Combining DataFrames with Pandas
Overview
Teaching: 20 min
Exercises: 25 minQuestions
Can I work with data from multiple sources?
How can I combine data from different data sets?
Objectives
Combine data from multiple files into a single DataFrame using merge and concat.
Combine two DataFrames using a unique ID found in both DataFrames.
Employ
to_csvto export a DataFrame in CSV format.Join DataFrames using common fields (join keys).
79
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 time and position files into pandas DataFrames. In iPython:
import pandas as pd
position_cols = pd.read_csv('data/position_cols.csv', keep_default_na=False, na_values=[""])
position_cols = position_cols[['record_id', 'site', 'transect', 'replicate']]
position_cols
record_id site transect replicate
0 1 IVEE 1 A
1 2 IVEE 5 B
2 3 IVEE 5 B
3 4 IVEE 5 B
4 5 IVEE 5 C
... ... ... ... ...
45 46 IVEE 3 C
46 47 IVEE 3 C
47 48 IVEE 4 A
48 49 NAPL 6 D
49 50 NAPL 8 A
50 51 NAPL 7 A
[50 rows x 4 columns]
time_cols = pd.read_csv('data/time_cols.csv', keep_default_na=False, na_values=[""])
time_cols = time_cols[['record_id', 'year', 'month', 'date']]
time_cols
record_id year month date
0 1 2012 8 2012-08-20
1 2 2012 8 2012-08-20
2 3 2012 8 2012-08-20
3 4 2012 8 2012-08-20
4 5 2012 8 2012-08-20
5 6 2012 8 2012-08-20
... ... ... ... ...
45 46 2012 8 2012-08-20
46 47 2012 8 2012-08-20
47 48 2012 8 2012-08-20
48 49 2012 8 2012-08-22
49 50 2012 8 2012-08-22
50 51 2012 8 2012-08-22
[50 rows x 4 columns]
Take note that the read_csv method we used can take some additional options which
we didn’t use previously. Many functions in Python have a set of options that
can be set by the user if needed. In this case, we have told pandas to assign
empty values in our CSV to NaN keep_default_na=False, na_values=[""].
More about all of the read_csv options here.
Concatenating DataFrames
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.
# Read in first 10 lines
lobster_sub = lobsters_df.head(10)
# Grab the last 10 rows
lobster_sub_last10 = lobsters_df.tail(10)
# Reset the index values to the second dataframe appends properly
lobster_sub_last10 = lobster_sub_last10.reset_index(drop=True)
# drop=True option avoids adding new index column with old index values
When we concatenate DataFrames, we need to specify the axis. axis=0 tells
pandas to stack the second DataFrame UNDER the first one. It will automatically
detect 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 the
first DataFrame. 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
horizontally, we want to make sure what we are doing makes sense (i.e. the data are
related in some way).
# Stack the DataFrames on top of each other
vertical_stack = pd.concat([lobster_sub, lobster_sub_last10], axis=0)
# Place the DataFrames side by side
horizontal_stack = pd.concat([lobster_sub, lobster_sub_last10], axis=1)
Row Index Values and Concat
Have a look at the vertical_stack dataframe? Notice anything unusual?
The row indexes for the two data frames lobster_sub and lobster_sub_last10
have been repeated. We can reindex the new dataframe using the reset_index() method.
Writing Out Data to CSV
We can use the to_csv command to do export a DataFrame in CSV format. 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
vertical_stack.to_csv('foldername/out.csv'). We use the ‘index=False’ so that
pandas doesn’t include the index number for each line.
# Write DataFrame to CSV
vertical_stack.to_csv('data/out.csv', index=False)
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.
# For kicks read our output back into Python and make sure all looks good
new_output = pd.read_csv('data/out.csv', keep_default_na=False, na_values=[""])
Challenge - Combine Data
In the data folder, there are two lobster data files:
lobsters_2012.csvandlobsters_2013.csv. Read the data into Python and combine the files to make one new data frame. Create a plot of average plot size by year grouped by site. Export your results as a CSV and make sure it reads back into Python properly.
Joining DataFrames
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.
NOTE: 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 different attributes (site) given each species is only
entered once. - It also makes it easy for us to make changes to a particular attribute information once, without having to find each instance of it in the larger data.
- It optimizes the size of our data.
Joining Two DataFrames
To better understand joins, let’s import a subset of our data to work with.
position_cols = pd.read_csv('data/position_cols.csv', keep_default_na=False, na_values=[""])
position_cols = position_cols[['record_id', 'site', 'transect', 'replicate']]
time_cols = pd.read_csv('data/time_cols.csv', keep_default_na=False, na_values=[""])
time_cols = time_cols[['record_id', 'year', 'month', 'date']]
Identifying join keys
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.
>>> position_cols.columns
Index(['record_id', 'site', 'transect', 'replicate'], dtype='object')
>>> time_cols.columns
Index(['record_id', 'year', 'month', 'date'], dtype='object')
In our example, the join key is the column containing the two-letter species
identifier, which is called record_id.
Now that we know the fields with the common record 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 joins
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.
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 merge and an Inner join is
the default option:
merged_inner = pd.merge(left=position_cols, right=time_cols, left_on='record_id', right_on='record_id')
# In this case `record_id` is the only column name in both dataframes, so if we skipped `left_on`
# And `right_on` arguments we would still get the same result
# What's the size of the output data?
merged_inner.shape
merged_inner
record_id site transect replicate year month date
0 1 IVEE 1 A 2012 8 2012-08-20
1 2 IVEE 5 B 2012 8 2012-08-20
2 3 IVEE 5 B 2012 8 2012-08-20
3 4 IVEE 5 B 2012 8 2012-08-20
4 5 IVEE 5 C 2012 8 2012-08-20
5 6 IVEE 5 D 2012 8 2012-08-20
6 7 IVEE 6 A 2012 8 2012-08-20
7 8 IVEE 6 B 2012 8 2012-08-20
8 9 IVEE 6 B 2012 8 2012-08-20
9 10 IVEE 6 C 2012 8 2012-08-20
10 11 IVEE 6 D 2012 8 2012-08-20
...
40 41 IVEE 4 D 2012 8 2012-08-20
41 42 IVEE 4 B 2012 8 2012-08-20
42 43 IVEE 4 C 2012 8 2012-08-20
43 44 IVEE 3 D 2012 8 2012-08-20
44 45 IVEE 3 C 2012 8 2012-08-20
45 46 IVEE 3 C 2012 8 2012-08-20
46 47 IVEE 3 C 2012 8 2012-08-20
47 48 IVEE 4 A 2012 8 2012-08-20
48 49 NAPL 6 D 2012 8 2012-08-22
49 50 NAPL 8 A 2012 8 2012-08-22
50 51 NAPL 7 A 2012 8 2012-08-22
The result of an inner join of position_cols and time_cols is a new DataFrame
that contains the combined set of columns from position_cols and time_cols. It
only contains rows that have record IDs that are the same in
both DataFrames. In other words, if a row in
position_cols has a value of record_id that does *not* appear in the record_id
column of time_cols, it will not be included in the DataFrame returned by an
inner join. Similarly, if a row in time_cols has a value of record_id
that does *not* appear in the record_id column of position_cols`, that row will not
be included in the DataFrame returned by an inner join.
The two DataFrames that we want to join are passed to the merge function using
the left and right argument. The left_on='species' argument tells merge
to use the record_id column as the join key from position_cols (the left
DataFrame). Similarly , the right_on='record_id' argument tells merge to
use the record_id column as the join key from position_cols (the right
DataFrame). For inner joins, the order of the left and right arguments does
not matter.
The result merged_inner DataFrame contains all of the columns from position_cols
as well as all the columns from time_cols.
Left joins
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.e., NaN or None) values
for those columns in the resulting joined DataFrame.
Note: a left join will still discard rows from the right DataFrame that do not
have values for the join key(s) in the left DataFrame.
A left join is performed in pandas by calling the same merge function used for
inner join, but using the how='left' argument:
merged_left = pd.merge(left=position_cols, right=time_cols, how='left', left_on='record_id', right_on='record_id')
merged_left
record_id site transect replicate year month date
0 1 IVEE 1 A 2012 8 2012-08-20
1 2 IVEE 5 B 2012 8 2012-08-20
2 3 IVEE 5 B 2012 8 2012-08-20
3 4 IVEE 5 B 2012 8 2012-08-20
4 5 IVEE 5 C 2012 8 2012-08-20
5 6 IVEE 5 D 2012 8 2012-08-20
6 7 IVEE 6 A 2012 8 2012-08-20
7 8 IVEE 6 B 2012 8 2012-08-20
8 9 IVEE 6 B 2012 8 2012-08-20
9 10 IVEE 6 C 2012 8 2012-08-20
10 11 IVEE 6 D 2012 8 2012-08-20
...
40 41 IVEE 4 D 2012 8 2012-08-20
41 42 IVEE 4 B 2012 8 2012-08-20
42 43 IVEE 4 C 2012 8 2012-08-20
43 44 IVEE 3 D 2012 8 2012-08-20
44 45 IVEE 3 C 2012 8 2012-08-20
45 46 IVEE 3 C 2012 8 2012-08-20
46 47 IVEE 3 C 2012 8 2012-08-20
47 48 IVEE 4 A 2012 8 2012-08-20
48 49 NAPL 6 D 2012 8 2012-08-22
49 50 NAPL 8 A 2012 8 2012-08-22
50 51 NAPL 7 A 2012 8 2012-08-22
Other join types
The pandas merge function supports two other join types:
- Right (outer) join: Invoked by passing
how='right'as an argument. Similar to a left join, except all rows from therightDataFrame are kept, while rows from theleftDataFrame without matching join key(s) values are discarded. - Full (outer) join: Invoked by passing
how='outer'as an argument. This join type returns the all pairwise combinations of rows from both DataFrames; i.e., the result DataFrame willNaNwhere data is missing in one of the dataframes. This join type is very rarely used.
Final Challenge
Challenge - Merging Comparisons
Please implement left, right, and inner joins on dummy_df1 and dummy_df2. Try to answer the questions below by observing the resulting three dataframes.
df_1 = {'site_rep': ['IVEE_A', 'NAPL_A', 'NAPL_B', 'AQUE_A', 'AQUE_B', 'AQUE_C', 'CARP_A', 'CARP_B', 'MOHK_A', 'MOHK_B'], 'value': np.random.normal(0, 1, size= 10), 'diver': ['foo', float("nan"), 'foo', 'baz', 'bar', 'baz', 'foo', float("nan"), 'baz', 'bar']} dummy_df1 = pd.DataFrame(df_1) df_2 = {'site_rep': ['IVEE_A', 'NAPL_A', 'NAPL_B', 'AQUE_A', 'AQUE_B', float("nan"), 'CARP_A', 'CARP_B', 'MOHK_A', 'MOHK_B'], 'transect': [1, 5, 6, 9, 7, 8, 4, 2, 3, float("nan")], 'protected': [True, False, False, True, False, True, True, True, False, False]} dummy_df2 = pd.DataFrame(df_2)
- Are all the dataframes the same dimensions?
- What join do you think is best for these dataframes?
- Another parameter that is available with the merge function is sort (e.g. sort = True, sort = False), what change do you observe ‘sort = True’ making?
Key Points
Pandas’
mergeandconcatcan 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.