- While merge is a module function.join is an object function that lives on your DataFrame. This enables you to specify only one DataFrame, which will join the DataFrame you call.join on. Under the hood.join uses merge , but it provides a more efficient way to.
- We have a method called pandas.merge that merges dataframes similar to the database join operations. Follow the below steps to achieve the desired output. Merge method uses the common column for the merge operation.
- To combine data frames based on a common column (s), i.e., adding columns of second data frame to the first data frame with respect to a common column (s), you can use merge function. To combine data frames: with rows of second data frame added to those of.
34 Reshaping and Joining Data Frames Gathering for Tidiness. The gather function in the tidyr package makes most untidy data frames tidier. Ungathering with spread. While this organization of the data—with each row being an observation and each column being. Splitting Columns. Groupbys and split-apply-combine to answer the question. Now that you've checked out out data, it's time for the fun part. You'll first use a groupby method to split the data into groups, where each group is the set of movies released in a given year.
Can I work with data from multiple sources?
How can I combine data from different data sets?
Combine data from multiple files into a single DataFrame using merge and concat.
Combine two DataFrames using a unique ID found in both DataFrames.
to_csvto export a DataFrame in CSV format.
Join DataFrames using common fields (join keys).
In many “real world” situations, the data that we want to use come in multiplefiles. We often need to combine these files into a single DataFrame to analyzethe data. The pandas package provides various methods for combiningDataFrames including
To work through the examples below, we first need to load the species andsurveys files into pandas DataFrames. In iPython:
Take note that the
read_csv method we used can take some additional options whichwe didn’t use previously. Many functions in Python have a set of options thatcan be set by the user if needed. In this case, we have told pandas to assignempty values in our CSV to NaN
keep_default_na=False, na_values=['].More about all of the read_csv options here.
We can use the
concat function in pandas to append either columns or rows fromone DataFrame to another. Let’s grab two subsets of our data to see how thisworks.
When we concatenate DataFrames, we need to specify the axis.
axis=0 tellspandas to stack the second DataFrame UNDER the first one. It will automaticallydetect whether the column names are the same and will stack accordingly.
axis=1 will stack the columns in the second DataFrame to the RIGHT of thefirst DataFrame. To stack the data vertically, we need to make sure we have thesame columns and associated column format in both datasets. When we stackhorizontally, we want to make sure what we are doing makes sense (i.e. the data arerelated in some way).
Row Index Values and Concat
Have a look at the
vertical_stack dataframe? Notice anything unusual?The row indexes for the two data frames
survey_sub_last10have been repeated. We can reindex the new dataframe using the
Writing Out Data to CSV
We can use the
to_csv command to do export a DataFrame in CSV format. Note that the codebelow will by default save the data into the current working directory. We cansave it to a different folder by adding the foldername and a slash to the file
vertical_stack.to_csv('foldername/out.csv'). We use the ‘index=False’ so thatpandas doesn’t include the index number for each line.
Check out your working directory to make sure the CSV wrote out properly, andthat you can open it! If you want, try to bring it back into Python to make sureit imports properly.
Challenge - Combine Data
In the data folder, there are two survey data files:
surveys2002.csv. Read the data into Python and combine the files to make onenew 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 combineDataFrames is to use columns in each dataset that contain common values (acommon 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 “lookuptable” 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 anSQL database.
For example, the
species.csv file that we’ve been working with is a lookuptable. This table contains the genus, species and taxa code for 55 species. Thespecies code is unique for each line. These species are identified in our surveydata as well using the unique species code. Rather than adding 3 more columnsfor the genus, species and taxa to each of the 35,549 line Survey data table, wecan maintain the shorter table with the species information. When we want toaccess that information, we can create a query that joins the additional columnsof information to the Survey data.
Storing data in this way has many benefits including:
- It ensures consistency in the spelling of species attributes (genus, speciesand taxa) given each species is only entered once. Imagine the possibilitiesfor 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 oncewithout 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 asubset to work with. We’ll use the
.head method to do this. We’ll also readin a subset of the species table.
In this example,
species_sub is the lookup table containing genus, species, andtaxa names that we want to join with the data in
survey_sub to produce a newDataFrame that contains all of the columns from both
Identifying join keys
To identify appropriate join keys we first need to know which field(s) areshared between the files (DataFrames). We might inspect both DataFrames toidentify these columns. If we are lucky, both DataFrames will have columns withthe same name that also contain the same data. If we are less lucky, we need toidentify a (differently-named) column in each DataFrame that contains the sameinformation.
In our example, the join key is the column containing the two-letter speciesidentifier, which is called
Now that we know the fields with the common species ID attributes in eachDataFrame, we are almost ready to join our data. However, since there aredifferent types of joins, wealso 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 combinestwo DataFrames based on a join key and returns a new DataFrame that containsonly those rows that have matching values in both of the originalDataFrames.
Combine Two Data Frames
Inner joins yield a DataFrame that contains only rows where the value beingjoined 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
merge and an Inner join isthe default option:
The result of an inner join of
species_sub is a new DataFramethat contains the combined set of columns from
species_sub. Itonly contains rows that have two-letter species codes that are the same inboth the
species_sub DataFrames. In other words, if a row in
survey_sub has a value of
species_id that does not appear in the
species, it will not be included in the DataFrame returned by aninner join. Similarly, if a row in
species_sub has a value of
species_idthat does not appear in the
species_id column of
survey_sub, that row will notbe included in the DataFrame returned by an inner join.
The two DataFrames that we want to join are passed to the
merge function usingthe
right argument. The
left_on='species' argument tells
mergeto use the
species_id column as the join key from
leftDataFrame). Similarly , the
right_on='species_id' argument tells
merge touse the
species_id column as the join key from
rightDataFrame). For inner joins, the order of the
right arguments doesnot matter.
merged_inner DataFrame contains all of the columns from
survey_sub(record id, month, day, etc.) as well as all the columns from
species_sub(species_id, genus, species, and taxa).
merged_inner has fewer rows than
survey_sub. This is anindication that there were rows in
surveys_df with value(s) for
species_id thatdo not exist as value(s) for
What if we want to add information from
survey_sub withoutlosing any of the information from
survey_sub? In this case, we use a differenttype 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. Unlikean inner join, a left join will return all of the rows from the
leftDataFrame, even those rows whose join key(s) do not have values in the
rightDataFrame. Rows in the
left DataFrame that are missing values for the joinkey(s) in the
right DataFrame will simply have null (i.e., NaN or None) valuesfor those columns in the resulting joined DataFrame.
Note: a left join will still discard rows from the
right DataFrame that do nothave values for the join key(s) in the
A left join is performed in pandas by calling the same
merge function used forinner join, but using the
The result DataFrame from a left join (
merged_left) looks very much like theresult DataFrame from an inner join (
merged_inner) in terms of the columns itcontains. However, unlike
merged_left contains the samenumber of rows as the original
survey_sub DataFrame. When we inspect
merged_left, we find there are rows where the information that should havecome from
taxa) ismissing (they contain NaN values):
These rows are the ones where the value of
survey_sub (in thiscase,
PF) does not occur in
Other join types
merge function supports two other join types:
- Right (outer) join: Invoked by passing
how='right'as an argument. Similarto a left join, except all rows from the
rightDataFrame are kept, whilerows from the
leftDataFrame without matching join key(s) values arediscarded.
- Full (outer) join: Invoked by passing
how='outer'as an argument. This jointype returns the all pairwise combinations of rows from both DataFrames; i.e.,the result DataFrame will
NaNwhere data is missing in one of the dataframes. This join type isvery rarely used.
Challenge - Distributions
Create a new DataFrame by joining the contents of the
species.csv tables. Then calculate and plot the distribution of:
- taxa by plot
- taxa by sex by plot
Challenge - Diversity Index
- In the data folder, there is a
plots.csvfile that contains information about thetype associated with each plot. Use that data to summarize the number ofplots by plot type.
Calculate a diversity index of your choice for control vs rodent exclosureplots. The index should consider both species abundance and number ofspecies. You might choose to use the simple biodiversity index describedherewhich calculates diversity as:
the number of species in the plot / the total number of individuals in the plot = Biodiversity index.
Combine Data Frames In A List R
concatcan be used to combine subsets of a DataFrame, or even data from different files.
joinfunction combines DataFrames based on index or column.
Joining two DataFrames can be done in multiple ways (left, right, and inner) depending on what data must be in the final DataFrame.
to_csvcan be used to write out DataFrames in CSV format.
WIP Alert This is a work in progress. Current information is correct but more content may be added in the future.
View examples on this jupyter notebook
The default is an inner join. Use
'how'='left' 'right' 'outer' to change join types.
pd.merge by index
Joins by index are much faster than join on arbitrary columns!
If the columns you want to join on are Indices, use
'how'='left' 'right' 'outer' to change join types. The default join type is
Join on multiple columns
Joining by multiple columns is useful for dealing with time-stamped data.
Just pass an array of column names to
merge vs join
Joining by index (using
df.join) is much faster than joins on arbtitrary columns!
The difference between
dataframe.join() is that with dataframe.merge() you can join on any columns, whereas dataframe.join() only lets you join on index columns.
pd.merge() vs dataframe.join() vs dataframe.merge()
pd.merge() is the most generic.
df.merge() is the same as
pd.merge() with an implicit left dataframe. Use
df.join() for merging on index columns exclusively.
df.join is much faster because it joins by index
|How to call||Pandas global method||Dataframe method||Dataframe method|
|Join on||Join on any column||Join on index columns only||Join on any column|
|Performance||Slow unless using indices||Fast!||Slow unless using indices|
(i.e. how to write it
Rename duplicate columns
Note that column year was not duplicated,
pandas correctly identified it
was the same in both sides.
Combine Data Frames Python