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PANDAS DATA TYPES
The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. EFFICIENTLY CLEANING TEXT WITH PANDAS Cleaning attempt #2. Another approach that is very performant and flexible is to use np.select to run multiple matches and apply a specified value upon match.. There are several good resources that I used to learn how to use np.select.This article from Dataquest is a good overview. I also found this presentation from Nathan Cheever very interesting and information. MONTE CARLO SIMULATION WITH PYTHON The real “magic” of the Monte Carlo simulation is that if we run a simulation many times, we start to develop a picture of the likely distribution of results. In Excel, you would need VBA or another plugin to run multiple iterations. In python, we can use a for loop to run as many simulations as we’d like. CREATING POWERPOINT PRESENTATIONS WITH PYTHON Creating tables in PowerPoint is a good news / bad news story. The good news is that there is an API to create one. The bad news is that you can’t easily convert a pandas DataFrame to a table using the built in API.However, we are very fortunate that someone has already done all the hard work for us and created PandasToPowerPoint.. This excellent piece of code takes a DataFrame and converts CLEANING UP CURRENCY DATA WITH PANDAS In this example, the data is a mixture of currency labeled and non-currency labeled values. For a small example like this, you might want to clean it up at the source file. INTRODUCTION TO MARKET BASKET ANALYSIS IN PYTHON A useful (but somewhat overlooked) technique is called association analysis which attempts to find common patterns of items in large data sets. One specific application is often called market basket analysis. The most commonly cited example of market basket analysis is the so-called “beer and diapers” case. GUIDE TO ENCODING CATEGORICAL VALUES IN PYTHON The Data Set. For this article, I was able to find a good dataset at the UCI Machine Learning Repository.This particular Automobile Data Set includes a good mix of categorical values as well as continuous values and serves as a useful example that is relatively easy to understand. Since domain understanding is an important aspect when deciding how to encode various categorical values - this CREATING A WATERFALL CHART IN PYTHON Creating the Chart. Execute the standard imports and make sure IPython will display matplot plots. Setup the data we want to waterfall chart and load it into a dataframe. The data needs to start with your starting value but you leave out the final total. We will calculateit.
GENERATING EXCEL REPORTS FROM A PANDAS PIVOT TABLE Introduction. The previous pivot table article described how to use the pandas pivot_table function to combine and present data in an easy to view manner. This concept is probably familiar to anyone that has used pivot tables in Excel. However, pandas has the capability to easily take a cross section of the data and manipulate it. COMBINE MULTIPLE EXCEL WORKSHEETS INTO A SINGLE PANDAS This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command. For those of you that want the TLDR, here is the command: df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True) Read on for an explanation of when to use this and PRACTICAL BUSINESS PYTHONABOUTRESOURCESMAILING LISTARCHIVESOVERVIEW OFPANDAS DATA TYPES
The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. EFFICIENTLY CLEANING TEXT WITH PANDAS Cleaning attempt #2. Another approach that is very performant and flexible is to use np.select to run multiple matches and apply a specified value upon match.. There are several good resources that I used to learn how to use np.select.This article from Dataquest is a good overview. I also found this presentation from Nathan Cheever very interesting and information. MONTE CARLO SIMULATION WITH PYTHON The real “magic” of the Monte Carlo simulation is that if we run a simulation many times, we start to develop a picture of the likely distribution of results. In Excel, you would need VBA or another plugin to run multiple iterations. In python, we can use a for loop to run as many simulations as we’d like. CREATING POWERPOINT PRESENTATIONS WITH PYTHON Creating tables in PowerPoint is a good news / bad news story. The good news is that there is an API to create one. The bad news is that you can’t easily convert a pandas DataFrame to a table using the built in API.However, we are very fortunate that someone has already done all the hard work for us and created PandasToPowerPoint.. This excellent piece of code takes a DataFrame and converts CLEANING UP CURRENCY DATA WITH PANDAS In this example, the data is a mixture of currency labeled and non-currency labeled values. For a small example like this, you might want to clean it up at the source file. INTRODUCTION TO MARKET BASKET ANALYSIS IN PYTHON A useful (but somewhat overlooked) technique is called association analysis which attempts to find common patterns of items in large data sets. One specific application is often called market basket analysis. The most commonly cited example of market basket analysis is the so-called “beer and diapers” case. GUIDE TO ENCODING CATEGORICAL VALUES IN PYTHON The Data Set. For this article, I was able to find a good dataset at the UCI Machine Learning Repository.This particular Automobile Data Set includes a good mix of categorical values as well as continuous values and serves as a useful example that is relatively easy to understand. Since domain understanding is an important aspect when deciding how to encode various categorical values - this CREATING A WATERFALL CHART IN PYTHON Creating the Chart. Execute the standard imports and make sure IPython will display matplot plots. Setup the data we want to waterfall chart and load it into a dataframe. The data needs to start with your starting value but you leave out the final total. We will calculateit.
GENERATING EXCEL REPORTS FROM A PANDAS PIVOT TABLE Introduction. The previous pivot table article described how to use the pandas pivot_table function to combine and present data in an easy to view manner. This concept is probably familiar to anyone that has used pivot tables in Excel. However, pandas has the capability to easily take a cross section of the data and manipulate it. COMBINE MULTIPLE EXCEL WORKSHEETS INTO A SINGLE PANDAS This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command. For those of you that want the TLDR, here is the command: df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True) Read on for an explanation of when to use this and COMPREHENSIVE GUIDE TO GROUPING AND AGGREGATING WITH The most common built in aggregation functions are basic math functions including sum, mean, median, minimum, maximum, standard deviation, variance, mean absolute deviation and product. We can apply all these functions to the fare while grouping by the embark_town : This is all relatively straightforward math. OVERVIEW OF PANDAS DATA TYPES RangeIndex: 5 entries, 0 to 4 Data columns (total 10 columns): Customer Number 5 non-null float64 Customer Name 5 non-null object 2016 5 non-null object 2017 5 non-null object Percent Growth 5 non-null object Jan Units 5 non-null object Month 5 non-null int64 Day 5 non-null int64 Year 5 non-null int64 Active 5 non-null object dtypes: float64(1), int64(3 READING HTML TABLES WITH PANDAS Introduction. The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. BINNING DATA WITH PANDAS QCUT AND CUT Binning. One of the most common instances of binning is done behind the scenes for you when creating a histogram. The histogram below of customer sales data, shows how a continuous set of sales numbers can be divided into discrete bins (for example: $60,000 - $70,000) and then used to group and count account instances. CREATING A WATERFALL CHART IN PYTHON Creating the Chart. Execute the standard imports and make sure IPython will display matplot plots. Setup the data we want to waterfall chart and load it into a dataframe. The data needs to start with your starting value but you leave out the final total. We will calculateit.
CLEANING UP CURRENCY DATA WITH PANDAS In this example, the data is a mixture of currency labeled and non-currency labeled values. For a small example like this, you might want to clean it up at the source file. IMPROVING PANDAS EXCEL OUTPUT First, we resize the sheet by adjusting the zoom. worksheet.set_zoom(90) Some of our biggest improvements come through formatting the columns to make the data more readable. add_format is very useful for improving your standard output. Here are two examples of formatting numbers: COMBINING DATA FROM MULTIPLE EXCEL FILES The Problem. Before, I get into the examples, here is a simple diagram showing the challenges with the common process used in businesses all over the world to consolidate data from multiple Excel files, clean it up and perform some analysis. COMBINE MULTIPLE EXCEL WORKSHEETS INTO A SINGLE PANDAS This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command. For those of you that want the TLDR, here is the command: df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True) Read on for an explanation of when to use this and EXCEL “FILTER AND EDIT” Excel: “Filter and Edit” Outside of the Pivot Table, one of the top go-to tools in Excel is the Filter. This simple tool allows a user to quickly filter and sort the data by various numeric, text and formatting criteria. PRACTICAL BUSINESS PYTHONABOUTRESOURCESMAILING LISTARCHIVESOVERVIEW OFPANDAS DATA TYPES
The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. EFFICIENTLY CLEANING TEXT WITH PANDAS Cleaning attempt #2. Another approach that is very performant and flexible is to use np.select to run multiple matches and apply a specified value upon match.. There are several good resources that I used to learn how to use np.select.This article from Dataquest is a good overview. I also found this presentation from Nathan Cheever very interesting and information. MONTE CARLO SIMULATION WITH PYTHON The real “magic” of the Monte Carlo simulation is that if we run a simulation many times, we start to develop a picture of the likely distribution of results. In Excel, you would need VBA or another plugin to run multiple iterations. In python, we can use a for loop to run as many simulations as we’d like. CREATING POWERPOINT PRESENTATIONS WITH PYTHON Creating tables in PowerPoint is a good news / bad news story. The good news is that there is an API to create one. The bad news is that you can’t easily convert a pandas DataFrame to a table using the built in API.However, we are very fortunate that someone has already done all the hard work for us and created PandasToPowerPoint.. This excellent piece of code takes a DataFrame and converts CLEANING UP CURRENCY DATA WITH PANDAS In this example, the data is a mixture of currency labeled and non-currency labeled values. For a small example like this, you might want to clean it up at the source file. INTRODUCTION TO MARKET BASKET ANALYSIS IN PYTHON A useful (but somewhat overlooked) technique is called association analysis which attempts to find common patterns of items in large data sets. One specific application is often called market basket analysis. The most commonly cited example of market basket analysis is the so-called “beer and diapers” case. GUIDE TO ENCODING CATEGORICAL VALUES IN PYTHON The Data Set. For this article, I was able to find a good dataset at the UCI Machine Learning Repository.This particular Automobile Data Set includes a good mix of categorical values as well as continuous values and serves as a useful example that is relatively easy to understand. Since domain understanding is an important aspect when deciding how to encode various categorical values - this CREATING A WATERFALL CHART IN PYTHON Creating the Chart. Execute the standard imports and make sure IPython will display matplot plots. Setup the data we want to waterfall chart and load it into a dataframe. The data needs to start with your starting value but you leave out the final total. We will calculateit.
GENERATING EXCEL REPORTS FROM A PANDAS PIVOT TABLE Introduction. The previous pivot table article described how to use the pandas pivot_table function to combine and present data in an easy to view manner. This concept is probably familiar to anyone that has used pivot tables in Excel. However, pandas has the capability to easily take a cross section of the data and manipulate it. COMBINE MULTIPLE EXCEL WORKSHEETS INTO A SINGLE PANDAS This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command. For those of you that want the TLDR, here is the command: df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True) Read on for an explanation of when to use this and PRACTICAL BUSINESS PYTHONABOUTRESOURCESMAILING LISTARCHIVESOVERVIEW OFPANDAS DATA TYPES
The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. EFFICIENTLY CLEANING TEXT WITH PANDAS Cleaning attempt #2. Another approach that is very performant and flexible is to use np.select to run multiple matches and apply a specified value upon match.. There are several good resources that I used to learn how to use np.select.This article from Dataquest is a good overview. I also found this presentation from Nathan Cheever very interesting and information. MONTE CARLO SIMULATION WITH PYTHON The real “magic” of the Monte Carlo simulation is that if we run a simulation many times, we start to develop a picture of the likely distribution of results. In Excel, you would need VBA or another plugin to run multiple iterations. In python, we can use a for loop to run as many simulations as we’d like. CREATING POWERPOINT PRESENTATIONS WITH PYTHON Creating tables in PowerPoint is a good news / bad news story. The good news is that there is an API to create one. The bad news is that you can’t easily convert a pandas DataFrame to a table using the built in API.However, we are very fortunate that someone has already done all the hard work for us and created PandasToPowerPoint.. This excellent piece of code takes a DataFrame and converts CLEANING UP CURRENCY DATA WITH PANDAS In this example, the data is a mixture of currency labeled and non-currency labeled values. For a small example like this, you might want to clean it up at the source file. INTRODUCTION TO MARKET BASKET ANALYSIS IN PYTHON A useful (but somewhat overlooked) technique is called association analysis which attempts to find common patterns of items in large data sets. One specific application is often called market basket analysis. The most commonly cited example of market basket analysis is the so-called “beer and diapers” case. GUIDE TO ENCODING CATEGORICAL VALUES IN PYTHON The Data Set. For this article, I was able to find a good dataset at the UCI Machine Learning Repository.This particular Automobile Data Set includes a good mix of categorical values as well as continuous values and serves as a useful example that is relatively easy to understand. Since domain understanding is an important aspect when deciding how to encode various categorical values - this CREATING A WATERFALL CHART IN PYTHON Creating the Chart. Execute the standard imports and make sure IPython will display matplot plots. Setup the data we want to waterfall chart and load it into a dataframe. The data needs to start with your starting value but you leave out the final total. We will calculateit.
GENERATING EXCEL REPORTS FROM A PANDAS PIVOT TABLE Introduction. The previous pivot table article described how to use the pandas pivot_table function to combine and present data in an easy to view manner. This concept is probably familiar to anyone that has used pivot tables in Excel. However, pandas has the capability to easily take a cross section of the data and manipulate it. COMBINE MULTIPLE EXCEL WORKSHEETS INTO A SINGLE PANDAS This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command. For those of you that want the TLDR, here is the command: df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True) Read on for an explanation of when to use this and COMPREHENSIVE GUIDE TO GROUPING AND AGGREGATING WITH The most common built in aggregation functions are basic math functions including sum, mean, median, minimum, maximum, standard deviation, variance, mean absolute deviation and product. We can apply all these functions to the fare while grouping by the embark_town : This is all relatively straightforward math. READING HTML TABLES WITH PANDAS Introduction. The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. OVERVIEW OF PANDAS DATA TYPES RangeIndex: 5 entries, 0 to 4 Data columns (total 10 columns): Customer Number 5 non-null float64 Customer Name 5 non-null object 2016 5 non-null object 2017 5 non-null object Percent Growth 5 non-null object Jan Units 5 non-null object Month 5 non-null int64 Day 5 non-null int64 Year 5 non-null int64 Active 5 non-null object dtypes: float64(1), int64(3 BINNING DATA WITH PANDAS QCUT AND CUT Binning. One of the most common instances of binning is done behind the scenes for you when creating a histogram. The histogram below of customer sales data, shows how a continuous set of sales numbers can be divided into discrete bins (for example: $60,000 - $70,000) and then used to group and count account instances. CREATING A WATERFALL CHART IN PYTHON Creating the Chart. Execute the standard imports and make sure IPython will display matplot plots. Setup the data we want to waterfall chart and load it into a dataframe. The data needs to start with your starting value but you leave out the final total. We will calculateit.
CLEANING UP CURRENCY DATA WITH PANDAS In this example, the data is a mixture of currency labeled and non-currency labeled values. For a small example like this, you might want to clean it up at the source file. IMPROVING PANDAS EXCEL OUTPUT First, we resize the sheet by adjusting the zoom. worksheet.set_zoom(90) Some of our biggest improvements come through formatting the columns to make the data more readable. add_format is very useful for improving your standard output. Here are two examples of formatting numbers: COMBINING DATA FROM MULTIPLE EXCEL FILES The Problem. Before, I get into the examples, here is a simple diagram showing the challenges with the common process used in businesses all over the world to consolidate data from multiple Excel files, clean it up and perform some analysis. COMBINE MULTIPLE EXCEL WORKSHEETS INTO A SINGLE PANDAS This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command. For those of you that want the TLDR, here is the command: df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True) Read on for an explanation of when to use this and EXCEL “FILTER AND EDIT” Excel: “Filter and Edit” Outside of the Pivot Table, one of the top go-to tools in Excel is the Filter. This simple tool allows a user to quickly filter and sort the data by various numeric, text and formatting criteria. PRACTICAL BUSINESS PYTHONABOUTRESOURCESMAILING LISTARCHIVESOVERVIEW OFPANDAS DATA TYPES
The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. EFFICIENTLY CLEANING TEXT WITH PANDAS Cleaning attempt #2. Another approach that is very performant and flexible is to use np.select to run multiple matches and apply a specified value upon match.. There are several good resources that I used to learn how to use np.select.This article from Dataquest is a good overview. I also found this presentation from Nathan Cheever very interesting and information. MONTE CARLO SIMULATION WITH PYTHON The real “magic” of the Monte Carlo simulation is that if we run a simulation many times, we start to develop a picture of the likely distribution of results. In Excel, you would need VBA or another plugin to run multiple iterations. In python, we can use a for loop to run as many simulations as we’d like. CREATING POWERPOINT PRESENTATIONS WITH PYTHON Creating tables in PowerPoint is a good news / bad news story. The good news is that there is an API to create one. The bad news is that you can’t easily convert a pandas DataFrame to a table using the built in API.However, we are very fortunate that someone has already done all the hard work for us and created PandasToPowerPoint.. This excellent piece of code takes a DataFrame and converts READING HTML TABLES WITH PANDAS Introduction. The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. INTRODUCTION TO MARKET BASKET ANALYSIS IN PYTHON A useful (but somewhat overlooked) technique is called association analysis which attempts to find common patterns of items in large data sets. One specific application is often called market basket analysis. The most commonly cited example of market basket analysis is the so-called “beer and diapers” case. EXCEL “FILTER AND EDIT” There is a special bonus of $250 plus a 4.5% commission for all shoe sales > $1000 in a single transaction. In order to do this in Excel, using the Filter and edit approach: Add a commission column with 2%. Add a bonus column of $0. Filter on shirts and change the vale to 2.5%. Clear the filter. GUIDE TO ENCODING CATEGORICAL VALUES IN PYTHON The Data Set. For this article, I was able to find a good dataset at the UCI Machine Learning Repository.This particular Automobile Data Set includes a good mix of categorical values as well as continuous values and serves as a useful example that is relatively easy to understand. Since domain understanding is an important aspect when deciding how to encode various categorical values - this USING PANDAS TO CREATE AN EXCEL DIFF Introduction. As part of my continued exploration of pandas, I am going to walk through a real world example of how to use pandas to automate a process that could be very difficult to do in Excel.My business problem is that I have two Excel files that are structured similarly but have different data and I would like to easily understand what has changed between the two files. COMBINE MULTIPLE EXCEL WORKSHEETS INTO A SINGLE PANDAS This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command. For those of you that want the TLDR, here is the command: df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True) Read on for an explanation of when to use this and PRACTICAL BUSINESS PYTHONABOUTRESOURCESMAILING LISTARCHIVESOVERVIEW OFPANDAS DATA TYPES
The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. EFFICIENTLY CLEANING TEXT WITH PANDAS Cleaning attempt #2. Another approach that is very performant and flexible is to use np.select to run multiple matches and apply a specified value upon match.. There are several good resources that I used to learn how to use np.select.This article from Dataquest is a good overview. I also found this presentation from Nathan Cheever very interesting and information. MONTE CARLO SIMULATION WITH PYTHON The real “magic” of the Monte Carlo simulation is that if we run a simulation many times, we start to develop a picture of the likely distribution of results. In Excel, you would need VBA or another plugin to run multiple iterations. In python, we can use a for loop to run as many simulations as we’d like. CREATING POWERPOINT PRESENTATIONS WITH PYTHON Creating tables in PowerPoint is a good news / bad news story. The good news is that there is an API to create one. The bad news is that you can’t easily convert a pandas DataFrame to a table using the built in API.However, we are very fortunate that someone has already done all the hard work for us and created PandasToPowerPoint.. This excellent piece of code takes a DataFrame and converts READING HTML TABLES WITH PANDAS Introduction. The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. INTRODUCTION TO MARKET BASKET ANALYSIS IN PYTHON A useful (but somewhat overlooked) technique is called association analysis which attempts to find common patterns of items in large data sets. One specific application is often called market basket analysis. The most commonly cited example of market basket analysis is the so-called “beer and diapers” case. EXCEL “FILTER AND EDIT” There is a special bonus of $250 plus a 4.5% commission for all shoe sales > $1000 in a single transaction. In order to do this in Excel, using the Filter and edit approach: Add a commission column with 2%. Add a bonus column of $0. Filter on shirts and change the vale to 2.5%. Clear the filter. GUIDE TO ENCODING CATEGORICAL VALUES IN PYTHON The Data Set. For this article, I was able to find a good dataset at the UCI Machine Learning Repository.This particular Automobile Data Set includes a good mix of categorical values as well as continuous values and serves as a useful example that is relatively easy to understand. Since domain understanding is an important aspect when deciding how to encode various categorical values - this USING PANDAS TO CREATE AN EXCEL DIFF Introduction. As part of my continued exploration of pandas, I am going to walk through a real world example of how to use pandas to automate a process that could be very difficult to do in Excel.My business problem is that I have two Excel files that are structured similarly but have different data and I would like to easily understand what has changed between the two files. COMBINE MULTIPLE EXCEL WORKSHEETS INTO A SINGLE PANDAS This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command. For those of you that want the TLDR, here is the command: df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True) Read on for an explanation of when to use this and AUTOMATING WINDOWS APPLICATIONS USING COM Pywin32 is basically a very thin wrapper of python that allows us to interact with COM objects and automate Windows applications with python. The power of this approach is that you can pretty much do anything that a Microsoft Application can do through python. The downside is that you have to run this on a Windows system withMicrosoft Office
PANDAS DATAFRAME VISUALIZATION TOOLS Data Analysis Applications. The second category of GUI applications are full-fledged applications typically using a web back-end like Flask or a separate application based on Qt. These applications vary in complexity and capability from simple table views and plotting capabilities to robust statistical analysis. READING HTML TABLES WITH PANDAS Introduction. The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML.However, there can be some challenges in cleaning and formatting the data before analyzing it. OVERVIEW OF PANDAS DATA TYPES RangeIndex: 5 entries, 0 to 4 Data columns (total 10 columns): Customer Number 5 non-null float64 Customer Name 5 non-null object 2016 5 non-null object 2017 5 non-null object Percent Growth 5 non-null object Jan Units 5 non-null object Month 5 non-null int64 Day 5 non-null int64 Year 5 non-null int64 Active 5 non-null object dtypes: float64(1), int64(3 CREATING A WATERFALL CHART IN PYTHON Creating the Chart. Execute the standard imports and make sure IPython will display matplot plots. Setup the data we want to waterfall chart and load it into a dataframe. The data needs to start with your starting value but you leave out the final total. We will calculateit.
IMPROVING PANDAS EXCEL OUTPUT First, we resize the sheet by adjusting the zoom. worksheet.set_zoom(90) Some of our biggest improvements come through formatting the columns to make the data more readable. add_format is very useful for improving your standard output. Here are two examples of formatting numbers: COMBINING DATA FROM MULTIPLE EXCEL FILES The Problem. Before, I get into the examples, here is a simple diagram showing the challenges with the common process used in businesses all over the world to consolidate data from multiple Excel files, clean it up and perform some analysis. BINNING DATA WITH PANDAS QCUT AND CUT Binning. One of the most common instances of binning is done behind the scenes for you when creating a histogram. The histogram below of customer sales data, shows how a continuous set of sales numbers can be divided into discrete bins (for example: $60,000 - $70,000) and then used to group and count account instances. CLEANING UP CURRENCY DATA WITH PANDAS In this example, the data is a mixture of currency labeled and non-currency labeled values. For a small example like this, you might want to clean it up at the source file. COMBINE MULTIPLE EXCEL WORKSHEETS INTO A SINGLE PANDAS This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command. For those of you that want the TLDR, here is the command: df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True) Read on for an explanation of when to use this and Toggle navigation _ _* Home
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PRACTICAL BUSINESS PYTHON Taking care of business, one python script at a time Mon 09 November 2020 COMPREHENSIVE GUIDE TO GROUPING AND AGGREGATING WITH PANDAS Posted by Chris Moffittin articles
One of the most basic analysis functions is grouping and aggregating data. In some cases, this level of analysis may be sufficient to answer business questions. In other instances, this activity might be the first step in a more complex data science analysis. In pandas, the groupby function can be combined with one or more aggregation functions to quickly and easily summarize data. This concept is deceptively simple and most new pandas users will understand this concept. However, they might be surprised at how useful complex aggregation functions can be for supporting sophisticated analysis. This article will quickly summarize the basic pandas aggregation functions and show examples of more complex custom aggregations. Whether you are a new or more experienced pandas user, I think you will learn a few things from this article.Read more... __
-------------------------Mon 19 October 2020
READING POORLY STRUCTURED EXCEL FILES WITH PANDAS Posted by Chris Moffittin articles
With pandas it is easy to read Excel files and convert the data into a DataFrame. Unfortunately Excel files in the real world are often poorly constructed. In those cases where the data is scattered across the worksheet, you may need to customize the way you read the data. This article will discuss how to use pandas and openpyxl to read these types of Excel files and cleanly convert the data to a DataFrame suitable for further analysis.Read more... __
-------------------------Mon 12 October 2020
CASE STUDY: PROCESSING HISTORICAL WEATHER PATTERN DATA Posted by Chris Moffittin articles
The main purpose of this blog is to show people how to use Python to solve real world problems. Over the years, I have been fortunate enough to hear from readers about how they have used tips and tricks from this site to solve their own problems. In this post, I am extremely delighted to present a real world case study. I hope it will give you some ideas about how you can apply these concepts to yourown problems.
This example comes from Michael Biermann from Germany. He had the challenging task of trying to gather detailed historical weather data in order to do analysis on the relationship between air temperature and power consumption. This article will show how he used a pipeline of Python programs to automate the process of collecting, cleaning and processing gigabytes of weather data in order to performhis analysis.
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------------------------- Mon 21 September 2020 PB PYTHON ARTICLE ROADMAP Posted by Chris Moffittin articles
September 17th is Practical Business Python’s anniversary. Last year , I reflected on 5 years of growth. This year, I wanted to take a step back and develop a guide to guide readers through the content on PB PythonRead more... __
------------------------- Mon 14 September 2020 READING HTML TABLES WITH PANDAS Posted by Chris Moffittin articles
The pandas read_html() function is a quick and convenient way to turn an HTML table into a pandas DataFrame. This function can be useful for quickly incorporating tables from various websites without figuring out how to scrape the site’s HTML. However, there can be some challenges in cleaning and formatting the data before analyzing it. In this article, I will discuss how to use pandas read_html() to read and clean several Wikipedia HTML tables so that you can use them for furthernumeric analysis.
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-------------------------Mon 17 August 2020
TAKING ANOTHER LOOK AT PLOTLY Posted by Chris Moffittin articles
I’ve written quite a bit about visualization in python - partially because the landscape is always evolving. Plotly stands out as one of the tools that has undergone a significant amount of change since my first post in 2015. If you have not looked at using Plotly for python data visualization lately, you might want to take it for a spin. This article will discuss some of the most recent changes with Plotly, what the benefits are and why Plotly is worth considering for your data visualization needs.Read more... __
-------------------------Tue 02 June 2020
SIDETABLE - CREATE SIMPLE SUMMARY TABLES IN PANDAS Posted by Chris Moffittin articles
Today I am happy to announce the release of a new pandas utility library called sidetable . This library makes it easy to build a frequency table and simple summary of missing values in a DataFrame. I have found it to be a useful tool when starting data exploration on a new data set and I hope others find it useful as well. This project is also an opportunity to illustrate how to use pandasnew API
to register custom DataFrame accessors. This API allows you to build custom functions for working with pandas DataFrames and Series and could be really useful for building out your own library of custom pandas accessor functions.Read more... __
-------------------------Mon 04 May 2020
EXPLORING AN ALTERNATIVE TO JUPYTER NOTEBOOKS FOR PYTHON DEVELOPMENT Posted by Chris Moffittin articles
Jupyter notebooks are an amazing tool for evaluating and exploring data. I have been using them as an integral part of my day to day analysis for several years and reach for them almost any time I need to do data analysis or exploration. Despite how much I like using python in Jupyter notebooks, I do wish for the editor capabilities you can find in VS Code. I also would like my files to work better when versioning them with git. Recently, I have started using a solution that supports the interactivity of the Jupyter notebook and the developer friendliness of plain .py text files. Visual Studio Code enables this approach through Jupyter code cells and the Python Interactive Window. Using this combination, you can visualize and explore your data in real time with a plain python file that includes some lightweight markup. The resulting file works seamlessly with all VS Code editing features and supports clean git check ins. The rest of this article will discuss how to use this python development workflow within VS Code and some of the primary reasons why you may or may not want to do so.Read more... __
-------------------------Mon 30 March 2020
USING WSL TO BUILD A PYTHON DEVELOPMENT ENVIRONMENT ON WINDOWS Posted by Chris Moffittin articles
In 2016, Microsoft launched Windows Subsystem for Linux (WSL) which brought robust unix functionality to Windows. In May 2019, Microsoft announced the release of WSL 2 which includes an updated architecture that improved many aspects of WSL - especially file system performance. I have been following WSL for a while but now that WSL 2 is nearing general release, I decided to install it and try it out. In the few days I have been using it, I have really enjoyed the experience. The combo of using Windows 10 and a full Linux distro like Ubuntu is a really powerful development solution that workssurprisingly well.
Read more... __
------------------------- Tue 18 February 2020 PYTHON TOOLS FOR RECORD LINKING AND FUZZY MATCHING Posted by Chris Moffittin articles
Record linking and fuzzy matching are terms used to describe the process of joining two data sets together that do not have a common unique identifier. Examples include trying to join files based on people’s names or merging data that only have organization’s nameand address.
This problem is a common business challenge and difficult to solve in a systematic way - especially when the data sets are large. A naive approach using Excel and vlookup statements can work but requires a lot of human intervention. Fortunately, python provides two libraries that are useful for these types of problems and can support complex matching algorithms with a relatively simple API.Read more... __
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