Combining DataFrames with pandas

Overview

Teaching: 20 min
Exercises: 25 min
Questions
  • 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_csv to 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 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. In iPython:

import pandas as pd
authors_df = pd.read_csv("authors.csv",
                         keep_default_na=False, na_values=[""])
authors_df

        TCP                                             Author
0    A00002                         Aylett, Robert, 1583-1655?
1    A00005  Higden, Ranulf, d. 1364. Polycronicon. English...
2    A00007             Higden, Ranulf, d. 1364. Polycronicon.
3    A00008          Wood, William, fl. 1623, attributed name.
4    A00011

places_df = pd.read_csv("places.csv",
                         keep_default_na=False, na_values=[""])
places_df
    A00002                         London
0   A00005                         London
1   A00007                         London
2   A00008               The Netherlands?
3   A00011                      Amsterdam
4   A00012                         London
5   A00014                         London

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 of surveys table
place_sub = places_df.head(10)
# grab the last 20 rows 
place_sub_last10 = places_df.tail(20)
#reset the index values to the second dataframe appends properly
place_sub_last10 = place_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 horizonally, we want to make sure what we are doing makes sense (ie the data are related in some way).

# stack the DataFrames on top of each other
vertical_stack = pd.concat([place_sub, place_sub_last10], axis=0)

# place the DataFrames side by side
horizontal_stack = pd.concat([place_sub, place_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 place_sub and place_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('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('out.csv', keep_default_na=False, na_values=[""])

Challenge - Combine Data

In the data folder, there are two catalogue data files: 1640.csv and 1641.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.

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.

The places.csv file is table that contains the place and EEBO id for some titles. When we want to access that information, we can create a query that joins the additional columns of information to the author data.

Storing data in this way has many benefits including:

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.

>>> authors_df.columns

Index([u'TCP', u'EEBO', u'VID', u'STC', u'Status', u'Author', u'Date', u'Title', u'Terms', u'Pages'], dtype='object')

>>> places_df.columns

Index([u'EEBO', u'Place'], dtype='object')

In our example, the join key is the column containing the identifier, which is called TCP.

Now that we know the fields with the common TCP 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 joins exists in BOTH tables. An example of an inner join, adapted from this page is below:

Inner join -- courtesy of codinghorror.com

The pandas function for performing joins is called merge and an Inner join is the default option:

merged_inner = pd.merge(left=authors_df,right=places_df, left_on='TCP', right_on='TCP')
# in this case `species_id` is the only column name in  both dataframes, so if we skippd `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

OUTPUT:

      TCP                                             Author             Place
0  A00002                         Aylett, Robert, 1583-1655?            London
1  A00005  Higden, Ranulf, d. 1364. Polycronicon. English...            London
2  A00007             Higden, Ranulf, d. 1364. Polycronicon.            London
3  A00008          Wood, William, fl. 1623, attributed name.  The Netherlands?
4  A00011                                                NaN         Amsterdam

The result of an inner join of authors_df and places_df is a new DataFrame that contains the combined set of columns from those tables. It only contains rows that have two-letter species codes that are the same in both the authos_df and place_df DataFrames. In other words, if a row in authors_df has a value of TCP that does not appear in the TCP column of TCP, it will not be included in the DataFrame returned by an inner join. Similarly, if a row in places_df has a value of TCP that does not appear in the TCP column of places_df, 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='TCP' argument tells merge to use the TCP column as the join key from places_df (the left DataFrame). Similarly , the right_on='TCP' argument tells merge to use the TCP column as the join key from authors_df (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 authors (TCP, Person) as well as all the columns from places_df (TCP, Place).

Notice that merged_inner has fewer rows than place_sub. This is an indication that there were rows in place_df with value(s) for EEBO that do not exist as value(s) for EEBO in authors_df.

Left joins

What if we want to add information from cat_sub to 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 left DataFrame.

Left Join

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=places_df,right=authors_df, how='left', left_on='TCP', right_on='TCP')
merged_left

**OUTPUT:**
      TCP             Place                                             Author
0  A00002            London                         Aylett, Robert, 1583-1655?
1  A00005            London  Higden, Ranulf, d. 1364. Polycronicon. English...
2  A00007            London             Higden, Ranulf, d. 1364. Polycronicon.
3  A00008  The Netherlands?          Wood, William, fl. 1623, attributed name.
4  A00011         Amsterdam                                                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_inner, merged_left contains the same number of rows as the original place_sub DataFrame. When we inspect merged_left, we find there are rows where the information that should have come from authors_df (i.e., Author) is missing (they contain NaN values):

 merged_inner[ pd.isnull(merged_inner.Author) ]
**OUTPUT:**
        TCP Author      Place
4    A00011    NaN  Amsterdam
6    A00014    NaN     London
8    A00018    NaN   Germany?

These rows are the ones where the value of Author from authors_df does not occur in places_df.

Other join types

The pandas merge function supports two other join types:

Final Challenges

Challenge - Distributions

Create a new DataFrame by joining the contents of the eebo.csv and places.csv tables. Then calculate and plot the distribution of:

  1. title by author by place

Key Points