or preferable for data science work — in my opinion, from personal experience.Here are times where using pandas is more beneficial than SQL — while also having the same functionality as SQL:creating calculated fields from existing featuresWhen incorporating a more complex SQL query, you often are incorporating subqueries as well in order to divide values from different columns. In pandas you can simply divide features much easier like the following:df["new_column"] = df["first_column"]/df["second_column"]The code above is showing how you can divide two separate columns, and assign those values to a new column — in this case, you are performing the feature creation on the whole entire dataset or dataframe. You can use this function in both feature exploration and feature engineering in the process of data science.grouping byAlso referring to subqueries, grouping by in SQL can become quite complex and require lines and lines of code that can be visually overwhelming. In pandas, you can simply group by one line of code. I am not referring to the group by at the end of a simple select from table query, but one where there are multiple subqueries involved.df.groupby(by="first_column").mean()This result would be returning the mean of the first_column for every column in the dataframe. There are many other ways to use this grouping function, of which are outlined nicely in the pandas documentation linked below.checking data typesIn SQL, you will often have to cast types, but sometimes it can be a little clearer to see the way pandas lays out data types in a vertical format, rather than scrolling through a horizontal output in SQL. You can expect some examples of data types returned to be int64, float64, datetime64[ns], and object.df.dtypesWhile these are all fairly simple functions of pandas and SQL, in SQL, they are particularly tricky, and sometimes just much easier to implement in a pandas dataframe. Now, let’s look at what SQL is better at performing.SQLPhoto by Caspar Camille Rubin on Unsplash [4].SQL is probably the language that is used most by the most amount of different positions. For example, a data engineer could use SQL, a Tableau developer, or a product manager. With that being said, data scientists tend to use SQL frequently. It is important to note that there are several different versions of SQL, usually all having a similar function, just slightly formatted differently.Here are times where using SQL is more beneficial than pandas — while also having the same functionality as pandas:WHERE clauseThis clause in SQL is used frequently and can also be performed in pandas. In pandas, however, it is slightly more difficult, or less intuitive. For example, you have to write out redundant code, whereas in SQL, you simply need the WHERE.SELECT ID FROM TABLE WHERE ID > 100In pandas, it would be something like:df[df["ID"] > 100]["ID"]Yes, both are simple, one is just a little more intuitive.JOINSPandas has a few ways to join, which can be a little overwhelming, whereas in SQL you can perform simple joins like the following: INNER, LEFT, RIGHTSELECT one.column_A, two.column_B FROM FIRST_TABLE one INNER JOIN SECOND_TABLE two on two.ID = one.IDIn this code, joining is slightly easier to read, than in pandas, where you have to merge dataframes, and especially as you merge more than two dataframes, it can be quite complex in pandas. SQL can perform multiple joins whether it be INNER, etc., all in the same query.All of these examples, whether it be SQL or pandas, can be used in at least the exploratory data analysis portion of the data science process, as well as in feature engineer, and querying model results once they are stored in a database.SummaryThis comparison of pandas versus SQL is more of a personal preference. With that being said, you may feel the opposite of my opinion. However, I hope it still sheds light on the differences between pandas and SQL, as well as what you can perform the same in both tools, using slightly different coding techniques and a different language altogether.To summarize, we have compared the benefits of using pandas over SQL and vice versa for a few of their shared functions:* creating calculated fields from existing features * grouping by * checking data types * WHERE clause * JOINSI hope you found my article both interesting and useful. Please feel free to comment down below if you agree with these comparisons — why or why not? Do you think one tool, in particular, is better than the other? What other data science tools can you think of that would have a similar comparison? What other functions of pandas and SQL could we compare?
Feb 23, 2021
I’ve been coding in Python for more than 10 years. There was a time when I thought I knew it all, which was a clear sign I was getting complacent.Then I decided to do a bit of research about Python improvements. Those 3.6, 3.7, 3.8 Python versions aren’t there for nothing, right?After going through release notes, I found these neat tricks that I would like to share with you.By reading this article, you’ll learn:How to format big integers more clearlyA better way to work with file pathsThe proper way of string formating1. Formatting big integersUsing Underscores in Numeric Literals. Image by Roman OracFrom Python 3.6 (and onwards) you can use underscores to make numbers easier to read. See PEP 515 for more details.Let’s look at an example:a = 1000000000 # Is variable a billion or 100 millions? # Let's use underscores to make it easier to read a = 1_000_000_000 # You can group numbers as you like b = 1_0_9_0It also works with hexadecimal addresses and grouping bits.# grouping hexadecimal addresses by words addr = 0xCAFE_F00D # grouping bits into nibbles in a binary literal flags = 0b_0011_1111_0100_1110Also read: Why Decorators In Python Are Pure Genius?2. PathlibPhoto by Alice Donovan Rouse on UnsplashWorking with paths can be challenging especially if your code needs to run on multiple operating systems.Luckily for us, Python standard library has pathllib.Let’s look at an example:from pathlib import Path path = Path("some_folder") print(path) # output: some_folder # We can add more subfolders in a readable way path = path / "sub_folder" / "sub_sub_folder" print(path) # output: some_folder/sub_folder/sub_sub_folder # make path absolute print(path.resolve()) # output: /Users/r.orac/some_folder/sub_folter/sub_sub_folderDespite using / sign as the separator to concatenate paths, pathlib makes it operating system agnostic, meaning it will also work on Windows OS.Also read: Using Python And Pandas Datareader to Analyze Financial Data3. Simplify string formattingf-string formatting in Python. Image by Roman OracI’m used to using old school string formatting in Python:person = 'Roman' exercise = 0 print("%d-times %s exercised during corona epidemic" % (exercise, person)) # output # 0-times Roman exercised during corona epidemicUntil recently, I didn’t know there is a better (more modern) way of string formatting in Python.In Python 3.6, PEP 498 introduces Literal String Interpolation, which simplifies the string formatting.We can rewrite the example above to:person = 'roman' exercise = 0 print(f"{exercise}-times {person} exercised during corona epidemic") # output # 0-times Roman exercised during corona epidemicA string prefixed with f is known as fstring.fstrings even support math operations:print(f"{exercise+1}-times {person} exercised during corona epidemic") # Output # '1-times roman exercised during corona epidemic'But I didn’t exercise during the corona epidemic so adding +1 in the fstring would simply be a lie 😂What about formatting float values?f = 0.333333 print(f"this is f={f:.2f} rounded to 2 decimals") # Output this is f=0.33 rounded to 2 decimalsAlso read: How to Use Python Datetimes Correctly?ConclusionThese Python tricks will make your code more concise and will make coding more enjoyable.Many Python developers don’t know about these tricks — you’re not one of them anymore.Before you goFollow me on Twitter, where I regularly tweet about Data Science and Machine Learning.Also read: Understanding Python's Collections Module
Feb 23, 2021
In this new post we want to talk about the most useful Python extensions for Visual Studio Code. Visual Studio Code is an integrated development environment created by Microsoft for Windows, Linux and macOS. Among its features are debugging, syntax highlighting, smart code completion, snippets, code refactoring and integrated Git. Users can change the theme, keyboard shortcuts, preferences and install extensions that add additional functionality.Precisely we are going to talk about the extensions you can install for VS. Here is a list of our favorites1- PythonLink: https://github.com/Microsoft/vscode-pythonPython extension for Visual Studio CodeA Visual Studio Code extension with rich support for the Python language (for all actively supported versions of the language: >=3.6), including features such as IntelliSense (Pylance), linting, debugging, code navigation, code formatting, refactoring, variable explorer, test explorer, and more!NOTE: Web support -- e.g., github.dev -- is limited.Installed extensionsThe Python extension will automatically install the Pylance and Jupyter extensions to give you the best experience when working with Python files and Jupyter notebooks. However, Pylance is an optional dependency, which means that the Python extension will remain fully functional if it is not installed. You can also uninstall it at the expense of some features if you are using a different language server.2- Python IndentLink: https://github.com/kbrose/vsc-python-indentIt is used to correct Python indentation in Visual Studio Code. How it worksEvery time you press the Enter key in a Python context, this extension will parse your Python file down to the location of your cursor, and determine exactly how much to indent the next line (or two in the case of hanging indents) and how much to indent nearby lines.There are three main cases when determining the correct indent. Review the documentation here: https://github.com/kbrose/vsc-python-indent3- Python Doctring GeneratorLink: https://github.com/NilsJPWerner/autoDocstringVisual Studio Code extension to quickly generate docstrings for python functions.FeaturesQuickly generate a docstring fragment that can be tabbed.Choose from several types of docstring formats.Infer parameter types via pep484 type hints, default values and var names.Support for args, kwargs, decorators, errors and parameter types.Docstring FormatsGoogle (default)docBlockrNumpySphinxPEP0257 (coming soon)UsageThe cursor must be on the line directly below the definition to generate a complete auto-populated docstring.Press enter after opening the docstring with triple quotes ("""" or ''')Keyboard shortcut: ctrl+shift+2 or cmd+shift+2 for macCan be changed in Preferences -> Keyboard shortcuts -> extension.generateDocstringCommand: Generate DocstringRight-click menu: Generate DocstringAlso read: 4 Must-Know Python Pandas Functions for Time Series Analysis4- Python ExtendedLink: https://github.com/tushortz/vscode-Python-ExtendedPython Extended is a vscode snippet that makes it easy to write Python code by providing completion options along with all arguments.UsageRun vscode and in a python file, type the name of the method to complete and press tab or enter on selection.How to installOpen vscode. Press F1, search for "ext install" followed by the extension name, in this case "ext install Python Extended" without the ">". Or if you prefer ">ext install", press enter, search for "Python Extended".5- Python PreviewLink: https://github.com/dongli0x00/python-previewA Visual Studio Code extension with debug preview support for the Python language.RequirementsInstall a version of Python 3.6 or Python 2.7. Make sure that the location of your Python interpreter is included in your PATH environment variable.It is best to install the Python extension for Python Intellisense.6- AREPL for PythonLink: https://github.com/almenon/arepl-vscodeAREPL automatically evaluates Python code in real time as you type.UsageFirst, make sure you have python 3.7 or higher installed.Open a python file and click on the cat in the top right bar to open AREPL. You can click the cat again to close it.Or run AREPL via the search command: control-shift-por use the shortcuts: control-shift-a (current document) / control-shift-q (new document)FeaturesReal-time evaluation: no need to run - AREPL evaluates your code automatically. You can control this (or even disable it) in the settings.Variable display: The final state of your local variables is displayed in a collapsible JSON format.Error display: The moment you make a mistake an error is displayed with the stack trace.Settings: AREPL offers many settings to suit your user experience. Customize the look and feel, bounce time, python options and much more.Aldo read: 3 Python Tricks That Will Improve Your Code7- Python PathLink: https://github.com/mgesbert/vscode-python-pathThis extension adds a set of tools to help generate internal import statements in a Python project.Features"Copy Python Path" is accessible from:Command lineExplorer context menuEditor context menuEditor title context menu8- Python Test ExplorerLink: https://github.com/kondratyev-nv/vscode-python-test-adapterThis extension allows you to run your Python Unittest, Pytest or Testplan tests with the Test Explorer user interface.How to get startedInstall the extensionConfigure Visual Studio Code to discover your tests (see the Configuration section and the documentation for the test framework of your choice:Unittest documentationPytest documentationTestplan documentationOpen the sidebar of the test viewExecute your tests via the Run icon in the Test ExplorerFeaturesDisplays a Test Explorer in the test view in the VS Code sidebar with all detected tests and suites and their statusConvenient error reporting during test detectionUnittest, Pytest and Testplan debuggingDisplays the log of a failed test when the test is selected in the explorerTest rerun when saving testsSupports multi-root workspacesSupports Unittest, Pytest and Testplan test frameworks and their plugins9- Python SnippetsLink: https://github.com/ylcnfrht/vscode-python-snippet-packA snippet package to make working with Python more productive This snippet package contains all of the following Python methodsall built-in python snippets and contains at least one example for each methodall python string snippets contain at least one example for each methodall python list snippets contain at least one example for each methodall Python set snippets contain at least one example for each methodall Python tuple snippets contain at least one example for each methodall python dictionary snippets contain at least one example for each methodAnd it contains many other code snippets (such as if/else, for, while, while/else, try/catch, file process, andclass snippets and class examples for oop (polymorphism, encapsulation, inheritance, etc.).If you don't use a method don't worry this extension contains a lot of code examples for each python method.This extension is not just a code snippet, it will also be useful for learning the python programming language.You will learn all python methods with a lot of code examples.For example, if you want to use the string replacement method, you just need to use .replace.But if you don't know how to use the replace method then use string.replace =>10- JupyterLink: https://github.com/Microsoft/vscode-jupyterA Visual Studio Code extension that provides basic notebook support for language kernels that are compatible with Jupyter Notebooks today. Many language kernels will work without any modifications. To enable advanced features, modifications to the VS Code language extensions may be necessary.Notebook supportThe Jupyter Extension uses VS code's built-in notebook support. This interface offers a number of advantages to notebook users:Out-of-the-box support for VS Code's wide range of basic code editing functions, such as hot output, search and replace, and code folding.Editor extensions such as VIM, bracket coloring, linters and many more are available while editing a cell.Deep integration with the general workbench and file-based features of VS Code, such as outline view (table of contents), breadcrumbs, and other operations.Fast load times for Jupyter notebook (.ipynb) files. Any notebook file is loaded and rendered as quickly as possible, while execution-related operations are initialized behind the scenes.Includes a notebook diff tool, which makes it easy to compare and visualize differences between code cells, results and metadata.Extensibility beyond what the Jupyter extension provides. Extensions can now add their own specific language or runtime to notebooks, such as the .NET and Gather interactive notebooks.Although the Jupyter extension comes with a comprehensive set of the most commonly used renderers for output, the marketplace supports installable custom renderers to make working with your notebooks even more productive. To get started writing your own, check out the VS Code renderer api documentation.You can also read data science posts in Spanish here.ConclusionThere are many extensions that you can use with your Visual Studio Code, and deciding which one to use will involve testing, reviewing utilities, use cases and so on in order to make your work easier while coding!Also read: Why Decorators In Python Are Pure Genius?
Feb 23, 2021
Keep up to date by participating in our global community of data scientists and AI enthusiasts. We discuss the latest developments in data science competitions, new techniques for solving complex challenges, AI and machine learning models, and much more!
