Using Python And Pandas Datareader to Analyze Financial Data

Daniel Morales
Mar 09, 2021

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Using Python And Pandas Datareader to Analyze Financial Data

Mar 09, 2021 33 seconds read

Finance and economics are becoming more and more interesting for all kinds of people, regardless of their career or profession. This is because we are all affected by economic data, or at least we are increasingly interested in being up-to-date, and we have a lot of information at hand.

Every day billions of bytes of financial data are sent over the Internet. Whether it is the price of a share, an e-commerce transaction, or even information on a country's GDP. All this data, when properly organized and managed can be used to build some amazing and insightful software applications.

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4 Must-Know Python Pandas Functions for Time Series Analysis

How to Build Your Data Analytics Team

16 Underrated Pandas Series Methods And When To Use Them

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Libraries
Pandas
4 Must-Know Python Pandas Functions for Time Series Analysis

Time series data consists of data points attached to sequential time stamps. Daily sales, hourly temperature values, and second-level measurements in a chemical process are some examples of time series data.Time series data has different characteristics than ordinary tabular data. Thus, time series analysis has its own dynamics and can be considered as a separate field. There are books over 500 pages to cover time series analysis concepts and techniques in depth.Pandas was created by Wes Mckinney to provide an efficient and flexible tool to work with financial data which is kind of a time series. In this article, we will go over 4 Pandas functions that can be used for time series analysis.We need data for the examples. Let’s start with creating our own time series data.import numpy as np import pandas as pd df = pd.DataFrame({ "date": pd.date_range(start="2020-05-01", periods=100, freq="D"), "temperature": np.random.randint(18, 30, size=100) + np.random.random(100).round(1) }) df.head()(image by author)We have created a data frame that contains temperature measurements during a period of 100 days. The date_range function of Pandas can be used for generating a date range with customized frequency. The temperature values are generated randomly using Numpy functions.We can now start on the functions.1. ShiftIt is a common operation to shift time series data. We may need to make a comparison between lagged or lead features. In our data frame, we can create a new feature that contains the temperature of the previous day.df["temperature_lag_1"] = df["temperature"].shift(1) df.head()(image by author)The scalar value passed to the shift function indicates the number of periods to shift. The first row of the new column is filled with NaN because there is no previous value for the first row.The fill_value parameter can be used for filling the missing values with a scalar. Let’s replace the NaN with the average value of the temperature column.df["temperature_lag_1"] = df["temperature"]\ .shift(1, fill_value = df.temperature.mean()) df.head()(image by author)If you are interested in the future values, you can shift backwards by passing negative values to the shift function. For instance, “-1” brings the temperature in the next day.2. ResampleAnother common operation performed on time series data is resampling. It involves in changing the frequency of the periods. For instance, we may be interested in the weekly temperature data rather than daily measurements.The resample function creates groups (or bins) of a specified internal. Then, we can apply aggregation functions to the groups to calculate the value based on resampled frequency.Let’s calculate the average weekly temperatures. The first step is to resample the data to week level. Then, we will apply the mean function to calculate the average.df_weekly = df.resample("W", on="date").mean() df_weekly.head()(image by author)The first parameter specifies the frequency for resampling. “W” stands for week, surprisingly. If the data frame does not have a datetime index, the column that contains the date or time related information needs to be passed to the on parameter.3. AsfreqThe asfreq function provides a different technique for resampling. It returns the value at the end of the specified interval. For instance, asfreq(“W”)returns the value on the last day of each week.In order to use the asfreq function, we should set the date column as the index of the data frame.df.set_index("date").asfreq("W").head()(image by author)Since we are getting a value at a specific day, it is not necessary to apply an aggregation function.4. RollingThe rolling function can be used for calculating moving average which is a highly common operation for time series data. It creates a window of a particular size. Then, we can use this window to make calculations as it rolls through the data points.The figure below explains the concept of rolling.(image by author)Let’s create a rolling window of 3 and use it to calculate the moving average.df.set_index("date").rolling(3).mean().head()(image by author)For any day, the values show the average of the day and the previous 2 days. The values of the first 3 days are 18.9, 23.8, and 19.9. Thus, the moving average on the third day is the average of these values which is 20.7.The first 2 values are NaN because they do not have previous 2 values. We can also use this rolling window to cover the previous and next day for any given day. It can be done by setting the center parameter as true.df.set_index("date").rolling(3, center=True).mean().head()(image by author)The values of the first 3 days are 18.9, 23.8, and 19.9. Thus, the moving average in the second day is the average of these 3 values. In this setting, only the first value is NaN because we only need 1 previous value.ConclusionWe have covered 4 Pandas functions that are commonly used in time series analysis. Predictive analytics is an essential part of data science. Time series analysis is at the core of many problems that predictive analytics aims to solve. Hence, if you plan to work on predictive analytics, you should definitely learn how to handle time series data.Thank you for reading. 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Business
How to Build Your Data Analytics Team

Peer reviewed by Kat Holmes — Data Director ITV‍As businesses recognize the decisive power of data to achieve business goals, most are hoping to put data in the driver’s seat of their business and product strategies. This entails putting together a strong data team which can effectively propagate its insights across different areas of the business. Unfortunately, this is no easy task.To be truly data driven, companies need to build three capabilities: data strategy, data governance and data analytics.3 pillars for data-driven companies — Image from PitchStrategy: Data strategy is your organization’s roadmap for using data to achieve its goals. It requires a clear understanding of the data needs inherent to the business strategy. Why are you collecting data? Are you trying to make money, save money, manage risk, deliver exceptional customer experience, all the above?Governance: Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the efficient use of information in enabling your organization to achieve its goals. A well-crafted data governance strategy ensures that data in your company is trusted, accurate and available.Analytics: The term data analytics refers to the process of analyzing raw data to draw conclusions about the information they contain. Typically, those involved with data analytics in an organization are data engineers, data analysts and data scientists.Ultimately, your ability to leverage data will depend on these three pillars. If you’re reading this and realizing that your organization possesses none of these, don’t worry. That’s why we’re here. A good place to start is to build a strong analytics team, one that is closely tied with the strategic goals of your business. It is the first pillar of your data organization, and the focus of the article.When building a data analytics team, heads of data typically grapple with the following questions:How big should this team be?How many data engineers, data analysts, data scientists?How does the team interact with the rest of the organization?Which structure for the data team? Centralized or embedded?They rightly do so; having a strong data team is not a luxury anymore, but essential to the very survival of a company today.Let’s start with the basics though.Where are you in your data journey?Before building a data team, it’s important that you realize where you are in your “data journey”, because this will directly affect the structure of your team. This part is thus dedicated to a simplified data maturity assessment. Beware, company size and data maturity are two different things. Your organization can be large but immature on a data level.Data maturity is the journey towards seeing tangible value from your data assets. We propose a simple framework of data maturity assessment, in which you measure your ability to understand your past, know your present and predict your future. What do I mean by this?Well, in most companies each department has its own set of KPIs that support the execution of the corporate strategy. It’s not enough just to define them, they must also be clearly tracked, and you must also have the ability to predict future outcomes against these KPIs. This ability rests on a clear knowledge of your present, which, in turn, builds on a strong understanding of the past. Do this, and you have found a simple way to assess your data maturity. For example, if you’re unable to identify the revenue drivers for your company ( your past), it means you need to work on your data maturity by bringing visibility to your business before you seek to predict future outcomes. We don’t recommend skipping steps. It’s like Maslow’s hierarchy of needs, but for data.Read Also: What Are the Expected Results of a Data Science Project?Data hierarchy of needs — Image by Louise de LeyritzLet’s look at a couple of practical examples:Marketing ROI. Define your ROI, across multiple channels, by using an identified attribution model. Then understand its evolution in the previous 12 months, and especially its drivers (identify performing channels, time of the year, product, ….). Then track on a daily/weekly/monthly basis its evolution thanks to a reporting tool you trust ( present). Forecast your marketing budget based on these predictive models ( future).Customer Satisfaction. Define your customer satisfaction measure. Is it NPS, CSAT? Everyone in your company should share a common understanding of how it is computed. As with our previous example, compute its evolution in the previous 12 months, find its drivers ( past). Then track daily the satisfaction of your customers with trusted dashboards. Identify action to take from today to increase it. Your understanding of the past and the present state of customers satisfaction will allow to predict churn efficiently ( future)Understanding your past and present is commonly referred to as performing descriptive analytics. Descriptive analytics helps an organization understand its performance by providing context to help key stakeholders interpret information. This context is usually in the form of data visualization, including graphs, dashboards, reports and charts. When you are analysing data to forecast the future, you’re engaging in predictive analytics. The idea with predictive analytics is to take historical data, feed it into a machine learning model that considers key patterns. Apply this model to current data, and hope that it will forecast the future. We’ll use the terms of descriptive and predictive analytics throughout the article to refer to understanding the past, present or predicting the future.If you realize that your organization is not fully mature (ie. you don’t have a clear understanding of your past and present), here are our recommendations for what should be the next steps of your data team.Key players on a data analytics teamA data analytics team is usually composed of four core functions, which are detailed below.Data engineer: They are responsible for designing, building, and maintaining datasets that can be leveraged in data projects. As such, data engineers closely work with both data scientists and data analysts. We also include the new role of analytics engineer here, although, in practice, this role lies between analytics and engineering.Data scientist: They use advanced mathematics and statistics, and programming tools to build predictive models. The roles of data scientists and data analysts are pretty similar, but data scientists focuse more on predictive analytics than descriptive analytics.Data analyst: They use data to perform reporting and direct analysis. Whereas data scientists and engineers typically interact with data in its raw or unrefined states, analysts work with data that’s already been cleaned and transformed into more user-friendly formats.Business analyst/ops analyst: They help the organization improve its processes and systems. They focus on dashboarding, answer business questions and propose their interpretation. They are agile and straddle the line between IT and the business to help bridge the gap and improve efficiency. They frequently work with a specific business area such as marketing or finance, and their SQL literacy can range from basic dashboarding to advanced analysis.Head of data analytics: They provide strategic oversight to the data team. Their goal is to create an environment that allows all different parties to access the data they need painlessly, build the skills of the business to draw meaningful insights from the data, and ensure data governance. They also act as a bridge between the data team and the main business unit, acting both as a visionary and a technical lead.‍Read Also: What Is Open Innovation In Data Science?How large should the team be?Different companies will build data teams of different sizes, no one size fits all. We have studied the data team’s structure of 300+ companies, with a 300–1000 employee range and derived the following insights:As a general rule, you should aim to have a total of 5–10% of data analysis savvy employees in your company. Some companies such as Amazon or Facebook are training a huge portion of their employees, but we have excluded them for our analysis.The first hires of a brand-new data teams are often a data engineer and a data analyst. With just these two roles, organizations can already engage in some basic descriptive analytics. When building a larger team, think in terms of the skillset you need. A typical data project requires the following skills: database, software development, machine learning, visualization, collaboration, and communication skills. It is very rare to find individuals who possess all these skills. You should thus be aware of which skill each candidate brings to the table. Regardless of how many people you decide to hire, your team should ideally cover this skill set. Where you are in your data journey also impacts who you hire and at which stage. Generally, data analysts focus on understanding the past. That is, they take the data you have and try to understand the drivers of growth and other metrics. Business analysts/obs analysts are oriented towards the present (dashboarding). Finally, data scientists focus on predicting future outcomes. So, if you have trouble understanding your past, hire a data analyst instead ahead of a data scientist.What should ultimately guide the size of your data team is the number of business problem statements and the complexity of the most serious problems. Look at the size of your roadmap and establish how many people you need to complete your data projects within a reasonable amount of time. If you realize it would take more than a year for your data team to complete its projects, then it’s probably time to expand the team. We also encourage you to look at your run vs build ratio. Members of your data team ‘run’ when they work on daily business operations, focusing on the present performance of the organization. They ‘build’ when they work on long-term projects, such as adding new features to the product. Your data team should be running 2/3 of the time and building 1/3 of the time. If your data team spends all its time focusing on day-to-day needs, you are jeopardising the future of your company, and it is probably time to expand the team.Finally, you might have to make some project-specific hirings. If you’re a fintech conducting a project on fraud detection, or a company specialising in dispatching for logistics, you might want to hire someone who knows the specifics of your industry.How does the data team integrate with the company?There is no perfect structure for an analytics team, and your structure is likely to change many times. If your data team structure hasn’t changed for the past 2 years, then it’s likely to be a sub-optimal structure. Why? Because the data needs of your company are evolving rapidly, calling for an adaptation of your data team’s structure. Also keep in mind that the more static your organization, the harder the next change will be. For this reason, we don’t prescribe a given structure, but rather present the most common models and how they can be suited to different types of businesses.The very first step to take when structuring your data team is to find the data people that already exist in your organization. They might not be just the people with the term “data” in their title, but they could be any employee who’s not afraid of data analysis or has SQL skills already, such as business analysts/ ops analysts. If you don’t take the time to locate pre-existing data people carefully, you are likely to end up with an unplanned data team structure, unlikely to fit your business needs.‍Centralized modelCentralized model for data teams — Image by Louise de LeyritzThe centralized model is the most straightforward structure to implement, and it is usually the first step for companies who aim to be data driven. There are, however, a few drawbacks to this model, which are referenced below. This structure usually leads to a centralized data “platform”, where the data team has access to all the data, and services the whole organization in a variety of projects. All data engineers, analysts and scientists within this team are managed directly by the head of data. With this structure, the data team is reporting in a dotted line to data stakeholders based in business units, in a consultant/client-type relationship.Read Also: The 3 Basic Principles of a Data-Driven CompanyThis flexible model is adaptable to the continuously evolving needs of a growing business. If you’re at the beginning of your data journey, that is, you still struggle to have a clear vision of your past and present, this is the structure we recommend. The data team’s first projects will seek to bring visibility to the business, ensuring all departments in your organization have KPIs and dashboards they can trust. This kind of structure is particularly good for analytics where reusability and data governance are important.Advantages✅ The data team can help with other teams’ projects while working on its own agenda.✅ The team can prioritise projects across the company.✅ There are more opportunities for talent and skillset development in a centralized team. In fact, the data team works on a broader variety of projects, and data engineers, scientists and analysts can benefit from their peers’s insights.✅ The head of data has a centralized view of the company’s strategy and can assign data people to projects that are the most suited to their capabilities.✅ Encourages career growth, as data engineers, scientists and have clear perspectives of seniority roles.‍Drawbacks❌ High chance of disconnect between the data analytics team and other business units. In this model, data engineers and data scientists are not immersed in the day-to-day activities of other teams, making it difficult for them to identify the most relevant problems to tackle.❌ Risk for the analytics group to be reduced to a “support” function, with other departments not taking their responsibilities.❌ As the data team serves the rest of the business, other business units might feel like their needs are not properly addressed, or that the planning process is too bureaucratic and slow.‍Decentralized/Embedded modelDecentralized model for data teams — Image by Louise de LeyritzIn a decentralized model, each department hires its “own” data people, with a centralized data platform. In this model, data analysts and scientists focus on the problems faced by their specific business unit, with little interaction with data people from other areas of the company. With this structure, data analysts report directly to the head of their respective business unit.Advantages✅ Embedded teams of data people are agile and responsive, because they are dedicated to their respective business functions and have good domain knowledge.✅ Product managers can assign data tasks to the people most qualified to work on them.✅ Business data teams don’t have to fight for resources to build their data project because the resources sit in the teams.Drawbacks❌ Lack of source of truth, duplication of data content❌ Data people end up working on redundant issues due to a lack of communication between different teams.❌ The creation of silos leads to productivity erosion since data people can’t draw on their colleagues’ expertise as they do in the centralized model.❌ This model makes it harder to optimally staff data people on different projects.❌ Business managers, usually lacking technical backgrounds, will find it hard to manage data people and understand the quality of their work.‍Federated model/ Centre of excellenceA federated model is most suited to companies that have reached data maturity, have a clear data strategy and engage in predictive analytics.Center of excellence mode l- Image by Louise de LeyritzIn the Centre of Excellence model (COE), data people are embedded in business units, but a centralized group that provides leadership, support and training remains. If data analysts and scientists are deployed across business departments, you would still have a data leader (or a core of data leaders according to company size) who prioritizes and supervises data projects. This ensures that the most beneficial data projects are tackled first.This strategy is most suited to larger, enterprise-scale companies with a clear data roadmap. The centre of excellence model entails a larger data team, as you need data scientists both in the COE and in the different business branches. If you are a small or medium company, your needs might not require a data team of this size.Read Also: How to Make Your Company a Data-driven Organization?This approach retains the advantages of both the centralized and the embedded model. It is a more balanced structure in which the data team’s actions are coordinated, but also keeps the data experts embedded in business units.Again, it’s extremely important that you know who your data people are. When building a centralized team at the beginning of your data journey, make sure you don’t have business analysts/ops embedded in other departments. Otherwise, you will end up with an unwanted mixed model, creating complete chaos in your organization. When creating a COE, you need to ensure it’s wanted and planned.Advantages✅ The Centre of Excellence model provides the advantages of both the centralized and the embedded models.It still presents some drawbacks, though:Drawbacks❌ This model requires an additional layer of coordination and communication needed to ensure alignment between COE and business units.❌ Not fit for purpose for small — medium sized organizations, so these companies can then hook it to the benefits that can come with this hub and spoke model.Final wordsBuilding a strong analytics team is a key pillar you need to build if your company is to become data-driven. The extent to which you will extract business value from data ultimately depends on the strength of this team, and how symbiotic it is with the rest of your business. There is no made-to-order advice for the size, composition and structure of your data team. That’s why you need to understand the data maturity level of your organization, so that you can build a data team suited to your business’ needs and aligned with your business strategy.At Castor, we write about all the processes involved when leveraging data assets: from the modern data stack, to data teams composition, to data governance. Our blog covers the technical and the less technical aspects of creating tangible value from data.At Castor, we are building a data documentation tool for the Notion, Figma, Slack generation. Or data-wise for the Fivetran, Looker, Snowflake, DBT aficionados. We designed our catalog to be easy to use, delightful and friendly.Want to check it out? Reach out to us and we will show you a demo.Originally published at https://www.castordoc.com.

Libraries
Pandas
16 Underrated Pandas Series Methods And When To Use Them

In this article, we’re going to explore some lesser-known but very useful pandas methods for manipulating Series objects. Some of these methods are related only to Series, the others — both to Series and DataFrames, having, however, specific features when used with both structure types.1. is_uniqueAs its name sugests, this method checks if all the values of a Series are unique:import pandas as pd print(pd.Series([1, 2, 3, 4]).is_unique) print(pd.Series([1, 2, 3, 1]).is_unique) Output: True False 2 & 3. is_monotonic and is_monotonic_decreasingWith these 2 methods, we can check if the values of a Series are in ascending/descending order:print(pd.Series([1, 2, 3, 8]).is_monotonic) print(pd.Series([1, 2, 3, 1]).is_monotonic) print(pd.Series([9, 8, 4, 0]).is_monotonic_decreasing) Output: True False TrueBoth methods work also for a Series with string values. In this case, Python uses a lexicographical ordering under the hood, comparing two subsequent strings character by character. It’s not the same as just an alphabetical ordering, and actually, the example with the numeric data above is a particular case of such an ordering. As the Python documentation says,Lexicographical ordering for strings uses the Unicode code point number to order individual characters.In practice, it mainly means that the letter case and special symbols are also taken into account:print(pd.Series(['fox', 'koala', 'panda']).is_monotonic) print(pd.Series(['FOX', 'Fox', 'fox']).is_monotonic) print(pd.Series(['*', '&', '_']).is_monotonic) Output: True True FalseA curious exception happens when all the values of a Series are the same. In this case, both methods return True:print(pd.Series([1, 1, 1, 1, 1]).is_monotonic) print(pd.Series(['fish', 'fish']).is_monotonic_decreasing) Output: True TrueAlso Read: 4 Must-Know Python Pandas Functions for Time Series Analysis4. hasnansThis method checks if a Series contains NaN values:import numpy as np print(pd.Series([1, 2, 3, np.nan]).hasnans) print(pd.Series([1, 2, 3, 10, 20]).hasnans) Output: True False5. emptySometimes, we might want to know if a Series is completely empty, not containing even NaN values:print(pd.Series().empty) print(pd.Series(np.nan).empty) Output: True FalseA Series can become empty after some manipulations with it, for example, filtering:s = pd.Series([1, 2, 3]) s[s > 3].empty Output: True 6 & 7. first_valid_index() and last_valid_index()These 2 methods return index for first/last non-NaN value and are particularly useful for Series objects with many NaNs:print(pd.Series([np.nan, np.nan, 1, 2, 3, np.nan]).first_valid_index()) print(pd.Series([np.nan, np.nan, 1, 2, 3, np.nan]).last_valid_index()) Output: 2 4If all the values of a Series are NaN, both methods return None:print(pd.Series([np.nan, np.nan, np.nan]).first_valid_index()) print(pd.Series([np.nan, np.nan, np.nan]).last_valid_index()) Output: None None 8. truncate()This method allows truncating a Series before and after some index value. Let’s truncate the Series from the previous section leaving only non-NaN values:s = pd.Series([np.nan, np.nan, 1, 2, 3, np.nan]) s.truncate(before=2, after=4) Output: 2 1.0 3 2.0 4 3.0 dtype: float64The original index of the Series was preserved. We may want to reset it and also to assign the truncated Series to a variable:s_truncated = s.truncate(before=2, after=4).reset_index(drop=True) print(s_truncated) Output: 0 1.0 1 2.0 2 3.0 dtype: float64Also Read: Pandas vs SQL. When Data Scientists Should Use One Over the Other9. convert_dtypes()As the pandas documentation says, this method is used toConvert columns to best possible dtypes using dtypes supporting pd.NA.If to consider only Series objects and not DataFrames, the only application of this method is to convert all nullable integers (i.e. float numbers with a decimal part equal to 0, such as 1.0, 2.0, etc.) back to “normal” integers. Such float numbers appear when the original Series contains both integers and NaN values. Since NaN is a float in numpy and pandas, it leads to the whole Series with any missing values to become of float type as well.Let’s take a look at the example from the previous section to see how it works:print(pd.Series([np.nan, np.nan, 1, 2, 3, np.nan])) print('\n') print(pd.Series([np.nan, np.nan, 1, 2, 3, np.nan]).convert_dtypes()) Output: 0 NaN 1 NaN 2 1.0 3 2.0 4 3.0 5 NaN dtype: float64 0 <NA> 1 <NA> 2 1 3 2 4 3 5 <NA> dtype: Int64 10. clip()We can clip all the values of a Series at input thresholds (lower and upper parameters):s = pd.Series(range(1, 11)) print(s) s_clipped = s.clip(lower=2, upper=7) print(s_clipped) Output: 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 dtype: int64 0 2 1 2 2 3 3 4 4 5 5 6 6 7 7 7 8 7 9 7 dtype: int64 11. rename_axis()In the case of a Series object, this method sets the name of the index:s = pd.Series({'flour': '300 g', 'butter': '150 g', 'sugar': '100 g'}) print(s) s=s.rename_axis('ingredients') print(s) Output: flour 300 g butter 150 g sugar 100 g dtype: object ingredients flour 300 g butter 150 g sugar 100 g dtype: object 12 & 13. nsmallest() and nlargest()These 2 methods return the smallest/largest elements of a Series. By default, they return 5 values, in ascending order for nsmallest() and in descending - for nlargest().s = pd.Series([3, 2, 1, 100, 200, 300, 4, 5, 6]) s.nsmallest() Output: 2 1 1 2 0 3 6 4 7 5 dtype: int64It’s possible to specify another number of the smallest/largest values to be returned. Also, we may want to reset the index and assign the result to a variable:largest_3 = s.nlargest(3).reset_index(drop=True) print(largest_3) Output: 0 300 1 200 2 100 dtype: int64Also Read: Pandas vs SQL. When Data Scientists Should Use One Over the Other14. pct_change()For a Series object, we can calculate percentage change (or, more precisely, fraction change) between the current and a prior element. This approach can be helpful, for example, when working with time series, or for creating a waterfall chart in % or fractions.s = pd.Series([20, 33, 14, 97, 19]) s.pct_change() Output: 0 NaN 1 0.650000 2 -0.575758 3 5.928571 4 -0.804124 dtype: float64To make the resulting Series more readable, let’s round it:s.pct_change().round(2) Output: 0 NaN 1 0.65 2 -0.58 3 5.93 4 -0.80 dtype: float64 15. explode()This method transforms each list-like element of a Series (lists, tuples, sets, Series, ndarrays) to a row. Empty list-likes will be transformed in a row with NaN. To avoid repeated indices in the resulting Series, it’s better to reset index:s = pd.Series([[np.nan], {1, 2}, 3, (4, 5)]) print(s) s_exploded = s.explode().reset_index(drop=True) print(s_exploded) Output: 0 [nan] 1 {1, 2} 2 3 3 (4, 5) dtype: object 0 NaN 1 1 2 2 3 3 4 4 5 5 dtype: object 16. repeat()This method is used for consecutive repeating each element of a Series a defined number of times. Also in this case, it makes sense to reset index:s = pd.Series([1, 2, 3]) print(s) s_repeated = s.repeat(2).reset_index(drop=True) print(s_repeated) Output: 0 1 1 2 2 3 dtype: int64 0 1 1 1 2 2 3 2 4 3 5 3 dtype: int64If the number of repetitions is assigned to 0, an empty Series will be returned:s.repeat(0) Output: Series([], dtype: int64) ConclusionTo sum up, we investigated 16 rarely used pandas methods for working with Series and some of their application cases. If you know some other interesting ways to manipulate pandas Series, you’re very welcome to share them in the comments.Thanks for reading!Also read: Using Python And Pandas Datareader to Analyze Financial Data

Business
The Impact of AI and Data Science on Modern Industry Challenges

The digital transformation sweeping through industries is making data science and artificialintelligence (AI) more essential than ever. From manufacturing to healthcare, companies areleveraging data and AI not just for operational efficiency but also for strategic growth. Here, we’llexplore how real-life data science and AI applications are solving industry challenges andshaping the future.Predictive Maintenance in ManufacturingManufacturers have long sought ways to reduce equipment downtime and prolong machinerylife. Predictive maintenance, powered by AI, enables businesses to foresee issues before theyarise. By analyzing data from sensors attached to machinery, AI can detect early warning signsof potential failures. This proactive approach reduces unexpected breakdowns and associatedcosts, boosting overall productivity.A prime example is the use of predictive maintenance in the elevator industry. Elevators arenow connected via GSM gateways, enabling real-time data communication through networkslike 3G and 4G. AI analyzes sensor data from various elevator components to detect anomaliessuch as changes in motor vibration or cable wear. When detected, these anomalies triggeralerts for technicians to address the issues before a breakdown occurs. Companies like KONEhave leveraged platforms like IBM Watson to enhance their predictive maintenance capabilities,ensuring safer and more reliable operations.Fraud Detection in Financial ServicesFraud is a persistent challenge in financial services, threatening the security of institutions andtheir customers. AI-driven solutions have revolutionized fraud detection by leveraging machinelearning algorithms to identify unusual transaction patterns and flag potential fraudulent activityin real-time.These systems are trained on extensive datasets, enabling them to learn and adapt to changingfraud tactics. For example, machine learning models analyze historical transaction data torecognize deviations from typical customer behavior. This allows financial institutions to quicklyidentify and halt suspicious transactions, minimizing the impact of fraud. Companies thatincorporate tools like Microsoft Power BI can further optimize their insights, making informeddecisions and bolstering security measures across the board.Healthcare DiagnosticsThe application of data science in healthcare is transformative, enabling faster and moreaccurate diagnostics. AI algorithms analyze complex medical data, such as imaging scans andpatient records, to identify diseases early and recommend treatment plans. This assists doctorsin diagnosing conditions more precisely and allows for more personalized patient care.In radiology, for instance, AI tools can process thousands of X-rays to detect abnormalities withan accuracy that sometimes surpasses that of human experts. AI is also proving indispensablein genomics, helping to identify hereditary disease markers and guiding the development ofpersonalized treatments. According to DataScientest, advancements in healthcare analytics arenot only improving diagnostic processes but also facilitating better patient outcomes byproviding actionable insights into medical data.Supply Chain OptimizationThe supply chain is the backbone of any product-driven industry. Effective supply chainmanagement ensures that products reach customers promptly and efficiently. AI plays asignificant role here by improving demand forecasting, inventory management, and deliveryprocesses.Predictive analytics, for example, use historical sales data and external factors such as weatherand economic indicators to forecast product demand more accurately. This helps companiesavoid overstocking or understocking, leading to more efficient inventory management.Additionally, AI-driven route optimization ensures faster delivery times and reducedtransportation costs.Customer Experience EnhancementBusinesses today are increasingly turning to AI to enhance customer experiences. By analyzingcustomer data, AI can help predict customer needs and personalize interactions, makingservices more engaging and effective. AI-powered chatbots, for example, have become commonplace in handling basic customer inquiries. These bots, equipped with natural language processing (NLP), can understand and respond to questions, improving response times and overall customer satisfaction. Beyond chatbots, advanced recommendation engines are used in e-commerce platforms to suggestproducts based on user behavior.Key Challenges and ConsiderationsWhile the benefits of integrating AI and data science are clear, industries must navigate severalchallenges to make the most of these technologies. Data security is of paramount importance,especially in sectors like healthcare and finance where sensitive data is handled. Companiesmust ensure robust information security protocols and adhere to regulations such as theGeneral Data Protection Regulation (GDPR) in the EU and the Health Insurance Portability andAccountability Act (HIPAA) in the United States.Bias in AI models is another challenge that requires attention. If machine learning algorithms aretrained on non-representative data, they can perpetuate biases, leading to unfair outcomes. Forinstance, biased models in hiring practices could lead to skewed decisions, while biasedhealthcare algorithms might overlook critical patient needs. Regular audits and training ondiverse datasets can help mitigate these risks.Ethical Considerations and SustainabilityThe use of AI and data science should align with ethical practices. This includes ensuringtransparency in AI-driven decision-making and minimizing potential biases. It is also importantto prioritize sustainability. Companies should strive to implement energy-efficient AI models andconsider the environmental impact of their data centers and computation needs.Ethical data use and model interpretability are crucial for building trust with consumers andstakeholders. When companies openly communicate how their AI systems work and the stepstaken to prevent biases, they foster trust and encourage wider adoption.Advancing Workforce SkillsTo leverage the full potential of AI and data science, businesses must invest in upskilling theirworkforce. This includes training employees to understand and work with AI technologies, aswell as fostering a culture of data-driven decision-making.The Future of AI and Data Science in IndustryEmerging trends such as AI-powered automation and more sophisticated machine learningalgorithms will redefine how industries operate. Businesses that embrace these technologiesand focus on building a data-centric culture will be better positioned for long-term success.Theintegration of data science and AI into industry practices is more than just a trend—it is a crucialstrategy for gaining a competitive edge.

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