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 To work through the examples below, we first need to load the species and surveys files into pandas DataFrames. In iPython:
Take note that the Concatenating DataFramesWe can use the
When we concatenate DataFrames, we need to specify the axis.
Row Index Values and ConcatHave a look at the Writing Out Data to CSVWe can use the
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.
Joining DataFramesWhen 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. NOTE: This process of joining tables is similar to what we do with tables in an SQL database. For example, the Storing data in this way has many benefits including:
Joining Two DataFramesTo better understand joins, let’s grab the first 10 lines of our data as a subset to work with. We’ll use the
In this example, Identifying join keysTo 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.
In our example, the join key is the column containing the two-letter species identifier, which is called 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. Inner joinsThe 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. Inner joins yield a DataFrame that contains only rows where the value being joined exists in BOTH tables. An example of an inner join, adapted from Jeff Atwood’s blogpost about SQL joins is below: The pandas function for performing joins is called
The result of an inner
join of The two DataFrames that we want to join are passed to the The result Notice that Left joinsWhat if we want to add information from 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 Note: a left join will still discard rows from the A left join is performed in
pandas by calling the same
The result DataFrame from a left join (
These rows are the ones where the value of Other join typesThe pandas
Final Challenges
How do I merge 3 data frames?How do I merge multiple DataFrames with the same column names?. Import module.. Create or load first dataframe.. Create or load second dataframe.. Concatenate on the basis of same column names.. Display result.. How do I merge 3 DataFrames in pandas Python?Pandas merge() function is used to merge multiple Dataframes. We can use either pandas. merge() or DataFrame. merge() to merge multiple Dataframes.
How do you join 3 tables in Python?The concat() function performs concatenation operations of multiple tables along one of the axes (row-wise or column-wise).
Which method is used to merge two DataFrames?To concatenate an arbitrary number of pandas objects ( DataFrame or Series ), use concat .
|