Join And Merge Pandas Dataframe

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The materials for this course were produced by the Anaconda training team. As a Data Scientist, you'll often find that the data you need is not in a single file. It may be spread across a number merge multiple data frames pandas text files, spreadsheets, or databases. You want to be able to import the data of interest merge multiple data frames pandas a collection of DataFrames and figure out how to combine them to answer your central questions. This course is all about the act of merge multiple data frames pandas, or merging, DataFrames, an essential part of any working Data Scientist's toolbox.

You'll hone your pandas skills by learning how to organize, reshape, and aggregate multiple data sets to answer your specific questions. In this chapter, you'll learn about different techniques you can use to import multiple files into DataFrames.

Having imported your data into individual DataFrames, you'll then learn how to share information between DataFrames using their Indexes. Understanding how Indexes work is essential information that you'll need for merging DataFrames later in the course.

Having learned how to import multiple DataFrames and share information using Indexes, in this chapter you'll learn how to perform database-style operations to combine DataFrames. In particular, you'll learn about appending and concatenating DataFrames while working with a variety of real-world datasets. Here, you'll learn all about merging pandas DataFrames.

You'll explore different techniques for merging, and learn about left joins, right joins, inner joins, and outer joins, as well as when to use which.

You'll also learn about ordered merging, which is useful when you want to merge DataFrames whose columns have natural orderings, like merge multiple data frames pandas columns. To cement your new skills, you'll apply them by working on an in-depth study involving Olympic medal data.

The analysis involves integrating your multi-DataFrame skills from this course and also skills you've gained in previous pandas courses. This is a rich dataset that will allow you to fully leverage your pandas data manipulation skills.

Community Projects DataChats Episodes This course is part of these tracks: Dhavide Aruliah Director of Training at Anaconda. Prerequisites pandas Foundations Manipulating DataFrames with pandas.

Course Description As a Data Scientist, you'll often find that the data you need is not in a single file. Case Study - Summer Olympics.

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In many "real world" situations, the data that we want to use come in multiple files. We often need to combine these files into a single DataFrame to analyze the data. The pandas package provides various methods for combining DataFrames including merge and concat. To work through the examples below, we first need to load the species and surveys files into pandas DataFrames. Many functions in python have a set of options that can be set by the user if needed. We can use the concat function in Pandas to append either columns or rows from one DataFrame to another.

Let's grab two subsets of our data to see how this works. When we concatenate DataFrames, we need to specify the axis. It will automatically detect whether the column names are the same and will stack accordingly. To stack the data vertically, we need to make sure we have the same columns and associated column format in both datasets. When we stack horizonally, we want to make sure what we are doing makes sense ie the data are related in some way.

Have a look at the horizontalStack dataframe? The row indexes for the two data frames surveySub and surveySubLast10 are not the same. Thus, when Python tries to concatenate the two dataframes it can't place them next to each other. The new horizontalStack dataframe is now side by side without the extra NaN values.

Note that the code below will by default save the data into the current working directory. We can save it to a different folder by adding the foldername and a slash to the file verticalStack. Check out your working directory to make sure the CSV wrote out properly, and that you can open it! If you want, try to bring it back into python to make sure it imports properly. In the data folder, there are two survey data files: Read the data into python and combine the files to make one new data frame.

Create a plot of average plot weight by year grouped by sex. Export your results as a CSV and make sure it reads back into python properly. When we concatenated our DataFrames we simply added them to each other - stacking them either vertically or side by side. Another way to combine DataFrames is to use columns in each dataset that contain common values a common unique id.

Combining DataFrames using a common field is called "joining". The columns containing the common values are called "join key s ". Joining DataFrames in this way is often useful when one DataFrame is a "lookup table" containing additional data that we want to include in the other. For example, the species. This table contains the genus, species and taxa code for 55 species. The species code is unique for each line. These species are identified in our survey data as well using the unique species code.

Rather than adding 3 more columns for the genus, species and taxa to each of the 35, line Survey data table, we can maintain the shorter table with the species information. When we want to access that information, we can create a query that joins the additional columns of information to the Survey data.

To better understand joins, let's grab the first 10 lines of our data as a subset to work with. We'll also read in a subset of the species table. To identify appropriate join keys we first need to know which field s are shared between the files DataFrames. We might inspect both DataFrames to identify these columns.

If we are lucky, both DataFrames will have columns with the same name that also contain the same data. If we are less lucky, we need to identify a differently-named column in each DataFrame that contains the same information. Now that we know the fields with the common species ID attributes in each DataFrame, we are almost ready to join our data. However, since there are different types of joins , we also need to decide which type of join makes sense for our analysis.

The most common type of join is called an inner join. An inner join combines two DataFrames based on a join key and returns a new DataFrame that contains only those rows that have matching values in both of the original DataFrames.

An example of an inner join, adapted from this page is below:. The pandas function for performing joins is called merge and an Inner join is the default option:. The result of an inner join of surveySub and speciesSub is a new DataFrame that contains the combined set of columns from surveySub and speciesSub. It only contains rows that have two-letter species codes that are the same in both the surveysSub and speciesSub DataFrames. The two DataFrames that we want to join are passed to the merge function using the left and right argument.

For inner joins, the order of the left and right arguments does not matter. This happened because we had a species field in both tables with information that could not be joined. What if we want to add information from speciesSub to surveysSub without losing any of the information from surveySub?

In this case, we use a different type of join called a "left outer join", or a "left join". Like an inner join, a left join uses join keys to combine two DataFrames. Unlike an inner join, a left join will return all of the rows from the left DataFrame, even those rows whose join key s do not have values in the right DataFrame.

Rows in the left DataFrame that are missing values for the join key s in the right DataFrame will simply have null i. These rows are the ones where the value of species from surveySub in this case, NaN does not occur in speciesSub.

Create a new DataFrame by joining the contents of the surveys. Then calculate and plot the distribution of:. Calculate a diversity index of your choice for control vs rodent exclosure plots.

The index should consider both species abundance and number of species. You might choose to use the simple biodiversity index described here which calculates diversity as:. Combining DataFrames with pandas In many "real world" situations, the data that we want to use come in multiple files. Learning Objectives Learn how to concatenate two DataFrames together append one dataFrame to a second dataFrame Learn how to join two DataFrames together using a uniqueID found in both DataFrames Learn how to write out a DataFrame to csv using Pandas To work through the examples below, we first need to load the species and surveys files into pandas DataFrames.

Rodent 51 US Sparrow sp. Joining DataFrames When we concatenated our DataFrames we simply added them to each other - stacking them either vertically or side by side. This process of joining tables is similar to what we do with tables in an SQL database. Storing data in this way has many benefits including: It ensures consistency in the spelling of species attributes genus, species and taxa given each species is only entered once.

Imagine the possibilities for spelling errors when entering the genus and species thousands of times! It also makes it easy for us to make changes to the species information once without having to find each instance of it in the larger survey data.

It optimizes the size of our data. Joining Two DataFrames To better understand joins, let's grab the first 10 lines of our data as a subset to work with.

Identifying join keys To identify appropriate join keys we first need to know which field s are shared between the files DataFrames. Inner joins The most common type of join is called an inner join. An example of an inner join, adapted from this page is below: The pandas function for performing joins is called merge and an Inner join is the default option: Do the results look cleaner? Left joins What if we want to add information from speciesSub to surveysSub without losing any of the information from surveySub?

Other join types The pandas merge function supports two other join types: Similar to a left join, except all rows from the right DataFrame are kept, while rows from the left DataFrame without matching join key s values are discarded.

This join type returns the all pairwise combinations of rows from both DataFrames; i. This join type is very rarely used. Then calculate and plot the distribution of: Use that data to summarize the number of plots by plot type. You might choose to use the simple biodiversity index described here which calculates diversity as: