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:
import pandas as pd surveys_df = pd.read_csv("data/surveys.csv", keep_default_na=False, na_values=[""]) surveys_df record_id month day year plot species sex hindfoot_length weight 0 1 7 16 1977 2 NA M 32 NaN 1 2 7 16 1977 3 NA M 33 NaN 2 3 7 16 1977 2 DM F 37 NaN 3 4 7 16 1977 7 DM M 36 NaN 4 5 7 16 1977 3 DM M 35 NaN ... ... ... ... ... ... ... ... ... ... 35544 35545 12 31 2002 15 AH NaN NaN NaN 35545 35546 12 31 2002 15 AH NaN NaN NaN 35546 35547 12 31 2002 10 RM F 15 14 35547 35548 12 31 2002 7 DO M 36 51 35548 35549 12 31 2002 5 NaN NaN NaN NaN [35549 rows x 9 columns] species_df = pd.read_csv("data/species.csv", keep_default_na=False, na_values=[""]) species_df species_id genus species taxa 0 AB Amphispiza bilineata Bird 1 AH Ammospermophilus harrisi Rodent 2 AS Ammodramus savannarum Bird 3 BA Baiomys taylori Rodent 4 CB Campylorhynchus brunneicapillus Bird .. ... ... ... ... 49 UP Pipilo sp. Bird 50 UR Rodent sp. Rodent 51 US Sparrow sp. Bird 52 ZL Zonotrichia leucophrys Bird 53 ZM Zenaida macroura Bird [54 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
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 of surveys table survey_sub = surveys_df.head(10) # Grab the last 10 rows survey_sub_last10 = surveys_df.tail(10) # Reset the index values to the second dataframe appends properly survey_sub_last10 = survey_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([survey_sub, survey_sub_last10], axis=0) # Place the DataFrames side by side horizontal_stack = pd.concat([survey_sub, survey_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
survey_sub_last10 have 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 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_output/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_output/out.csv', keep_default_na=False, na_values=[""])
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 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.
NOTE: This process of joining tables is similar to what we do with tables in an SQL database.
For example, the
species.csv file that we’ve been working with is a lookup table. 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 35,549 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.
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. We’ll use the
.head method to do this. We’ll also read in a subset of the species table.
# Read in first 10 lines of surveys table survey_sub = surveys_df.head(10) # Import a small subset of the species data designed for this part of the lesson. # It is stored in the data folder. species_sub = pd.read_csv('data/speciesSubset.csv', keep_default_na=False, na_values=[""])
In this example,
species_sub is the lookup table containing genus, species, and taxa names that we want to join with the data in
survey_sub to produce a new DataFrame that contains all of the columns from both
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.
>>> species_sub.columns Index([u'species_id', u'genus', u'species', u'taxa'], dtype='object') >>> survey_sub.columns Index([u'record_id', u'month', u'day', u'year', u'plot_id', u'species_id', u'sex', u'hindfoot_length', u'weight'], dtype='object')
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.
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=survey_sub, right=species_sub, left_on='species_id', right_on='species_id') # In this case `species_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 month day year plot_id species_id sex hindfoot_length \ 0 1 7 16 1977 2 NL M 32 1 2 7 16 1977 3 NL M 33 2 3 7 16 1977 2 DM F 37 3 4 7 16 1977 7 DM M 36 4 5 7 16 1977 3 DM M 35 5 8 7 16 1977 1 DM M 37 6 9 7 16 1977 1 DM F 34 7 7 7 16 1977 2 PE F NaN weight genus species taxa 0 NaN Neotoma albigula Rodent 1 NaN Neotoma albigula Rodent 2 NaN Dipodomys merriami Rodent 3 NaN Dipodomys merriami Rodent 4 NaN Dipodomys merriami Rodent 5 NaN Dipodomys merriami Rodent 6 NaN Dipodomys merriami Rodent 7 NaN Peromyscus eremicus Rodent
The result of an inner join of
species_sub is a new DataFrame that contains the combined set of columns from
species_sub. It only contains rows that have two-letter species codes that are the same in both 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_id column of
species, it will not be included in the DataFrame returned by an inner join. Similarly, if a row in
species_sub has a value of
species_id that does not appear in the
species_id column of
survey_sub, 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
right argument. The
left_on='species' argument tells
merge to use the
species_id column as the join key from
left DataFrame). Similarly , the
right_on='species_id' argument tells
merge to use the
species_id column as the join key from
right DataFrame). For inner joins, the order of the
right arguments does not 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 an indication that there were rows in
surveys_df with value(s) for
species_id that do not exist as value(s) for
What if we want to add information from
survey_sub without losing any of the information from
survey_sub? 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.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
A left join is performed in pandas by calling the same
merge function used for inner join, but using the
merged_left = pd.merge(left=survey_sub, right=species_sub, how='left', left_on='species_id', right_on='species_id') merged_left
record_id month day year plot_id species_id sex hindfoot_length \ 0 1 7 16 1977 2 NL M 32 1 2 7 16 1977 3 NL M 33 2 3 7 16 1977 2 DM F 37 3 4 7 16 1977 7 DM M 36 4 5 7 16 1977 3 DM M 35 5 6 7 16 1977 1 PF M 14 6 7 7 16 1977 2 PE F NaN 7 8 7 16 1977 1 DM M 37 8 9 7 16 1977 1 DM F 34 9 10 7 16 1977 6 PF F 20 weight genus species taxa 0 NaN Neotoma albigula Rodent 1 NaN Neotoma albigula Rodent 2 NaN Dipodomys merriami Rodent 3 NaN Dipodomys merriami Rodent 4 NaN Dipodomys merriami Rodent 5 NaN NaN NaN NaN 6 NaN Peromyscus eremicus Rodent 7 NaN Dipodomys merriami Rodent 8 NaN Dipodomys merriami Rodent 9 NaN NaN NaN NaN
The result DataFrame from a left join (
merged_left) looks very much like the result DataFrame from an inner join (
merged_inner) in terms of the columns it contains. However, unlike
merged_left contains the same number of rows as the original
survey_sub DataFrame. When we inspect
merged_left, we find there are rows where the information that should have come from
taxa) is missing (they contain NaN values):
merged_left[ pd.isnull(merged_left.genus) ]
record_id month day year plot_id species_id sex hindfoot_length \ 5 6 7 16 1977 1 PF M 14 9 10 7 16 1977 6 PF F 20 weight genus species taxa 5 NaN NaN NaN NaN 9 NaN NaN NaN NaN
These rows are the ones where the value of
survey_sub (in this case,
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. Similar to a left join, except all rows from the
rightDataFrame are kept, while rows from the
leftDataFrame 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 will
NaNwhere data is missing in one of the dataframes. This join type is very rarely used.