Combining DataFrames with pandas

Last updated on 2024-08-05 | Edit this page

Overview

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.

In order to get more practice with slicing and subsetting, we will opt to create subsets of the works_df instead of loading in data from multiple files.

PYTHON

top_concat_df = works_df.head()
bottom_concat_df = works_df.tail().reset_index()
left_concat_df = works_df.iloc[:,0:3]
right_concat_df = works_df.iloc[:,3:]
left_merge_df = works_df[['mms_id','title']]
left_merge_df = left_merge_df.drop_duplicates()
right_merge_df = works_df[['mms_id','author']].sample(frac=.8,random_state=42).drop_duplicates()

Concatenating DataFrames

We can use the concat function in Pandas to append either columns or rows from one DataFrame to another. 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).

PYTHON

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

# place the DataFrames side by side
horizontal_stack = pd.concat([left_concat_df, right_concat_df], 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 top_concat_df and bottom_concat_df have been repeated. We can reindex the new dataframe using the reset_index() method.

PYTHON

# reset index and drop previous index values rather than convert to data column
vertical_stack.reset_index(drop=True,inplace=True)

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.

PYTHON

# 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.

PYTHON

# read our output back into python and make sure all looks good
new_output = pd.read_csv('out.csv')
new_output

Challenge - Combine Data

Make your own subsets and combine them however you like. Output at least once and read it back in. Did you get what you expected?

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.

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.

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

Now that we have a common key, 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.

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

PYTHON

merged_inner = pd.merge(left=left_merge_df,right=right_merge_df, on='mms_id')

# what's the size of the output data?
print(merged_inner.shape)
merged_inner

OUTPUT:

(12848, 3)
mms_id	title	author
0	991004617919702908	"A god of justice?" : the problem of evil in t...	Whitted, Qiana J., 1974-
1	991004617919702908	"A god of justice?" : the problem of evil in t...	Whitted, Qiana J., 1974-
2	991003607949702908	"Baad bitches" and sassy supermamas : Black po...	Dunn, Stephane, 1967-
3	991003607949702908	"Baad bitches" and sassy supermamas : Black po...	Dunn, Stephane, 1967-
4	991013190057102908	"Codependent lesbian space alien seeks same"	Olnek, Madeleine.; Space Aliens, LLC.

The result of an inner join of left_merge_df and right_merge_df is a new DataFrame that contains the combined set of columns from those tables. It only contains rows that have mms_id codes that are the same in both DataFrames.

The two DataFrames that we want to join were passed to the merge function using on since the key colums in boht dataframes was the same. If they had been different, we could still do it but we would have to use left_on and right_on arguments with the appropriate column names instead of just one.

Notice that merged_inner has fewer rows than left_merge_df. This is an indication that there were rows in left_merge_df with value(s) for mms_id that do not exist as value(s) for mms_id in right_merge_df.

Left joins

What if we wanted to merge these two without losing any of the information from left_merge_df? 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.

A left join is performed in pandas by calling the same merge function used for inner join, but using the how='left' argument:

PYTHON

merged_left = pd.merge(left=left_merge_df,right=right_merge_df, on='mms_id',how='left')

# what's the size of the output data?
print("shape:", merged_left.shape)
print("Missing Authors:", merged_left.author.isnull().sum())
merged_left

**OUTPUT:**
shape: (14232, 3)
Missing Authors: 1384
mms_id	title	author
0	991004617919702908	"A god of justice?" : the problem of evil in t...	Whitted, Qiana J., 1974-
1	991003607949702908	"Baad bitches" and sassy supermamas : Black po...	Dunn, Stephane, 1967-
2	991013190057102908	"Codependent lesbian space alien seeks same"	Olnek, Madeleine.; Space Aliens, LLC.
3	991003662979702908	"Lactilla tends her fav'rite cow" : ecocritica...	Milne, Anne.
4	991008089169702908	"The useless mouths", and other literary writings	Beauvoir, Simone de, 1908-1986.
...	...	...	...
14227	991013597402302908	¡Ban c/s this! : the BSP anthology of Xican@ l...	Rivera, Santino J., editor.
14228	991010673989702908	¡Muy pop! : conversations on Latino popular cu...	Stavans, Ilan.
14229	991004452179702908	¡Viva la historieta! : Mexican comics, NAFTA, ...	Campbell, Bruce, 1964- author.
14230	991013093859602908	Đời về cơ bản là buồn cười	Lê, Bích, author.
14231	991013054957702908	Đường vào văn chương : phê bình lý trí ...	Đặng, Phùng Quân, 1942- author.
14232 rows × 3 columns

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 left_merge_df DataFrame. When we inspect merged_left, we find there are rows where the information that should have come from right_merge_df (i.e., author) is missing (they contain NaN values):

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 the right DataFrame are kept, while rows from the left DataFrame 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 NaN where data is missing in one of the dataframes. This join type is very rarely used.

Making Composite Keys

Sometimes a data file might not have an obvious key in a single column, but we may be able to generate a suitable key by combining two or more columns into what is known as a composite key.

Let’s see an example of this by importing the ethnicity.csv and gender.csv, and then examining one of them.

PYTHON

ethicity_df = pd.read_csv('ethnicity.csv')
gender_df = pd.read_csv('gender.csv')
print(gender_df.head())
print(ethicity_df.head())

They both have term and year columns that contain duplication. However, we could combine these colums into a composite key that would be unique for each dataframe. We will also strip away any potential leading or trailing whitespace as a precaution.

PYTHON

gender_df["key"] = gender_df.year.astype(str).str.strip() + gender_df.term.str.strip()
ethnicity_df["key"] = ethnicity_df.year.astype(str).str.strip() + ethnicity_df.term.str.strip()
gender_df

Now that we have a shared key, we will drop the term and year columns from one of the dataframes as we merge. Otherwise, we would end up with duplicate columns that are automatically renamed with a prefix to prevent naming collision. Simply dropping won’t always be the best approach, but is safe to do so in our case using a inner join.

PYTHON

gender_ethnicity_df = pd.merge(gender_df.drop(columns=["term","year"]), ethnicity_df, on='key')
print(gender_ethnicity_df.shape)
gender_ethnicity_df

Challenge - Joins

Create a new DataFrame by joining the contents of the gender.csv and ethnicity.csv tables. Make it a left join where gender is the left dataframe.

Calculate the:

Number of ethnicity records that were dropped
Number of rows where ethnicity data is NaN

Solution to challenge

PYTHON

merged = pd.merge(
                  left=pd.read_csv("gender.csv"),
                  right=pd.read_csv("ethnicity.csv"),
                  on="['year','term']"
                  )
# Part 1: Number of ethnicity records that were dropped
ethnicity_df.shape[0] - merged.shape[0]
# Part 2: Number of rows where ethnicity data is NaN
merged.isnull().sum() # this will find all columns with null values and effectively count them
# you can *or* the columns with *nulls* to filter the dataframe and take the first value of shape
merged[merged.intl.isnull()| merged.pacific_islander.isnull()].shape[0]